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Individual and group assessments of quiz accuracy and students' discrimination of what they know and what they do not know regarding course material were examined using confidence ratings from 22 graduate students, 47 undergraduates, and their 23 heterogeneous learning groups over 6 quizzes. Students first took each multiple choice quiz as individuals and then as a group. Students received instruction regarding metamemory, confidence calibrations, and overconfidence after the first three quizzes. It was hypothesized that individuals and their groups would use this information to adjust their confidence ratings to discriminate appropriately between correct and wrong quiz answers. Within groups, students improved their accuracy, but did not appropriately adjust their confidence judgments. Moreover, the improved accuracy in groups came at a cost of increased confidence for wrong answers. Neither relevant information about metamemory nor assignment to structured learning groups was effective at improving students' assignments of confidence judgments, and may even have made it worse. Factors affecting group decision making appear to be high individual confidence and a majority effect, with educational status a marginally contributing component. There are six figures and two tables. (Contains 19 references.) (Author/SLD)

Cohort groups in higher education have been established in response to the demographic shifts that have occurred with greater proportions of adult students and students who work off-campus. Cohort groups are defined as a group of students who begin coursework in a degree or certificate program together and who remain together for at least two-thirds of the classes in the program. This cohort learning study compared the learning outcomes of students in 12 matched groups, 6 representing degree programs scheduled in traditional, non-cohort formats and 6 representing the same degrees at the same institutions but in cohort formats. The institutions represented three large public research universities, one private research institution, and one private comprehensive institution. From a total of 353 students surveyed, usable response data came from 287. Learning outcomes were measured by grade point averages and the results of a student self-survey. Comparison between all cohort and non-cohort groups showed slightly higher cohort student learning on affective, cognitive, and learning transfer dimensions. Overall, there were more similarities than differences in learning outcomes between the cohort and non-cohort students surveyed. One figure and seven tables of data are appended. (Contains 25 references.) (JLS)

The use of cooperative learning at the college level was studied by investigating whether different instructor intervention approaches would affect the achievement of college students using cooperative learning methods. Subjects were 97 undergraduate elementary education majors enrolled in three sections of a curriculum course at the University of South Florida (Tampa) College of Education during the second semester of the 1990-91 academic year. Nineteen learning teams were formed and randomly assigned to treatment conditions as follows: (1) directions and materials provided, but no instructor assistance (control group); (2) instructors provided advance organizers; and (3) instructors met with groups after sessions to answer questions and provide assistance. All treatment groups read the same materials and followed a similar schedule of activities. A pretest was followed by a posttest and an attitude questionnaire after the 2-week period. On the posttest, the control and follow-up discussion groups had nearly identical mean scores, with the mean for the advance organizer group more than two points higher. Students liked the cooperative learning situation, believed they learned the materials well, and preferred instructor assistance to the control condition. Results suggest that advance organizers may yield greater learning than do follow-up discussions. Five tables present study findings, and a 17-item list of references is included. (SLD)

This study investigated the effect of using differentiated instruction by integrating multiple intelligences and learning styles on solving problems, achievement in, and attitudes towards math in six graders with learning disabilities in cooperative groups. A total of 60 students identified with LD were invited to participate. The sample was randomly divided into two groups; experimental (n = 30 boys) and control (n = 30 boys). ANCOVA and T.test were employed for data analysis. Findings from this study indicated the effectiveness of differentiated instruction by integrating multiple intelligences and learning styles on solving problems, achievement in, and attitudes towards math in the target students. On the basis of the findings, the study advocated for the effectiveness of using differentiated instruction by integrating multiple intelligences and learning styles on solving problems, achievement in, and attitudes towards math in learning disabled students.

Social loafing research has spanned several decades and fields of study. Research has provided support for both the existence of social loafing and its antecedents within the laboratory, classroom, and work place. Studies regarding the perceptions of social loafing and its effects in the online learning environment, however, are largely non-existent. This study surveyed 227 online learning students who were participating in online learning groups. The study seeks to determine whether the perception of social loafing exists within online learning groups. In addition, several psychosocial factors identified in face-to-face environments are analyzed to determine their impact in online learning groups. Evidence supports both the perception of social loafing in online learning groups as well as similarities between social loafing antecedents in face-to-face groups and those in the online learning environment. (Contains 2 tables.)

Social loafing research has spanned several decades and fields of study. Research has provided support for both the existence of social loafing and its antecedents within the laboratory, classroom, and work place. Studies regarding the perceptions of social loafing and its effects in the online learning environment, however, are largely non-existent. This study surveyed 227 online learning students who were participating in online learning groups. The study seeks to determine whether the perception of social loafing exists within online learning groups. In addition, several psychosocial factors identified in face-to-face environments are analyzed to determine their impact in online learning groups. Evidence supports both the perception of social loafing in online learning groups as well as similarities between social loafing antecedents in face-to-face groups and those in the online learning environment. (Contains 2 tables.)

This practicum aimed to improve global awareness among middle school students. One specific goal was to increase the students' concept of the world and their global perspective. A second goal was to increase the students' geographic knowledge, enabling them to locate sites of current world events and affairs on a map or globe. Students were taught the geographic locational skills needed for learning the global issues and their localities in the world. The students synthesized facts about global interdependence. They wrote essays and reports derived from library research. Each student kept a current events notebook throughout the implementation of the practicum process. They were provided with 32 weeks of lessons, activities, and projects divided into 3 phases. Outcomes from this practicum experience were very positive. All six of the practicum's objectives were achieved and surpassed. The students gained knowledge of global interdependence. Their geographic skills improved. Finally, the students gained an interest in worldwide current events and affairs. The global awareness survey instrument and the geographic locational skills test are appended. (DB)

This article is from PLoS Computational Biology , volume 10 . Abstract Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.

I. Progress on the behavioral and the molecular biological goals: (1) We have finished, as originally proposed, the software and first actual physical system for computer-controlled training procedures with which to shape animal behavior and to perform learning-conditioning experiments. (2) We have constructed molecular biological vectors for generating muscarinic cholinergic receptor proteins pertaining specifically to all of the five known muscarinic receptors--this work follows on previous AFOSR-funded work relating to cholinergic enhancement of associative learning 14,15,11-13. II. Progress into the implications of attractors, perturbation analysis of neurons, and the use of language theory: (3) We have developed the conceptual rationale and conducted computer experiments to show that attractor gradients provide an integrative principle that globally acts on all synapses in a network of cooperative neurons. The consequences of this are extensive, and much naturally falls out naturally, e.g: synaptic strengths are optimally set with one another; the size of the Attractors, Dissipative action, learning, Muscarinic receptors, Symbolic dynamics, Finite-state automata, Neural networks, Neuron membrane perturbation analysis.

I. Progress on the behavioral and the molecular biological goals: (1) We have finished, as originally proposed, the software and first actual physical system for computer-controlled training procedures with which to shape animal behavior and to perform learning-conditioning experiments. (2) We have constructed molecular biological vectors for generating muscarinic cholinergic receptor proteins pertaining specifically to all of the five known muscarinic receptors--this work follows on previous AFOSR-funded work relating to cholinergic enhancement of associative learning 14,15,11-13. II. Progress into the implications of attractors, perturbation analysis of neurons, and the use of language theory: (3) We have developed the conceptual rationale and conducted computer experiments to show that attractor gradients provide an integrative principle that globally acts on all synapses in a network of cooperative neurons. The consequences of this are extensive, and much naturally falls out naturally, e.g: synaptic strengths are optimally set with one another; the size of the Attractors, Dissipative action, learning, Muscarinic receptors, Symbolic dynamics, Finite-state automata, Neural networks, Neuron membrane perturbation analysis.

When pain persists in the absence of injury or beyond the normal healing time (typically >3 months) it no longer serves a protective role and can lead to disability and reduced quality of life. Previous research has demonstrated that altered learning processes play a crucial role in the development, maintenance, and reduction of chronic pain (Meulders et a., 2020; Gatzounis et al., 2021). Specifically, impaired safety learning (Schlitt et al., 2021: Zaman et al., 2015) excessive fear generalization (Meulders et al., 2014; Meulders et al., 2015) and resistance to extinction learning (Meulders et al., 2015) are typically observed in comparisons of adults with chronic pain compared to pain-free peers. Furthermore, these processes can result in individual engaging in maladaptive behaviors that maintain disability (Meulders et al., 2019; Breivik et al., 2006). While this research has informed contemporary models of pain-related disability and its treatment, investigation in adolescents (12-24 years) is limited, despite an equally high prevalence of chronic pain in this group. Research into learning processes in this population has largely been in the field of anxiety, and has demonstrated less differential learning, greater generalization, and reduced extinction when compared to adults. Therefore, we propose a comprehensive study of pain-related fear acquisition, generalization and extinction across adolescents and adults in both chronic pain and pain-free participants, with the aim of ascertaining the degree of generalizability of findings from adults to adolescents.

When pain persists in the absence of injury or beyond the normal healing time (typically >3 months) it no longer serves a protective role and can lead to disability and reduced quality of life. Previous research has demonstrated that altered learning processes play a crucial role in the development, maintenance, and reduction of chronic pain (Meulders et a., 2020; Gatzounis et al., 2021). Specifically, impaired safety learning (Schlitt et al., 2021: Zaman et al., 2015) excessive fear generalization (Meulders et al., 2014; Meulders et al., 2015) and resistance to extinction learning (Meulders et al., 2015) are typically observed in comparisons of adults with chronic pain compared to pain-free peers. Furthermore, these processes can result in individual engaging in maladaptive behaviors that maintain disability (Meulders et al., 2019; Breivik et al., 2006). While this research has informed contemporary models of pain-related disability and its treatment, investigation in adolescents (12-24 years) is limited, despite an equally high prevalence of chronic pain in this group. Research into learning processes in this population has largely been in the field of anxiety, and has demonstrated less differential learning, greater generalization, and reduced extinction when compared to adults. Therefore, we propose a comprehensive study of pain-related fear acquisition, generalization and extinction across adolescents and adults in both chronic pain and pain-free participants, with the aim of ascertaining the degree of generalizability of findings from adults to adolescents.

The theme for the 63rd annual conference of the Association of Literacy Educators and Researchers Building Bridges with and for Literacy. The first section of the Yearbook begins with Connie Briggs' Presidential address, followed by an article from the Betty Sturtevant Award recipients Aimee Morewood, Susan Taylor, Allison Swan Dagen, Julie W. Ankrum, and Christina Glance. Next, Kristal Elaine Vallie shared out the findings from her dissertation research titled Middle School Librarians' Perceptions and Promotion of Lesbian, Gay, Bisexual, transgender, and Questioning (LGBTQ) Books. Following this is the Masters Research Award Winner and the Spotlight article for the Judy Richardson Literacy as a Living Legacy Award. This section is concluded with the work of Stephanie Grote-Garcia, Evan Ortlieb, Bethanie Pletcher, Micharl Manderino, Vassiliki Zygouris-Coe, Juan Araujo, and Alexandra Babino titled Building Bridges Between Research and Practice: Reflecting Upon the Results of the 2019 What's Hot in Literacy Survey. In section two, titled Building Bridges with English Language Learners and Families, articles focus on ways to connect literacy with English language learners and their families. Section three focuses on connections between literacy, content, and Online learning. Section four focuses on making connections about diversity through literacy. Finally, section five focuses on learning with and for literacy. After a peer-review process for conference acceptance, the ensuing articles underwent an additional round of double blind peer review for acceptance in the Yearbook. The articles reflect the conference theme, Building Bridges Through Literacy, and expand upon it to explore ways to connect literacy through technology, with families, for English language learners, and diverse groups.

This study investigated congruence in ego stage and group relevant aspects of teachers' and learners' personalities, and between their preferences for and perceptions of group work and emotionality behaviors, as related to learners' learning achievement and to teacher and student satisfaction with learning group membership. Subjects (130 learners and seven teachers) were given the Ideal Group and Self-Description questionnaires, and measures of perception and satisfaction; each teacher also constructed a test of terminal student proficiency. Overall evidence from analysis of the ego-stage dyadic congruency, the group-relevant congruency, and the preference and perception congruency variables in association with teacher and learner satisfaction and learning achievement, did not form any assumptions as to their predictive relationships. The data did reveal some strength by the ego-stage dyadic congruency variable, especially in association with the dependent variables of learner achievement. Supplemental analysis of the eight ego stages indicated that teacher-learner dyadic congruency was not significantly related to learner satisfaction. Reasons for the findings were suggested, along with wider implications. Thesis is available from University Microfilms. (LY)

This study investigated congruence in ego stage and group relevant aspects of teachers' and learners' personalities, and between their preferences for and perceptions of group work and emotionality behaviors, as related to learners' learning achievement and to teacher and student satisfaction with learning group membership. Subjects (130 learners and seven teachers) were given the Ideal Group and Self-Description questionnaires, and measures of perception and satisfaction; each teacher also constructed a test of terminal student proficiency. Overall evidence from analysis of the ego-stage dyadic congruency, the group-relevant congruency, and the preference and perception congruency variables in association with teacher and learner satisfaction and learning achievement, did not form any assumptions as to their predictive relationships. The data did reveal some strength by the ego-stage dyadic congruency variable, especially in association with the dependent variables of learner achievement. Supplemental analysis of the eight ego stages indicated that teacher-learner dyadic congruency was not significantly related to learner satisfaction. Reasons for the findings were suggested, along with wider implications. Thesis is available from University Microfilms. (LY)

The present research aims at contributing to the understanding of the different consequences of multi-attributional diversity and activation of stereotypes for targets and perceivers in CSCL groups in higher distance education. This is done by analysing self-reported and behavioral data.

A survey of informal learning among 318 teachers and HRD professionals was conducted. Analysis of the data found that teachers rely to a greater extent on interactive informal learning activities while HRD professionals rely to a greater extent on independent learning activities. Data analysis also found that six environmental factors inhibit engagement in informal learning and seven personal characteristics enhance motivation to participate in informal learning. Implications for HRD theory, research, and practice are discussed. (Contains 1 table.)

A survey of informal learning among 318 teachers and HRD professionals was conducted. Analysis of the data found that teachers rely to a greater extent on interactive informal learning activities while HRD professionals rely to a greater extent on independent learning activities. Data analysis also found that six environmental factors inhibit engagement in informal learning and seven personal characteristics enhance motivation to participate in informal learning. Implications for HRD theory, research, and practice are discussed. (Contains 1 table.)

It has been documented that Facebook is the most popular social networking site among students. Given that most students are already users of Facebook, implementing it into the curriculum provides an easy way for students to actively participate in class activities. This paper explores the idea that the use of Facebook Groups to complement classroom teaching leads to a more meaningful learning experience and increases student engagement in finance. The instantaneous availability of course notes, problem solutions, PowerPoint slides, videos, and all course content to students on Facebook increases their interest in the subject matter and has a positive effect on learning. This paper presents an innovative method of using Facebook Groups to support teaching and learning in finance, and indeed in many other subject areas. The article reviews the literature on the use of Facebook in education and presents a model for effectively integrating Facebook Groups into the finance curriculum.

Despite a great deal of time and energy went into digitalisation of the world around us, education has been lagging behind. A question therefore arises to what extent higher education institutions should introduce e-learning as part of their programmes. The purpose of this study is to add to the body of knowledge on e-learning by examining perceptions and intentions of students regarding e-learning. There are two broader research objectives pursued in order to achieve the purpose. The first objective is to identify students' knowledge and perception of e-learning, along with their attitudes and experience with it. The second research objective is to assess readiness of students to engage in e-learning and determine their willingness to pay for it. This study uses mixed method research design. First, results of two focus groups are reported, followed by results of survey on 104 respondents in a country of Central Europe. Results show that, students are in general positively inclined towards e-learning and would be willing to take online courses. However, there are still some reservations connected to it and the preference is towards the blended format. In spite of e-learning's European roots, the majority of students do not want the full integration of Information Technology into the study process, meaning that the traditional learning methods combined with IT are preferred.

This final report describes Project BLEND (Beginning Learning Experiences in Developmentally Inclusive Groups and at Home), a project designed to develop, implement, evaluate, and disseminate an ecological model for early intervention for children with developmental delays. Project BLEND included the following components: (a) the partnership (family, child care, BLEND) that was the context in which all activities for supporting each child's development were designed and implemented; (b) service coordination that assisted parents in establishing child care services and coordinating services from other agencies; and transition planning that assisted the child and family in making the transition to their next environment. In the first phase of the project, this model was developed and refined. In the second phase, the model was fully implemented with an existing early intervention center and community child care programs in middle Tennessee. In the third phase, the model was replicated in two sites: a school system in an urban setting that serves young children with disabilities (ages 3-5) and a second site that serves infants and toddlers with developmental delays and their families in a rural community. The report describes the project's activities and includes project implementation and replication checklists in the appendices. (Contains 12 references.) (Author/CR)

This final report describes Project BLEND (Beginning Learning Experiences in Developmentally Inclusive Groups and at Home), a project designed to develop, implement, evaluate, and disseminate an ecological model for early intervention for children with developmental delays. Project BLEND included the following components: (a) the partnership (family, child care, BLEND) that was the context in which all activities for supporting each child's development were designed and implemented; (b) service coordination that assisted parents in establishing child care services and coordinating services from other agencies; and transition planning that assisted the child and family in making the transition to their next environment. In the first phase of the project, this model was developed and refined. In the second phase, the model was fully implemented with an existing early intervention center and community child care programs in middle Tennessee. In the third phase, the model was replicated in two sites: a school system in an urban setting that serves young children with disabilities (ages 3-5) and a second site that serves infants and toddlers with developmental delays and their families in a rural community. The report describes the project's activities and includes project implementation and replication checklists in the appendices. (Contains 12 references.) (Author/CR)

Compared were the characteristics of 60 learning disabled (LD) and 60 normal children (all between 8- and 11-years-old) participating in the Georgia Reading Research Program. The target group consisted of LD children who showed deficits in the psychological process of ordering/sequencing; while the LD reference group were average or above average in ordering/sequencing abilities. Instruments used to assess these deficits were the Wechsler Intelligence Scale for Children Sequencing Triad and the Wide Range Achievement Spelling Test. Other differences between the two groups were that the target group had instructional reading levels one or more years below their expected grade placement levels and were enrolled in special classes; while the LD reference children were enrolled in regular classes and were reading within six months of expected grade level. The program was to focus on an evaluation of specific curriculum treatments designed to facilitate reading achievement. (Author/DB)

This multi-method study explored how children conceptualize emergent leadership in collaborative learning groups, and whether emergent leadership was associated with student achievement motivation. Fourth and fifth grade students participated in a collaborative math activity. After the group math task, 294 students were surveyed on their achievement orientation and emergence of leadership. Within their learning groups, a subset of 18 students was individually interviewed. The interview data revealed that elementary school- aged children are aware of the emergence of leadership in collaborative learning groups, describing leadership behaviors in two domains: task-focused and relationship-focused. The survey data revealed that while task-focused leadership was only associated with performance goal orientations. Relationships-focused leadership was associated with both mastery and performance goals, though the association was stronger with mastery goals. Taken together, this study shows the importance of including emergent leadership in the study of collaborative learning groups. (Contains 39 references.) (Author)

Using small learning groups in graduate education is a way to prepare learners to meet the challenges they face as professionals and to enrich and facilitate adult learning in ways that cannot be accomplished as well by members working alone. This technique also helps graduate students develop the skills needed to work productively as group members. The most effective use for small groups is in researching and learning experiences that do not have well-structured processes and only one right answer so that the experiences and strengths of various group members can be used to solve problems or create projects. Examples of profitable group learning situations with graduate students include courses in which students learn to write grants or conduct program evaluation. Maturity, the ability of group members to respect each others' feelings and viewpoints, and managing conflict are qualities needed by members of successful learning groups. Group learning has several strengths: increasing group members' confidence, increased knowledge through exchange of ideas, increased creativity through shared responsibility, and the opportunity for people to get to know others in work settings. Limitations to group learning include the uneven contributions of group members, the knowledge levels of group participants, and the difficulty of evaluating performance and assigning grades. Instructors of small groups of graduate students should step back and assume the role of facilitator, offering help only when group members cannot solve their own problems. (Contains 22 references.) (KC)

This is a summative report based on Year Three data from the MSP Grant Project entitled, "Content Development for Investigations" (CoDE:I). The purpose of the MSP grant program was to develop standards-based elementary mathematics teachers by giving teachers the tools to teach with a new standards-based mathematics curriculum, "Investigations in Number, Data, and Space" ("Investigations"). The participants were teachers in two school systems located near a large metropolitan city in the southeastern United States. System One is a large, urban school system and System Two is a smaller suburban school system in a neighboring city. The two school systems conducted professional development separately and on different days throughout the grant program, but the overall content and focus of the professional development remained consistent. The professional development facilitators worked with both groups of teachers. Since the MSP project is not a longitudinal design, teachers participants exit from the program at the end of the academic year. The focus of this report is to examine the impacts of the professional development on teacher beliefs, practices, mathematics content knowledge, and student learning outcomes.

This bibliography contains some 2,000 articles on empirical investigation and theoretical considerations covering the topic of students' conceptions entered before September 1990. PUblications in journals, books, working papers, and contributions to conferences are included. English, German, and French publications, and ones in other languages are contained in the bibliography. The bibliography is divided into nine groups: (1) general considerations concerning research in this area; (2) everyday notions and scientific notions; (3) development of notions in the history of science as compared to development of notions of individuals; (4) language and notions; (5) methods of investigations; (6) investigations of students' notions; (7) instruction taking students' notions into account; (8) investigations of teachers notions; and (9) notions and teacher training. The entries include the author's name, year of publication, title, place of publication, and a set of keywords that help the reader to categorize the articles. Keywords indicate the group of the article (1-9), physics, chemistry, or biology, and further areas or concepts. Articles dealing with conceptions of the teaching and learning process, conceptions of science, conceptions on the use of science for technology and society, and empirical studies in which gender differences are investigated are also indicated by keywords. An author index, an appendix that contains publications added during the preparation of the present edition from October to December 1990, a second appendix that contains entries from another bibliography not listed in this bibliography, and a list of keywords are included. (KR)

Collaborative learning in an online classroom can take the form of discussion among the whole class or within smaller groups. This paper addresses the latter, examining first whether assessment makes a difference to the level of learner participation and then considering other factors involved in creating effective collaborative learning groups. Data collected over a three year period (15 cohorts) from the Foundations course in the Master of Distance Education (MDE) program offered jointly by University of Maryland University College (UMUC) and the University of Oldenburg does not support the authors' original hypothesis that assessment makes a significant difference to learner participation levels in small group learning projects and leads them to question how much emphasis should be placed on grading work completed in study groups to the exclusion of other strategies. Drawing on observations of two MDE courses, including the Foundations course, their extensive online teaching experience, and a review of the literature, the authors identify factors other than grading that contribute positively to the effectiveness of small collaborative learning groups in the online environment. In particular, the paper focuses on specific instructional strategies that facilitate learner participation in small group projects, which result in an enhanced sense of community, increased skill acquisition, and better learning outcomes. (Contains 2 figures and 1 table.)

This report presents conceptual frameworks for inclusive learning, good practices in learning cities and recommendations for the future. The COVID-19 pandemic has had an enormous impact on life in cities, as well as exposing and exacerbating almost all forms of inequality. Access to high-calibre, resilient infrastructure, reliable basic services and decent jobs must be provided to all urban and rural dwellers. Achieving this means ensuring learning opportunities in cities are of high quality, inclusive of the diverse backgrounds of all learners and offered on a continuous basis throughout life. This publication features chapters on learning cities' endeavours to promote inclusive lifelong learning for vulnerable groups. It is based on research papers prepared for the fourth International Conference on Learning Cities, which took place in 2019 in Medellín, Colombia, under the theme 'Inclusion -- A principle for lifelong learning and sustainable cities' and hence marks a transition between the learning city conferences of 2019 and 2021. Though the examples included in the publication were in place before the pandemic took hold, they show how populations that were made even more vulnerable by the pandemic can be effectively targeted by lifelong learning opportunities.

This action research examines how focused organization, group roles, and gender grouping impact student participation when working in a cooperative group setting. Fifty-two sixth graders were studied for a period of nine weeks. Results show when students are organized in their cooperative groups, there will be an increase in student participation. Participation also increased when students were given assigned roles. Lastly, this research shows that my hypothesis was incorrect by thinking participation would increase when students work in same gender cooperative groups. To come to these results, data was collected using a triangular approach focusing on observations, change in grades, and questionnaires. The following are appended: (1) Data Collection Matrix; (2) Teacher Observation Log: Focused Organization; (3) Focused Organization; (4) Teacher Observation Log: Group Roles; (5) Student Roles; (6) Group Roles; (7) Teacher Observation Log: Gender Grouping; and (8) Gender Grouping. (Contains 5 figures.)

Changes in the environment require constant adaptation to optimize future behavior. Learning through reinforcement is essential for adaptation in response to changes in the environment; such ability is thought to play a key role in human cognitive development as well as in the emergence and maintenance of psychiatric disorders, such as ADHD, depression or anxiety. Uncertainty and valence are considered to be two important factors for cognitive adaptation during reinforcement learning (RL). Clinical symptoms have been shown to modulate RL with respect to uncertainty and valence conditions, but little is known how psychopathology affects RL in clinically diagnosed compared to typically developing children and adolescents. In this study, we used a probabilistic learning task in which we experimentally manipulated uncertainty and valence levels. Uncertainty was manipulated by two uncertainty conditions, one which included stochasticity, and another which added volatility. Stochasticity or expected uncertainty refers to learning action-outcome contingencies that were probabilistic and stable (80% contingent and 20% non-contingent feedback). Uncertainty due to the probabilistic outcomes becomes expected once the preferred action-outcome contingency had been learned. Volatility further adds uncertainty to the probabilistic action-outcome contingencies: at change points, the learned action-outcome contingencies have to be reversed. Volatility represents unexpected uncertainty, since the exact points of the change cannot be anticipated. Valence has been examined by effects of both valence condition and prediction error (PE) valence which makes it challenging to compare and generalize results. In our study, our task allowed us to examine both these valence effects. For PE effects, valence is determined by whether the received outcome relative to the expected outcome was positive or negative at each trial (Eckstein, Master, Dahl, Wilbrecht, & Collins, 2022; Rosenbaum, Grassie, & Hartley, 2022). Notably, PE valence represents trial-to-trial valence effects, and learning from a positive PE may lead to a momentary positive surprise that can change quickly from positive to negative, if the next trial comes with a negative PE. For valence as block-wise condition effects, valence is determined by whether the absolute outcome value was positive or negative, such as in reward learning and punishment learning, respectively (Palminteri, Kilford, Coricelli, & Blakemore, 2016). Here, valence may unfold over multiple trials and lead to more enduring valence effects. During reward learning, the preferred outcome was to receive 3 coins compared to 1 coin only; during punishment learning, the loss of 1 coin was preferred than the loss of 3 coins. We also added a third condition of both mixed reward and punishments, which was commonly used in studies that only examined PE valence effects. In this third condition, which we will refer to as mixed condition, the preferred outcome was to receive 1 coin compared to losing 1 coin. Notably, PE valence can be additionally examined in any of the block-wise valence conditions. A pilot study indicated that the task can be applied to both clinical and neurotypical 8-18-year-old children and adolescents, and that their adaptation performance was modulated by valence and uncertainty. Our sample of the main study will include 120 children and adolescents between the age of 8 and 18, one clinical group (n=60) with diagnoses of anxiety, depression or ADHD, and a neurotypical group (n=60). We aim to delineate differences between psychiatric conditions (ADHD, anxiety, depression) for cognitive adaptation in respect to valence and uncertainty. We also aim to explore potential transdiagnostic effects of internalizing and externalizing psychopathology during cognitive adaptation. We will use both behavioral measures, such as accuracy, switching behavior and reaction time, as well as computational modeling measures, with the aim to capture adaptations in latent prediction-related parameters. How one learns in the learning conditions that differ by uncertainty and valence might depend upon the current symptom severity, both in clinical as well as neurotypical groups. Therefore, our aim of this study is to examine individual differences in clinical symptoms and their effects on uncertainty and valence during learning. We will further explore the moderating roles of the individual’s environment, state and trait, such as socioeconomic disparities, motivational traits and momentary affective state. Research Questions (RQ) Effects of Depression Major depressive disorder is an affective disorder whose primary symptoms are low mood and loss of motivation and pleasure in daily life. Despite its primarily affective symptoms, there is robust evidence of cognitive deficits in depression (Gotlib & Joormann, 2010), which may be related to the hypoactivity of serotonin, dopamine and noradrenaline (Eshel & Roiser, 2010; Ruhé, Mason, & Schene, 2007). Of note, depression is considered an internalizing disorder, as opposed to externalizing disorders (Achenbach, 1966; Carver, Johnson, & Timpano, 2017), with a large overlap in symptoms such as with anxiety disorders, specifically within a distress-based or negative affect symptom cluster (Watson, 2005). The overlap in negative affect symptoms between depression and anxiety may contribute to shared differences during RL. Although previous studies have found similarities and differences in the learning effects of depression and anxiety, a direct comparison with a task that manipulates both valence and uncertainty during RL has not been done. DEP-RQ1: What are the effects of depression on uncertainty conditions during RL? Studies that used RL with stable environments, reflecting low uncertainty, found reduced learning performance (Elliott, Sahakian, Herrod, Robbins, & Paykel, 1997; Steele, Meyer, & Ebmeier, 2004). Two studies used computational models to examine learning processes in depression, with in inconsistent results. The studies examined various parameters, including learning rate and reward sensitivity. Learning rate serves as a metric for quantifying how individuals update future values, whereas reward sensitivity reflects an individual's responsiveness to perceived outcomes. Reward learning rates were negatively related to anhedonic depression symptoms (Brown et al., 2021), but unrelated in a meta-analysis (Huys, Pizzagalli, Bogdan, & Dayan, 2013). Reward sensitivity in turn was either reduced (Huys et al., 2013) or increased (Brown et al., 2021). These contrasting findings may partly be explained by the use of a pavlovian and an instrumental learning task, respectively, and our study will focus on instrumental learning. One study reported reduced choice sensitivity in a more depressed student group (Kunisato et al., 2012), suggesting that depression is associated with less value-dependent choice behavior. Because the parameters of reward sensitivity and choice sensitivity are computationally interchangeable, current models cannot answer whether the parameters captured decision-related or feedback-related effects of depression (Browning, Paulus, & Huys, 2022). Additional parameter differences have been reported, such as a more negative valuation of received outcomes during punishment learning in depression (Brown et al., 2021), suggesting valence effects, or a decreased attentional breadth in relation to higher trait rumination (Hitchcock et al., 2022), suggesting impaired attention and memory processes. Further studies are needed to establish robust links between depressive symptoms and computational parameters in low uncertainty learning conditions during RL. Consistent with tasks of relatively low uncertainty, studies with more volatile tasks such as probabilistic reversal learning have reported impaired learning performance in depression (Dombrovski et al., 2010; Dombrovski, Szanto, Clark, Reynolds, & Siegle, 2013; Mukherjee, Filipowicz, Vo, Satterthwaite, & Kable, 2020; Mukherjee, Lee, Kazinka, D Satterthwaite, & Kable, 2020; Must, Horvath, Nemeth, & Janka, 2013; Rupprechter, Stankevicius, Huys, Steele, & Seriès, 2018). Less optimal switching behavior in term of lower win-stay and higher lose-switch behavior (particularly after misleading feedback) was also observed (Dickstein et al., 2010; Dombrovski et al., 2015; Mukherjee, Filipowicz, et al., 2020; Murphy, Michael, Robbins, & Sahakian, 2003; Taylor Tavares et al., 2008). In contrast, only one study also reported no learning differences in relation to depression (Brolsma et al., 2020). Some studies applied computational models, with inconsistent findings, similar to studies with low uncertainty tasks. One study reported reduced learning rates in depressed individuals compared to healthy controls (Mukherjee, Filipowicz, et al., 2020), while another study did not find such differences (Brolsma et al., 2020). Additionally, reduced choice sensitivity was reported (Mukherjee, Filipowicz, et al., 2020; Rupprechter et al., 2018), as well as a decreased memory of observed rewards, putatively due to working memory constraints (Rupprechter et al., 2018). Overall, behavioral findings show a relatively consistent picture of reduced learning performance in depression under both low and high uncertainty learning. However, these studies mostly used tasks of mixed-valence or reward conditions only, and the picture regarding uncertainty under punishment learning is less clear. Further, robust links between computational parameters, depression and uncertainty are yet to be established. There is some evidence that learning rate and choice sensitivity estimates are lower across uncertainty conditions. DEP-RQ2: What are the effects of depression on valence conditions during RL? Past RL studies of depression have usually used either conditions for reward and punishment learning separately, or they used PE valence within a reward task to determine valence effects (i.e. gaining more or less than expected). While both PE valence as well as valence condition (learning from positive or from negative feedback) have been studied extensively in depression, these valence effects may represent distinct characteristics on learning which need to be disentangled. To shed light on the relevance of the way valence is manipulated in relation to depression, our study includes 3 valence conditions: reward learning, punishment learning and mixed valence learning. Of note, many studies that have examined reward learning used a mixed valence feedback scheme. For reward learning, past studies found reduced learning performance in depression (Forbes, Shaw, & Dahl, 2007; Herzallah et al., 2013; Morris, Bylsma, Yaroslavsky, Kovacs, & Rottenberg, 2015; Robinson, Cools, Carlisi, Sahakian, & Drevets, 2012). Reduced reward learning was related to reduced striatal activation following positive PE (Robinson, Cools, Carlisi, et al., 2012). Further, both reduced striatal activations and reduced reward learning were a predictor of future depressive symptoms during adolescence (Forbes et al., 2007; Morgan, Olino, McMakin, Ryan, & Forbes, 2013). In terms of positive PE effects on learning, a simulation meta-analysis found lower learning rates, while results from the conventional meta-analysis shows only showed lower learning rates at trend (Pike & Robinson, 2022). Estimation tasks found a reduced positivity bias (Garrett et al., 2014; Sharot, Korn, & Dolan, 2011), which may implicate a reduced positivity and confirmation bias in depression during RL as well. Here, learning rates from positive and confirmatory PE are usually higher than from negative and disconfirmatory PE, which leads to higher learning performance compared to symmetric learning rates (Palminteri & Lebreton, 2022). In contrast, a developmental study found no effect of depressive symptoms on learning rate asymmetry (Nussenbaum, Velez, Washington, Hamling, & Hartley, 2022). In brief, both reward condition and positive PE effects suggest that reward learning may be reduced in depression, but the underlying computational mechanisms during RL are less clear. For punishment learning, two studies found higher learning performance relative to that of reward learning (Herzallah et al., 2013; Timmer, Sescousse, Van Der Schaaf, Esselink, & Cools, 2017). An acute lowering of serotonin levels in healthy individuals through depleting its precursor tryptophan lead to enhanced punishment learning, which suggests that low serotonin increase punishment learning, but do not affect reward learning (Cools, Robinson, & Sahakian, 2008; Robinson, Cools, & Sahakian, 2012). Indeed, depressed individuals with SSRI medication showed lower punishment learning relative to unmedicated depression individuals, but no differences in reward leaning (Herzallah et al., 2013). Studies with computational models examined either punishment learning rates or negative PE learning rates. Punishment learning rates showed inconsistent findings, with higher learning rates in depressed and anxious individuals compared to healthy controls (Aylward et al., 2019), or lower learning rates in depressed compared to healthy controls (Mukherjee, Filipowicz, et al., 2020). In terms of negative PE valence, a meta-analysis found higher learning rates in a simulation approach, while results in the conventional approach showed no effect (Pike & Robinson, 2022). Another study did not find changed learning rates in relation to depression, but a higher shift towards perceiving punishments as larger (Brown et al., 2021). To summarize, despite some inconsistencies in the literature, both punishment condition and negative PE effects point towards enhanced punishment learning in depression. Overall, the finding of both reduced reward learning and enhanced punishment learning in depression has led to the idea that depressed individuals differ in their subjective valuation and exhibit an exaggerated loss aversion compared to what prospect theory has established in healthy individuals (Chen, Takahashi, Nakagawa, Inoue, & Kusumi, 2015). There is evidence of learning deviations under both valence conditions in depression, but it is yet unclear whether these emerge in earlier developmental phases. DEP-RQ3: Are there differential effects of the anhedonic and negative affect symptoms clusters during RL? Depression is a highly heterogeneous disorder, which might explain some of the inconsistencies found during learning in depression. A closer look at specific symptom effects might prove useful to shed new light on the effects on RL. Depression is often distinguished by the symptom clusters of anhedonia and negative affect. Anhedonia is the loss of pleasure or lack of reactivity to pleasurable stimuli. While negative affect in depression shows considerable overlap with anxiety disorders, anhedonia is more independent of anxiety, but overlapping considerably with other disorders such as schizophrenia or addiction (Pizzagalli, 2014). Negative affect or distress putatively is influenced by serotonergic function, whereas anhedonia depends more upon dopaminergic function (Nutt, 2008). Therefore, these two symptom clusters within depression may have distinct effects on learning behavior. Since serotonergic function has been shown to specifically target punishment learning (Cools et al., 2008; Robinson, Cools, & Sahakian, 2012), it is possible that enhanced punishment learning can be linked to negative affect. A direct link between negative affect and punishment learning in depression has been previously reported, but remains understudied (Brown et al., 2021). Anhedonia in turn has been studied extensively, and consistently identified related reduced striatal activations during reward anticipation and reward delivery in adolescents and adults (Gradin et al., 2011; Stringaris et al., 2015). Anhedonic symptoms were related to less optimal switching behavior (Pizzagalli, Iosifescu, Hallett, Ratner, & Fava, 2008), reduced learning rates from positive and negative PE (Chase et al., 2010), specifically to reduced reward learning rates (Brown et al., 2021), and to more exploratory decision-making during learning (Harlé, Guo, Zhang, Paulus, & Yu, 2017). Overall, this suggests that anhedonic and negative affect symptoms clusters may have differential effects during RL, and further studies are needed to examine how negative affect modulates RL. Effects of Anxiety Anxiety disorders can differ with regard to the situation (e.g. social anxiety) or the objects (e.g. specific phobia) that cause symptoms such as aversive affective state, somatic stress symptoms and perception of sustained threat. Anxiety is increasingly recognized as developmental disorder (Leonardo & Hen, 2008), and together with depression and obsessive-compulsive disorder, it has been categorized as internalizing disorder, as opposed to externalizing disorders, e.g. ADHD and conduct disorder (Achenbach, 1966; Carver et al., 2017). Among internalizing disorders, conditions can be further divided into fear-based or distress-based symptom clusters (Watson, 2005), however, other symptom clusters have been distinguished as well (e.g. somatic and cognitive anxiety symptoms). Maladaptive uncertainty learning has been found for both the fear-based (Brown, Price, & Dombrovski, 2023) and the distress-based symptom clusters (Hammond, Xu, Ai, & Van Dam, 2023). A study that distinguished by somatic and cognitive symptom clusters found stronger influences of the somatic symptom cluster on RL (Fan, Gershman, & Phelps, 2022; Wise & Dolan, 2020). Neurally, individuals with anxiety disorders exhibited higher noradrenaline levels (Kalk, Nutt, & Lingford-Hughes, 2011) and dysregulated serotonergic modulation (Dayan & Huys, 2009), which may relate to the observed learning disruptions. Further, structural brain differences have been found to mediate the effect of unpredictability experienced during childhood on anxiety and depression symptoms in adults, which suggests that the uncertainty experienced as a child increases the risk for anxiety and depression and may lead to structural brain changes during development (Wang, Cao, Zheng, Chen, & Zhu, 2023). It remains unclear to what extent anxiety symptoms explain learning disruptions across both clinical and normative anxiety symptoms equally, and whether age modulates these effects during development. In this study, we will examine how effects of anxiety symptoms are present during RL under different levels of uncertainty and valence in both clinical and typically developing individuals. ANX-RQ1: What are the effects of anxiety on uncertainty conditions during RL? The current literature suggests that uncertainty processing is central to learning disruptions in anxiety symptoms (Brown et al., 2023). The aversive consequence of uncertainty in anxiety has been shown to impair behavioral learning performance already during low uncertainty, such as expected uncertainty, which is inherent in probabilistic but stable learning (LaFreniere & Newman, 2019). Under high uncertainty, such as in volatile environments, higher anxiety reduced learning performance and predicted aberrant switching behavior, such as reduced win-stay, increased or reduced lose-shift behavior (Dickstein et al., 2010; Hein, de Fockert, & Ruiz, 2021; Huang, Thompson, & Paulus, 2017; Piray, Ly, Roelofs, Cools, & Toni, 2019; Xia, Xu, Yang, Gu, & Zhang, 2021). Maladaptive uncertainty learning may be related to chronic underconfidence in individuals with anxiety and depression, as they showed disproportionally larger updates from low confidence decisions (Katyal, Huys, Dolan, & Fleming, 2023). One study also reported an inverted-u-shaped effect of trait anxiety on learning: both low and high trait anxiety were related to lower behavioral learning performance compared to intermediate trait anxiety (Aberg, Toren, & Paz, 2022). A nonlinear effect of anxiety on learning may be linked to the nonlinear effect of arousal on optimal task engagement that is attributed to noradrenaline function (Eckstein, Guerra-Carrillo, Miller Singley, & Bunge, 2017). Computational studies suggest that individuals with higher trait anxiety or with internalizing symptoms have difficulty adjusting their learning rate to the learning environments. Specifically, higher trait anxiety or internalizing symptoms predicted smaller learning rate adjustments between stable and volatile learning environments, which reflects less flexible learning (Browning, Behrens, Jocham, O’Reilly, & Bishop, 2015; Gagne, Zika, Dayan, & Bishop, 2020). The less flexible updating in relation to high internalizing symptoms was further characterized by a reduced updating after positive PE when action-outcome contingencies where changing throughout the learning block (Gagne et al., 2020). Beyond effects on learning rates, there is evidence that anxiety modulates decision-making during RL. Trait anxiety predicted the strategy by which participants explored alternative choices: higher trait anxiety participants exhibited a shift from value-based exploration towards uncertainty-related exploration (Aberg et al., 2022). This is in line with another study which reported that among individuals that were best described by a value-free choice strategy win-stay-lose-shift, higher state anxiety individuals exploited this value-free strategy more. Overall, anxiety may enhance less optimal choice strategies at the cost of value-based decision-making. To summarize, the currently literature found that anxiety has disruptive effects on learning under both low and high levels of uncertainty. Behavioral studies found reduced learning performance and less optimal switching behavior, but learning performance effects may also be nonlinearly related to anxiety symptoms. Computational studies have focused on anxiety effects between high and low uncertainty, but it is unclear how parameters of each uncertainty level are related to anxiety. These studies reported maladaptive learning rate adjustments to the statistics of the environment, as well as a shift from value-based to value-free decision behavior, but no studies have reported both effects of learning rate and decision behavior. Further, some studies reported combined effects of anxiety and depression, and it is not clear to what extent anxiety and depression show distinct effects on learning. Despite its relevance of anxiety disorders during development (Leonardo & Hen, 2008), it is unknown whether anxiety disrupts learning in children and adolescents similarly as in adults, since the only study with a pediatric sample found no behavioral effects of anxiety and did not apply computational models (Dickstein et al., 2010). This study will examine how anxiety symptoms in children and adolescents, both of clinical and normative symptoms range, show effects of uncertainty that are independent of depressive symptoms. ANX-RQ2: What are the effects of anxiety on valence conditions during RL? The effects of anxiety on uncertainty learning were most consistently found during punishment or threat learning, both behaviorally and computationally (Aberg et al., 2022; Browning et al., 2015; Gagne et al., 2020; LaFreniere & Newman, 2019; Piray et al., 2019). Some studies extended the found effects of uncertainty to both punishment and reward learning (Aberg et al., 2022; Gagne et al., 2020; LaFreniere & Newman, 2019) or did not find effects in either valence condition (Dickstein et al., 2010). Other studies did not examine reward and punishment separately, but they presented both rewards and punishments mixed within learning blocks; these studies found behaviorally reduced learning performance (Xia et al., 2021), and in a simulation meta-analysis higher negative PE learning rates and lower positive PE learning across depression and anxiety (Pike & Robinson, 2022). A recent study using naturalistic learning found that higher negative PE learning rates explained lower and less precise expectations about future academic outcomes and predicted the long-term development of anxiety (Villano et al., 2023). In brief, this new body of literature suggests that higher anxiety symptoms may predict aberrant processing of negative outcomes, both under punishment learning and from negative PE, and that this may be a risk factor for the development of anxiety. It is unknown whether anxiety modulates processing of negative outcomes already in children and adolescents. ANX-RQ3: Are there differential effects of anxiety symptom clusters during RL? The distinctions between fear-based and distress-based anxiety symptoms as well as somatic anxiety and cognitive anxiety symptoms have helped to examine learning disruptions in relation to more specific symptom clusters. Although it has been proposed that fear-based symptoms exert stronger effects on RL (Brown et al., 2023), the current literature suggests that multiple symptom clusters may have effects on RL. Studies that specified effects of symptom clusters found that somatic anxiety reduced uncertainty-directed exploration, whereas cognitive anxiety increased uncertainty-directed exploration (Fan et al., 2022). Similarly, divergent roles of these two symptoms clusters have been found during aversive learning (Wise & Dolan, 2020). The previously described maladaptive learning rate adjustment between stable and volatile environments was attributed to distress-based symptoms, which are less specific to anxiety and likely reflect internalizing symptoms more broadly (Gagne et al., 2020; Hammond et al., 2023). Importantly, the effects of fear-based symptoms have not been examined in these two studies. Therefore, it is unknown whether the effects on learning rate can be better attributed to fear-based symptoms rather than to distress-based symptoms. Since maladaptive learning rate adjustments were also linked to reduced pupil dilation changes, reflecting noradrenaline function and arousal, physiological anxiety symptoms may underlie uncertainty-related effects (Browning et al., 2015). The divergent effects of somatic and cognitive anxiety symptoms on decision behavior are difficult to interpret, but it seems that the role of somatic anxiety symptoms is more in line with the effect of a study that did not distinguish by symptom clusters (Aberg et al., 2022). The current definitions of symptoms clusters vary (eg. fear-based and distress-based symptoms, somatic and cognitive anxiety symptoms), which makes is difficult to integrate findings. Effects of ADHD Attention-deficit/hyperactivity disorder (ADHD) is commonly described as a developmental brain disorder, in which the hypofunctioning of noradrenaline and dopamine lead to reduced cognitive performance such as during RL (Plichta & Scheres, 2014; Ziegler, Pedersen, Mowinckel, & Biele, 2016). Notably, not just neurotransmitter hypofunction such as in ADHD, but also hyperfunction such as during stress (Arnsten, 1999, 2009) has been shown to impair performance. Therefore, an inverted-u-shaped relationship between neurotransmitter function and cognitive performance has been proposed (Biederman & Spencer, 1999; Del Campo, Chamberlain, Sahakian, & Robbins, 2011). Beyond functional differences, the effect of ADHD has been observed in the brain structure, likely as long-term consequence of brain neuroplasticity. Brain regions implicated in reward processing and value representation, such as the ventral striatum and the orbitofrontal cortex, exhibited smaller volumes in individuals with ADHD compared to controls (Carmona et al., 2009; Hesslinger et al., 2002). The well-established biological differences in ADHD have led to effective psychopharmacological treatments in ameliorating ADHD symptoms. Stimulants such as methylphenidate are used to target and normalize dopamine function. Some studies that controlled for methylphenidate use showed that it improved learning performance (Luman, Goos, & Oosterlaan, 2015; Pelham, Milich, & Walker, 1986), which suggests that medication use is a relevant confounding factor for examining effects of ADHD on learning. It remains unclear whether ADHD symptoms explain learning differences only by diagnostic categorization such as reported in case-control studies, or whether these learning effects also extend to subclinical ADHD symptoms in clinical and neurotypically developing groups. Further, it remains unclear whether the effects of ADHD on RL are more pronounced during sensitive developmental periods such as during childhood and adolescence. In this study, we will examine how effects of ADHD symptoms are present during RL under different levels of uncertainty and valence. ADHD-RQ1: What are the effects of ADHD on uncertainty conditions during RL? Several studies have examined the effects of ADHD diagnosis on RL under different levels of uncertainty (Hulsbosch et al., 2021). Under low uncertainty, studies showed mixed findings: some found no effects of ADHD (Luman et al., 2015; Oades & Müller, 1997; Wiesner, Molzow, Prehn-Kristensen, & Baving, 2017), while other found reduced learning (Frank, Santamaria, O’Reilly, & Willcutt, 2007; Gabay, Shahbari-Khateb, & Mendelsohn, 2018; Luman et al., 2021; Shephard, Jackson, & Groom, 2016). Additionally two studies reported slower and more variable reaction times (Frank et al., 2007; Gabay et al., 2018). Under high uncertainty, studies show similarly mixed evidence: two studies found no effects of ADHD (Chantiluke et al., 2015; Finger et al., 2008), one study reported computational effects (Hauser et al., 2014) in terms of a more explorative choice behavior, and two studies showed reduced learning performance in ADHD compared to a control group (Itami & Ca, 2002; Shephard et al., 2016). To summarize, the current literature shows mixed findings during RL under both low and high uncertainty. If an effect was found for ADHD, learning performance was reduced, choices were more erratic, and reaction times were slower and more variable. This study aims to shed new light on whether uncertainty modulates effects of ADHD symptoms during RL, and how these effects can be captured both behaviorally and computationally. ADHD-RQ2: What are the effects of ADHD on valence conditions during RL? No studies have examined the effects of punishment learning in ADHD so far. One study has reported differences in response to positive and negative feedback: negative feedback elicited stronger activations in children with ADHD compared to neurotypical children (Van Meel, Oosterlaan, Heslenfeld, & Sergeant, 2005). Given that the abovementioned brain deficits in ADHD were found in relation to reward learning, processing of negative feedback may be relatively spared and lead to an imbalance towards stronger processing of negative information compared to positive information. It is currently unknown whether the effects of ADHD might be less pronounced during punishment learning and learning from negative PE, compared to learning from reward and positive PE. ADHD-RQ3: Are there differential effects of the symptom clusters hyperactivity/impulsivity and inattentiveness during RL? ADHD consists of a set of symptoms, most notably the symptom clusters hyperactivity/impulsivity and inattentiveness. While previous studies reported correlations of the symptom clusters to learning deficits (Gabay et al., 2018; Luman et al., 2015), it is currently unclear whether the symptom clusters show separable contributions to RL. Computational models may help to identify separable contributions. For example, one theoretical model showed that aberrant learning parameters reproduced impulsive behavior in a delayed response time task (Williams & Dayan, 2005). Impulsive and hyperactive symptoms might be to learning parameters such as learning rate. No study has reported learning rate effects of ADHD, although all theoretical models of ADHD agree that dopamine hypofunction leads to aberrant learning parameters (Frank et al., 2007; Sagvolden, Johansen, Aase, & Russell, 2005; Tripp & Wickens, 2008). Thus, it is unknown whether hyperactivity/impulsivity symptoms and the underlying dopamine hypofunction specifically explain effects on learning rate. The attention deficit in ADHD has not been linked to computational parameters yet. However, noradrenaline function, which is fundamental in attentional processes, has been linked decision behavior during RL (Dubois et al., 2021, 2020; Frank et al., 2007). In contrast, one influential theoretical account of ADHD has linked attention deficits to the dopaminergic mesocortical pathway (Sagvolden et al., 2005). Therefore, it remains unclear whether inattentiveness in ADHD is specifically linked to noradrenergic function and attributed explorative decision behavior, as well as slower and more variable reaction times during RL.

Changes in the environment require constant adaptation to optimize future behavior. Learning through reinforcement is essential for adaptation in response to changes in the environment; such ability is thought to play a key role in human cognitive development as well as in the emergence and maintenance of psychiatric disorders, such as ADHD, depression or anxiety. Uncertainty and valence are considered to be two important factors for cognitive adaptation during reinforcement learning (RL). Clinical symptoms have been shown to modulate RL with respect to uncertainty and valence conditions, but little is known how psychopathology affects RL in clinically diagnosed compared to typically developing children and adolescents. In this study, we used a probabilistic learning task in which we experimentally manipulated uncertainty and valence levels. Uncertainty was manipulated by two uncertainty conditions, one which included stochasticity, and another which added volatility. Stochasticity or expected uncertainty refers to learning action-outcome contingencies that were probabilistic and stable (80% contingent and 20% non-contingent feedback). Uncertainty due to the probabilistic outcomes becomes expected once the preferred action-outcome contingency had been learned. Volatility further adds uncertainty to the probabilistic action-outcome contingencies: at change points, the learned action-outcome contingencies have to be reversed. Volatility represents unexpected uncertainty, since the exact points of the change cannot be anticipated. Valence has been examined by effects of both valence condition and prediction error (PE) valence which makes it challenging to compare and generalize results. In our study, our task allowed us to examine both these valence effects. For PE effects, valence is determined by whether the received outcome relative to the expected outcome was positive or negative at each trial (Eckstein, Master, Dahl, Wilbrecht, & Collins, 2022; Rosenbaum, Grassie, & Hartley, 2022). Notably, PE valence represents trial-to-trial valence effects, and learning from a positive PE may lead to a momentary positive surprise that can change quickly from positive to negative, if the next trial comes with a negative PE. For valence as block-wise condition effects, valence is determined by whether the absolute outcome value was positive or negative, such as in reward learning and punishment learning, respectively (Palminteri, Kilford, Coricelli, & Blakemore, 2016). Here, valence may unfold over multiple trials and lead to more enduring valence effects. During reward learning, the preferred outcome was to receive 3 coins compared to 1 coin only; during punishment learning, the loss of 1 coin was preferred than the loss of 3 coins. We also added a third condition of both mixed reward and punishments, which was commonly used in studies that only examined PE valence effects. In this third condition, which we will refer to as mixed condition, the preferred outcome was to receive 1 coin compared to losing 1 coin. Notably, PE valence can be additionally examined in any of the block-wise valence conditions. A pilot study indicated that the task can be applied to both clinical and neurotypical 8-18-year-old children and adolescents, and that their adaptation performance was modulated by valence and uncertainty. Our sample of the main study will include 120 children and adolescents between the age of 8 and 18, one clinical group (n=60) with diagnoses of anxiety, depression or ADHD, and a neurotypical group (n=60). We aim to delineate differences between psychiatric conditions (ADHD, anxiety, depression) for cognitive adaptation in respect to valence and uncertainty. We also aim to explore potential transdiagnostic effects of internalizing and externalizing psychopathology during cognitive adaptation. We will use both behavioral measures, such as accuracy, switching behavior and reaction time, as well as computational modeling measures, with the aim to capture adaptations in latent prediction-related parameters. How one learns in the learning conditions that differ by uncertainty and valence might depend upon the current symptom severity, both in clinical as well as neurotypical groups. Therefore, our aim of this study is to examine individual differences in clinical symptoms and their effects on uncertainty and valence during learning. We will further explore the moderating roles of the individual’s environment, state and trait, such as socioeconomic disparities, motivational traits and momentary affective state. Research Questions (RQ) Effects of Depression Major depressive disorder is an affective disorder whose primary symptoms are low mood and loss of motivation and pleasure in daily life. Despite its primarily affective symptoms, there is robust evidence of cognitive deficits in depression (Gotlib & Joormann, 2010), which may be related to the hypoactivity of serotonin, dopamine and noradrenaline (Eshel & Roiser, 2010; Ruhé, Mason, & Schene, 2007). Of note, depression is considered an internalizing disorder, as opposed to externalizing disorders (Achenbach, 1966; Carver, Johnson, & Timpano, 2017), with a large overlap in symptoms such as with anxiety disorders, specifically within a distress-based or negative affect symptom cluster (Watson, 2005). The overlap in negative affect symptoms between depression and anxiety may contribute to shared differences during RL. Although previous studies have found similarities and differences in the learning effects of depression and anxiety, a direct comparison with a task that manipulates both valence and uncertainty during RL has not been done. DEP-RQ1: What are the effects of depression on uncertainty conditions during RL? Studies that used RL with stable environments, reflecting low uncertainty, found reduced learning performance (Elliott, Sahakian, Herrod, Robbins, & Paykel, 1997; Steele, Meyer, & Ebmeier, 2004). Two studies used computational models to examine learning processes in depression, with in inconsistent results. The studies examined various parameters, including learning rate and reward sensitivity. Learning rate serves as a metric for quantifying how individuals update future values, whereas reward sensitivity reflects an individual's responsiveness to perceived outcomes. Reward learning rates were negatively related to anhedonic depression symptoms (Brown et al., 2021), but unrelated in a meta-analysis (Huys, Pizzagalli, Bogdan, & Dayan, 2013). Reward sensitivity in turn was either reduced (Huys et al., 2013) or increased (Brown et al., 2021). These contrasting findings may partly be explained by the use of a pavlovian and an instrumental learning task, respectively, and our study will focus on instrumental learning. One study reported reduced choice sensitivity in a more depressed student group (Kunisato et al., 2012), suggesting that depression is associated with less value-dependent choice behavior. Because the parameters of reward sensitivity and choice sensitivity are computationally interchangeable, current models cannot answer whether the parameters captured decision-related or feedback-related effects of depression (Browning, Paulus, & Huys, 2022). Additional parameter differences have been reported, such as a more negative valuation of received outcomes during punishment learning in depression (Brown et al., 2021), suggesting valence effects, or a decreased attentional breadth in relation to higher trait rumination (Hitchcock et al., 2022), suggesting impaired attention and memory processes. Further studies are needed to establish robust links between depressive symptoms and computational parameters in low uncertainty learning conditions during RL. Consistent with tasks of relatively low uncertainty, studies with more volatile tasks such as probabilistic reversal learning have reported impaired learning performance in depression (Dombrovski et al., 2010; Dombrovski, Szanto, Clark, Reynolds, & Siegle, 2013; Mukherjee, Filipowicz, Vo, Satterthwaite, & Kable, 2020; Mukherjee, Lee, Kazinka, D Satterthwaite, & Kable, 2020; Must, Horvath, Nemeth, & Janka, 2013; Rupprechter, Stankevicius, Huys, Steele, & Seriès, 2018). Less optimal switching behavior in term of lower win-stay and higher lose-switch behavior (particularly after misleading feedback) was also observed (Dickstein et al., 2010; Dombrovski et al., 2015; Mukherjee, Filipowicz, et al., 2020; Murphy, Michael, Robbins, & Sahakian, 2003; Taylor Tavares et al., 2008). In contrast, only one study also reported no learning differences in relation to depression (Brolsma et al., 2020). Some studies applied computational models, with inconsistent findings, similar to studies with low uncertainty tasks. One study reported reduced learning rates in depressed individuals compared to healthy controls (Mukherjee, Filipowicz, et al., 2020), while another study did not find such differences (Brolsma et al., 2020). Additionally, reduced choice sensitivity was reported (Mukherjee, Filipowicz, et al., 2020; Rupprechter et al., 2018), as well as a decreased memory of observed rewards, putatively due to working memory constraints (Rupprechter et al., 2018). Overall, behavioral findings show a relatively consistent picture of reduced learning performance in depression under both low and high uncertainty learning. However, these studies mostly used tasks of mixed-valence or reward conditions only, and the picture regarding uncertainty under punishment learning is less clear. Further, robust links between computational parameters, depression and uncertainty are yet to be established. There is some evidence that learning rate and choice sensitivity estimates are lower across uncertainty conditions. DEP-RQ2: What are the effects of depression on valence conditions during RL? Past RL studies of depression have usually used either conditions for reward and punishment learning separately, or they used PE valence within a reward task to determine valence effects (i.e. gaining more or less than expected). While both PE valence as well as valence condition (learning from positive or from negative feedback) have been studied extensively in depression, these valence effects may represent distinct characteristics on learning which need to be disentangled. To shed light on the relevance of the way valence is manipulated in relation to depression, our study includes 3 valence conditions: reward learning, punishment learning and mixed valence learning. Of note, many studies that have examined reward learning used a mixed valence feedback scheme. For reward learning, past studies found reduced learning performance in depression (Forbes, Shaw, & Dahl, 2007; Herzallah et al., 2013; Morris, Bylsma, Yaroslavsky, Kovacs, & Rottenberg, 2015; Robinson, Cools, Carlisi, Sahakian, & Drevets, 2012). Reduced reward learning was related to reduced striatal activation following positive PE (Robinson, Cools, Carlisi, et al., 2012). Further, both reduced striatal activations and reduced reward learning were a predictor of future depressive symptoms during adolescence (Forbes et al., 2007; Morgan, Olino, McMakin, Ryan, & Forbes, 2013). In terms of positive PE effects on learning, a simulation meta-analysis found lower learning rates, while results from the conventional meta-analysis shows only showed lower learning rates at trend (Pike & Robinson, 2022). Estimation tasks found a reduced positivity bias (Garrett et al., 2014; Sharot, Korn, & Dolan, 2011), which may implicate a reduced positivity and confirmation bias in depression during RL as well. Here, learning rates from positive and confirmatory PE are usually higher than from negative and disconfirmatory PE, which leads to higher learning performance compared to symmetric learning rates (Palminteri & Lebreton, 2022). In contrast, a developmental study found no effect of depressive symptoms on learning rate asymmetry (Nussenbaum, Velez, Washington, Hamling, & Hartley, 2022). In brief, both reward condition and positive PE effects suggest that reward learning may be reduced in depression, but the underlying computational mechanisms during RL are less clear. For punishment learning, two studies found higher learning performance relative to that of reward learning (Herzallah et al., 2013; Timmer, Sescousse, Van Der Schaaf, Esselink, & Cools, 2017). An acute lowering of serotonin levels in healthy individuals through depleting its precursor tryptophan lead to enhanced punishment learning, which suggests that low serotonin increase punishment learning, but do not affect reward learning (Cools, Robinson, & Sahakian, 2008; Robinson, Cools, & Sahakian, 2012). Indeed, depressed individuals with SSRI medication showed lower punishment learning relative to unmedicated depression individuals, but no differences in reward leaning (Herzallah et al., 2013). Studies with computational models examined either punishment learning rates or negative PE learning rates. Punishment learning rates showed inconsistent findings, with higher learning rates in depressed and anxious individuals compared to healthy controls (Aylward et al., 2019), or lower learning rates in depressed compared to healthy controls (Mukherjee, Filipowicz, et al., 2020). In terms of negative PE valence, a meta-analysis found higher learning rates in a simulation approach, while results in the conventional approach showed no effect (Pike & Robinson, 2022). Another study did not find changed learning rates in relation to depression, but a higher shift towards perceiving punishments as larger (Brown et al., 2021). To summarize, despite some inconsistencies in the literature, both punishment condition and negative PE effects point towards enhanced punishment learning in depression. Overall, the finding of both reduced reward learning and enhanced punishment learning in depression has led to the idea that depressed individuals differ in their subjective valuation and exhibit an exaggerated loss aversion compared to what prospect theory has established in healthy individuals (Chen, Takahashi, Nakagawa, Inoue, & Kusumi, 2015). There is evidence of learning deviations under both valence conditions in depression, but it is yet unclear whether these emerge in earlier developmental phases. DEP-RQ3: Are there differential effects of the anhedonic and negative affect symptoms clusters during RL? Depression is a highly heterogeneous disorder, which might explain some of the inconsistencies found during learning in depression. A closer look at specific symptom effects might prove useful to shed new light on the effects on RL. Depression is often distinguished by the symptom clusters of anhedonia and negative affect. Anhedonia is the loss of pleasure or lack of reactivity to pleasurable stimuli. While negative affect in depression shows considerable overlap with anxiety disorders, anhedonia is more independent of anxiety, but overlapping considerably with other disorders such as schizophrenia or addiction (Pizzagalli, 2014). Negative affect or distress putatively is influenced by serotonergic function, whereas anhedonia depends more upon dopaminergic function (Nutt, 2008). Therefore, these two symptom clusters within depression may have distinct effects on learning behavior. Since serotonergic function has been shown to specifically target punishment learning (Cools et al., 2008; Robinson, Cools, & Sahakian, 2012), it is possible that enhanced punishment learning can be linked to negative affect. A direct link between negative affect and punishment learning in depression has been previously reported, but remains understudied (Brown et al., 2021). Anhedonia in turn has been studied extensively, and consistently identified related reduced striatal activations during reward anticipation and reward delivery in adolescents and adults (Gradin et al., 2011; Stringaris et al., 2015). Anhedonic symptoms were related to less optimal switching behavior (Pizzagalli, Iosifescu, Hallett, Ratner, & Fava, 2008), reduced learning rates from positive and negative PE (Chase et al., 2010), specifically to reduced reward learning rates (Brown et al., 2021), and to more exploratory decision-making during learning (Harlé, Guo, Zhang, Paulus, & Yu, 2017). Overall, this suggests that anhedonic and negative affect symptoms clusters may have differential effects during RL, and further studies are needed to examine how negative affect modulates RL. Effects of Anxiety Anxiety disorders can differ with regard to the situation (e.g. social anxiety) or the objects (e.g. specific phobia) that cause symptoms such as aversive affective state, somatic stress symptoms and perception of sustained threat. Anxiety is increasingly recognized as developmental disorder (Leonardo & Hen, 2008), and together with depression and obsessive-compulsive disorder, it has been categorized as internalizing disorder, as opposed to externalizing disorders, e.g. ADHD and conduct disorder (Achenbach, 1966; Carver et al., 2017). Among internalizing disorders, conditions can be further divided into fear-based or distress-based symptom clusters (Watson, 2005), however, other symptom clusters have been distinguished as well (e.g. somatic and cognitive anxiety symptoms). Maladaptive uncertainty learning has been found for both the fear-based (Brown, Price, & Dombrovski, 2023) and the distress-based symptom clusters (Hammond, Xu, Ai, & Van Dam, 2023). A study that distinguished by somatic and cognitive symptom clusters found stronger influences of the somatic symptom cluster on RL (Fan, Gershman, & Phelps, 2022; Wise & Dolan, 2020). Neurally, individuals with anxiety disorders exhibited higher noradrenaline levels (Kalk, Nutt, & Lingford-Hughes, 2011) and dysregulated serotonergic modulation (Dayan & Huys, 2009), which may relate to the observed learning disruptions. Further, structural brain differences have been found to mediate the effect of unpredictability experienced during childhood on anxiety and depression symptoms in adults, which suggests that the uncertainty experienced as a child increases the risk for anxiety and depression and may lead to structural brain changes during development (Wang, Cao, Zheng, Chen, & Zhu, 2023). It remains unclear to what extent anxiety symptoms explain learning disruptions across both clinical and normative anxiety symptoms equally, and whether age modulates these effects during development. In this study, we will examine how effects of anxiety symptoms are present during RL under different levels of uncertainty and valence in both clinical and typically developing individuals. ANX-RQ1: What are the effects of anxiety on uncertainty conditions during RL? The current literature suggests that uncertainty processing is central to learning disruptions in anxiety symptoms (Brown et al., 2023). The aversive consequence of uncertainty in anxiety has been shown to impair behavioral learning performance already during low uncertainty, such as expected uncertainty, which is inherent in probabilistic but stable learning (LaFreniere & Newman, 2019). Under high uncertainty, such as in volatile environments, higher anxiety reduced learning performance and predicted aberrant switching behavior, such as reduced win-stay, increased or reduced lose-shift behavior (Dickstein et al., 2010; Hein, de Fockert, & Ruiz, 2021; Huang, Thompson, & Paulus, 2017; Piray, Ly, Roelofs, Cools, & Toni, 2019; Xia, Xu, Yang, Gu, & Zhang, 2021). Maladaptive uncertainty learning may be related to chronic underconfidence in individuals with anxiety and depression, as they showed disproportionally larger updates from low confidence decisions (Katyal, Huys, Dolan, & Fleming, 2023). One study also reported an inverted-u-shaped effect of trait anxiety on learning: both low and high trait anxiety were related to lower behavioral learning performance compared to intermediate trait anxiety (Aberg, Toren, & Paz, 2022). A nonlinear effect of anxiety on learning may be linked to the nonlinear effect of arousal on optimal task engagement that is attributed to noradrenaline function (Eckstein, Guerra-Carrillo, Miller Singley, & Bunge, 2017). Computational studies suggest that individuals with higher trait anxiety or with internalizing symptoms have difficulty adjusting their learning rate to the learning environments. Specifically, higher trait anxiety or internalizing symptoms predicted smaller learning rate adjustments between stable and volatile learning environments, which reflects less flexible learning (Browning, Behrens, Jocham, O’Reilly, & Bishop, 2015; Gagne, Zika, Dayan, & Bishop, 2020). The less flexible updating in relation to high internalizing symptoms was further characterized by a reduced updating after positive PE when action-outcome contingencies where changing throughout the learning block (Gagne et al., 2020). Beyond effects on learning rates, there is evidence that anxiety modulates decision-making during RL. Trait anxiety predicted the strategy by which participants explored alternative choices: higher trait anxiety participants exhibited a shift from value-based exploration towards uncertainty-related exploration (Aberg et al., 2022). This is in line with another study which reported that among individuals that were best described by a value-free choice strategy win-stay-lose-shift, higher state anxiety individuals exploited this value-free strategy more. Overall, anxiety may enhance less optimal choice strategies at the cost of value-based decision-making. To summarize, the currently literature found that anxiety has disruptive effects on learning under both low and high levels of uncertainty. Behavioral studies found reduced learning performance and less optimal switching behavior, but learning performance effects may also be nonlinearly related to anxiety symptoms. Computational studies have focused on anxiety effects between high and low uncertainty, but it is unclear how parameters of each uncertainty level are related to anxiety. These studies reported maladaptive learning rate adjustments to the statistics of the environment, as well as a shift from value-based to value-free decision behavior, but no studies have reported both effects of learning rate and decision behavior. Further, some studies reported combined effects of anxiety and depression, and it is not clear to what extent anxiety and depression show distinct effects on learning. Despite its relevance of anxiety disorders during development (Leonardo & Hen, 2008), it is unknown whether anxiety disrupts learning in children and adolescents similarly as in adults, since the only study with a pediatric sample found no behavioral effects of anxiety and did not apply computational models (Dickstein et al., 2010). This study will examine how anxiety symptoms in children and adolescents, both of clinical and normative symptoms range, show effects of uncertainty that are independent of depressive symptoms. ANX-RQ2: What are the effects of anxiety on valence conditions during RL? The effects of anxiety on uncertainty learning were most consistently found during punishment or threat learning, both behaviorally and computationally (Aberg et al., 2022; Browning et al., 2015; Gagne et al., 2020; LaFreniere & Newman, 2019; Piray et al., 2019). Some studies extended the found effects of uncertainty to both punishment and reward learning (Aberg et al., 2022; Gagne et al., 2020; LaFreniere & Newman, 2019) or did not find effects in either valence condition (Dickstein et al., 2010). Other studies did not examine reward and punishment separately, but they presented both rewards and punishments mixed within learning blocks; these studies found behaviorally reduced learning performance (Xia et al., 2021), and in a simulation meta-analysis higher negative PE learning rates and lower positive PE learning across depression and anxiety (Pike & Robinson, 2022). A recent study using naturalistic learning found that higher negative PE learning rates explained lower and less precise expectations about future academic outcomes and predicted the long-term development of anxiety (Villano et al., 2023). In brief, this new body of literature suggests that higher anxiety symptoms may predict aberrant processing of negative outcomes, both under punishment learning and from negative PE, and that this may be a risk factor for the development of anxiety. It is unknown whether anxiety modulates processing of negative outcomes already in children and adolescents. ANX-RQ3: Are there differential effects of anxiety symptom clusters during RL? The distinctions between fear-based and distress-based anxiety symptoms as well as somatic anxiety and cognitive anxiety symptoms have helped to examine learning disruptions in relation to more specific symptom clusters. Although it has been proposed that fear-based symptoms exert stronger effects on RL (Brown et al., 2023), the current literature suggests that multiple symptom clusters may have effects on RL. Studies that specified effects of symptom clusters found that somatic anxiety reduced uncertainty-directed exploration, whereas cognitive anxiety increased uncertainty-directed exploration (Fan et al., 2022). Similarly, divergent roles of these two symptoms clusters have been found during aversive learning (Wise & Dolan, 2020). The previously described maladaptive learning rate adjustment between stable and volatile environments was attributed to distress-based symptoms, which are less specific to anxiety and likely reflect internalizing symptoms more broadly (Gagne et al., 2020; Hammond et al., 2023). Importantly, the effects of fear-based symptoms have not been examined in these two studies. Therefore, it is unknown whether the effects on learning rate can be better attributed to fear-based symptoms rather than to distress-based symptoms. Since maladaptive learning rate adjustments were also linked to reduced pupil dilation changes, reflecting noradrenaline function and arousal, physiological anxiety symptoms may underlie uncertainty-related effects (Browning et al., 2015). The divergent effects of somatic and cognitive anxiety symptoms on decision behavior are difficult to interpret, but it seems that the role of somatic anxiety symptoms is more in line with the effect of a study that did not distinguish by symptom clusters (Aberg et al., 2022). The current definitions of symptoms clusters vary (eg. fear-based and distress-based symptoms, somatic and cognitive anxiety symptoms), which makes is difficult to integrate findings. Effects of ADHD Attention-deficit/hyperactivity disorder (ADHD) is commonly described as a developmental brain disorder, in which the hypofunctioning of noradrenaline and dopamine lead to reduced cognitive performance such as during RL (Plichta & Scheres, 2014; Ziegler, Pedersen, Mowinckel, & Biele, 2016). Notably, not just neurotransmitter hypofunction such as in ADHD, but also hyperfunction such as during stress (Arnsten, 1999, 2009) has been shown to impair performance. Therefore, an inverted-u-shaped relationship between neurotransmitter function and cognitive performance has been proposed (Biederman & Spencer, 1999; Del Campo, Chamberlain, Sahakian, & Robbins, 2011). Beyond functional differences, the effect of ADHD has been observed in the brain structure, likely as long-term consequence of brain neuroplasticity. Brain regions implicated in reward processing and value representation, such as the ventral striatum and the orbitofrontal cortex, exhibited smaller volumes in individuals with ADHD compared to controls (Carmona et al., 2009; Hesslinger et al., 2002). The well-established biological differences in ADHD have led to effective psychopharmacological treatments in ameliorating ADHD symptoms. Stimulants such as methylphenidate are used to target and normalize dopamine function. Some studies that controlled for methylphenidate use showed that it improved learning performance (Luman, Goos, & Oosterlaan, 2015; Pelham, Milich, & Walker, 1986), which suggests that medication use is a relevant confounding factor for examining effects of ADHD on learning. It remains unclear whether ADHD symptoms explain learning differences only by diagnostic categorization such as reported in case-control studies, or whether these learning effects also extend to subclinical ADHD symptoms in clinical and neurotypically developing groups. Further, it remains unclear whether the effects of ADHD on RL are more pronounced during sensitive developmental periods such as during childhood and adolescence. In this study, we will examine how effects of ADHD symptoms are present during RL under different levels of uncertainty and valence. ADHD-RQ1: What are the effects of ADHD on uncertainty conditions during RL? Several studies have examined the effects of ADHD diagnosis on RL under different levels of uncertainty (Hulsbosch et al., 2021). Under low uncertainty, studies showed mixed findings: some found no effects of ADHD (Luman et al., 2015; Oades & Müller, 1997; Wiesner, Molzow, Prehn-Kristensen, & Baving, 2017), while other found reduced learning (Frank, Santamaria, O’Reilly, & Willcutt, 2007; Gabay, Shahbari-Khateb, & Mendelsohn, 2018; Luman et al., 2021; Shephard, Jackson, & Groom, 2016). Additionally two studies reported slower and more variable reaction times (Frank et al., 2007; Gabay et al., 2018). Under high uncertainty, studies show similarly mixed evidence: two studies found no effects of ADHD (Chantiluke et al., 2015; Finger et al., 2008), one study reported computational effects (Hauser et al., 2014) in terms of a more explorative choice behavior, and two studies showed reduced learning performance in ADHD compared to a control group (Itami & Ca, 2002; Shephard et al., 2016). To summarize, the current literature shows mixed findings during RL under both low and high uncertainty. If an effect was found for ADHD, learning performance was reduced, choices were more erratic, and reaction times were slower and more variable. This study aims to shed new light on whether uncertainty modulates effects of ADHD symptoms during RL, and how these effects can be captured both behaviorally and computationally. ADHD-RQ2: What are the effects of ADHD on valence conditions during RL? No studies have examined the effects of punishment learning in ADHD so far. One study has reported differences in response to positive and negative feedback: negative feedback elicited stronger activations in children with ADHD compared to neurotypical children (Van Meel, Oosterlaan, Heslenfeld, & Sergeant, 2005). Given that the abovementioned brain deficits in ADHD were found in relation to reward learning, processing of negative feedback may be relatively spared and lead to an imbalance towards stronger processing of negative information compared to positive information. It is currently unknown whether the effects of ADHD might be less pronounced during punishment learning and learning from negative PE, compared to learning from reward and positive PE. ADHD-RQ3: Are there differential effects of the symptom clusters hyperactivity/impulsivity and inattentiveness during RL? ADHD consists of a set of symptoms, most notably the symptom clusters hyperactivity/impulsivity and inattentiveness. While previous studies reported correlations of the symptom clusters to learning deficits (Gabay et al., 2018; Luman et al., 2015), it is currently unclear whether the symptom clusters show separable contributions to RL. Computational models may help to identify separable contributions. For example, one theoretical model showed that aberrant learning parameters reproduced impulsive behavior in a delayed response time task (Williams & Dayan, 2005). Impulsive and hyperactive symptoms might be to learning parameters such as learning rate. No study has reported learning rate effects of ADHD, although all theoretical models of ADHD agree that dopamine hypofunction leads to aberrant learning parameters (Frank et al., 2007; Sagvolden, Johansen, Aase, & Russell, 2005; Tripp & Wickens, 2008). Thus, it is unknown whether hyperactivity/impulsivity symptoms and the underlying dopamine hypofunction specifically explain effects on learning rate. The attention deficit in ADHD has not been linked to computational parameters yet. However, noradrenaline function, which is fundamental in attentional processes, has been linked decision behavior during RL (Dubois et al., 2021, 2020; Frank et al., 2007). In contrast, one influential theoretical account of ADHD has linked attention deficits to the dopaminergic mesocortical pathway (Sagvolden et al., 2005). Therefore, it remains unclear whether inattentiveness in ADHD is specifically linked to noradrenergic function and attributed explorative decision behavior, as well as slower and more variable reaction times during RL.

Changes in the environment require constant adaptation to optimize future behavior. Learning through reinforcement is essential for adaptation in response to changes in the environment; such ability is thought to play a key role in human cognitive development as well as in the emergence and maintenance of psychiatric disorders, such as ADHD, depression or anxiety. Uncertainty and valence are considered to be two important factors for cognitive adaptation during reinforcement learning (RL). Clinical symptoms have been shown to modulate RL with respect to uncertainty and valence conditions, but little is known how psychopathology affects RL in clinically diagnosed compared to typically developing children and adolescents. In this study, we used a probabilistic learning task in which we experimentally manipulated uncertainty and valence levels. Uncertainty was manipulated by two uncertainty conditions, one which included stochasticity, and another which added volatility. Stochasticity or expected uncertainty refers to learning action-outcome contingencies that were probabilistic and stable (80% contingent and 20% non-contingent feedback). Uncertainty due to the probabilistic outcomes becomes expected once the preferred action-outcome contingency had been learned. Volatility further adds uncertainty to the probabilistic action-outcome contingencies: at change points, the learned action-outcome contingencies have to be reversed. Volatility represents unexpected uncertainty, since the exact points of the change cannot be anticipated. Valence has been examined by effects of both valence condition and prediction error (PE) valence which makes it challenging to compare and generalize results. In our study, our task allowed us to examine both these valence effects. For PE effects, valence is determined by whether the received outcome relative to the expected outcome was positive or negative at each trial (Eckstein, Master, Dahl, Wilbrecht, & Collins, 2022; Rosenbaum, Grassie, & Hartley, 2022). Notably, PE valence represents trial-to-trial valence effects, and learning from a positive PE may lead to a momentary positive surprise that can change quickly from positive to negative, if the next trial comes with a negative PE. For valence as block-wise condition effects, valence is determined by whether the absolute outcome value was positive or negative, such as in reward learning and punishment learning, respectively (Palminteri, Kilford, Coricelli, & Blakemore, 2016). Here, valence may unfold over multiple trials and lead to more enduring valence effects. During reward learning, the preferred outcome was to receive 3 coins compared to 1 coin only; during punishment learning, the loss of 1 coin was preferred than the loss of 3 coins. We also added a third condition of both mixed reward and punishments, which was commonly used in studies that only examined PE valence effects. In this third condition, which we will refer to as mixed condition, the preferred outcome was to receive 1 coin compared to losing 1 coin. Notably, PE valence can be additionally examined in any of the block-wise valence conditions. A pilot study indicated that the task can be applied to both clinical and neurotypical 8-18-year-old children and adolescents, and that their adaptation performance was modulated by valence and uncertainty. Our sample of the main study will include 120 children and adolescents between the age of 8 and 18, one clinical group (n=60) with diagnoses of anxiety, depression or ADHD, and a neurotypical group (n=60). We aim to delineate differences between psychiatric conditions (ADHD, anxiety, depression) for cognitive adaptation in respect to valence and uncertainty. We also aim to explore potential transdiagnostic effects of internalizing and externalizing psychopathology during cognitive adaptation. We will use both behavioral measures, such as accuracy, switching behavior and reaction time, as well as computational modeling measures, with the aim to capture adaptations in latent prediction-related parameters. How one learns in the learning conditions that differ by uncertainty and valence might depend upon the current symptom severity, both in clinical as well as neurotypical groups. Therefore, our aim of this study is to examine individual differences in clinical symptoms and their effects on uncertainty and valence during learning. We will further explore the moderating roles of the individual’s environment, state and trait, such as socioeconomic disparities, motivational traits and momentary affective state. Research Questions (RQ) Effects of Depression Major depressive disorder is an affective disorder whose primary symptoms are low mood and loss of motivation and pleasure in daily life. Despite its primarily affective symptoms, there is robust evidence of cognitive deficits in depression (Gotlib & Joormann, 2010), which may be related to the hypoactivity of serotonin, dopamine and noradrenaline (Eshel & Roiser, 2010; Ruhé, Mason, & Schene, 2007). Of note, depression is considered an internalizing disorder, as opposed to externalizing disorders (Achenbach, 1966; Carver, Johnson, & Timpano, 2017), with a large overlap in symptoms such as with anxiety disorders, specifically within a distress-based or negative affect symptom cluster (Watson, 2005). The overlap in negative affect symptoms between depression and anxiety may contribute to shared differences during RL. Although previous studies have found similarities and differences in the learning effects of depression and anxiety, a direct comparison with a task that manipulates both valence and uncertainty during RL has not been done. DEP-RQ1: What are the effects of depression on uncertainty conditions during RL? Studies that used RL with stable environments, reflecting low uncertainty, found reduced learning performance (Elliott, Sahakian, Herrod, Robbins, & Paykel, 1997; Steele, Meyer, & Ebmeier, 2004). Two studies used computational models to examine learning processes in depression, with in inconsistent results. The studies examined various parameters, including learning rate and reward sensitivity. Learning rate serves as a metric for quantifying how individuals update future values, whereas reward sensitivity reflects an individual's responsiveness to perceived outcomes. Reward learning rates were negatively related to anhedonic depression symptoms (Brown et al., 2021), but unrelated in a meta-analysis (Huys, Pizzagalli, Bogdan, & Dayan, 2013). Reward sensitivity in turn was either reduced (Huys et al., 2013) or increased (Brown et al., 2021). These contrasting findings may partly be explained by the use of a pavlovian and an instrumental learning task, respectively, and our study will focus on instrumental learning. One study reported reduced choice sensitivity in a more depressed student group (Kunisato et al., 2012), suggesting that depression is associated with less value-dependent choice behavior. Because the parameters of reward sensitivity and choice sensitivity are computationally interchangeable, current models cannot answer whether the parameters captured decision-related or feedback-related effects of depression (Browning, Paulus, & Huys, 2022). Additional parameter differences have been reported, such as a more negative valuation of received outcomes during punishment learning in depression (Brown et al., 2021), suggesting valence effects, or a decreased attentional breadth in relation to higher trait rumination (Hitchcock et al., 2022), suggesting impaired attention and memory processes. Further studies are needed to establish robust links between depressive symptoms and computational parameters in low uncertainty learning conditions during RL. Consistent with tasks of relatively low uncertainty, studies with more volatile tasks such as probabilistic reversal learning have reported impaired learning performance in depression (Dombrovski et al., 2010; Dombrovski, Szanto, Clark, Reynolds, & Siegle, 2013; Mukherjee, Filipowicz, Vo, Satterthwaite, & Kable, 2020; Mukherjee, Lee, Kazinka, D Satterthwaite, & Kable, 2020; Must, Horvath, Nemeth, & Janka, 2013; Rupprechter, Stankevicius, Huys, Steele, & Seriès, 2018). Less optimal switching behavior in term of lower win-stay and higher lose-switch behavior (particularly after misleading feedback) was also observed (Dickstein et al., 2010; Dombrovski et al., 2015; Mukherjee, Filipowicz, et al., 2020; Murphy, Michael, Robbins, & Sahakian, 2003; Taylor Tavares et al., 2008). In contrast, only one study also reported no learning differences in relation to depression (Brolsma et al., 2020). Some studies applied computational models, with inconsistent findings, similar to studies with low uncertainty tasks. One study reported reduced learning rates in depressed individuals compared to healthy controls (Mukherjee, Filipowicz, et al., 2020), while another study did not find such differences (Brolsma et al., 2020). Additionally, reduced choice sensitivity was reported (Mukherjee, Filipowicz, et al., 2020; Rupprechter et al., 2018), as well as a decreased memory of observed rewards, putatively due to working memory constraints (Rupprechter et al., 2018). Overall, behavioral findings show a relatively consistent picture of reduced learning performance in depression under both low and high uncertainty learning. However, these studies mostly used tasks of mixed-valence or reward conditions only, and the picture regarding uncertainty under punishment learning is less clear. Further, robust links between computational parameters, depression and uncertainty are yet to be established. There is some evidence that learning rate and choice sensitivity estimates are lower across uncertainty conditions. DEP-RQ2: What are the effects of depression on valence conditions during RL? Past RL studies of depression have usually used either conditions for reward and punishment learning separately, or they used PE valence within a reward task to determine valence effects (i.e. gaining more or less than expected). While both PE valence as well as valence condition (learning from positive or from negative feedback) have been studied extensively in depression, these valence effects may represent distinct characteristics on learning which need to be disentangled. To shed light on the relevance of the way valence is manipulated in relation to depression, our study includes 3 valence conditions: reward learning, punishment learning and mixed valence learning. Of note, many studies that have examined reward learning used a mixed valence feedback scheme. For reward learning, past studies found reduced learning performance in depression (Forbes, Shaw, & Dahl, 2007; Herzallah et al., 2013; Morris, Bylsma, Yaroslavsky, Kovacs, & Rottenberg, 2015; Robinson, Cools, Carlisi, Sahakian, & Drevets, 2012). Reduced reward learning was related to reduced striatal activation following positive PE (Robinson, Cools, Carlisi, et al., 2012). Further, both reduced striatal activations and reduced reward learning were a predictor of future depressive symptoms during adolescence (Forbes et al., 2007; Morgan, Olino, McMakin, Ryan, & Forbes, 2013). In terms of positive PE effects on learning, a simulation meta-analysis found lower learning rates, while results from the conventional meta-analysis shows only showed lower learning rates at trend (Pike & Robinson, 2022). Estimation tasks found a reduced positivity bias (Garrett et al., 2014; Sharot, Korn, & Dolan, 2011), which may implicate a reduced positivity and confirmation bias in depression during RL as well. Here, learning rates from positive and confirmatory PE are usually higher than from negative and disconfirmatory PE, which leads to higher learning performance compared to symmetric learning rates (Palminteri & Lebreton, 2022). In contrast, a developmental study found no effect of depressive symptoms on learning rate asymmetry (Nussenbaum, Velez, Washington, Hamling, & Hartley, 2022). In brief, both reward condition and positive PE effects suggest that reward learning may be reduced in depression, but the underlying computational mechanisms during RL are less clear. For punishment learning, two studies found higher learning performance relative to that of reward learning (Herzallah et al., 2013; Timmer, Sescousse, Van Der Schaaf, Esselink, & Cools, 2017). An acute lowering of serotonin levels in healthy individuals through depleting its precursor tryptophan lead to enhanced punishment learning, which suggests that low serotonin increase punishment learning, but do not affect reward learning (Cools, Robinson, & Sahakian, 2008; Robinson, Cools, & Sahakian, 2012). Indeed, depressed individuals with SSRI medication showed lower punishment learning relative to unmedicated depression individuals, but no differences in reward leaning (Herzallah et al., 2013). Studies with computational models examined either punishment learning rates or negative PE learning rates. Punishment learning rates showed inconsistent findings, with higher learning rates in depressed and anxious individuals compared to healthy controls (Aylward et al., 2019), or lower learning rates in depressed compared to healthy controls (Mukherjee, Filipowicz, et al., 2020). In terms of negative PE valence, a meta-analysis found higher learning rates in a simulation approach, while results in the conventional approach showed no effect (Pike & Robinson, 2022). Another study did not find changed learning rates in relation to depression, but a higher shift towards perceiving punishments as larger (Brown et al., 2021). To summarize, despite some inconsistencies in the literature, both punishment condition and negative PE effects point towards enhanced punishment learning in depression. Overall, the finding of both reduced reward learning and enhanced punishment learning in depression has led to the idea that depressed individuals differ in their subjective valuation and exhibit an exaggerated loss aversion compared to what prospect theory has established in healthy individuals (Chen, Takahashi, Nakagawa, Inoue, & Kusumi, 2015). There is evidence of learning deviations under both valence conditions in depression, but it is yet unclear whether these emerge in earlier developmental phases. DEP-RQ3: Are there differential effects of the anhedonic and negative affect symptoms clusters during RL? Depression is a highly heterogeneous disorder, which might explain some of the inconsistencies found during learning in depression. A closer look at specific symptom effects might prove useful to shed new light on the effects on RL. Depression is often distinguished by the symptom clusters of anhedonia and negative affect. Anhedonia is the loss of pleasure or lack of reactivity to pleasurable stimuli. While negative affect in depression shows considerable overlap with anxiety disorders, anhedonia is more independent of anxiety, but overlapping considerably with other disorders such as schizophrenia or addiction (Pizzagalli, 2014). Negative affect or distress putatively is influenced by serotonergic function, whereas anhedonia depends more upon dopaminergic function (Nutt, 2008). Therefore, these two symptom clusters within depression may have distinct effects on learning behavior. Since serotonergic function has been shown to specifically target punishment learning (Cools et al., 2008; Robinson, Cools, & Sahakian, 2012), it is possible that enhanced punishment learning can be linked to negative affect. A direct link between negative affect and punishment learning in depression has been previously reported, but remains understudied (Brown et al., 2021). Anhedonia in turn has been studied extensively, and consistently identified related reduced striatal activations during reward anticipation and reward delivery in adolescents and adults (Gradin et al., 2011; Stringaris et al., 2015). Anhedonic symptoms were related to less optimal switching behavior (Pizzagalli, Iosifescu, Hallett, Ratner, & Fava, 2008), reduced learning rates from positive and negative PE (Chase et al., 2010), specifically to reduced reward learning rates (Brown et al., 2021), and to more exploratory decision-making during learning (Harlé, Guo, Zhang, Paulus, & Yu, 2017). Overall, this suggests that anhedonic and negative affect symptoms clusters may have differential effects during RL, and further studies are needed to examine how negative affect modulates RL. Effects of Anxiety Anxiety disorders can differ with regard to the situation (e.g. social anxiety) or the objects (e.g. specific phobia) that cause symptoms such as aversive affective state, somatic stress symptoms and perception of sustained threat. Anxiety is increasingly recognized as developmental disorder (Leonardo & Hen, 2008), and together with depression and obsessive-compulsive disorder, it has been categorized as internalizing disorder, as opposed to externalizing disorders, e.g. ADHD and conduct disorder (Achenbach, 1966; Carver et al., 2017). Among internalizing disorders, conditions can be further divided into fear-based or distress-based symptom clusters (Watson, 2005), however, other symptom clusters have been distinguished as well (e.g. somatic and cognitive anxiety symptoms). Maladaptive uncertainty learning has been found for both the fear-based (Brown, Price, & Dombrovski, 2023) and the distress-based symptom clusters (Hammond, Xu, Ai, & Van Dam, 2023). A study that distinguished by somatic and cognitive symptom clusters found stronger influences of the somatic symptom cluster on RL (Fan, Gershman, & Phelps, 2022; Wise & Dolan, 2020). Neurally, individuals with anxiety disorders exhibited higher noradrenaline levels (Kalk, Nutt, & Lingford-Hughes, 2011) and dysregulated serotonergic modulation (Dayan & Huys, 2009), which may relate to the observed learning disruptions. Further, structural brain differences have been found to mediate the effect of unpredictability experienced during childhood on anxiety and depression symptoms in adults, which suggests that the uncertainty experienced as a child increases the risk for anxiety and depression and may lead to structural brain changes during development (Wang, Cao, Zheng, Chen, & Zhu, 2023). It remains unclear to what extent anxiety symptoms explain learning disruptions across both clinical and normative anxiety symptoms equally, and whether age modulates these effects during development. In this study, we will examine how effects of anxiety symptoms are present during RL under different levels of uncertainty and valence in both clinical and typically developing individuals. ANX-RQ1: What are the effects of anxiety on uncertainty conditions during RL? The current literature suggests that uncertainty processing is central to learning disruptions in anxiety symptoms (Brown et al., 2023). The aversive consequence of uncertainty in anxiety has been shown to impair behavioral learning performance already during low uncertainty, such as expected uncertainty, which is inherent in probabilistic but stable learning (LaFreniere & Newman, 2019). Under high uncertainty, such as in volatile environments, higher anxiety reduced learning performance and predicted aberrant switching behavior, such as reduced win-stay, increased or reduced lose-shift behavior (Dickstein et al., 2010; Hein, de Fockert, & Ruiz, 2021; Huang, Thompson, & Paulus, 2017; Piray, Ly, Roelofs, Cools, & Toni, 2019; Xia, Xu, Yang, Gu, & Zhang, 2021). Maladaptive uncertainty learning may be related to chronic underconfidence in individuals with anxiety and depression, as they showed disproportionally larger updates from low confidence decisions (Katyal, Huys, Dolan, & Fleming, 2023). One study also reported an inverted-u-shaped effect of trait anxiety on learning: both low and high trait anxiety were related to lower behavioral learning performance compared to intermediate trait anxiety (Aberg, Toren, & Paz, 2022). A nonlinear effect of anxiety on learning may be linked to the nonlinear effect of arousal on optimal task engagement that is attributed to noradrenaline function (Eckstein, Guerra-Carrillo, Miller Singley, & Bunge, 2017). Computational studies suggest that individuals with higher trait anxiety or with internalizing symptoms have difficulty adjusting their learning rate to the learning environments. Specifically, higher trait anxiety or internalizing symptoms predicted smaller learning rate adjustments between stable and volatile learning environments, which reflects less flexible learning (Browning, Behrens, Jocham, O’Reilly, & Bishop, 2015; Gagne, Zika, Dayan, & Bishop, 2020). The less flexible updating in relation to high internalizing symptoms was further characterized by a reduced updating after positive PE when action-outcome contingencies where changing throughout the learning block (Gagne et al., 2020). Beyond effects on learning rates, there is evidence that anxiety modulates decision-making during RL. Trait anxiety predicted the strategy by which participants explored alternative choices: higher trait anxiety participants exhibited a shift from value-based exploration towards uncertainty-related exploration (Aberg et al., 2022). This is in line with another study which reported that among individuals that were best described by a value-free choice strategy win-stay-lose-shift, higher state anxiety individuals exploited this value-free strategy more. Overall, anxiety may enhance less optimal choice strategies at the cost of value-based decision-making. To summarize, the currently literature found that anxiety has disruptive effects on learning under both low and high levels of uncertainty. Behavioral studies found reduced learning performance and less optimal switching behavior, but learning performance effects may also be nonlinearly related to anxiety symptoms. Computational studies have focused on anxiety effects between high and low uncertainty, but it is unclear how parameters of each uncertainty level are related to anxiety. These studies reported maladaptive learning rate adjustments to the statistics of the environment, as well as a shift from value-based to value-free decision behavior, but no studies have reported both effects of learning rate and decision behavior. Further, some studies reported combined effects of anxiety and depression, and it is not clear to what extent anxiety and depression show distinct effects on learning. Despite its relevance of anxiety disorders during development (Leonardo & Hen, 2008), it is unknown whether anxiety disrupts learning in children and adolescents similarly as in adults, since the only study with a pediatric sample found no behavioral effects of anxiety and did not apply computational models (Dickstein et al., 2010). This study will examine how anxiety symptoms in children and adolescents, both of clinical and normative symptoms range, show effects of uncertainty that are independent of depressive symptoms. ANX-RQ2: What are the effects of anxiety on valence conditions during RL? The effects of anxiety on uncertainty learning were most consistently found during punishment or threat learning, both behaviorally and computationally (Aberg et al., 2022; Browning et al., 2015; Gagne et al., 2020; LaFreniere & Newman, 2019; Piray et al., 2019). Some studies extended the found effects of uncertainty to both punishment and reward learning (Aberg et al., 2022; Gagne et al., 2020; LaFreniere & Newman, 2019) or did not find effects in either valence condition (Dickstein et al., 2010). Other studies did not examine reward and punishment separately, but they presented both rewards and punishments mixed within learning blocks; these studies found behaviorally reduced learning performance (Xia et al., 2021), and in a simulation meta-analysis higher negative PE learning rates and lower positive PE learning across depression and anxiety (Pike & Robinson, 2022). A recent study using naturalistic learning found that higher negative PE learning rates explained lower and less precise expectations about future academic outcomes and predicted the long-term development of anxiety (Villano et al., 2023). In brief, this new body of literature suggests that higher anxiety symptoms may predict aberrant processing of negative outcomes, both under punishment learning and from negative PE, and that this may be a risk factor for the development of anxiety. It is unknown whether anxiety modulates processing of negative outcomes already in children and adolescents. ANX-RQ3: Are there differential effects of anxiety symptom clusters during RL? The distinctions between fear-based and distress-based anxiety symptoms as well as somatic anxiety and cognitive anxiety symptoms have helped to examine learning disruptions in relation to more specific symptom clusters. Although it has been proposed that fear-based symptoms exert stronger effects on RL (Brown et al., 2023), the current literature suggests that multiple symptom clusters may have effects on RL. Studies that specified effects of symptom clusters found that somatic anxiety reduced uncertainty-directed exploration, whereas cognitive anxiety increased uncertainty-directed exploration (Fan et al., 2022). Similarly, divergent roles of these two symptoms clusters have been found during aversive learning (Wise & Dolan, 2020). The previously described maladaptive learning rate adjustment between stable and volatile environments was attributed to distress-based symptoms, which are less specific to anxiety and likely reflect internalizing symptoms more broadly (Gagne et al., 2020; Hammond et al., 2023). Importantly, the effects of fear-based symptoms have not been examined in these two studies. Therefore, it is unknown whether the effects on learning rate can be better attributed to fear-based symptoms rather than to distress-based symptoms. Since maladaptive learning rate adjustments were also linked to reduced pupil dilation changes, reflecting noradrenaline function and arousal, physiological anxiety symptoms may underlie uncertainty-related effects (Browning et al., 2015). The divergent effects of somatic and cognitive anxiety symptoms on decision behavior are difficult to interpret, but it seems that the role of somatic anxiety symptoms is more in line with the effect of a study that did not distinguish by symptom clusters (Aberg et al., 2022). The current definitions of symptoms clusters vary (eg. fear-based and distress-based symptoms, somatic and cognitive anxiety symptoms), which makes is difficult to integrate findings. Effects of ADHD Attention-deficit/hyperactivity disorder (ADHD) is commonly described as a developmental brain disorder, in which the hypofunctioning of noradrenaline and dopamine lead to reduced cognitive performance such as during RL (Plichta & Scheres, 2014; Ziegler, Pedersen, Mowinckel, & Biele, 2016). Notably, not just neurotransmitter hypofunction such as in ADHD, but also hyperfunction such as during stress (Arnsten, 1999, 2009) has been shown to impair performance. Therefore, an inverted-u-shaped relationship between neurotransmitter function and cognitive performance has been proposed (Biederman & Spencer, 1999; Del Campo, Chamberlain, Sahakian, & Robbins, 2011). Beyond functional differences, the effect of ADHD has been observed in the brain structure, likely as long-term consequence of brain neuroplasticity. Brain regions implicated in reward processing and value representation, such as the ventral striatum and the orbitofrontal cortex, exhibited smaller volumes in individuals with ADHD compared to controls (Carmona et al., 2009; Hesslinger et al., 2002). The well-established biological differences in ADHD have led to effective psychopharmacological treatments in ameliorating ADHD symptoms. Stimulants such as methylphenidate are used to target and normalize dopamine function. Some studies that controlled for methylphenidate use showed that it improved learning performance (Luman, Goos, & Oosterlaan, 2015; Pelham, Milich, & Walker, 1986), which suggests that medication use is a relevant confounding factor for examining effects of ADHD on learning. It remains unclear whether ADHD symptoms explain learning differences only by diagnostic categorization such as reported in case-control studies, or whether these learning effects also extend to subclinical ADHD symptoms in clinical and neurotypically developing groups. Further, it remains unclear whether the effects of ADHD on RL are more pronounced during sensitive developmental periods such as during childhood and adolescence. In this study, we will examine how effects of ADHD symptoms are present during RL under different levels of uncertainty and valence. ADHD-RQ1: What are the effects of ADHD on uncertainty conditions during RL? Several studies have examined the effects of ADHD diagnosis on RL under different levels of uncertainty (Hulsbosch et al., 2021). Under low uncertainty, studies showed mixed findings: some found no effects of ADHD (Luman et al., 2015; Oades & Müller, 1997; Wiesner, Molzow, Prehn-Kristensen, & Baving, 2017), while other found reduced learning (Frank, Santamaria, O’Reilly, & Willcutt, 2007; Gabay, Shahbari-Khateb, & Mendelsohn, 2018; Luman et al., 2021; Shephard, Jackson, & Groom, 2016). Additionally two studies reported slower and more variable reaction times (Frank et al., 2007; Gabay et al., 2018). Under high uncertainty, studies show similarly mixed evidence: two studies found no effects of ADHD (Chantiluke et al., 2015; Finger et al., 2008), one study reported computational effects (Hauser et al., 2014) in terms of a more explorative choice behavior, and two studies showed reduced learning performance in ADHD compared to a control group (Itami & Ca, 2002; Shephard et al., 2016). To summarize, the current literature shows mixed findings during RL under both low and high uncertainty. If an effect was found for ADHD, learning performance was reduced, choices were more erratic, and reaction times were slower and more variable. This study aims to shed new light on whether uncertainty modulates effects of ADHD symptoms during RL, and how these effects can be captured both behaviorally and computationally. ADHD-RQ2: What are the effects of ADHD on valence conditions during RL? No studies have examined the effects of punishment learning in ADHD so far. One study has reported differences in response to positive and negative feedback: negative feedback elicited stronger activations in children with ADHD compared to neurotypical children (Van Meel, Oosterlaan, Heslenfeld, & Sergeant, 2005). Given that the abovementioned brain deficits in ADHD were found in relation to reward learning, processing of negative feedback may be relatively spared and lead to an imbalance towards stronger processing of negative information compared to positive information. It is currently unknown whether the effects of ADHD might be less pronounced during punishment learning and learning from negative PE, compared to learning from reward and positive PE. ADHD-RQ3: Are there differential effects of the symptom clusters hyperactivity/impulsivity and inattentiveness during RL? ADHD consists of a set of symptoms, most notably the symptom clusters hyperactivity/impulsivity and inattentiveness. While previous studies reported correlations of the symptom clusters to learning deficits (Gabay et al., 2018; Luman et al., 2015), it is currently unclear whether the symptom clusters show separable contributions to RL. Computational models may help to identify separable contributions. For example, one theoretical model showed that aberrant learning parameters reproduced impulsive behavior in a delayed response time task (Williams & Dayan, 2005). Impulsive and hyperactive symptoms might be to learning parameters such as learning rate. No study has reported learning rate effects of ADHD, although all theoretical models of ADHD agree that dopamine hypofunction leads to aberrant learning parameters (Frank et al., 2007; Sagvolden, Johansen, Aase, & Russell, 2005; Tripp & Wickens, 2008). Thus, it is unknown whether hyperactivity/impulsivity symptoms and the underlying dopamine hypofunction specifically explain effects on learning rate. The attention deficit in ADHD has not been linked to computational parameters yet. However, noradrenaline function, which is fundamental in attentional processes, has been linked decision behavior during RL (Dubois et al., 2021, 2020; Frank et al., 2007). In contrast, one influential theoretical account of ADHD has linked attention deficits to the dopaminergic mesocortical pathway (Sagvolden et al., 2005). Therefore, it remains unclear whether inattentiveness in ADHD is specifically linked to noradrenergic function and attributed explorative decision behavior, as well as slower and more variable reaction times during RL.

Changes in the environment require constant adaptation to optimize future behavior. Learning through reinforcement is essential for adaptation in response to changes in the environment; such ability is thought to play a key role in human cognitive development as well as in the emergence and maintenance of psychiatric disorders, such as ADHD, depression or anxiety. Uncertainty and valence are considered to be two important factors for cognitive adaptation during reinforcement learning (RL). Clinical symptoms have been shown to modulate RL with respect to uncertainty and valence conditions, but little is known how psychopathology affects RL in clinically diagnosed compared to typically developing children and adolescents. In this study, we used a probabilistic learning task in which we experimentally manipulated uncertainty and valence levels. Uncertainty was manipulated by two uncertainty conditions, one which included stochasticity, and another which added volatility. Stochasticity or expected uncertainty refers to learning action-outcome contingencies that were probabilistic and stable (80% contingent and 20% non-contingent feedback). Uncertainty due to the probabilistic outcomes becomes expected once the preferred action-outcome contingency had been learned. Volatility further adds uncertainty to the probabilistic action-outcome contingencies: at change points, the learned action-outcome contingencies have to be reversed. Volatility represents unexpected uncertainty, since the exact points of the change cannot be anticipated. Valence has been examined by effects of both valence condition and prediction error (PE) valence which makes it challenging to compare and generalize results. In our study, our task allowed us to examine both these valence effects. For PE effects, valence is determined by whether the received outcome relative to the expected outcome was positive or negative at each trial (Eckstein, Master, Dahl, Wilbrecht, & Collins, 2022; Rosenbaum, Grassie, & Hartley, 2022). Notably, PE valence represents trial-to-trial valence effects, and learning from a positive PE may lead to a momentary positive surprise that can change quickly from positive to negative, if the next trial comes with a negative PE. For valence as block-wise condition effects, valence is determined by whether the absolute outcome value was positive or negative, such as in reward learning and punishment learning, respectively (Palminteri, Kilford, Coricelli, & Blakemore, 2016). Here, valence may unfold over multiple trials and lead to more enduring valence effects. During reward learning, the preferred outcome was to receive 3 coins compared to 1 coin only; during punishment learning, the loss of 1 coin was preferred than the loss of 3 coins. We also added a third condition of both mixed reward and punishments, which was commonly used in studies that only examined PE valence effects. In this third condition, which we will refer to as mixed condition, the preferred outcome was to receive 1 coin compared to losing 1 coin. Notably, PE valence can be additionally examined in any of the block-wise valence conditions. A pilot study indicated that the task can be applied to both clinical and neurotypical 8-18-year-old children and adolescents, and that their adaptation performance was modulated by valence and uncertainty. Our sample of the main study will include 120 children and adolescents between the age of 8 and 18, one clinical group (n=60) with diagnoses of anxiety, depression or ADHD, and a neurotypical group (n=60). We aim to delineate differences between psychiatric conditions (ADHD, anxiety, depression) for cognitive adaptation in respect to valence and uncertainty. We also aim to explore potential transdiagnostic effects of internalizing and externalizing psychopathology during cognitive adaptation. We will use both behavioral measures, such as accuracy, switching behavior and reaction time, as well as computational modeling measures, with the aim to capture adaptations in latent prediction-related parameters. How one learns in the learning conditions that differ by uncertainty and valence might depend upon the current symptom severity, both in clinical as well as neurotypical groups. Therefore, our aim of this study is to examine individual differences in clinical symptoms and their effects on uncertainty and valence during learning. We will further explore the moderating roles of the individual’s environment, state and trait, such as socioeconomic disparities, motivational traits and momentary affective state. Research Questions (RQ) Effects of Depression Major depressive disorder is an affective disorder whose primary symptoms are low mood and loss of motivation and pleasure in daily life. Despite its primarily affective symptoms, there is robust evidence of cognitive deficits in depression (Gotlib & Joormann, 2010), which may be related to the hypoactivity of serotonin, dopamine and noradrenaline (Eshel & Roiser, 2010; Ruhé, Mason, & Schene, 2007). Of note, depression is considered an internalizing disorder, as opposed to externalizing disorders (Achenbach, 1966; Carver, Johnson, & Timpano, 2017), with a large overlap in symptoms such as with anxiety disorders, specifically within a distress-based or negative affect symptom cluster (Watson, 2005). The overlap in negative affect symptoms between depression and anxiety may contribute to shared differences during RL. Although previous studies have found similarities and differences in the learning effects of depression and anxiety, a direct comparison with a task that manipulates both valence and uncertainty during RL has not been done. DEP-RQ1: What are the effects of depression on uncertainty conditions during RL? Studies that used RL with stable environments, reflecting low uncertainty, found reduced learning performance (Elliott, Sahakian, Herrod, Robbins, & Paykel, 1997; Steele, Meyer, & Ebmeier, 2004). Two studies used computational models to examine learning processes in depression, with in inconsistent results. The studies examined various parameters, including learning rate and reward sensitivity. Learning rate serves as a metric for quantifying how individuals update future values, whereas reward sensitivity reflects an individual's responsiveness to perceived outcomes. Reward learning rates were negatively related to anhedonic depression symptoms (Brown et al., 2021), but unrelated in a meta-analysis (Huys, Pizzagalli, Bogdan, & Dayan, 2013). Reward sensitivity in turn was either reduced (Huys et al., 2013) or increased (Brown et al., 2021). These contrasting findings may partly be explained by the use of a pavlovian and an instrumental learning task, respectively, and our study will focus on instrumental learning. One study reported reduced choice sensitivity in a more depressed student group (Kunisato et al., 2012), suggesting that depression is associated with less value-dependent choice behavior. Because the parameters of reward sensitivity and choice sensitivity are computationally interchangeable, current models cannot answer whether the parameters captured decision-related or feedback-related effects of depression (Browning, Paulus, & Huys, 2022). Additional parameter differences have been reported, such as a more negative valuation of received outcomes during punishment learning in depression (Brown et al., 2021), suggesting valence effects, or a decreased attentional breadth in relation to higher trait rumination (Hitchcock et al., 2022), suggesting impaired attention and memory processes. Further studies are needed to establish robust links between depressive symptoms and computational parameters in low uncertainty learning conditions during RL. Consistent with tasks of relatively low uncertainty, studies with more volatile tasks such as probabilistic reversal learning have reported impaired learning performance in depression (Dombrovski et al., 2010; Dombrovski, Szanto, Clark, Reynolds, & Siegle, 2013; Mukherjee, Filipowicz, Vo, Satterthwaite, & Kable, 2020; Mukherjee, Lee, Kazinka, D Satterthwaite, & Kable, 2020; Must, Horvath, Nemeth, & Janka, 2013; Rupprechter, Stankevicius, Huys, Steele, & Seriès, 2018). Less optimal switching behavior in term of lower win-stay and higher lose-switch behavior (particularly after misleading feedback) was also observed (Dickstein et al., 2010; Dombrovski et al., 2015; Mukherjee, Filipowicz, et al., 2020; Murphy, Michael, Robbins, & Sahakian, 2003; Taylor Tavares et al., 2008). In contrast, only one study also reported no learning differences in relation to depression (Brolsma et al., 2020). Some studies applied computational models, with inconsistent findings, similar to studies with low uncertainty tasks. One study reported reduced learning rates in depressed individuals compared to healthy controls (Mukherjee, Filipowicz, et al., 2020), while another study did not find such differences (Brolsma et al., 2020). Additionally, reduced choice sensitivity was reported (Mukherjee, Filipowicz, et al., 2020; Rupprechter et al., 2018), as well as a decreased memory of observed rewards, putatively due to working memory constraints (Rupprechter et al., 2018). Overall, behavioral findings show a relatively consistent picture of reduced learning performance in depression under both low and high uncertainty learning. However, these studies mostly used tasks of mixed-valence or reward conditions only, and the picture regarding uncertainty under punishment learning is less clear. Further, robust links between computational parameters, depression and uncertainty are yet to be established. There is some evidence that learning rate and choice sensitivity estimates are lower across uncertainty conditions. DEP-RQ2: What are the effects of depression on valence conditions during RL? Past RL studies of depression have usually used either conditions for reward and punishment learning separately, or they used PE valence within a reward task to determine valence effects (i.e. gaining more or less than expected). While both PE valence as well as valence condition (learning from positive or from negative feedback) have been studied extensively in depression, these valence effects may represent distinct characteristics on learning which need to be disentangled. To shed light on the relevance of the way valence is manipulated in relation to depression, our study includes 3 valence conditions: reward learning, punishment learning and mixed valence learning. Of note, many studies that have examined reward learning used a mixed valence feedback scheme. For reward learning, past studies found reduced learning performance in depression (Forbes, Shaw, & Dahl, 2007; Herzallah et al., 2013; Morris, Bylsma, Yaroslavsky, Kovacs, & Rottenberg, 2015; Robinson, Cools, Carlisi, Sahakian, & Drevets, 2012). Reduced reward learning was related to reduced striatal activation following positive PE (Robinson, Cools, Carlisi, et al., 2012). Further, both reduced striatal activations and reduced reward learning were a predictor of future depressive symptoms during adolescence (Forbes et al., 2007; Morgan, Olino, McMakin, Ryan, & Forbes, 2013). In terms of positive PE effects on learning, a simulation meta-analysis found lower learning rates, while results from the conventional meta-analysis shows only showed lower learning rates at trend (Pike & Robinson, 2022). Estimation tasks found a reduced positivity bias (Garrett et al., 2014; Sharot, Korn, & Dolan, 2011), which may implicate a reduced positivity and confirmation bias in depression during RL as well. Here, learning rates from positive and confirmatory PE are usually higher than from negative and disconfirmatory PE, which leads to higher learning performance compared to symmetric learning rates (Palminteri & Lebreton, 2022). In contrast, a developmental study found no effect of depressive symptoms on learning rate asymmetry (Nussenbaum, Velez, Washington, Hamling, & Hartley, 2022). In brief, both reward condition and positive PE effects suggest that reward learning may be reduced in depression, but the underlying computational mechanisms during RL are less clear. For punishment learning, two studies found higher learning performance relative to that of reward learning (Herzallah et al., 2013; Timmer, Sescousse, Van Der Schaaf, Esselink, & Cools, 2017). An acute lowering of serotonin levels in healthy individuals through depleting its precursor tryptophan lead to enhanced punishment learning, which suggests that low serotonin increase punishment learning, but do not affect reward learning (Cools, Robinson, & Sahakian, 2008; Robinson, Cools, & Sahakian, 2012). Indeed, depressed individuals with SSRI medication showed lower punishment learning relative to unmedicated depression individuals, but no differences in reward leaning (Herzallah et al., 2013). Studies with computational models examined either punishment learning rates or negative PE learning rates. Punishment learning rates showed inconsistent findings, with higher learning rates in depressed and anxious individuals compared to healthy controls (Aylward et al., 2019), or lower learning rates in depressed compared to healthy controls (Mukherjee, Filipowicz, et al., 2020). In terms of negative PE valence, a meta-analysis found higher learning rates in a simulation approach, while results in the conventional approach showed no effect (Pike & Robinson, 2022). Another study did not find changed learning rates in relation to depression, but a higher shift towards perceiving punishments as larger (Brown et al., 2021). To summarize, despite some inconsistencies in the literature, both punishment condition and negative PE effects point towards enhanced punishment learning in depression. Overall, the finding of both reduced reward learning and enhanced punishment learning in depression has led to the idea that depressed individuals differ in their subjective valuation and exhibit an exaggerated loss aversion compared to what prospect theory has established in healthy individuals (Chen, Takahashi, Nakagawa, Inoue, & Kusumi, 2015). There is evidence of learning deviations under both valence conditions in depression, but it is yet unclear whether these emerge in earlier developmental phases. DEP-RQ3: Are there differential effects of the anhedonic and negative affect symptoms clusters during RL? Depression is a highly heterogeneous disorder, which might explain some of the inconsistencies found during learning in depression. A closer look at specific symptom effects might prove useful to shed new light on the effects on RL. Depression is often distinguished by the symptom clusters of anhedonia and negative affect. Anhedonia is the loss of pleasure or lack of reactivity to pleasurable stimuli. While negative affect in depression shows considerable overlap with anxiety disorders, anhedonia is more independent of anxiety, but overlapping considerably with other disorders such as schizophrenia or addiction (Pizzagalli, 2014). Negative affect or distress putatively is influenced by serotonergic function, whereas anhedonia depends more upon dopaminergic function (Nutt, 2008). Therefore, these two symptom clusters within depression may have distinct effects on learning behavior. Since serotonergic function has been shown to specifically target punishment learning (Cools et al., 2008; Robinson, Cools, & Sahakian, 2012), it is possible that enhanced punishment learning can be linked to negative affect. A direct link between negative affect and punishment learning in depression has been previously reported, but remains understudied (Brown et al., 2021). Anhedonia in turn has been studied extensively, and consistently identified related reduced striatal activations during reward anticipation and reward delivery in adolescents and adults (Gradin et al., 2011; Stringaris et al., 2015). Anhedonic symptoms were related to less optimal switching behavior (Pizzagalli, Iosifescu, Hallett, Ratner, & Fava, 2008), reduced learning rates from positive and negative PE (Chase et al., 2010), specifically to reduced reward learning rates (Brown et al., 2021), and to more exploratory decision-making during learning (Harlé, Guo, Zhang, Paulus, & Yu, 2017). Overall, this suggests that anhedonic and negative affect symptoms clusters may have differential effects during RL, and further studies are needed to examine how negative affect modulates RL. Effects of Anxiety Anxiety disorders can differ with regard to the situation (e.g. social anxiety) or the objects (e.g. specific phobia) that cause symptoms such as aversive affective state, somatic stress symptoms and perception of sustained threat. Anxiety is increasingly recognized as developmental disorder (Leonardo & Hen, 2008), and together with depression and obsessive-compulsive disorder, it has been categorized as internalizing disorder, as opposed to externalizing disorders, e.g. ADHD and conduct disorder (Achenbach, 1966; Carver et al., 2017). Among internalizing disorders, conditions can be further divided into fear-based or distress-based symptom clusters (Watson, 2005), however, other symptom clusters have been distinguished as well (e.g. somatic and cognitive anxiety symptoms). Maladaptive uncertainty learning has been found for both the fear-based (Brown, Price, & Dombrovski, 2023) and the distress-based symptom clusters (Hammond, Xu, Ai, & Van Dam, 2023). A study that distinguished by somatic and cognitive symptom clusters found stronger influences of the somatic symptom cluster on RL (Fan, Gershman, & Phelps, 2022; Wise & Dolan, 2020). Neurally, individuals with anxiety disorders exhibited higher noradrenaline levels (Kalk, Nutt, & Lingford-Hughes, 2011) and dysregulated serotonergic modulation (Dayan & Huys, 2009), which may relate to the observed learning disruptions. Further, structural brain differences have been found to mediate the effect of unpredictability experienced during childhood on anxiety and depression symptoms in adults, which suggests that the uncertainty experienced as a child increases the risk for anxiety and depression and may lead to structural brain changes during development (Wang, Cao, Zheng, Chen, & Zhu, 2023). It remains unclear to what extent anxiety symptoms explain learning disruptions across both clinical and normative anxiety symptoms equally, and whether age modulates these effects during development. In this study, we will examine how effects of anxiety symptoms are present during RL under different levels of uncertainty and valence in both clinical and typically developing individuals. ANX-RQ1: What are the effects of anxiety on uncertainty conditions during RL? The current literature suggests that uncertainty processing is central to learning disruptions in anxiety symptoms (Brown et al., 2023). The aversive consequence of uncertainty in anxiety has been shown to impair behavioral learning performance already during low uncertainty, such as expected uncertainty, which is inherent in probabilistic but stable learning (LaFreniere & Newman, 2019). Under high uncertainty, such as in volatile environments, higher anxiety reduced learning performance and predicted aberrant switching behavior, such as reduced win-stay, increased or reduced lose-shift behavior (Dickstein et al., 2010; Hein, de Fockert, & Ruiz, 2021; Huang, Thompson, & Paulus, 2017; Piray, Ly, Roelofs, Cools, & Toni, 2019; Xia, Xu, Yang, Gu, & Zhang, 2021). Maladaptive uncertainty learning may be related to chronic underconfidence in individuals with anxiety and depression, as they showed disproportionally larger updates from low confidence decisions (Katyal, Huys, Dolan, & Fleming, 2023). One study also reported an inverted-u-shaped effect of trait anxiety on learning: both low and high trait anxiety were related to lower behavioral learning performance compared to intermediate trait anxiety (Aberg, Toren, & Paz, 2022). A nonlinear effect of anxiety on learning may be linked to the nonlinear effect of arousal on optimal task engagement that is attributed to noradrenaline function (Eckstein, Guerra-Carrillo, Miller Singley, & Bunge, 2017). Computational studies suggest that individuals with higher trait anxiety or with internalizing symptoms have difficulty adjusting their learning rate to the learning environments. Specifically, higher trait anxiety or internalizing symptoms predicted smaller learning rate adjustments between stable and volatile learning environments, which reflects less flexible learning (Browning, Behrens, Jocham, O’Reilly, & Bishop, 2015; Gagne, Zika, Dayan, & Bishop, 2020). The less flexible updating in relation to high internalizing symptoms was further characterized by a reduced updating after positive PE when action-outcome contingencies where changing throughout the learning block (Gagne et al., 2020). Beyond effects on learning rates, there is evidence that anxiety modulates decision-making during RL. Trait anxiety predicted the strategy by which participants explored alternative choices: higher trait anxiety participants exhibited a shift from value-based exploration towards uncertainty-related exploration (Aberg et al., 2022). This is in line with another study which reported that among individuals that were best described by a value-free choice strategy win-stay-lose-shift, higher state anxiety individuals exploited this value-free strategy more. Overall, anxiety may enhance less optimal choice strategies at the cost of value-based decision-making. To summarize, the currently literature found that anxiety has disruptive effects on learning under both low and high levels of uncertainty. Behavioral studies found reduced learning performance and less optimal switching behavior, but learning performance effects may also be nonlinearly related to anxiety symptoms. Computational studies have focused on anxiety effects between high and low uncertainty, but it is unclear how parameters of each uncertainty level are related to anxiety. These studies reported maladaptive learning rate adjustments to the statistics of the environment, as well as a shift from value-based to value-free decision behavior, but no studies have reported both effects of learning rate and decision behavior. Further, some studies reported combined effects of anxiety and depression, and it is not clear to what extent anxiety and depression show distinct effects on learning. Despite its relevance of anxiety disorders during development (Leonardo & Hen, 2008), it is unknown whether anxiety disrupts learning in children and adolescents similarly as in adults, since the only study with a pediatric sample found no behavioral effects of anxiety and did not apply computational models (Dickstein et al., 2010). This study will examine how anxiety symptoms in children and adolescents, both of clinical and normative symptoms range, show effects of uncertainty that are independent of depressive symptoms. ANX-RQ2: What are the effects of anxiety on valence conditions during RL? The effects of anxiety on uncertainty learning were most consistently found during punishment or threat learning, both behaviorally and computationally (Aberg et al., 2022; Browning et al., 2015; Gagne et al., 2020; LaFreniere & Newman, 2019; Piray et al., 2019). Some studies extended the found effects of uncertainty to both punishment and reward learning (Aberg et al., 2022; Gagne et al., 2020; LaFreniere & Newman, 2019) or did not find effects in either valence condition (Dickstein et al., 2010). Other studies did not examine reward and punishment separately, but they presented both rewards and punishments mixed within learning blocks; these studies found behaviorally reduced learning performance (Xia et al., 2021), and in a simulation meta-analysis higher negative PE learning rates and lower positive PE learning across depression and anxiety (Pike & Robinson, 2022). A recent study using naturalistic learning found that higher negative PE learning rates explained lower and less precise expectations about future academic outcomes and predicted the long-term development of anxiety (Villano et al., 2023). In brief, this new body of literature suggests that higher anxiety symptoms may predict aberrant processing of negative outcomes, both under punishment learning and from negative PE, and that this may be a risk factor for the development of anxiety. It is unknown whether anxiety modulates processing of negative outcomes already in children and adolescents. ANX-RQ3: Are there differential effects of anxiety symptom clusters during RL? The distinctions between fear-based and distress-based anxiety symptoms as well as somatic anxiety and cognitive anxiety symptoms have helped to examine learning disruptions in relation to more specific symptom clusters. Although it has been proposed that fear-based symptoms exert stronger effects on RL (Brown et al., 2023), the current literature suggests that multiple symptom clusters may have effects on RL. Studies that specified effects of symptom clusters found that somatic anxiety reduced uncertainty-directed exploration, whereas cognitive anxiety increased uncertainty-directed exploration (Fan et al., 2022). Similarly, divergent roles of these two symptoms clusters have been found during aversive learning (Wise & Dolan, 2020). The previously described maladaptive learning rate adjustment between stable and volatile environments was attributed to distress-based symptoms, which are less specific to anxiety and likely reflect internalizing symptoms more broadly (Gagne et al., 2020; Hammond et al., 2023). Importantly, the effects of fear-based symptoms have not been examined in these two studies. Therefore, it is unknown whether the effects on learning rate can be better attributed to fear-based symptoms rather than to distress-based symptoms. Since maladaptive learning rate adjustments were also linked to reduced pupil dilation changes, reflecting noradrenaline function and arousal, physiological anxiety symptoms may underlie uncertainty-related effects (Browning et al., 2015). The divergent effects of somatic and cognitive anxiety symptoms on decision behavior are difficult to interpret, but it seems that the role of somatic anxiety symptoms is more in line with the effect of a study that did not distinguish by symptom clusters (Aberg et al., 2022). The current definitions of symptoms clusters vary (eg. fear-based and distress-based symptoms, somatic and cognitive anxiety symptoms), which makes is difficult to integrate findings. Effects of ADHD Attention-deficit/hyperactivity disorder (ADHD) is commonly described as a developmental brain disorder, in which the hypofunctioning of noradrenaline and dopamine lead to reduced cognitive performance such as during RL (Plichta & Scheres, 2014; Ziegler, Pedersen, Mowinckel, & Biele, 2016). Notably, not just neurotransmitter hypofunction such as in ADHD, but also hyperfunction such as during stress (Arnsten, 1999, 2009) has been shown to impair performance. Therefore, an inverted-u-shaped relationship between neurotransmitter function and cognitive performance has been proposed (Biederman & Spencer, 1999; Del Campo, Chamberlain, Sahakian, & Robbins, 2011). Beyond functional differences, the effect of ADHD has been observed in the brain structure, likely as long-term consequence of brain neuroplasticity. Brain regions implicated in reward processing and value representation, such as the ventral striatum and the orbitofrontal cortex, exhibited smaller volumes in individuals with ADHD compared to controls (Carmona et al., 2009; Hesslinger et al., 2002). The well-established biological differences in ADHD have led to effective psychopharmacological treatments in ameliorating ADHD symptoms. Stimulants such as methylphenidate are used to target and normalize dopamine function. Some studies that controlled for methylphenidate use showed that it improved learning performance (Luman, Goos, & Oosterlaan, 2015; Pelham, Milich, & Walker, 1986), which suggests that medication use is a relevant confounding factor for examining effects of ADHD on learning. It remains unclear whether ADHD symptoms explain learning differences only by diagnostic categorization such as reported in case-control studies, or whether these learning effects also extend to subclinical ADHD symptoms in clinical and neurotypically developing groups. Further, it remains unclear whether the effects of ADHD on RL are more pronounced during sensitive developmental periods such as during childhood and adolescence. In this study, we will examine how effects of ADHD symptoms are present during RL under different levels of uncertainty and valence. ADHD-RQ1: What are the effects of ADHD on uncertainty conditions during RL? Several studies have examined the effects of ADHD diagnosis on RL under different levels of uncertainty (Hulsbosch et al., 2021). Under low uncertainty, studies showed mixed findings: some found no effects of ADHD (Luman et al., 2015; Oades & Müller, 1997; Wiesner, Molzow, Prehn-Kristensen, & Baving, 2017), while other found reduced learning (Frank, Santamaria, O’Reilly, & Willcutt, 2007; Gabay, Shahbari-Khateb, & Mendelsohn, 2018; Luman et al., 2021; Shephard, Jackson, & Groom, 2016). Additionally two studies reported slower and more variable reaction times (Frank et al., 2007; Gabay et al., 2018). Under high uncertainty, studies show similarly mixed evidence: two studies found no effects of ADHD (Chantiluke et al., 2015; Finger et al., 2008), one study reported computational effects (Hauser et al., 2014) in terms of a more explorative choice behavior, and two studies showed reduced learning performance in ADHD compared to a control group (Itami & Ca, 2002; Shephard et al., 2016). To summarize, the current literature shows mixed findings during RL under both low and high uncertainty. If an effect was found for ADHD, learning performance was reduced, choices were more erratic, and reaction times were slower and more variable. This study aims to shed new light on whether uncertainty modulates effects of ADHD symptoms during RL, and how these effects can be captured both behaviorally and computationally. ADHD-RQ2: What are the effects of ADHD on valence conditions during RL? No studies have examined the effects of punishment learning in ADHD so far. One study has reported differences in response to positive and negative feedback: negative feedback elicited stronger activations in children with ADHD compared to neurotypical children (Van Meel, Oosterlaan, Heslenfeld, & Sergeant, 2005). Given that the abovementioned brain deficits in ADHD were found in relation to reward learning, processing of negative feedback may be relatively spared and lead to an imbalance towards stronger processing of negative information compared to positive information. It is currently unknown whether the effects of ADHD might be less pronounced during punishment learning and learning from negative PE, compared to learning from reward and positive PE. ADHD-RQ3: Are there differential effects of the symptom clusters hyperactivity/impulsivity and inattentiveness during RL? ADHD consists of a set of symptoms, most notably the symptom clusters hyperactivity/impulsivity and inattentiveness. While previous studies reported correlations of the symptom clusters to learning deficits (Gabay et al., 2018; Luman et al., 2015), it is currently unclear whether the symptom clusters show separable contributions to RL. Computational models may help to identify separable contributions. For example, one theoretical model showed that aberrant learning parameters reproduced impulsive behavior in a delayed response time task (Williams & Dayan, 2005). Impulsive and hyperactive symptoms might be to learning parameters such as learning rate. No study has reported learning rate effects of ADHD, although all theoretical models of ADHD agree that dopamine hypofunction leads to aberrant learning parameters (Frank et al., 2007; Sagvolden, Johansen, Aase, & Russell, 2005; Tripp & Wickens, 2008). Thus, it is unknown whether hyperactivity/impulsivity symptoms and the underlying dopamine hypofunction specifically explain effects on learning rate. The attention deficit in ADHD has not been linked to computational parameters yet. However, noradrenaline function, which is fundamental in attentional processes, has been linked decision behavior during RL (Dubois et al., 2021, 2020; Frank et al., 2007). In contrast, one influential theoretical account of ADHD has linked attention deficits to the dopaminergic mesocortical pathway (Sagvolden et al., 2005). Therefore, it remains unclear whether inattentiveness in ADHD is specifically linked to noradrenergic function and attributed explorative decision behavior, as well as slower and more variable reaction times during RL.

The aim of this paper is to present "the three steps method" of the self-formation of collaborative groups in a problem-based learning environment. The self-formation of collaborative groups is based on sharing of accountability among students for solving instructional problems. The steps of the method are planning collaborative problem solving, self-evaluation of students, and building collaborative groups. The planning comprises determination of the nomenclature of higher order thinking (HOT) skills, defining the instructional problems and their complexity levels, creating problem groups according to the complexity levels, setting the problem-relevant HOT skills, determining the accountability measure and the assessments of accountability for solving the problems. The self-evaluation includes self-detection of personal HOT skills, measurement of the diversity between the personal HOT skills and the problem-relevant skills based on the proposed diversity measure, and self-evaluation of willingness and desire of a student to take accountability for solving the instructional problems. The personal willingness is evaluated by the diversity measure. The desire is guided by the accountability assessments for problem solving. Coordination of the self-evaluation outcomes allows building collaborative groups. A group's composition is adjusted by the specific requirements of an instructor.

What the Marine Corps achieved in Anbar constituted a major turning point in the Iraq War. Many had declared the fight there lost at the very time I MEF was launching a three-dimensional strategy in 2006 that culminated in 2007 with a strategic defeat for AQI and those insurgents aligned with it. The fight for Anbar Province demonstrated the Marine Corps s capacity to learn and change in order to address complicated and very violent challenges. The four-year fight in the Sunni heartland is an important illustration of that Marine capacity to improvise and adapt, which is infused into the Corps training routines and warrior ethos. This study details how I MEF designed and implemented a counterinsurgency approach that was contextualized for Anbar, consisting of (1) the COIN phases of clear, hold, build ; (2) tribal engagement to expand the operating force available to maintain local security; and (3) targeted counterterrorism aimed at degrading al Qaeda s clandestine apparatus.

What the Marine Corps achieved in Anbar constituted a major turning point in the Iraq War. Many had declared the fight there lost at the very time I MEF was launching a three-dimensional strategy in 2006 that culminated in 2007 with a strategic defeat for AQI and those insurgents aligned with it. The fight for Anbar Province demonstrated the Marine Corps s capacity to learn and change in order to address complicated and very violent challenges. The four-year fight in the Sunni heartland is an important illustration of that Marine capacity to improvise and adapt, which is infused into the Corps training routines and warrior ethos. This study details how I MEF designed and implemented a counterinsurgency approach that was contextualized for Anbar, consisting of (1) the COIN phases of clear, hold, build ; (2) tribal engagement to expand the operating force available to maintain local security; and (3) targeted counterterrorism aimed at degrading al Qaeda s clandestine apparatus.

What the Marine Corps achieved in Anbar constituted a major turning point in the Iraq War. Many had declared the fight there lost at the very time I MEF was launching a three-dimensional strategy in 2006 that culminated in 2007 with a strategic defeat for AQI and those insurgents aligned with it. The fight for Anbar Province demonstrated the Marine Corps s capacity to learn and change in order to address complicated and very violent challenges. The four-year fight in the Sunni heartland is an important illustration of that Marine capacity to improvise and adapt, which is infused into the Corps training routines and warrior ethos. This study details how I MEF designed and implemented a counterinsurgency approach that was contextualized for Anbar, consisting of (1) the COIN phases of clear, hold, build ; (2) tribal engagement to expand the operating force available to maintain local security; and (3) targeted counterterrorism aimed at degrading al Qaeda s clandestine apparatus.

The article discusses the optimal types of tasks and exercises that help students to effectively enrich their vocabulary in the Russian language textbook. In addition to the use of traditional types of vocabulary work, innovative ways to increase vocabulary that develop speech and broaden teachers' worldviews are also demonstrated

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This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 47 titles deal with a variety of topics, including the following: (1) the effect of source variation upon children's reactions to television commercials; (2) covert observation as a means of measuring the affective states of television viewers; (3) viewers' responses to a film as a socially situated event; (4) children's understanding of television reality; (5) media learning and adolescent developmental tasks; (6) the dynamics of communication and information in groups; (7) an information processing approach to uses and gratifications; (8) privacy and media encroachment; (9) the changing images of females and males in television commercials; (10) commercial broadcasters' perceptions of entry-level employment requirements for college graduates in the broadcast industry; (11) transnational advertising and the mass media in Latin America; (12) longitudinal patterns of television viewing and adolescent role socialization; (13) the relationship between persistence, attention, and observational learning of television's program content; (14) the film industry and the Vietnam war; (15) the effect of televised violence; (16) the use of videotape for studying nonverbal behavior in social settings; (17) United States Supreme Court interpretations of "public interest" in decisions concerning the media from 1927 to 1979. (FL)

The present study attempts to expand current question categorization schemes to identify question types that discriminate good learners from their peers in collaborative problem-solving groups. The study also explores the effects of person and task variables on students' question-asking behaviors in an effort to identify those that facilitate mathematics problem solving. Forty-seven fifth graders from two independent urban schools that use cooperative-learning methods participated in the study. Students were asked to solve fraction problems in one set containing continuous problems and in another set consisting of discrete problems. A question-categorization scheme was devised to code student requests to each other for information or assistance. No significant differences were found in the numbers of questions asked by students of high, low, or average ability. Although this appears contradictory to some previous results, it may be that including question types that were not exclusively help seeking explains the discrepancy. Overall, results indicate that type of task interacts with student characteristics and the setting to affect performance and students' peer-directed questions. Prior experience with cooperative-learning groups appears to have affected problem-solving and questioning performance. Two figures present study findings. (Contains 73 references.) (SLD)

Continuing conflicts between violent groups and states generate an ever-present demand for higher quality and more timely information to support operations to combat terrorism. In particular, better ways are needed to understand how terrorist and insurgent groups adapt over time into more-effective organizations and increasingly dangerous threats. To adapt, terrorist organizations must learn. A group's ability to learn determines its chance of success, since learning is the link between what the group wants to do and its ability to gather the needed information and resources to actually do it. Despite the importance of terrorist group learning, comparatively little focused research effort has been directed at understanding this process and identifying the factors that influence group learning ability. While relevant data and insights can be found in the literature on terrorism and terrorist organizations, this information has not been collected and systematically analyzed to assess its importance from the perspective of efforts to combat terrorism. This study addresses that need in an effort to both analyze current understanding and stimulate further study and research in this area. The RAND Corporation conducted an analysis of organizational learning in terrorist groups and assessed its implications for efforts to combat terrorism. The work was performed between November 2003 and November 2004, a period during which the threat of international terrorism was high and concern about the capabilities of terrorist organizations and how they might change over time was a central focus of policy debate and U.S. homeland security planning. The study is described in this report and in Aptitude for Destruction, Volume 1: Organizational Learning in Terrorist Groups and Its Implications for Combating Terrorism, which applies the analytical framework described in the second part of this report to the practical demands of intelligence and law enforcement activities.

Bulletin (West Virginia University. Agricultural Experiment Station)

Bulletin (West Virginia University. Agricultural Experiment Station)

Bulletin (West Virginia University. Agricultural Experiment Station)

Thesis (M.A.)--Boston University, 1938

This article describes the teaching of process-oriented counselling skills in a group. The interweaving of theory and practice is discussed. The need for and a method of integrating the personal and professional growth of group members with the experiential and conceptual learning of counselling skills are outlined . The congruence of the content and the educational approach is an important element in the training. The development and significance of the community of learners, an intrinsic dimension of the training, are also described.

This article describes the teaching of process-oriented counselling skills in a group. The interweaving of theory and practice is discussed. The need for and a method of integrating the personal and professional growth of group members with the experiential and conceptual learning of counselling skills are outlined . The congruence of the content and the educational approach is an important element in the training. The development and significance of the community of learners, an intrinsic dimension of the training, are also described.