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Over the last half century, mathematics education has seen numerous reform initiatives and standards. About every ten years, a new wave of documents offers recommendations on how to best teach mathematics. The Common Core State Standards for Mathematics (CCSSM; National Governors Association Center for Best Practices & Council of Chief State School Officers, 2010) builds on the concept of learning trajectories (LT) (Daro, Mosher, & Corcoran, 2011) and outlines the mathematics content and practices to be addressed at particular grade levels. With the widespread adoption of these standards, mathematics teacher educators have worked to share ideas about trajectories with teachers. Because research on learning largely developed separately from research on teaching, the authors' work used LTs to link these two bodies of research. They theorized the concept of Learning Trajectories Based Instruction (LTBI) as a model of teaching where instructional decisions are grounded in research on student learning in the form of trajectories and they interpreted several highly developed domains of research on mathematics teaching in relation to these trajectories (Sztajn, Confrey, Wilson, & Edgington, 2012). Since that time, the authors have worked to share this model with teachers in professional development settings, and their research has empirically examined and elaborated the affordances of LTBI. In this commentary, they critically analyze the LTBI model using Gutiérrez's (2007) dimensions of equity as a comprehensive framework for equity in mathematics education. Through this theoretical examination, they make explicit the assumptions inherent in the initial model and identify opportunities for LTBI to enhance equitable mathematics instruction. First, they briefly introduce current research on LTs and highlight principles of LTs that they contend are aligned with equitable instruction. Next, they present Gutiérrez's framework and a rationale for its selection as a tool for their theoretical analysis, briefly describing each of its dimensions. They detail their analysis of LTBI and conjecture what equity-oriented uses of the model might look like in instruction. The authors conclude with an invitation to the mathematics teacher education community to discuss the potentials and challenges of using LTs to support equitable mathematics instruction.

Over the last half century, mathematics education has seen numerous reform initiatives and standards. About every ten years, a new wave of documents offers recommendations on how to best teach mathematics. The Common Core State Standards for Mathematics (CCSSM; National Governors Association Center for Best Practices & Council of Chief State School Officers, 2010) builds on the concept of learning trajectories (LT) (Daro, Mosher, & Corcoran, 2011) and outlines the mathematics content and practices to be addressed at particular grade levels. With the widespread adoption of these standards, mathematics teacher educators have worked to share ideas about trajectories with teachers. Because research on learning largely developed separately from research on teaching, the authors' work used LTs to link these two bodies of research. They theorized the concept of Learning Trajectories Based Instruction (LTBI) as a model of teaching where instructional decisions are grounded in research on student learning in the form of trajectories and they interpreted several highly developed domains of research on mathematics teaching in relation to these trajectories (Sztajn, Confrey, Wilson, & Edgington, 2012). Since that time, the authors have worked to share this model with teachers in professional development settings, and their research has empirically examined and elaborated the affordances of LTBI. In this commentary, they critically analyze the LTBI model using Gutiérrez's (2007) dimensions of equity as a comprehensive framework for equity in mathematics education. Through this theoretical examination, they make explicit the assumptions inherent in the initial model and identify opportunities for LTBI to enhance equitable mathematics instruction. First, they briefly introduce current research on LTs and highlight principles of LTs that they contend are aligned with equitable instruction. Next, they present Gutiérrez's framework and a rationale for its selection as a tool for their theoretical analysis, briefly describing each of its dimensions. They detail their analysis of LTBI and conjecture what equity-oriented uses of the model might look like in instruction. The authors conclude with an invitation to the mathematics teacher education community to discuss the potentials and challenges of using LTs to support equitable mathematics instruction.

In the present paper we show that distributional information is particularly important when considering concept availability under implicit language learning conditions. Based on results from different behavioural experiments we argue that the implicit learnability of semantic regularities depends on the degree to which the relevant concept is reflected in language use. In our simulations, we train a Vector-Space model on either an English or a Chinese corpus and then feed the resulting representations to a feed-forward neural network. The task of the neural network was to find a mapping between the word representations and the novel words. Using datasets from four behavioural experiments, which used different semantic manipulations, we were able to obtain learning patterns very similar to those obtained by humans.

This article is from Nature and Science of Sleep , volume 6 . Abstract There is increasing evidence that sleep promotes off-line enhancement of a variety of explicitly learned motor tasks in young adults. However, whether sleep promotes off-line consolidation of implicitly learned motor tasks is still under question. Furthermore, the role of sleep in promoting transfer of learning remains unknown. This study examined the role of sleep in learning and transfer of learning of an implicit continuous motor task. Twenty-three neurologically intact individuals (mean age 26.4 years) were randomly assigned to either a sleep group or a no-sleep group. The sleep group practiced a continuous tracking task in the evening and underwent retention and transfer testing the following morning, while the no-sleep group practiced the tracking task in the morning and underwent retention and transfer testing in the evening. The results show that in both the sleep and no-sleep groups, performance improved off-line without further practice for both the general skill and the sequence-specific skill. The results also indicate that sleep and time promote transfer of learning of both sequence-specific and general skill learning to a spatial and temporal variation of the motor task. These findings demonstrate that sleep does not play a critical role in promoting off-line learning and transfer of learning of an implicit continuous motor task.

The process of implicit learning is a phenomenon that has been studied for decades, but its implications for, and practical applications to, applied game design have been almost completely unexplored. This is seen as a major gap in the game-based learning literature, as the field has historically struggled to facilitate transfer of knowledge and skill gained in the game to scenarios outside the game (such as standardised competence tests). It is suggested that a lot of learning in games is implicit and results in tacit knowledge - difficult to explicate - which is why we believe it is prudent to study the process of implicit learning as it happens in games and how it is impacted by game design choices. This will allow educators to improve overall learning outcomes and contribute to our understanding of the complex learning mechanism at work during interactive media gameplay. In this study we seek to introduce implicit learning to applied game design, by exploring it in the context of cognitive load theory and attentional effects. To do this we will use a variation of the sequence learning paradigm in the context of a web-based game.

The process of implicit learning is a phenomenon that has been studied for decades, but its implications for, and practical applications to, applied game design have been almost completely unexplored. This is seen as a major gap in the game-based learning literature, as the field has historically struggled to facilitate transfer of knowledge and skill gained in the game to scenarios outside the game (such as standardised competence tests). It is suggested that a lot of learning in games is implicit and results in tacit knowledge - difficult to explicate - which is why we believe it is prudent to study the process of implicit learning as it happens in games and how it is impacted by game design choices. This will allow educators to improve overall learning outcomes and contribute to our understanding of the complex learning mechanism at work during interactive media gameplay. In this study we seek to introduce implicit learning to applied game design, by exploring it in the context of cognitive load theory and attentional effects. To do this we will use a variation of the sequence learning paradigm in the context of a web-based game.

Study exploring the impact of different instructional contexts on learning a symbolic math task.

This study investigates whether reward-conditioned stimuli influence attention, motivation, and action selection implicitly. Specifically, we examine Pavlovian-to-Instrumental Transfer (PIT), an effect by which reward conditioned stimuli (CS)s influence instrumental behavior, leading to increased responses toward reward-related cues. Additionally, we assess attentional responses towards reward-conditioned stimuli using an Emotional Attentional Blink (EAB) task, in which CS can overcome attentional distraction by emotionally salient distractors. Importantly, we will use a state-of-the-art Bayesian procedure to determine participants’ awareness of stimulus-outcome contingencies. This will allow to examine whether PIT effects, and attention to high reward CS overcoming aversive distractors in the EAB, can occur implicitly in individuals who are unaware of the contingencies. Finally, we will explore whether these effects are more pronounced in individuals with risky alcohol use. Before the experimental tasks, all participants will complete the Alcohol Use Disorder Identification Test (AUDIT) to categorise participants into high-risk and low-risk alcohol use groups. The experiment consists of three phases: - First, in the Pavlovian reward-conditioning task, geometric shapes (octagon and square) are associated with an 80% probability of earning different reward magnitudes (conditioned stimuli of 10 cents or 50 cents, hereafter CS10 and CS50) and a 20% probability of earning no reward. The assignment of rewards to shapes is counterbalanced across participants. Participants are instructed only to identify the yellow or green color overlaid on the shapes by pressing the left or right key and that they possibly win a monetary reward. No mention is made of the role of the geometric shapes, ensuring that learning of reward contingencies remains implicit. In the Pavlovian phase, 25% of trials include an additional question where participants estimate the potential winnings (10 cents or 50 cents) associated with the shape and rate their confidence on a scale from 1 (no idea) to 5 (very sure). These expectancy measures will be used to differentiate participants who consciously identify the reward contingencies from those who remain unaware. This will allow us to investigate evidence for implicit reward conditioning. - Next, in the Instrumental task, participants engage in a response-contingent reward-learning process, where they repeatedly press a designated key (R10 or R50, corresponding to the left and right arrow keys) to receive a monetary reward. Specifically, pressing the R10 key results in a 50% probability of receiving 10 cents, while pressing the R50 key results in a 10% probability of receiving 50 cents. To ensure the utility of each response option is equivalent, the assignment of rewards to keys is counterbalanced across participants. To reinforce learning and encourage continued engagement, participants are required to press their chosen key multiple times within a trial. The task includes a designated response window that begins randomly between 1.5 and 2.5 seconds after trial onset and lasts for 1 second. During this window, participants must press the same key at least twice for the response to be considered valid. This ensures participants engage actively during the trial rather than relying on single, late responses. If multiple keys are pressed or if the key is pressed fewer than two times in the response window, the trial is marked invalid, and no feedback is given. When a valid response is made, the screen displays the reward (e.g., “you win 10p” or “50p”), reinforcing the response–reward association. - Finally, in the Transfer phase, participants are presented with a sequence of rapidly displayed abstract images in the so-called Rapid Serial Visual Presentation (RSVP) task. Each trial consists of a RSVP of 17 stimuli, each displayed for 95 ms. Embedded within this sequence is a single neutral or aversive distractor image and a target CS (e.g., a geometric shape). Fillers are squares, hexagons and octagons combined into one shape and appear at the beginning of the trial. Aversive or neutral distractors appear on the 4th, 6th or 8th position of the series, followed by another filler and the presentation of the target. Finally, more fillers are presented to complete the stream of 17 images. Examples of aversive images are pictures of certain objects (guns or knives), animals (predators or poisonous animals) or violent scenes (fights, aggressions or mutilated bodies). The target CS are shapes participants previously encountered during the Pavlovian conditioning phase, where each shape (square and octagon) was associated with a specific monetary reward (e.g., 10 cents or 50 cents). A hexagon shape is included as a control condition, always linked to no reward. Thus, there are three target CS (square, hexagon, octagon), each linked to a different outcome: 10 cents, 50 cents, or no reward. After each RSVP sequence, participants either complete the Instrumental task before identifying the target stimulus or identify the target stimulus before completing the Instrumental task. This order is counterbalanced between blocks (3 blocks of 48 trials) to control for any sequence effects. This phase measures the extent to which conditioned stimuli influence action selection (instrumental response, PIT effect) and attentional capture (target stimulus detection accuracy). The study will determine whether these effects occur independently of contingency awareness and whether stronger PIT effects, and a greater ability to overcome attentional capture by aversive distractors during CS50 trials, serve as risk markers for problematic alcohol use.

Continued series of experiments looking at re-enterant visual processing and how it relates to regularity learning.

Approved protocol for the manuscript submitted to Consciousness and Cognition (in princinple acceptance).

Similarities and differences in implicit procedural learning and recording of ERP between subjects in developmental coordination disorder group and in control group using an adapted version of a serial reaction time task in motor imagery and action execution conditions

This article is from Frontiers in Human Neuroscience , volume 8 . Abstract We introduced haptic cues to the serial reaction time (SRT) sequence learning task alongside the standard visual cues to assess the relative contributions of visual and haptic stimuli to the formation of motor and perceptual memories. We used motorized keys to deliver brief pulse-like displacements to the resting fingers, expecting that the proximity and similarity of these cues to the subsequent response motor actions (finger-activated key-presses) would strengthen the motor memory trace in particular. We adopted the experimental protocol developed by Willingham (1999) to explore whether haptic cues contribute differently than visual cues to the balance of motor and perceptual learning. We found that sequence learning occurs with haptic stimuli as well as with visual stimuli and we found that irrespective of the stimuli (visual or haptic) the SRT task leads to a greater amount of motor learning than perceptual learning.

In a pre-registered experiment (https://doi.org/10.17605/OSF.IO/3D6H8) we tested whether meaningful actions drive implicit learning of temporal regularities. In the experiment, participants responded to targets that could randomly appear in three colors at three locations. They responded by pressing color-coded buttons with different fingers, thereby directly relating each color to a specific action. In half of the blocks one color (and thereby action) occurred regularly (i.e., every 3s), in the other half targets at one location occurred regularly. All other targets appeared at random moments. Results showed no behavioral benefits of either temporal regularity (i.e., neither in the regular action, nor in the regular location condition). This would be in line with one of our predictions: A correspondence between action and location is a prerequisite for learning temporal regularities. However, this null-findings could also be explained by (1) the color-response key-mapping being too difficult (i.e., there may be an additional, cognitive translation step from the color seen on screen to which finger to move/button to press); (2) the paradigm being too easy (i.e., learning the temporal regularity is not necessary in order to improve performance); or (3) Simon effects covering up effects of implicit learning (in Experiment 1 'fast' congruent responses (e.g., left key - left target) are interleaved with 'slow' incongruent responses (e.g., left key - right target) . To test these alternative explanations, we will modify our previous experimental design in the current study, such that the action and location regularity will be coupled. This design will also be in line with the outcome of our previous experiment (Salet et al. 2021; a correspondence between action and location is a prerequisite for learning temporal regularities). Throughout the experiment, each target location will be associated with one of three colors, and the color-mapped response keys will additionally be location-mapped (left key for the leftmost target, right key for the rightmost target, middle key for the middle-top target). This way, regular targets will have both a location- and action-regularity (location = action blocks), addressing explanation (1). With this setup, we will also avoid Simon interference, as the response-key is always congruent with the target location, addressing explanation (3). After this block, participants complete two additional blocks, in which we further attempt to disentangle the contribution of action- and location-regularity to implicit learning of temporal regularities. In the present design, we sought to do so without introducing strong Simon interference. Therefore, we present all three targets vertically in these blocks, aligned in the horizontal center of the screen. We use two block types to assess the independent contribution of regular locations and regular actions: That is, in action blocks we will present regular targets at random locations but with a consistent color, probing a consistent response-action. In location blocks the regular target will be presented at one location, but with unpredictable colors probing an unpredictable response. Possible outcomes of this follow-up experiment are: (1) Participants still do not learn the temporal regularity in any condition: Correspondence between action and location is not a prerequisite for implicit learning of temporal regularities, or the motor action must be more elaborate (e.g., mouse cursor movements). (2) Participants learn the temporal regularity in the location=action condition, but not in the location or action condition: Correspondence between action and location is a prerequisite for implicit learning of temporal regularities. (3) Participants learn the temporal regularity in the location=action and action condition, but not in the location condition: Only the action regularity is a prerequisite for implicit learning of temporal regularities, and the null findings in the previous experiment may have been caused by Simon interference. (4) Participants learn the temporal regularity in the location=action and location condition, but not in the action condition: Only the location regularity is a prerequisite for implicit learning of temporal regularities, null findings in the previous experiment may have been caused by Simon effects. (5) Participants learn the temporal regularity in all conditions: Both location regularities and action regularities can induce learning, but in the previous experiment such effects were probably disrupted by Simon interference. (6) Participants learn the temporal regularity in all conditions, but with varying strength: Both action and location are a prerequisite for implicit learning of temporal regularities, and they have a superadditive effect when occurring together.

Raw data associated with manuscript "Deferred feedback does not dissociate implicit and explicit category learning systems: Commentary on Smith et al. (2014)"

Raw data associated with manuscript "Deferred feedback does not dissociate implicit and explicit category learning systems: Commentary on Smith et al. (2014)"

In a psychophysical experiment based on the arcade game Whac-A-Mole (WAM), in which targets (moles) had to be hit using a computer cursor, Salet et al. (2021) showed that temporal regularities built into the WAM experiment (here: one of three targets appeared every 3 s) can be learned implicitly and improve performance expressed by reaction time (RT) and hit rate. That is, although their participants were completely unaware of the timing aspect of the experiment, they unintentionally utilized temporal information to guide behavior. In this study, the aim is to assess the action-perception coupling of implicit temporal learning. That is, whether the implicit temporal adaptation observed in Salet et al. (2021) results from learning where the regular stimulus is presented at regular onsets (perception); or learning what motor action to perform at regular onsets. Salet et al. (2021)'s study does not allow to distinguish between these two possibilities as the regularity’s spatial location was directly coupled to a unique motor action: Their participants could have learned to do three things at regular onsets: (1) where to spatially orient their attention; (2) what motor action to prepare in anticipation of regular’s onsets; or (3) both. With an extension of the WAM-design of Salet et al. (2021) we aim to distinguish between these three possible learning possibilities. To do so, we manipulate whether the regular feature is linked to a unique location or a unique motor action. We modify the WAM-design such that targets vary in location (left, top, right) and color (orange, green, purple). Participants place their index, middle, and ring finger on three buttons, each button corresponding to one of the three colors. In one half of the experiment, the temporal regularity concerns the feature 'location' (regular location condition; as in Salet et al., 2021). For example, every 3 s a target appears on the left, with an unpredictable color. This means that the motor action (button press with one of the three fingers) is not associated with this regularity. In the other half of the experiment, the temporal regularity concerns the feature 'color' thereby linking motor action with the regularity (regular action condition): For example, every 3 s a green target occurs, while the location of this target was randomized. Because the correct response (i.e. specific finger press) is defined by this color, a repetitive and meaningful motor action occurs only if the regular feature is 'color': Every three seconds a green target prompts a response with the same finger. Possible outcomes of this experiment, based on the three possible learning outcomes outlined above, are: (1) Implicit learning occurs in the regular location and action condition: Participants can both implicitly learn at regular onsets where to spatially orient their attention and what motor action to perform. (2) Implicit learning occurs in the regular location but not in the action condition: Participants only learn where to spatially orient their attention at regular onsets, but not what motor action to perform. (3) Implicit learning occurs in the regular action but not location condition: Participants only learn what motor action to perform at regular onsets, but not where to spatially orient their attention. (4) Implicit learning occurs neither in the regular location nor in the action condition: Participants do not pick up on any temporal regularity in this paradigm, which might suggest that a correspondence between action and location are a prerequisite for learning temporal regularities. Salet JM, Kruijne W, Van Rijn H (2021). Implicit learning of temporal behavior in complex dynamic environments. Psychonomic Bulletin and Review, 28:1270–1280. https://doi.org/10.3758/s13423-020-01873-x

The present preregistration document concerns an online experiment.

The aim is to investigate whether patients with Korsakoff syndrome acquire information in the same manner as patients with alcohol use disorder, or whether they benefit more/less from certain learning conditions (e.g., instructions) because of their explicit memory deficit. Participants of both groups undergo testing in a new contingency learning task using ecologically valid stimuli (object-location associations). This assesses the impact of implicit or explicit instructions, or a combination of both, on later memory performance. Based on these assessments we determine task parameters that enhance behavioural indicators of learning and memory.

The aim is to investigate whether patients with Korsakoff syndrome acquire information in the same manner as patients with alcohol use disorder, or whether they benefit more/less from certain learning conditions (e.g., instructions) because of their explicit memory deficit. Participants of both groups undergo testing in a new contingency learning task using ecologically valid stimuli (object-location associations). This assesses the impact of implicit or explicit instructions, or a combination of both, on later memory performance. Based on these assessments we determine task parameters that enhance behavioural indicators of learning and memory.

As robots enter human environments, they will be expected to accomplish a tremendous range of tasks. It is not feasible for robot designers to pre-program these behaviors or know them in advance, so one way to address this is through end-user programming, such as via learning from demonstration (LfD). While significant work has been done on the mechanics of enabling robot learning from human teachers, one unexplored aspect is enabling mutual feedback between both the human teacher and robot during the learning process, i.e., implicit learning. In this paper, we explore one aspect of this mutual understanding, grounding sequences, where both a human and robot provide non-verbal feedback to signify their mutual understanding during interaction. We conducted a study where people taught an autonomous humanoid robot a dance, and performed gesture analysis to measure people's responses to the robot during correct and incorrect demonstrations.

Self-paced learning (SPL) mimics the cognitive mechanism of humans and animals that gradually learns from easy to hard samples. One key issue in SPL is to obtain better weighting strategy that is determined by minimizer function. Existing methods usually pursue this by artificially designing the explicit form of SPL regularizer. In this paper, we focus on the minimizer function, and study a group of new regularizer, named self-paced implicit regularizer that is deduced from robust loss function. Based on the convex conjugacy theory, the minimizer function for self-paced implicit regularizer can be directly learned from the latent loss function, while the analytic form of the regularizer can be even known. A general framework (named SPL-IR) for SPL is developed accordingly. We demonstrate that the learning procedure of SPL-IR is associated with latent robust loss functions, thus can provide some theoretical inspirations for its working mechanism. We further analyze the relation between SPL-IR and half-quadratic optimization. Finally, we implement SPL-IR to both supervised and unsupervised tasks, and experimental results corroborate our ideas and demonstrate the correctness and effectiveness of implicit regularizers.

The main purpose of this study is to examine the implications of tacit knowledge in the field of teaching and learning. The argument is that tacit knowledge cannot be described explicitly in the form of propositions. Tacit teaching and learning is based on factors such as personal experience, relation to text, and subjectivity of knowledge than being dominated by scientific objectivity and rationalism on a series of fixed and predetermined rules and principles. In this research, conceptual analysis method with the aim of clarification was used through the analysis of the concepts of teaching and tacit learning. In this method, the concepts used in a new structure are analyzed and redefined. The results of this research are that belief in the existence of a tacit and implicit dimension of knowledge can lead to a new attitude in the education system. Therefore, the purpose of learning and teaching in the tacit approach is not the transfer of knowledge but the discovery and creation of knowledge in the close relationship between the teacher and the student.

This paper evaluates the robustness of learning from implicit feedback in web search. In particular, we create a model of user behavior by drawing upon user studies in laboratory and real-world settings. The model is used to understand the effect of user behavior on the performance of a learning algorithm for ranked retrieval. We explore a wide range of possible user behaviors and find that learning from implicit feedback can be surprisingly robust. This complements previous results that demonstrated our algorithm's effectiveness in a real-world search engine application.

Informal learning and its validation has become a major issue in European and national education policy, raising the following questions. May learning be constituent for political action? Is learning the focus of validation? Is informality a feature of learning? Is implicit learning solely related to informality? To give answers, a general learning concept is introduced, focusing on the acting individual in socioculturally shaped environments. using this concept, learning is exclusively realised by the individual and therefore may not be a constituent element of political actions. Learning outcomes, not learning, are validated. Implicit learning is not only related to informality, and formality is not a feature of learning. From this perspective, "informality" and the opposite "formality" have to be located in conditions external to the learner, characterised by the "extent of educational arrangement", "certification" and "approved public regulations". Consequently, the term "informal learning" is triggering inappropriate associations. (Contains 1 table and 2 figures.)

Informal learning and its validation has become a major issue in European and national education policy, raising the following questions. May learning be constituent for political action? Is learning the focus of validation? Is informality a feature of learning? Is implicit learning solely related to informality? To give answers, a general learning concept is introduced, focusing on the acting individual in socioculturally shaped environments. using this concept, learning is exclusively realised by the individual and therefore may not be a constituent element of political actions. Learning outcomes, not learning, are validated. Implicit learning is not only related to informality, and formality is not a feature of learning. From this perspective, "informality" and the opposite "formality" have to be located in conditions external to the learner, characterised by the "extent of educational arrangement", "certification" and "approved public regulations". Consequently, the term "informal learning" is triggering inappropriate associations. (Contains 1 table and 2 figures.)

This article is from Advances in Cognitive Psychology , volume 8 . Abstract Accounts of statistical learning, both implicit and explicit, often invoke predictive processes as central to learning, yet practically all experiments employ non-predictive measures during training. We argue that the common theoretical assumption of anticipation and prediction needs clearer, more direct evidence for it during learning. We offer a novel experimental context to explore prediction, and report results from a simple sequential learning task designed to promote predictive behaviors in participants as they responded to a short sequence of simple stimulus events. Predictive tendencies in participants were measured using their computer mouse, the trajectories of which served as a means of tapping into predictive behavior while participants were exposed to very short and simple sequences of events. A total of 143 participants were randomly assigned to stimulus sequences along a continuum of regularity. Analysis of computer-mouse trajectories revealed that (a) participants almost always anticipate events in some manner, (b) participants exhibit two stable patterns of behavior, either reacting to vs. predicting future events, (c) the extent to which participants predict relates to performance on a recall test, and (d) explicit reports of perceiving patterns in the brief sequence correlates with extent of prediction. We end with a discussion of implicit and explicit statistical learning and of the role prediction may play in both kinds of learning.

This article is from PLoS ONE , volume 9 . Abstract In this study we aim to examine how the implicit learning of statistical regularities of successive stimuli affects the ability to exert cognitive control. In three experiments, sequences of flanker stimuli were segregated into pairs, with the second stimulus contingent on the first. Response times were reliably faster for the second stimulus if its congruence tended to match the congruence of the preceding stimulus, even though most participants were not explicitly aware of the statistical regularities (Experiment 1). In contrast, performance was not enhanced if the congruence of the second stimuli tended to mismatch the congruence of the first stimulus (Experiment 2). The lack of improvement appears to result from a failure of learning mismatch contingencies (Experiment 3). The results suggest that implicit learning of inter-stimulus relationships can facilitate cognitive control.

https://3dgv.github.io/ 3DGV is a virtual seminar series on Geometry Processing and 3D Computer Vision. Seminars take place every Wednesday, cycling through three different time zones (Europe, America, and Asia).

Findings from two studies of fourth grade children learning the concept of mathematical equivalence are presented. The questions studied were: (1) Can knowledge conveyed in gesture but not in speech be tapped by a recognition technique; and (2) Does having implicit knowledge that is expressed in gesture but not in speech have implications for learning? The first question was addressed in a study of 17 fourth grade students who solved six pretest problems of the form 3 + 4 + 5 = --- + 5 incorrectly. The students were presented six additional problems, one at a time, and were asked to rate the acceptability of six preferred solutions to each problem derived from strategies children often use. The second question was addressed in a study of 43 students who were unsuccessful on a pretest of six addition and four multiplication problems similar in form to the above example. These students were trained in the concept of equivalence and were retested. Results suggest that the knowledge a child expresses in gesture but not in speech reflects the first step in acquiring a concept and sets the agenda for future learning. (DE)

Research into second language (L2) pragmatics has addressed a number of learner variables likely to implicate in speech act knowledge. Subscribing to the same line of research, the present study addressed the development and validation of a pragmatic learning strategy inventory (PRALSI), and the relationship between English as a foreign language (EFL) learners' pragmatic learning strategy (PLS) use and their speech act knowledge. A total of 245 Iranian English-major undergraduates, ranging in age from 19 to 31, participated in PRALSI's validation study. Of these, 117 further participated in the second phase of the study to unearth the relationship between PLS use and speech act knowledge, measured through a 24-item written discourse completion test (WDCT). Principal component analysis confirmed the 3-factor structure of PRALSI, comprising implicit, inductive explicit, and deductive explicit strategies. The inventory contains 41 strategies for the learning of three main aspects of pragmatic competence: speech acts, implicature, and conversational routines. Two one-way ANOVAs also showed strategy use, particularly the use of explicit strategies, to be positively correlated with speech act knowledge. The findings reveal the implications of the implicit/explicit learning dichotomy for pragmatic development and strategy use, and the potentially greater advantage explicit strategies offer for pragmatic knowledge.

In recent years, interest in recommender research has shifted from explicit feedback towards implicit feedback data. A diversity of complex models has been proposed for a wide variety of applications. Despite this, learning from implicit feedback is still computationally challenging. So far, most work relies on stochastic gradient descent (SGD) solvers which are easy to derive, but in practice challenging to apply, especially for tasks with many items. For the simple matrix factorization model, an efficient coordinate descent (CD) solver has been previously proposed. However, efficient CD approaches have not been derived for more complex models. In this paper, we provide a new framework for deriving efficient CD algorithms for complex recommender models. We identify and introduce the property of k-separable models. We show that k-separability is a sufficient property to allow efficient optimization of implicit recommender problems with CD. We illustrate this framework on a variety of state-of-the-art models including factorization machines and Tucker decomposition. To summarize, our work provides the theory and building blocks to derive efficient implicit CD algorithms for complex recommender models.

In recent years, interest in recommender research has shifted from explicit feedback towards implicit feedback data. A diversity of complex models has been proposed for a wide variety of applications. Despite this, learning from implicit feedback is still computationally challenging. So far, most work relies on stochastic gradient descent (SGD) solvers which are easy to derive, but in practice challenging to apply, especially for tasks with many items. For the simple matrix factorization model, an efficient coordinate descent (CD) solver has been previously proposed. However, efficient CD approaches have not been derived for more complex models. In this paper, we provide a new framework for deriving efficient CD algorithms for complex recommender models. We identify and introduce the property of k-separable models. We show that k-separability is a sufficient property to allow efficient optimization of implicit recommender problems with CD. We illustrate this framework on a variety of state-of-the-art models including factorization machines and Tucker decomposition. To summarize, our work provides the theory and building blocks to derive efficient implicit CD algorithms for complex recommender models.

This article is from Frontiers in Psychology , volume 5 . Abstract Although there is now substantial evidence that developmental change occurs in implicit learning abilities over the lifespan, disparate results exist regarding the specific developmental trajectory of implicit learning skills. One possible reason for discrepancies across implicit learning studies may be that younger children show an increased sensitivity to variations in implicit learning task procedures and demands relative to adults. Studies using serial-reaction time (SRT) tasks have suggested that in adults, measurements of implicit learning are robust across variations in task procedures. Most classic SRT tasks have used response-contingent pacing in which the participant's own reaction time determines the duration of each trial. However, recent paradigms with adults and children have used fixed trial pacing, which leads to alterations in both response and attention demands, accuracy feedback, perceived agency, and task motivation for participants. In the current study, we compared learning on fixed-paced and self-paced versions of a spatial sequence learning paradigm in 4-year-old children and adults. Results indicated that preschool-aged children showed reduced evidence of implicit sequence learning in comparison to adults, regardless of the SRT paradigm used. In addition, we found the preschoolers showed significantly greater learning when stimulus presentation was self-paced. These data provide evidence for developmental differences in implicit sequence learning that are dependent on specific task demands such as stimulus pacing, which may be related to developmental changes in the impact of broader constructs such as attention and task motivation on implicit learning.

In many African countries, English is the medium of instruction in higher education even though students may not always be entirely familiar with "standard" English. This study aimed at investigating the relevance of English language competencies for teaching and learning from the perspective of students and lecturers. The study was carried out in Namibia and guided by the conceptual framework of implicit theories. Through a self-administered questionnaire, data were collected from a sample of 286 undergraduate students and 34 lecturers. Students and lecturers differed statistically significantly in all their views on the topic under investigation. While most of the lecturers (85.3%) believed that their students would not have good English language competencies, the majority of students (87.8%) rated their English between good and excellent. Most lecturers believed that insufficient English language competencies would cause a variety of problems for students such as having difficulty expressing themselves in English, following lectures, taking good notes during lectures, understanding academic texts, and writing coherent essays; in contrast, the majority of students believed that they had no such problems. The results are discussed with regard to practical implications for teaching and learning in higher education.

Research has shown that teaching second language (L2) learning strategies explicitly promotes an increase in strategy use and in oral proficiency. Consequently, a checklist based on the six types of strategies from Oxford's taxonomy (1990) was created to analyze strategic instruction in the most common textbooks used in the last year of Higher Secondary Education in Spain. The study considered whether there was explicit strategy instruction and its location (within the units [Internal] or in other sections [External]) in course books, and what specific strategies were explicitly taught for the two oral skills, listening and speaking. Results showed, first, that there was explicit internal and external instruction of L2 learning strategies both for listening and speaking, but not in all the books; second, that there was significantly more implicit use than explicit instruction, and less explicit instruction in the units of the textbooks than in specific extra sections in the textbook or in support material; and finally, that internal explicit instruction of strategies remains very limited; and thus, L2 learning strategy instruction, competence and use may not be sufficiently encouraged in those textbooks.

This systematic review will explore the following question: For late talkers and preschool children (i.e., 1;6-6;11) with Developmental Language Disorder/ Specific Language Impairment, is implicit vocabulary intervention associated with gains in receptive and expressive vocabulary, compared with other non-implicit vocabulary interventions or no intervention?

This systematic review will explore the following question: For late talkers and preschool children (i.e., 1;6-6;11) with Developmental Language Disorder/ Specific Language Impairment, is implicit vocabulary intervention associated with gains in receptive and expressive vocabulary, compared with other non-implicit vocabulary interventions or no intervention?

Imitation can be viewed as a means of enhancing learning in multiagent environments. It augments an agent's ability to learn useful behaviors by making intelligent use of the knowledge implicit in behaviors demonstrated by cooperative teachers or other more experienced agents. We propose and study a formal model of implicit imitation that can accelerate reinforcement learning dramatically in certain cases. Roughly, by observing a mentor, a reinforcement-learning agent can extract information about its own capabilities in, and the relative value of, unvisited parts of the state space. We study two specific instantiations of this model, one in which the learning agent and the mentor have identical abilities, and one designed to deal with agents and mentors with different action sets. We illustrate the benefits of implicit imitation by integrating it with prioritized sweeping, and demonstrating improved performance and convergence through observation of single and multiple mentors. Though we make some stringent assumptions regarding observability and possible interactions, we briefly comment on extensions of the model that relax these restricitions.

As part of the “Emotion recognition in schizophrenia spectrum disorders” (ERIS) project, Study 1 seeks to improve our understanding of implicit statistical learning (ISL) and associated factors in schizophrenia spectrum disorders (SSDs). To this end, we conduct a systematic review of the existing literature (see PROSPERO, link to be added) and an empirical investigation of ISL performance in individuals with SSDs and healthy controls (HCs) (described herein). ISL performance is measured from a standard tone-based transitional probabilities (TP) task. In this task, participants listen to a continuous stream of tones that—unbeknownst to them—contains hidden "words": sequences of three tones that are more likely to occur together. Later, participants are presented with two tritones (one word and one foil) and asked to choose the one that sounds more familiar. As part of Study 1, we develop the TP stimuli and presentation scripts based on Saffran et al. (1999) and related work, making these publicly available to facilitate further replication work. Our primary prediction is that individuals with SSDs show preserved ISL performance, both relative to chance and to HCs. This “preserved module” is expected to play a role in subsequent ERIS studies, which are scheduled for separate registrations. See the ERIS project at https://osf.io/m3kr4/ and Study 1 at https://osf.io/u9nfe/.

Generative adversarial networks (GANs) provide an algorithmic framework for constructing generative models with several appealing properties: they do not require a likelihood function to be specified, only a generating procedure; they provide samples that are sharp and compelling; and they allow us to harness our knowledge of building highly accurate neural network classifiers. Here, we develop our understanding of GANs with the aim of forming a rich view of this growing area of machine learning---to build connections to the diverse set of statistical thinking on this topic, of which much can be gained by a mutual exchange of ideas. We frame GANs within the wider landscape of algorithms for learning in implicit generative models--models that only specify a stochastic procedure with which to generate data--and relate these ideas to modelling problems in related fields, such as econometrics and approximate Bayesian computation. We develop likelihood-free inference methods and highlight hypothesis testing as a principle for learning in implicit generative models, using which we are able to derive the objective function used by GANs, and many other related objectives. The testing viewpoint directs our focus to the general problem of density ratio estimation. There are four approaches for density ratio estimation, one of which is a solution using classifiers to distinguish real from generated data. Other approaches such as divergence minimisation and moment matching have also been explored in the GAN literature, and we synthesise these views to form an understanding in terms of the relationships between them and the wider literature, highlighting avenues for future exploration and cross-pollination.

http://uf.catalog.fcla.edu/uf.jsp?st=UF027600704&ix=pm&I=0&V=D&pm=1

The aim is to investigate whether patients with amnesic mild cognitive impairment acquire information in the same manner as cognitively unimpaired older adults, or whether they benefit more/less from certain learning conditions (e.g., instructions) because of their explicit memory deficit. Participants of both groups undergo testing in a new contingency learning task using ecologically valid stimuli (object-location associations). This assesses the impact of implicit or explicit instructions, or a combination of both, on later memory performance. Based on these assessments we determine task parameters that enhance behavioural indicators of learning and memory.

This article is from Advances in Cognitive Psychology , volume 8 . Abstract In the current paper, we first evaluate the suitability of traditional serial reaction time (SRT) and artificial grammar learning (AGL) experiments for measuring implicit learning of social signals. We then report the results of a novel sequence learning task which combines aspects of the SRT and AGL paradigms to meet our suggested criteria for how implicit learning experiments can be adapted to increase their relevance to situations of social intuition. The sequences followed standard finite-state grammars. Sequence learning and consciousness of acquired knowledge were compared between 2 groups of 24 participants viewing either sequences of individually presented letters or sequences of body-posture pictures, which were described as series of yoga movements. Participants in both conditions showed above-chance classification accuracy, indicating that sequence learning had occurred in both stimulus conditions. This shows that sequence learning can still be found when learning procedures reflect the characteristics of social intuition. Rule awareness was measured using trial-by-trial evaluation of decision strategy (Dienes & Scott, 2005; Scott & Dienes, 2008). For letters, sequence classification was best on trials where participants reported responding on the basis of explicit rules or memory, indicating some explicit learning in this condition. For body-posture, classification was not above chance on these types of trial, but instead showed a trend to be best on those trials where participants reported that their responses were based on intuition, familiarity, or random choice, suggesting that learning was more implicit. Results therefore indicate that the use of traditional stimuli in research on sequence learning might underestimate the extent to which learning is implicit in domains such as social learning, contributing to ongoing debate about levels of conscious awareness in implicit learning.

This article is from Frontiers in Psychology , volume 5 . Abstract A host of research has now shown that our explicit goals and intentions can, in large part, overcome the capture of visual attention by objects that differ from their surroundings in terms of size, shape, or color. Surprisingly however, there is little evidence for the role of implicit learning in mitigating capture effects despite the fact that such learning has been shown to strongly affect behavior in a host of other performance domains. Here, we employ a modified attention capture paradigm, based on the work of Theeuwes (1991, 1992), in which participants must search for an odd-shaped target amongst homogeneous distracters. On each trial, there is also a salient, but irrelevant odd-colored distracter. Across the experiments reported, we intermix two search contexts: for one set of distracters (e.g., squares) the shape singleton and color singleton coincide on a majority of trials (high proportion congruent condition), whereas for the other set of distracters (e.g., circles) the shape and color singletons are highly unlikely to coincide (low proportion congruent condition). Crucially, we find that observers learn to allow the capture of attention by the salient distracter to a greater extent in the high, compared to the low proportion congruent condition, albeit only when search is sufficiently difficult. Moreover, this effect of prior experience on search behavior occurs in the absence of awareness of our proportion manipulation. We argue that low-level properties of the search displays recruit representations of prior experience in a rapid, flexible, and implicit manner.

Teacher immediacy has surfaced as an important instructional communication variable, yet little is known about how it functions to effect learning. To offer an explanation as to how teacher immediacy facilitates learning, a study investigated implicit communication theory. Subjects consisted of 625 undergraduate students who completed questionnaires later subjected to regression analyses. As in previous research, teacher verbal and nonverbal immediacy effected cognitive and affective learning. Findings indicate that implicit communication theory helps explain why learning occurs. Specifically, the dimensions of pleasure and arousal accounted for over half of learning variance. Further, implicit communication theory is significantly related to teacher immediacy. Results suggest that the integration of implicit communication theory with learning in general and specific instructional variables such as teacher immediacy is appropriate and fruitful. (Nine tables of data are included; 103 references are attached.) (Author)

Former meta-analyses on SR training (De Boer et al., 2018; Dignath et al., 2008; Dignath & Büttner, 2008; Donker et al., 2014; Hattie et al., 1996) have delivered insights into whether students can be trained in terms of SR strategies and which type of strategies are most effective, but no evidence is provided yet about how SR strategies can be trained best since former studies have not investigated the type of instructional approach as potential moderator for the effectiveness of SR training. Moreover research has shown that students can learn from peers as models (Bandura, 1997); also that they can learn how to apply SR strategies (Kostons, Van Gog & Paas, 2012). Yet, it remains unclear whether implicit strategy instruction or explicit instruction, that addresses strategy knowledge and utility of strategies, are more effective for young learners. Aim of the study is to investigate the effectiveness of explicit vs. implicit instruction videos for supporting students SRL. Testing the effectiveness of the implicit vs. explicit strategy instruction will be carried out by providing learners with instructional videos showing a coping model and a mastery model that demonstrates strategy use. Three video conditions will be carried out, showing (1) no SRL strategy use (video on healthy diet), (2) implicit modelling of strategy use (child applies strategies while learning without addressing utility and knowledge), and (3) explicit modelling of strategy use (child applies strategies while learning, and addresses utility and knowledge). The videos will focus on planning, monitoring, regulation and evaluation strategies. The experiment will test whether any of these conditions affect students’ use of SRL strategies, their strategy knowledge, and their motivation for strategy use and whether explicit strategy instruction is more effective than implicit instruction. The study will be conducted as an experimental study in order to test the effects of the videos on students’ strategy knowledge, their motivation to use these strategies (utility beliefs and self-regulatory efficacy), and their use of SR strategies (thinking-aloud and observation while executing the task).

The current experiment is considered a “Proof-of-Concept” in which we aim to establish the feasibility and rationale for an integrated theory that has been developed as part of the German Research Foundation (DFG) project “Motor heuristics and movement analogies in performance and health” (PIs: Markus Raab and Laura Voigt, RA 940/27-1 Ι VO 2789/1-1). For successful sports performance, athletes must choose between actions (movement selection) and then execute the selected action skillfully (movement execution; Raab, 2017; Voigt et al., 2022). A table tennis player who can execute a backhand stroke effectively but does not know when to use the backhand stroke will not succeed in the sport. Equally, a player who knows when to employ the backhand stroke but cannot execute it effectively is unlikely to succeed. Movement selection and execution often need to be performed when time is limited, stress is present, more than one task needs to be processed, or when fatigue or unfamiliar situations occur. Given limited ability to process multiple channels of information, performers need to find ways to manage the wealth of available information economically to make appropriate decisions and execute the chosen movement skillfully in their discipline. The idea that economic management of information is central for successful performance is supported by a common understanding of expertise in sports: During the development of expertise, the nature of the knowledge structures that support motor performance gradually changes over time, with an increasing degree of implicit (unconscious) processing and a decreasing level of explicit (conscious) processing (Fitts & Posner, 1967). Explicit processes involve the retrieval of consciously accessible (declarative) knowledge and depend on working memory. Thus, highly explicit sport performance has often been shown to be disrupted by demands resulting from performance pressure or multiple task requirements (Masters & Maxwell, 2008; Nieuwenhuys & Oudejans, 2012, 2017). In contrast, implicit processes are faster and involve sophisticated complexes of procedural knowledge that can be applied without conscious thought, with greater automaticity and fewer demands for attentional resources (e.g., Anderson, 1983; Lewicki et al., 1992; Kal et al., 2018; Masters & Maxwell, 2004; Shiffrin & Schneider, 1977; Willingham, 1998). Implicit processes are therefore less dependent on working memory, which allows the expert to economically manage multiple streams of information for movement selection and movement execution while being taxed with other demands (for a review of the theoretical architecture and function of working memory, see Baddeley, 2003). But how exactly are these streams of information for movement selection and movement execution managed? In the current study, we aim to deliver a “Proof-of-Concept” for the integration of two theories – motor heuristics (Raab, 2017) and motor analogies (Masters, 2000, 2012). We aim to investigate whether implicit learning helps athletes to manage information during movement selection and movement execution and protects performance under demanding circumstances. Motor heuristics have their theoretical basis in the simple heuristics used for decision making (Gigerenzer & Goldstein, 1996) and advise “what” movement to choose (i.e., movement selection). Motor heuristics are fast and frugal decision-making strategies (i.e., rules of thumb) that exploit information in the environment by ordering pieces of information (i.e., cues) by their validity. Decision makers judge the cues’ validity (i.e., how often in the past a particular piece of information was helpful in making a choice), and then choose the option that is favored by the cue with the highest validity. In sports, a large body of empirical findings demonstrates that superior decision making by experts is characterized by focusing on fewer (task-relevant) options and higher-quality options and decisions (Basevitch et al., 2020; Belling et al., 2015; Laborde & Raab, 2013; Musculus, 2018; Musculus et al., 2021; Raab & Johnson, 2007), especially in demanding circumstances, such as time pressure, opponent pressure, and stress (Musculus et al., 2021). For heuristics to be successful, their use needs to be matched to the environmental structures. Leuker et al. (2018, 2019) showed that statistical regularities (i.e., risk-reward structures) can be learned without explicit instructions via incidental, unsupervised learning and then utilized as heuristics for decision making under uncertainty. Implicit learning processes can thus be an important aspect in building a representation of the environment that, in turn, guides choice behavior (Hertwig et al., 2022). Importantly, the so-called description-experience gap further suggests that learning environmental structures by experience is more beneficial for performance than receiving explicit information about the environmental structures (e.g., Hertwig et al., 2004; Armstrong & Spaniol, 2017). Motor analogies have their theoretical basis in the theory of implicit motor learning (Masters, 1992, 2000) and advise ‘how’ to move (i.e., movement execution). Motor analogies leverage a concept that is already well known, such as “strike the ball while bringing the bat up the hypotenuse of a triangle” in order to convey the complex structure of the motor skill (e.g., a table tennis topspin forehand; Liao & Masters, 2001). It has been proposed that they promote economic management of information for movement control by chunking fundamental technical information (i.e., relevant pieces of information) into one well-known concept – many small “bits” of information are collapsed to into fewer larger chunks (Poolton & Masters, 2014). Although fewer chunks are processed, they contain the relevant information, meaning that information can be processed with relatively less cognitive effort and processing becomes more efficient. At the same time, motor analogies minimize accrual of conscious knowledge of the underlying rules governing the mechanics of movements (e.g., Liao & Masters, 2001). Learning movements implicitly through analogies has been shown to result in more robust motor performance under demanding conditions than learning movements by explicit step-by-step instructions (e.g., Koedijker et al., 2007, Lam et al., 2009; Liao & Masters, 2001; Schlapkohl et al., 2012; for a meta-analysis see Cabral et al., 2022), which has been attributed to the placement of fewer demands on cognitive resources than explicit motor learning. To date, motor heuristics and analogies have only been researched independently from each other. Notably, it has been shown that implicit motor learning through motor analogies did not only improve motor performance, but also decision making, potentially because implicit processing of movement execution left sufficient cognitive resources available for decision making (Lola & Tzetzis, 2021; Masters et al., 2008). Likewise, the use of heuristics (instead of a complex step-by-step planning strategy) showed combined selection and execution advantages, when participants were required to select one of two computer cursors that displace in different directions (i.e., selection) and subsequently navigate from a starting position to a goal as efficiently as possible (i.e., execution; Dundon et al., 2023). We aim to extend these findings by testing performance in demanding circumstances after promoting the economic management of information for both movement selection and execution via implicit learning of motor heuristics and motor analogies.

The current experiment is considered a “Proof-of-Concept” in which we aim to establish the feasibility and rationale for an integrated theory that has been developed as part of the German Research Foundation (DFG) project “Motor heuristics and movement analogies in performance and health” (PIs: Markus Raab and Laura Voigt, RA 940/27-1 Ι VO 2789/1-1). For successful sports performance, athletes must choose between actions (movement selection) and then execute the selected action skillfully (movement execution; Raab, 2017; Voigt et al., 2022). A table tennis player who can execute a backhand stroke effectively but does not know when to use the backhand stroke will not succeed in the sport. Equally, a player who knows when to employ the backhand stroke but cannot execute it effectively is unlikely to succeed. Movement selection and execution often need to be performed when time is limited, stress is present, more than one task needs to be processed, or when fatigue or unfamiliar situations occur. Given limited ability to process multiple channels of information, performers need to find ways to manage the wealth of available information economically to make appropriate decisions and execute the chosen movement skillfully in their discipline. The idea that economic management of information is central for successful performance is supported by a common understanding of expertise in sports: During the development of expertise, the nature of the knowledge structures that support motor performance gradually changes over time, with an increasing degree of implicit (unconscious) processing and a decreasing level of explicit (conscious) processing (Fitts & Posner, 1967). Explicit processes involve the retrieval of consciously accessible (declarative) knowledge and depend on working memory. Thus, highly explicit sport performance has often been shown to be disrupted by demands resulting from performance pressure or multiple task requirements (Masters & Maxwell, 2008; Nieuwenhuys & Oudejans, 2012, 2017). In contrast, implicit processes are faster and involve sophisticated complexes of procedural knowledge that can be applied without conscious thought, with greater automaticity and fewer demands for attentional resources (e.g., Anderson, 1983; Lewicki et al., 1992; Kal et al., 2018; Masters & Maxwell, 2004; Shiffrin & Schneider, 1977; Willingham, 1998). Implicit processes are therefore less dependent on working memory, which allows the expert to economically manage multiple streams of information for movement selection and movement execution while being taxed with other demands (for a review of the theoretical architecture and function of working memory, see Baddeley, 2003). But how exactly are these streams of information for movement selection and movement execution managed? In the current study, we aim to deliver a “Proof-of-Concept” for the integration of two theories – motor heuristics (Raab, 2017) and motor analogies (Masters, 2000, 2012). We aim to investigate whether implicit learning helps athletes to manage information during movement selection and movement execution and protects performance under demanding circumstances. Motor heuristics have their theoretical basis in the simple heuristics used for decision making (Gigerenzer & Goldstein, 1996) and advise “what” movement to choose (i.e., movement selection). Motor heuristics are fast and frugal decision-making strategies (i.e., rules of thumb) that exploit information in the environment by ordering pieces of information (i.e., cues) by their validity. Decision makers judge the cues’ validity (i.e., how often in the past a particular piece of information was helpful in making a choice), and then choose the option that is favored by the cue with the highest validity. In sports, a large body of empirical findings demonstrates that superior decision making by experts is characterized by focusing on fewer (task-relevant) options and higher-quality options and decisions (Basevitch et al., 2020; Belling et al., 2015; Laborde & Raab, 2013; Musculus, 2018; Musculus et al., 2021; Raab & Johnson, 2007), especially in demanding circumstances, such as time pressure, opponent pressure, and stress (Musculus et al., 2021). For heuristics to be successful, their use needs to be matched to the environmental structures. Leuker et al. (2018, 2019) showed that statistical regularities (i.e., risk-reward structures) can be learned without explicit instructions via incidental, unsupervised learning and then utilized as heuristics for decision making under uncertainty. Implicit learning processes can thus be an important aspect in building a representation of the environment that, in turn, guides choice behavior (Hertwig et al., 2022). Importantly, the so-called description-experience gap further suggests that learning environmental structures by experience is more beneficial for performance than receiving explicit information about the environmental structures (e.g., Hertwig et al., 2004; Armstrong & Spaniol, 2017). Motor analogies have their theoretical basis in the theory of implicit motor learning (Masters, 1992, 2000) and advise ‘how’ to move (i.e., movement execution). Motor analogies leverage a concept that is already well known, such as “strike the ball while bringing the bat up the hypotenuse of a triangle” in order to convey the complex structure of the motor skill (e.g., a table tennis topspin forehand; Liao & Masters, 2001). It has been proposed that they promote economic management of information for movement control by chunking fundamental technical information (i.e., relevant pieces of information) into one well-known concept – many small “bits” of information are collapsed to into fewer larger chunks (Poolton & Masters, 2014). Although fewer chunks are processed, they contain the relevant information, meaning that information can be processed with relatively less cognitive effort and processing becomes more efficient. At the same time, motor analogies minimize accrual of conscious knowledge of the underlying rules governing the mechanics of movements (e.g., Liao & Masters, 2001). Learning movements implicitly through analogies has been shown to result in more robust motor performance under demanding conditions than learning movements by explicit step-by-step instructions (e.g., Koedijker et al., 2007, Lam et al., 2009; Liao & Masters, 2001; Schlapkohl et al., 2012; for a meta-analysis see Cabral et al., 2022), which has been attributed to the placement of fewer demands on cognitive resources than explicit motor learning. To date, motor heuristics and analogies have only been researched independently from each other. Notably, it has been shown that implicit motor learning through motor analogies did not only improve motor performance, but also decision making, potentially because implicit processing of movement execution left sufficient cognitive resources available for decision making (Lola & Tzetzis, 2021; Masters et al., 2008). Likewise, the use of heuristics (instead of a complex step-by-step planning strategy) showed combined selection and execution advantages, when participants were required to select one of two computer cursors that displace in different directions (i.e., selection) and subsequently navigate from a starting position to a goal as efficiently as possible (i.e., execution; Dundon et al., 2023). We aim to extend these findings by testing performance in demanding circumstances after promoting the economic management of information for both movement selection and execution via implicit learning of motor heuristics and motor analogies.

This article is from Frontiers in Computational Neuroscience , volume 7 . Abstract It has been reported recently that while general sequence learning across ages conforms to the typical inverted-U shape pattern, with best performance in early adulthood, surprisingly, the basic ability of picking up in an implicit manner triplets that occur with high vs. low probability in the sequence is best before 12 years of age and it significantly weakens afterwards. Based on these findings, it has been hypothesized that the cognitively controlled processes coming online at around 12 are useful for more targeted explicit learning at the cost of becoming relatively less sensitive to raw probabilities of events. To test this hypothesis, we collected data in a sequence learning task using probabilistic sequences in five age groups from 11 to 39 years of age (N = 288), replicating the original implicit learning paradigm in an explicit task setting where subjects were guided to find repeating sequences. We found that in contrast to the implicit results, performance with the high- vs. low-probability triplets was at the same level in all age groups when subjects sought patterns in the sequence explicitly. Importantly, measurements of explicit knowledge about the identity of the sequences revealed a significant increase in ability to explicitly access the true sequences exactly around the age where the earlier study found the significant drop in ability to learn implicitly raw probabilities. These findings support the conjecture that the gradually increasing involvement of more complex internal models optimizes our skill learning abilities by compensating for the performance loss due to down-weighting the raw probabilities of the sensory input, while expanding our ability to acquire more sophisticated skills.

This paper presents a literature review on self-regulated learning and then reports two investigations. The two investigations identified situations that seemed likely to afford opportunities for self-regulated learning to occur and followed what parents and teachers did to provide implicit support in these situations. The hypothesis was that the development in students of orientations to engage in self-regulatory effort and to value or even enjoy this experience occurs in part through certain relevant and recurring, elemental activities and interactions with significant others. The first investigation examined the implicit teachings involved in parent-child interactions around homework. It found that homework sessions were catalysts for parent-child dialogue around issues of autonomy and conformity. The second investigation coded high school students' essays for elements of self-regulated learning; most frequently used elements were emotion control, planning, and self-evaluation. The paper concludes that self-regulated learning comes about from the continuing interchange between students and the educating elements of their extended environments--adults and knowledgeable peers, various enacting curricula, and affording activities. (Contains approximately 70 references.) (JDD)