Intuitive Supervision Of Analytical Thinking - An Empirical Study On Bat And Ball-structured Problems Analyzed Using Signal Detection Theory - Info and Reading Options
By Mattias Lauridsen, Joakim Sundh, August Collsiöö and Peter Juslin
“Intuitive Supervision Of Analytical Thinking - An Empirical Study On Bat And Ball-structured Problems Analyzed Using Signal Detection Theory” Metadata:
- Title: ➤ Intuitive Supervision Of Analytical Thinking - An Empirical Study On Bat And Ball-structured Problems Analyzed Using Signal Detection Theory
- Authors: Mattias LauridsenJoakim SundhAugust CollsiööPeter Juslin
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- Internet Archive ID: osf-registrations-amzh4-v1
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Reliable and consistent definition of analytical and intuitive processes have been difficult to establish. Often, the two processes have been defined by verbal properties such as fast or slow, conscious or nonconscious and require working memory or do not require working memory. An alternative, operational definition is provided by the Precise/Not Precise-model (PNP-model) which define the processes by modeling answer distributions (Sundh, Collsiöö, Millroth, & Juslin, 2021). It suggests that analytic responses result in a leptokurtic answer distribution as they rely on precise, deterministic processes. This means that if a normative strategy, such as a mental algorithm, is applied flawlessly it consistently leads to a normative answer. However, if one applies the wrong strategy or if a mistake occurs in the execution of the deterministic, analytical process, it can result in potentially large errors. On the other hand, intuitive processes rely on mental models of the state and function of the world which are learned from past experience. These mental models provide a range of more or less plausible answers which result in non-precise, gaussian response distribution. Many dual process accounts of intuition and analysis have addressed the interplay between analytical and intuitive processes (Kahneman, 2011; Evans & Stanovich, 2013; Frederick, 2005). The role of analytical processes has been described as a supervisory system to the more error-prone intuitive processes. That is, the analytical processes “intervene” when the “lower-order” intuitive processes fail to provide a normative solution (Evans & Stanovich, 2013). However, when viewing intuition as based on mental models that are shaped by passed experience, the role of intuitive processes could also be to evaluate the plausibility of the answer provided by the deterministic, analytic process. That is, the role of the intuitive process is to make sure that the solution reached by an analytic algorithm that is plausible given one’s past experience of the states of the world. If the result of an algorithm is implausible, another algorithm should be applied. When defining analytical processes as the application of content-independent logical or mathematical rules, the context should be irrelevant to reaching a correct solution when relying only on analytical processes. The intuitive process, on the other hand, uses contextual cues to suggest a range of plausible answers. This provides information that can be used either to evaluate answers suggested by a deterministic algorithm (i.e. does the answer fall within the range of plausible answers?) or to provide an (most often) approximately correct but non-precise answer. Consider for example the bat and ball-problem from the Cognitive Reflection Test (Frederick, 2005): A bat and a ball cost $1.1. A bat cost $1 more than a ball. How much does a ball cost? People mainly provide two answers to this problem: $.1 and $0.05. $.1 is the most common answer although it is wrong. The correct answer is $0.05. When applying the PNP-model, the two leptokurtic answer distributions tells us that the answers are mainly a result of analytical, deterministic thinking. Most people rely on an incorrect, but simpler, algorithm which we call the additive algorithm: 1 + x = 1.1. The normative solution is more advanced and require more numerical skills: (1 + x) + x = 1.1. In the original article, Frederick (2005) argued that the participants make the mistake because the additive solution is an intuitive answer where the answer “springs quickly to mind” and the analytical/reflective processes fail to intervene. However, from our perspective, the analytical processes fail as it relies on a faulty analytic algorithm (the additive algorithm) and the intuitive processes fail to “intervene”. We hypothesize that the intuitive supervision fails because the answer provided by the additive algorithm falls within the range of what is intuitively plausible. If one should alter the numbers in the bat and ball-problem so that the additive algorithm leads to an implausible answer, the intuitive supervisory process should detect this and initiate search for an algorithm that leads to a more plausible answer. There are two ways that the additive algorithm can be implausible. Consider the following example: A loaf of bread and a 2-liter coca cola cost $4.5 in total. The loaf of bread cost $0.5 more than two-liter coca cola. How much does 2-liter coca cola cost? If the additive algorithm is applied the coca cola would cost $4 (0.5 + x = 4.50). This is an implausible answer because (1) we may know from prior, real-world experience that a 2-liter coca cola usually does not cost that much (a contextual cue) and (2) the loaf of bread is supposed to cost more than the coca-cola, still the coca cola would cost $4 of the total of $4.5 for both products. This makes the additive algorithm mathematically implausible (mathematical cue). The plausibility of the additive algorithm as a solution to the bat and ball problem can therefore be varied by altering the difference of cost between the two products which provides numerical cues of its plausibility. Less relative difference in cost between products makes the additive algorithm mathematically less plausible. Furthermore, the absolute difference of the solutions provided by the normative algorithm and the additive algorithm can also be manipulated, although the additive algorithm always results in the double cost compared to the normative algorithm. In problems where the absolute difference of additive and normative algorithm is greater, the additive algorithm also seems less plausible. As the intuitive processes relies on contextual cues to judge the plausibility of a solution, it should be easier to detect the faulty additive algorithm when there are contextual cues available. The intuitive supervisory process can be regarded as a signal detection process. Its function is to detect signals that are implausible solutions, i.e. solutions that are not consistent with the mental models about states and functions of the world. In this study, we aim to apply the signal detection theory (SDT) to investigate the properties of the intuitive supervision system. We do this by showing multiple content-modified versions of the bat and ball-problem to the participants combined with a suggested solution (either a normative solution or the solution reached by the additive algorithm). The participants will have to judge if the solution provided is false or true. The participants will also provide a certainty rating for each answer that will be used to further investigate the participants’ sensitivity to detect incorrect solutions. SDT allows for a measurement of the participants ability to discriminate faulty and correct solutions that is not affected by response bias.
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