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Epistemic emotions affect learning and exploration. Specifically, beliefs held with high confidence elicit high levels of surprise and curiosity when proven wrong. In turn, these emotions lead to searching for more elaborative details about the belief topic. Do epistemic emotions also motivate exploration of how widely a belief is held? After answering a trivia question and indicating how confident they were, participants were shown an answer submitted by another participant, reported their surprise and curiosity, and then given the option of seeing up to three responses from different participants. The results supported serial mediation, with certainty predicting surprise, surprise predicting curiosity, and curiosity predicting the number of additional sources explored. However, unlike prior findings, high-certainty errors did not result in stronger emotions or more exploration than low-certainty errors. Thus, epistemic emotions motivate not just elaborative exploration but also exploration of opinion convergence - two complementary ways to justify beliefs.

CogSci 2022

Looking for a second opinion: Epistemic emotions and the exploration of information sources

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Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/07/Cogsci-2022-PAAG-Detailed-July-12final-1.pdf)
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The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





We tackle the problem of an agent interacting with humans in a general-sum environment, i.e., a non-zero sum, non-fully cooperative setting, where the agent's goal is to increase its own utility.
We show that when data is limited, building an accurate human model is very challenging, and that a reinforcement learning agent, which is based on this data, does not perform well in practice. Therefore, we propose that the agent should try maximizing a linear combination of the human's utility and its own utility rather than simply trying to maximize only its own utility.


Reinforcement Learning Agents for Interacting with Humans

Previous research indicates that using the internet in knowledge related tasks increases overestimation. We attempted to replicate this finding and extended previous research by explicitly manipulating the standards that participants used for the explanatory knowledge task in order to reduce the metacognitive bias. We conducted a 2x2 within-subject experiment with N = 166 participants. Replicating previous findings, the results show significantly more overestimation in Internet than in No-Internet conditions. However, with an alignment to external standards participants elicited more accurate metacognitive judgments. We conclude that explicit standards may be an important factor in knowledge-related activities involving the internet because of their effect on metacognitive judgments. On a theoretical level, this has implications for determining the basis of overestimation in knowledge tasks with the internet. On a practical level, providing external standards could be a feasible aid for buffering against this bias, for example in the educational context.


An Alignment of Standards Enhances Metacognitive Judgment Accuracy in Explanatory Knowledge Tasks with Internet Search

We present a user error model that simulates a user's errors using an eyes and hands extension to cognitive architectures. We developed a complete interactive cognitive model that performs a spreadsheet task. This model is compared with an existing cognitive model that performs the same task in a different spreadsheet tool. Also, the predictions are compared to human data (N=23) on the same uninstrumented interfaces. The comparison suggests that the interactive cognitive model moves us closer to having a user model that can directly test interfaces by predicting human behavior and performing the task on the same interface that users interact. The error model also allows exploration of error detection, error correction, and different knowledge types.


Including Errors and Errors Correction in a Cognitive User Model

In our study we examined the ability to distinguish between verbal and visual representation in preschool children. We developed tasks in which children had to learn new signs denoting different types of representation and then use the new symbols to compare different formats of imagination (inner speech and visual images) in story characters, and then apply these symbols to describe their own representation in solving different tasks. We will present the results of testing children between the ages of 4 and 9, which show that the majority of preschool and school-age children successfully learn the symbols and use them to distinguish verbal representation from visual representation in characters. We will discuss the further development and application of this technique to the study of preschool children’s meta-cognitive abilities. The reported study was funded by RFBR, project number 20-013-00698.


Understanding of differences in verbal and figurative representations by children 4-6 years old.

There’s increasing evidence from studies of human performance in artificial classification learning tasks that a plurality of mechanisms or strategies are available to learners. A recent investigation from our lab group found that a linearly-separable category structure was harder to learn than a comparable non-linearly separable structure; and furthermore, that there were qualitative individual differences reflecting different paths to learning. In the present experiment, we take a deeper dive into performance on the LS category structure using a bank of test phase measures to more fully reveal what each individual has learned and represented. We identify a systematic set of profiles to characterize individual learners and demonstrate novel evidence on the nature and role of traditional psychological constructs: exemplars, prototypes, and rules. In particular we show classic prototype effects arising not as a broad-based phenomenon but only from a specific path to learning.
Keywords: categorization; tacl: prototype effect; individual differences


Paths to Learning in Traditional Artificial Classification Tasks

The results of research by Hélie, Turner, and Cousineau (Hélie et al., 2018) show that category representation (rule-based and information integration categories) may influence performance in the visual search categorization task. In the present experiment, we replicated their results using other types of categories that contained discrete features: categories based on verbal rules and prototypes. We found that after learning the verbal rule as well as after learning rule-based categories target-present and target-absent trials are not much affected by display size. The effect of display size is bigger for prototypes, but unlike the results of Hélie et al, it does not collapse for target absent trials. We explain these results by the fact that the categories with discrete features have a stronger and more conscious representation, which is easier to transfer to the visual search task. The reported study was funded by RFBR, project number 20-013-00698.


The impact of categorization rule on categorical visual search

Categorization is a critically important aspect of cognition that undergoes development. Previous studies found that children of age four acquired categories through multiple features, forming a similarity-based representation, whereas adults relied on a single feature, forming a rule-based representation. One possible interpretation is that children cannot inhibit the distraction of multiple features. We designed a categorization task with all features covered in the present study. Children were asked to uncover one feature at a time until they could categorize the items. As a result, we minimized distraction and encouraged rule-based learning. Nonetheless, even when given incentives to uncover fewer features, children learned the categories with significantly higher entropy, as calculated by the number of features they uncovered, than adults did. Such a result suggests that inhibition failures cannot fully explain categorization in children. Instead, children’s tendency to over-explore (and thus over-sample features) may also contribute to early category learning.


The role of exploration in the development of category learning

Navigation is a process that humans use to get from A to B. Landmarks used during navigation and wayfinding can address different sensory modalities. We examined landmark information in four different variants: as a written word, as a spoken word, as a picture, or as an odor. Our 51 participants were separated into four groups. Each group received one specific variant of landmark information integrated into a learning and wayfinding video of a virtual maze with 12 intersections. At each intersection, one landmark information was presented. To assess how well the relevant landmarks could be distinguished from unknown distractor items of the same condition, the experiment concluded with a recognition phase, where 24 stimuli were presented (12 landmarks + 12 distractors). Relative frequencies of correct responses and mean response times were measured for wayfinding and recognition. Odors lead to similar correctness in wayfinding compared to the more common landmarks (pictures, written and spoken words), even though requiring longer response times. We stepped away from the traditional but limited view on landmarks towards a more holistic (i.e. including all senses) view of human orientation. Implications for future scientific research are being discussed.

Landmark Modality in Wayfinding: Does it Make a Difference?

People talk about time using the language of space. The future is "ahead." Endless events are "long." Cross-linguistically, these conventions exhibit both universality and striking diversity. These mappings in language, therefore, might originate from a combination of shared cognitive biases and sociocultural processes. To investigate the mechanisms involved in the emergence of space-time mappings - and linguistic metaphor more broadly - we conducted an experiment in which participants had to communicate about abstract temporal concepts using entirely spatial signals. The spatial signals developed by one pair of participants were then transmitted to the next pair, creating chains of multiple generations. Together, these processes of interaction and transmission sometimes generated fully systematic, compositional systems - although sometimes also generated systems that lacked structure entirely. The deciding factor may have been how people responded to errors - with incremental adjustments or radical reconfiguration. Systematic metaphors, therefore, may emerge from a heterogeneous mix of mechanisms.

Interaction dynamics affect the emergence of compositional structure in cultural transmission of space-time mappings

Time is one of the most fundamental phenomena in our universe. From the cognitive perspective, it is interesting how we perceive time. To verify the most common cognitive model of time perception (the Scalar Expectancy Theory) time reproduction experiment was conducted. Participants underwent a standard number sense procedure (comparing the number of dots). Next, they were asked to memorise some random letters. Afterwards, a face picture (neutral, happy, angry, fearful) was displayed for 400-1600 ms. After that, participants were asked to reproduce the string of letters and the picture's duration by pressing the space key. The final stage of the study was data modelling using Gaussian Process Regression. The results showed inconsistency with the previous results on the relationship between emotion and time perception. The relationship of memory overload with time perception may have been found.

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