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Category learning is a core competence for minimizing cognitive load and optimizing decision-making. An identical problem can be solved by employing a rule-based or a similarity-based strategy. This research examined whether the use of the two strategies was supported by common or distinct neural substrates. We conducted a category learning experiment with rule-plus-similarity stimuli using EEG-fNIRS fusion methodology. Participants learned two artificial categories using either a rule- or similarity-based strategy. The results showed a common visual-perceptual-analysis process and distinct decision-making processes between the uses of the two strategies. Larger P300 and N400 amplitudes and Wernicke&#39;s area activation indicated that hypotheses testing and verbal rule abstraction processes were critical for rule-based categorization. In contrast, increased frontopolar cortex activity indicated that integration of multiple dimensions was critical for similarity-based categorization. These results were consistent with COVIS theory, implying an explicit system in rule-based category learning whereas an implicit system in similarity-based learning.

CogSci 2023

Common and distinct neural bases for rule- and similarity-based category learning

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**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

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CogSci 2021

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

Category learning is a core competence for minimizing cognitive load and optimizing decision-making. An identical problem can be solved by employing a rule-based or a similarity-based strategy. This research examined whether the use of the two strategies was supported by common or distinct neural substrates. We conducted a category learning experiment with rule-plus-similarity stimuli using EEG-fNIRS fusion methodology. Participants learned two artificial categories using either a rule- or similarity-based strategy. The results showed a common visual-perceptual-analysis process and distinct decision-making processes between the uses of the two strategies. Larger P300 and N400 amplitudes and Wernicke's area activation indicated that hypotheses testing and verbal rule abstraction processes were critical for rule-based categorization. In contrast, increased frontopolar cortex activity indicated that integration of multiple dimensions was critical for similarity-based categorization. These results were consistent with COVIS theory, implying an explicit system in rule-based category learning whereas an implicit system in similarity-based learning.

poster

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

Join us!

<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
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The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

When making category decisions, humans sample features following their dynamic informativeness. Attention optimization models successfully predict these categorization behaviors, but optimization is not the only solution. Alternatively, categorization can be viewed as a Reinforcement Learning (RL) task in which learners sample information based on its expected utility. However, RL models of information sampling have high computational load, even though human learners solve this problem on the millisecond timescale. Therefore, we propose ATHENA-RSS, a model that implements reward-based information search in a more computationally efficient way, via the rapid sequential storage of memories and recurrent retrieval. To test the model, we conducted an experiment where participants (N = 99) learned hierarchically structured categories by uncovering stimulus features. We then conducted a simulation study, where ATHENA-RSS successfully reproduced all search patterns exhibited by participants. We conclude that rapid sequential storage and recurrent memory retrieval were sufficient to achieve human-like information sampling in this task.

Dynamic Information Sampling via Rapid Sequential Storage and Recurrence

The ability to selectively attend to stimuli increases the efficiency of learning. However, learning traps can develop when attention prematurely narrows to a subset of the features that predict outcomes, resulting in suboptimal decisions. The current work investigated the potential for learning traps to be particularly damaging in dynamic environments, where the features that predict rewards and losses change during learning. Two experiments (N=316) found that when learners received choice-contingent feedback, they frequently fell into a learning trap, using a suboptimal categorisation rule. Critically, these learners were unlikely to detect a subsequent rule change nor learn the new optimal rule. This change blindness was not attenuated by priming participants to expect change. These results show that the pernicious effects of learning traps are amplified in dynamic environments.

Learning traps lead to change blindness in dynamic environments

Data visualizations are powerful tools for communicating quantitative information. While prior work has focused on how experts design informative graphs, little is known about the intuitions non-experts have about what makes a graph effective for communicating a specific message. In the current study, we asked participants (N=398) which of 8 graphs would be most useful for answering a particular question, where all graphs were generated from the same dataset but varied in how data were arranged. We tested the degree to which participants based their decisions on sensitivity to how easily other participants (N=542) would actually be able to answer that question with that graph. Our results suggest that while people were biased towards graphs that were minimally informative (i.e., contained the relevant variables), their decisions did not necessarily reflect sensitivity to more graded, but systematic variation in actual graph comprehension performance.

How do communicative goals guide which data visualizations people think are effective?

The risk–reward correlation refers to a tendency to infer the inverse proportionality between probability of an outcome and the outcome's magnitude and is considered to reflect the statistical structure of gambling environments. Existing studies have demonstrated this inverse proportionality using numerical probabilities. However, verbal probability phrases can also convey contextual information about uncertainty without numbers, and express contextual information that is not contained by numerical probabilities. Specifically, verbal probabilities have positive and negative directionalities that can attune the listener to the occurrence or non-occurrence of an outcome and consequently, the listener’s decision making. Therefore, this study aimed to examine whether the risk–reward correlation also hold in verbal probability phrases, by conducting two empirical studies in which the participants were asked to estimate the reward values and winning probability of gambles expressed by verbal probability phrases such as “certain” or “impossible.” The results indicate that risk–reward correlation also hold in verbal probability phrases, and that negative probability phrases were associated with the larger rewards than the positive probability phrases.

Does “never” implicate a larger reward than “possible”? : Risk–reward correlation in verbal probability phrases

Theories in cognitive science are primarily aimed at explaining human behavior in general, appealing to universal constructs such as perception or attention. When it is considered, modeling of individual differences is typically performed by adapting model parameters. The implicit assumption of this standard approach is that people are relatively similar, employing the same basic cognitive processes in a given problem domain. In this work, we consider a broader evaluation of the way in which people may differ. We evaluate 23 models of risky choice on around 300 individuals, and find that most models---spanning various constructs from heuristic rules and attention to regret and subjective perception---explain the behavior of different subpopulations of individuals. These results may account for part of the difficulty in obtaining a single elegant explanation of behavior in some long-studied domains, and suggest a more serious consideration of individual variability in theory comparisons going forward.

To each their own theory: Exploring the limits of individual differences in decisions under risk

Probability judgments appear to violate basic axioms of probability theory, which seems to contradict with the recent successes of Bayesian models of cognition. To explain these violations, we propose the Quantum Sequential Sampler model, which combines quantum probability for explaining conjunction and disjunction fallacies, and a sequential sampling model that maps subjective quantum probabilities into responses. Our model explains probability judgments by a dynamical process, and achieves state-of-the-art performance in the biggest dataset for probability judgments to-date. Comparing with existing Bayesian models, our model predicts both probability judgments and violations of probability identities better.

Quantum Sequential Sampler: a dynamical model for human probability reasoning and judgments

Social cognition is shaped by cultural and social-psychological contexts. Depending on the social cues displayed, the cognitive processes that emerge during embodied social interactions can vary. This study explored the influence of gender diverse social cues on interpersonal coordination and decision making during dyadic interaction in virtual reality. Participants embodied either a transgender or cisgender avatar and interacted with a transgender confederate (human-controlled virtual agent). Our results suggest that transgender embodied women and men displayed more interpersonal affiliation via their coordination dynamics with the confederate after an explicit gender identity disclosure. Additionally, we found that cognitive flexibility for ambiguous decisions was influenced by attributions of responsibility made toward the confederate.

Embodied Transgender Interactions: Exploring Dyadic Interpersonal Coordination and Decision Making in Virtual Reality

Through training using distorted vision, the perception of the trained limb position is shifted based on visual information. We investigated whether and how such proprioceptive recalibration transfers to untrained limbs. The results of experiments using human-like virtual limbs confirmed that transfer to some limbs occurred. The manner in which the transfer occurred varied according to the participants' body posture and the type of trained limb. When the participants sat, the recalibration transferred from one arm to another arm symmetrically around the body midline. Conversely, in the case of a sitting leg and standing arm, it was directly copied to another leg or arm.

Interlimb Transfer of Proprioceptive Recalibration and Effect of Body Posture

This study aims to investigate whether autistic individuals (e.g., ASD) have consistent or inconsistent age-related changes for recognizing different facial emotions compared to typical ones. Particularly, do facial recognition abilities for different emotions display various developmental trends in both groups? Pictures of six primary facial types (happy, sad, angry, surprised, and confused faces) of real persons were used for 91 ASD patients and 177 normal participants from kindergartens, elementary schools, and colleges to examine the performance of facial emotion. The results showed that, although, in general, normal participants had greater recognition performance as expected, both groups of participants had three distinct categories of development patterns: (1) angry and fear, (2) happy and surprised, and the last, (3) sad and confused. These findings revealed significant theoretical and practical values for further research and treatment.

The Effects of Age on Facial Recognition of Autisms

Cognitively reappraising a stressful situation—reinterpreting it to alter its emotional impact—is effective for regulating negative emotions. When reappraising, English speakers spontaneously engage in linguistic distancing, using fewer first-person singular pronouns and present-tense verbs. We investigated whether this pattern generalizes to Spanish, a language proposed to offer unique distancing tools. Spanish and English bilinguals and English monolinguals (N = 163) transcribed their thoughts while responding naturally to negative images or reappraising them (for bilinguals, in both languages). We found that reappraisal increased standard markers of linguistic distancing similarly in English and Spanish. Moreover, stronger linguistic distancing when reappraising in either language was associated with greater reduction in self-reported negative affect. Notably, bilinguals did not utilize Spanish-specific distancing words (e.g., "estar" and diminutives) when reappraising. In showing that English-derived linguistic signatures of emotion regulation extend to Spanish, our findings offer initial evidence that linguistic distancing reflects emotional well-being across languages.

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Dynamic Information Sampling via Rapid Sequential Storage and Recurrence

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