United States

Is “cow” more closely related to “grass” or to “chicken”? Speakers of different languages judge similarity in this context differently, but why? One possibility is that cultures co-varying with these languages induce differences in conceptualizations of similarity. Specifically, East Asian cultures may promote reasoning about thematic similarity, by which cow and grass are more related, whereas Western cultures may bias judgments toward taxonomic relations, like cow and chicken. We measure similarity judgments across the US, China, and Vietnam and replicate US-China differences, but do not find that responding in Vietnam patterns with China. Instead, similarity judgments in Vietnam are intermediate between the US and China. We also show that word embedding models (fastText models for each language) are related to judgments within each country, suggesting a possible alternative interpretation of cross cultural differences. Perhaps notions of similarity are similar across contexts, but the statistics of the linguistic environment vary.


CogSci 2023

Re-examining cross-cultural similarity judgments using language statistics

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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.

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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.

Technology to predict responses is a key element in human-to-human messaging that has increasingly been utilized to enable AI-mediated communication. When response suggestions from AI are incorporated into human messaging, it will have an impact on the flow and content of the dialogue. In this paper, we investigated the effect of AI response suggestion sentences used for chat messaging on dialogue flow from two aspects: dialogue act and initiative. Usage rates of response suggestions for different dialogue acts were measured with BLEU scores, and we found that the response suggestions contributed to the establishment of a proper dialogue flow, such as an answer in response to a question. The results of our case study indicated that users who take the initiative in the dialogue tend to utilize response suggestions less frequently. We also found that some written responses were based on the suggested sentence structure but conveyed different messages.

The Effect of Response Suggestion on Dialog Flow: Analysis Based on Dialogue Act and Initiative

This study used XAI, which shows its purposes and attention as explanations of its process, and investigated how these explanations affect human trust in and use of AI. In this study, we generated heat maps indicating AI attention, conducted Experiment 1 to confirm the validity of the interpretability of the heat maps, and conducted Experiment 2 to investigate the effects of the purpose and heat maps in terms of reliance (depending on AI) and compliance (accepting answers of AI). The results of structural equation modeling (SEM) analyses showed that (1) displaying the purpose of AI positively and negatively influenced trust depending on the types of AI usage, reliance or compliance, and task difficulty, (2) just displaying the heat maps negatively influenced trust in a more difficult task, and (3) the heat maps positively influenced trust according to their interpretability in a more difficult task.

Modeling Reliance on XAI Indicating Its Purpose and Attention

To inform Explainable AI (XAI) design for updating users’ beliefs about AI based on their mental models, we examined the similarities and differences between humans and AI in object detection in driving scenarios. In humans, individuals differed in adopting focused or explorative attention strategies, with better performance associated with the focused strategy. AI (Yolo-v5s) had higher similarity in attended features to the focused than the explorative strategy in humans, and achieved human-expert-level performance in vehicle detection even in difficult cases such as occlusion and degradation. In contrast, it performed much poorer than humans in detecting humans with low attended feature similarity due to humans’ attention bias for stimuli with evolutionary significance. Also, higher similarity to humans’ attended features was associated with better AI performance, suggesting that human attention may be used for guiding AI design. These findings have significant implications for both AI and XAI designs.

Humans vs. AI in Detecting Vehicles and Humans in Driving Scenarios

As more of society uses virtual environments from collaborative learning environments to the now ubiquitous Zoom online meeting platform, there is an opportunity for artificial agents (AAs) to foster and support human collaboration. However, little appears to be known about whether people would actually want to collaborate with such an AA and what impact if any it has on cognitive processes underlying human collaboration. To begin to answer these questions, we devised a Wizard of Oz (WoZ) study where teams of 3 participants were tasked with having to locate and corral targets into a containment area in a virtual desert environment with the Wizard (confederate) playing the role of the Operator who could see the location of participants, targets and the containment area, and could communicate this information to participants. Although agent responsiveness had limited impact on performance and workload, as expected the responsiveness of the agent did influence participant's perceptions of whether the agent understand them and provided timely responses.

How do People Perceive Collaborative Conversational Agents?

Understanding problem solving or planning has been a shared challenge for both AI and cognitive science since the birth of both fields. We explore the extent to which modern planners from the field of AI can account for human performance on the Tower of London (TOL) task, a close relative of the Tower of Hanoi problem that has been extensively studied by psychologists. We characterize the task using the Planning Domain Definition Language (PDDL) and evaluate an adaptive online planner and a family of well-known planners, including online planners, optimal planners and satisficing planners. Each planner is evaluated based on its ability to predict the actions and planning times of participants in a new behavioral experiment. Our results suggest that participants use a range of strategies but that an adaptive lookahead planner provides the best overall account of both human actions and human planning times. This finding is consistent with the view that humans differ from standard AI planners by integrating a mechanism for evidence accumulation.

Comparing AI planning algorithms with humans on the Tower of London task

While saliency-based explainable AI (XAI) methods have been well developed for image classification models, they fall short in comparison with human explanations. Here we examined human explanation strategies for image classification and their relationship with explanation quality to inform better XAI designs. We found that individuals differed in attention strategies during explanation: Participants adopting more explorative strategies used more visual information in their explanations, whereas those adopting more focused strategies included more conceptual information. In addition, visual explanations were rated higher for effectiveness in teaching learners without prior category knowledge, whereas conceptual explanations were more diagnostic for observers with prior knowledge to infer the class label. Thus, individuals differ in the use of visual and conceptual information to explain image classification, which facilitate different aspects of explanation quality and suit learners with different experiences. These findings have important implications for adaptive use of visual and conceptual information in XAI development.

Individual differences in explanation strategies for image classification and implications for explainable AI

Since Darwin (1871), researchers have proposed that
musicality evolved in a reproductive context in which males
produce music to signal their mate quality. The extent to
which evidence supports this contention, however, remains
unclear. Related traits in many non-human animals are
sexually differentiated, and while some sex differences in
human auditory perception have been documented, the
pattern of results is murky. Here, we study melodic
discrimination, mistuning perception, and beat alignment
perception in 360,009 men and 194,291 women from 208
countries. We find that, in contrast to other non-music human
traits, and in contrast to non-human traits, there was no
overall advantage for either gender, and the observed gender
differences were minuscule (Cohen’s d: 0.009 - 0.11) and of
inconsistent direction. These results do not provide
compelling support for human music perception being a
sexually dimorphic trait, and therefore it is unlikely to have
been shaped by sexual selection.

Human music perception ability is not a sexually dimorphic trait

People often prefer simpler explanations (that posit the presence of fewer causes), judging these more probable than more complex alternatives. However, simplicity preferences are only mathematically justified under certain conditions. We examine one case where, mathematically, complexity preferences are justified, and test whether this corresponds to a boundary condition on simplicity preferences. Specifically, we focus on cases where causes occur frequently (rather than rarely), and where explanations specify the absence of additional causes (e.g., “Cause A and not B or C”) rather than remaining agnostic about their presence or absence (e.g., “Cause A”). Study 1 showed that, in these cases, simplicity preferences were attenuated, but not reversed. Study 2 suggests that simplicity preferences partly stemmed from failures to explicitly represent absent causes. We suggest that biases towards oversimplification may arise due to over-application of a cognitively simple version of Ockham’s razor, that is insensitive to the probability of absent causes.


The Edge of Ockham’s Razor: Examining Boundary Conditions on Preferences for Simpler Explanations

What shape do people's mental models take? We hypothesize that people build causal models that are suited to the task at hand. These models abstract away information to represent what matters. To test this idea empirically, we presented participants with causal learning paradigms where some features were outcome-relevant and others weren't. In Experiment 1, participants had to learn what objects of different shape and color made a machine turn on. In Experiment 2, they had to predict whether blocks sliding down ramps would cross a finish line. In both experiments, participants made systematic errors in a surprise test that asked them to recall what they had seen earlier. The errors people made suggest that they had build mental models of the task that privileged causally relevant information. Our results contribute to recent efforts trying to characterize the important role that causal abstraction plays in human learning and inference.


Learning what matters: Causal abstraction in human inference

Deciding whether an event could happen, or should happen, is critical to learning and decision-making yet takes years to develop, as young children are prone to deny the possibility and permissibility of any event that violates their expectations. Here, we explore whether this form of reasoning—modal cognition—is aided by cognitive reflection, or the disposition to privilege analysis over intuition. Ninety-nine children between the ages of 4 and 12 judged the possibility and permissibility of several hypothetical events, and their judgments were compared to their score on a developmental version of the cognitive reflection test, the CRT-D. Children’s CRT-D scores predicted their ability to differentiate possible events from impossible ones and impermissible events from permissible ones, as well as their ability to differentiate possibility from permissibility in general. Such differentiations were predicted by children’s CRT-D scores independent of age and executive function. These findings suggest that mature modal cognition may require the ability to reflect on, and override, the intuition that unexpected events cannot happen.


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The Effect of Response Suggestion on Dialog Flow: Analysis Based on Dialogue Act and Initiative

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Re-examining cross-cultural similarity judgements using lexical co-occurrence