Canada

Three-dimensional objects pose a challenge for our visual system, since we can only view objects from a single limited perspective at a given moment. Previous work found that given a limited perspective, infants represent 3D objects as complete volumes. Our study replicated this finding in 4- to 7-year-olds and adults, using an explicit prediction measure rather than looking times. We also explored whether humans have a bias to represent visually limited 3D objects as symmetrical rather than asymmetrical across shape, size, texture, and color. Overall, there was an above-chance preference for full volumetric and symmetrical object completion that increased with age. Low-level perceptual similarity of choices did not predict participants’ choices. Moreover, we evaluated ResNet-50 neural networks on the same tasks: they represented objects as complete volumes, but did not show substantial preference for symmetrical 3D representations. This raises the possibility that incorporating human symmetry biases could improve computer vision.

CogSci 2022

Three-Dimensional Object Completion in Humans and Computational Models

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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.

We hope that you enjoy the conference this year!<br><br>

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**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

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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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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.
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<br>[**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)
<br>[**The Detailed Program can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/08/DeArmond-CogSci2022-program-v113-final.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. 





Because there are many situations in our daily life in which the option space is not discrete but continuous, recently developed decision models have been able to examine the cognitive processes underlying choice in laboratory tasks with a continuous outcome space. One of the most important of these continuous models is the circular diffusion model (CDM) by Smith, which has been shown to account for continuous space data from a wide range of paradigms, including color identification, orientation, brightness, pricing. However, in addition to the inherent complexity of this model, it has become more complex in order to predict reliable data patterns, making it a tool only for experts. Here we propose a more easy version of the CDM, the EZ version, to fit the model on continuous scale data. The EZ-CDM for continuous choice space tasks can estimate the parameter values for the cognitive processes underlying without considering the response time distribution but only using traditionally favored summary statistics (i.e. the mean and variance of response time, and angular variance of accuracy.) by simple formulas that can be computed easily and needs neither theoretical knowledge of model fitting nor much programming skills. Here, we formulate the EZ method and show that, despite being easy and fast to calculate, it’s performance in recovering true parameters is acceptable.

An EZ-circular diffusion model of continuous decision processes

Affective factors such as anxiety, confidence, and motivation can impair and enhance task performance. Here, we used drift diffusion modeling (DDM) to examine how these variables affect visualization, manipulation, and decision making on a mental rotation task (MRT). The effects of affective factors on visuospatial reasoning are largely unknown, perhaps in part because analyses are generally concerned with overall accuracy and reaction time (RT), without decomposing the stages of processing. With DDM, we decompose performance on a MRT into separate processing components, particularly the speed of information update (drift rate) and the amount of evidence accumulation (decision threshold). 106 adult participants performed a two-alternative forced-choice (2-AFC) MRT, and throughout, they rated their levels of anxiety, confidence, and motivation. We found that although anxiety, confidence, and motivation all impacted drift rate, only confidence affected the decision threshold. Moreover, we observed a unique role for confidence in mediating the links between gender and model parameters, as well as a unique moderating role of motivation in this mediation. Altogether, these findings shed light on the interrelations between affective factors in accounting for mental rotation performance in men and women, including the unique combination of confidence and motivation in explaining the gender difference in mental rotation performance.

Affective Factors Affect Visuospatial Decision-making: A Dri Diffusion Modeling Approach

To achieve goals, people leverage cognitive control to adjust how they process information. Here we show that frequent adjustments in information processing strategies (e.g., response threshold) within a single task give rise to reconfiguration costs. In two experiments we induced different performance goals in a Stroop task via explicit instruction or incentives, and these goals either varied or were fixed across different blocks. Across both experiments, we find smaller adjustments in control intensity when people frequently adjust the amount of control they exert, relative to blocks in which they don’t. We show that these results can be accounted for with a model that maximizes reward rate while minimizing reconfiguration costs (proportional to the Euclidean distances between the previous and current control signals). These findings suggest that cognitive control adjustments are regularized to constrain larger adjustments in control, which has important implications for computational modeling and measurement of motivated cognitive control.

Empirical and Computational Evidence for Reconfiguration Costs During Within-Task Adjustments in Cognitive Control

People are often reluctant to reconsider their choices, sticking with their goals even when it is clear that they would be better off abandoning them. Explanations for this abound, including loss aversion, sunk costs, social and reputational pressures, and resource rational consideration of the costs of replanning. Here we propose another hypothesis: In adopting a goal, you immediately reap the rewards of gaining information about what to do and how to act. Insofar as goals are rewarding in themselves, we predict that unless a goal is specifically devalued or the costs associated with it are very high, the default is not to engage in any reconsideration at all. We test this hypothesis by creating a stripped down scenario involving choices between two goals with transparently obvious cost differentials. The task is designed to minimize other factors that might contribute to goal persistence and indeed, we test both adults and very young children on virtually the same task to ensure that the cognitive load for adults is negligible. Both adults (Experiments 1-2) and 4-6-year-old children (Experiments 3-4) choose the less costly of two goals when shown the costs and goals together. However, when participants are shown the goals first, and only then shown that their chosen goal is more costly than the alternative, participants stick with higher cost goals, unless the goals are explicitly devalued.

"Because I want to": Valuing goals for their own sake

When deciding whether and how to punish, people consider not only the potential direct consequences, but also, how their choice will affect observers’ judgements about the values and motives underlying the choice. We formalize the decision to punish as a rational communicative social action (RCSA). The model generates rational decisions to punish, incorporating anticipated observers’ judgements obtained from a recursive model of inference using an intuitive theory of mind. Using this model, we synthesize patterns of human punishment from recently published papers. RCSA thus offers a formal model of the cognitive process that humans use to balance preferences for how they are perceived, with other goals for punishing.

Modeling punishment as a rational communicative social action

We explore whether people can recognise the epistemic goal of active learners. In a novel online experiment, 110 adults watched screen recordings of other adults (``players'') manipulating objects in a 2D simulated physical microworld. Players had the goal of either identifying the magnet-like force connecting two of the objects, or their relative masses. Observers were asked to identify the learning goal of the player. By drawing from a previously collected dataset of active physical learning interactions and an ideal observer analysis, we manipulated how informative the players' actions are about the target property, and observers' level of access to the players' micro-control actions. We found observers were better at identifying the goals of successful players and of players trying to identify force, while the micro-dynamic evidence improved accuracy on identifying the mass goal. We use mixed methods to explore what cues observers used to make these judgments.

Inferring epistemic intention in simulated physical microworlds

It is commonly believed that expressions visually signal rich diagnostic information to human observers. We studied how observers interpret the dynamic expressions that people spontaneously produced during a real-life high-stakes televised game. We find that human observers are remarkably poor at recovering what events elicited others' facial and bodily expressions. Beyond simple inaccuracy, people's causal reasoning exhibits systematic model-based patterns of errors. We show that latent emotion representations can explain people's reasoning about the unseen causes of expressions. A hierarchical Bayesian model simulates which events people infer to be the cause of others' expressions by comparing the emotions inferred from the expressions against the emotions people were predicted to experience in various situations. This causal model provides a close, parameter-free fit to human causal judgments, suggesting that humans interpret expressions in the context of emotion predictions generated by a causally-structured mental model of other minds.

Reasoning about the antecedents of emotions: Bayesian causal inference over an intuitive theory of mind

Spatial perspective taking, in which people mentally adopt another person's view of the world, is a crucial component of everyday communication. We investigate spatial perspective taking in listeners interpreting ambiguous instructions from a partner, looking at how this behaviour varies with a human vs. computer partner (Exp. 1 and 2), and with individual differences in social and cognitive abilities (Exp. 3). Listeners' perspective taking tendencies vary with their individual differences in spatial orientation ability, with more othercentricism associated with better spatial orientation. In addition, partner identity influences perspective taking; however, in contrast to previous work, we find higher levels of egocentricism with a computer than a human partner.

Partner effects and individual differences on perspective taking

Speakers often use different names to refer to the same entity (e.g., "woman" vs. "tennis player"). We explore factors that affect naming variation for visually presented objects, analyzing a large dataset of object names with realistic images, and focusing on two factors: visual typicality (of objects and contexts) and name frequency. We develop a computational approach to estimate typicality, and not only study object names used by most annotators (top names), but also the second most used ones (alternative names). Our results show that variation increases for objects that are less typical for their top name, more typical for their alternative name, or whose top name has relatively low frequency. Context typicality does not show a general effect. Overall, we show that characteristics relating to name candidates beyond the top name are informative of naming variation, and the potential of using computational methods to inform models of human object naming.

Woman or tennis player? Visual typicality and lexical frequency affect variation in object naming

Research on sound symbolism has shown that speakers of different languages associate specific consonants and vowels with round and pointy shapes, a phenomenon commonly dubbed the bouba-kiki effect. Most of this work rely on forced-choice tasks in which participants assign previously crafted pseudowords (like kiki or bouba) to visual stimuli. Here we investigate this phenomenon with a production written task. Participants had to create a new word they thought would be a good name for round and spiky images. In this less constrained task, spiky images received names with more high/front vowels and voiceless stops, while round shapes were named with more back/rounded vowels and lateral and nasal consonants. In great part these results replicate previous findings, showing that participants recourse to sound symbolism even when the task at hand gives them more freedom to create names to abstract shapes.

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An EZ-circular diffusion model of continuous decision processes

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