United States

One of the consequences of the pandemic is that throughout 2020 virtual interactions largely replaced face-to-face interactions. Though there are few studies of how social media impacts body image perception across genders, research suggests that socializing through a virtual self-body image might have distinct implications for men and women. On an online study, we examined whether type of social pressure and body-ideal exert distinct pressures on members of the X, Y, and Z generations. Results showed media pressure affected body image satisfaction significantly more than other kinds of social pressure across genders and generations, with young males reporting a higher impact compared to older males. Males experienced more pressure to be muscular and women to be thin, especially for the younger generation.Future research should focus on social media as a potential intervention tool for the detection and prevention of body image disorders in both young female and male adults.

CogSci 2021

Body Image During Quarantine; Generational Effects of Social Media Pressure on Body Appearance Perception

### Looking for Cogsci 2023? Please [click here](https://underline.io/events/405/reception) to switch to 2023 edition.


**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>

**Your Program Chairs,**
![](https://assets.underline.io/uploads/markdown_image/1/image/99984908caba5e6453e5fc5ba34f3507.jpg)

**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

<iframe width="560" height="315" src="https://www.youtube.com/embed/464h1m3iU68" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

To review the proceedings for the conference or review the searchable program, please visit the [website](https://cognitivesciencesociety.org/program/).


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.

poster

Due to information processing constraints and cognitive limitations, humans must form limited representations of complex decision making tasks. However, the mechanisms by which humans generate representations of task-relevant stimulus remain unclear. We develop a model that seeks to account for the formation of these representations using a β-variational autoencoder (β-VAE) trained with a novel utility based learning objective. The proposed model forms latent representations of decision making tasks that are constrained in their information complexity. We show through simulation that this approach can account for important phenomena in human economic decision making tasks. This model provides a method of forming task-relevant representations that can be used to make decisions in a human-like manner.

Modeling Capacity-Limited Decision Making Using a Variational Autoencoder

We model the sense-making process of diagrams as conceptual blends of the diagrams’ geometric configurations with apt image schemas.We specify image schemas and geometric configurations with typed FOL theories. In addition, for the latter, we utilise some Qualitative Spatial Reasoning formalisms. Using an algebraic specification language, we can compute the conceptual blends of image schemas and geometry as category-theoretic colimits.We show through several examples how this model captures the sort of direct inferences we confer to diagrammatic representations due to our embodied cognition. We argue that this approach to sense-making might be of value for the design and application of diagrammatic and graphical visualisations, as well as for AI in general.

Modelling the Sense-Making of Diagrams Using Image Schemas

Human learning transfer takes advantage of important cognitive building blocks such as an abstract representation of concepts underlying tasks and causal models of the environment. One way to build abstract representations of the environment when the task involves interactions with others is to build a model of the opponent that may inform what actions they are likely to take next. In this study, we explore opponent modelling and its role in learning transfer by letting human participants play different games against the same computer agent, who possesses human-like theory of mind abilities with a limited degree of iterated reasoning. We find that participants deviate from Nash equilibrium play and learn to adapt to the opponent's strategy to exploit it. Moreover, we show that participants transfer their learning to new games and that this transfer is moderated by the level of sophistication of the opponent. Computational modelling shows that it is likely that players start each game using a model-based learning strategy that facilitates generalisation and opponent model transfer, but then switch to behaviour that is consistent with a model-free learning strategy in the later stages of the interaction.

Transfer of learned opponent models in repeated games

Most theoretical accounts of non-instrumental information seeking suggest that the magnitude of rewards has a direct influence on the attractiveness of the information. Specifically, the magnitude of rewards is assumed to be proportional to the strength of information seeking (or avoidant) behaviour. In a series of experiments using numerical and pictorial stimuli, we explore the extent to which observed information seeking behaviour tracks these predictions. Our findings indicate a robust independence of information seeking from outcome magnitude and valence with preferences for information largely remaining constant across different reward valence and magnitudes. We discuss these results in the context of current computational models with suggestions for future theoretical and empirical work.

Invariance of Information Seeking Across Reward Magnitudes

To glean information from social networks, people must be adept at distinguishing real evidence from hearsay. Here, we investigate human inferences within a simulated social network. We introduce a novel social learning task in which participants infer their surrounding social network structure while also using social communications to aid judgments about the shared environment. We find that the majority of adults correctly identified independent and acyclic network structures, though they struggled to identify cyclic communications. Comparison of judgments with several social inference models supports a naïve social learning account that integrates social evidence with the learner’s own observations in a structure insensitive way. This suggests that people are capable of using communication patterns to identify social influences, but they may still be misled by the distortions of evidence that these network dynamics can produce.

Know your network: Sensitivity to structure in social learning

The other-race effect refers to the difficulty of discriminating between faces from ethnic and racial groups other than one’s own. We challenged the hypotheses that same-race faces are holistically encoded while the other-race faces are analytically encoded. We proposed that the analytic-holistic hypotheses could be discriminated based on their information processing properties: (1) processing order of facial features, (2) stopping rule and (3) process dependency. We compared Eastern and Western participants using the psychophysically adjusted configural facial features in a face categorization task. The two cultures showed markedly different processing strategies: the Easterners demonstrated a higher level of holistic processing, than the Westerners. The computational modeling results show a weak other-race effect for Westerners, and a reversed effect for the Easterners. Overall, all subjects demonstrated parallel processing of facial features. In addition, some showed a presence of the across-feature process dependency, which supports a strong form of facial holistic hypothesis.

Computational Modelling of the Cross-Cultural Differences in Face Perception

It is an ancient insight that human actions are driven by desires. Yet it misses one mental representation, intention, with which agents regulate conflicting desires by committing to an admissible plan. Here we demonstrate four behavioral signatures of intention only observed in humans: disruption resistance as sticking with a plan despite setbacks; exclusiveness as avoiding paths with temptations of re-planning; deliberation as the gradual emergence of a commitment plan; temporal leap as forming future plans before finishing the current one. Humans were compared against an optimal model formulated as Markov Decision Process (MDP), who acts only to maximize expected future rewards. Conflicting desires are defined as a reward function returning positive rewards for multiple states. It showed none of the behavioral signatures of intention. These results reveal that humans regulate conflicting desires with intentions, which directly drive actions.

Intention beyond Desire: Humans Spontaneously Commit to Future Actions

Deep Neural Network representations correlate very well with neural responses measured in primates' brains and with psychological representations of human similarity judgement tasks, making them possible models for human behavior-related tasks. This study investigates whether DNNs can learn an implicit association (between colors and emotions) for images. An experiment was conducted in which subjects were asked to select a color for a given emotion-inducing image. These human responses (decision probabilities) were modeled on neural networks using representations extracted from pre-trained DNNs for the images and colors (a square of the color). The model presented showed a fuzzy linear relationship with the decision probabilities. Finally, this model was presented as a model for emotion classification tasks, specifically with very few training examples, showing an improvement in accuracy from a standard classification model. This analysis can be of relevance to psychologists studying these associations and AI researchers modelling emotional intelligence in machines.

Emotion-Color Association in Biologically Inspired Deep Neural Networks

A critical aspect of the way humans recognize and understand meaning in sequential data is the ability to identify abstract structural repetitions. We present a novel approach to discovering structural repetitions within sequences that uses a multiple Smith-Waterman self-alignment. We illustrate our approach in the context of finding different forms of structural repetition in music composition. Feature-specific alignment scoring functions enable structure finding in primitive features such as rhythm, melody, and lyrics. These can be compounded to create scoring functions that find higher-level structure including verse-chorus structure. We demonstrate our approach by finding harmonic, pitch, rhythmic, and lyrical structure in symbolic music and compounding these viewpoints to identify the abstract structure of verse-chorus segmentation.

Inferring Structural Constraints in Musical Sequences via Multiple Self-Alignment

The goal of the present study was to use content analysis to gain insight into the process of problem-solving of novice and expert programmers. While classic work on programmers identifies goals / plans as key constructs needed to code, there is relatively little work using protocol analysis. We recruited 7 expert and 12 novice rogrammers who completed up to 3 brief programming problems while providing a talk-aloud of their inner problem solving process. Based on analysis of the transcriptions of this talk aloud data, we identified the goals and steps used, as well as the broad differences between experts and novices in their problem solving process. These differences were formalized into python ACT-R models, and model output was compared to programs written by human participants.

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Next from CogSci 2021

Modeling Capacity-Limited Decision Making Using a Variational Autoencoder

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