United States

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.

CogSci 2021

Know your network: Sensitivity to structure in social learning

### 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

The ability to represent semantic structure in the environment — objects, parts, and relations — is a core aspect of human visual perception and cognition. Here we leverage recent advances in program synthesis to develop an algorithm for learning the part-based structure of drawings as represented by graphics programs. This algorithm iteratively learns a library of abstract subroutines that can be used to more compactly represent a set of drawings by capturing common structural elements. Our experiments explore how this algorithm exploits statistical reg- ularities across drawings to learn new subroutines. Together, these findings highlight the potential for understanding human visual concept learning via program-like abstractions.

Learning part-based abstractions for visual object concepts

We present the first analysis of multi-frequency neural entrainment to dynamic visual features which drives sign language comprehension. Using the measure of EEG coherence to optical flow in video stimuli, we are able to classify fluent signers’ brain states as denoting online language comprehension, or non-comprehension during watching of non-linguistic videos that are equivalent in low-level spatiotemporal features and high-level scene parameters. The data also indicates that lower frequencies, such as 1 Hz and 4 Hz, contribute substantially to brain state classification, indicating relevance of neural coherence to the signal at these frequencies to language comprehension. These findings suggest that fluent signers rely on predictive processing during online comprehension.

Application of machine learning to signal entrainment identifies predictive processing in sign language.

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

Deep reinforcement learning (Deep RL) methods can train artificial agents (AAs) to reach or exceed human-level performance. However, in multiagent contexts requiring competitive behavior or where the aim is to use AAs for human training, the qualitative behaviors AAs adopt may be just as important as their performance to ensure representative training. This paper compares human behaviors and performance when competing against either a human expert or an AA opponent trained using Deep RL on a 2-dimensional version of Pong. Results show that participants were not sensitive to the movement differences between the human expert and AA. Further, the participants did not alter their behaviors, except to compensate for differences in the environmental states caused by the opponents. The paper concludes with discussion on the potential impacts of AA training on human behavior with regard to representative design in the areas of skill development and team training.

Perceptual Sensitivity to an Artificial Co-Actor in Competitive 2D Pong

Quantum probability theory has successfully provided accurate descriptions of behavior in decision making, and here we apply the same principles to two category learning tasks, one using overlapping, information-integration (II) categories and the other using overlapping, rule-based (RB) categories. Since II categories lack verbalizable descriptions, unlike RB categories, we assert that an II categorization decision is characterized by quantum probability theory, whereas an RB categorization decision is governed by classical probability theory. In our experiment, participants learn to categorize stimuli as members of either category S or K during an acquisition phase, and then rate the likelihood on a scale of 0 to 5 that a stimulus belongs to one category and subsequently perform the same rating for the other category during a transfer phase. With II categories but not RB ones, the quantum model notably outperforms an analogous Markov model and the order effects on likelihood ratings are significant.

Comparing Markov and quantum random walk models of categorization decisions

The visual system does not require extensive signal in its inputs to compute rich, three-dimensional (3D) shape percepts. Even under highly degraded stimuli conditions, we can accurately interpret images in terms of volumetric objects. What computations support such broad generalization in the visual system? To answer, we exploit two degraded image modalities – silhouettes and two-tone “Mooney” images – alongside regular shaded images. We test two distinct approaches to vision: deep networks for classification and analysis-by-synthesis for scene inference. Deep networks perform substantially sub-human even after training on 18 times more images per category compared to the existing large-scale image sets for object classification. We also present a novel analysis-by-synthesis architecture that infers 3D scenes from images via optimization in a differentiable, physically-based renderer. This model also performs substantially sub-human. Nevertheless, both approaches can explain some of the key behavioral patterns. We discuss the insights these results provide for reverse-engineering visual cognition.

Seeing in the dark: Testing deep neural network and analysis-by-synthesis accounts of 3D shape perception with highly degraded images

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

Interaction with environmental resources such as technological tools facilitates conceptual learning and preparation for future learning. Writing and compiling code, failing, and trying again fosters deep understanding of computational concepts. This study investigates knowledge gap awareness as potential mediating cognitive mechanism in the learning process from situated interaction with a technological tool to internalized conceptual understanding. Students engaged in a tool-mediated or tool-dissociated learning activity on functional programming and were subsequently assessed on their learning again under tool-mediated or tool-dissociated conditions. Students’ assessment performances were triangulated with their open comments and questionnaire responses on the study’s learning and assessment activities. The findings support the assumption of tool-mediation facilitating knowledge gap awareness, which in turn facilitates conceptual learning.

Knowledge-Gap Awareness as Mediating Cognitive Mechanism in Tool-Mediated Learning in Computer Science: A Multi-Method Experimental Study

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.

Investigating novice and expert programmers' problem solving via protocol analysis

We use a model to explore the implications of ACT-R's learning and forgetting mechanisms to understand learning and retention on a complex task. The model performs a spreadsheet task that has 14 non-iterated subtasks. The model predicts a learning curve and knowledge decay for different learning stages. The model's learning curve fits the human data well for the first four trials without decay. When decay is examined, however, we have to make modifications to the retention equation for the model's predictions to match data and the shapes predicted by the other learning theories. To fix this anomaly, we modified the effect of time on decay (adjusting time outside the experiment to less than the effect of time in the experiment) and the strength of newly learned memories (less well known than the previous default value). From these results, we learn that training and testing have been confounded in many studies.

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