Decades of empirical and theoretical research on human decision-making has broadly categorized it into two, separate realms: decision-making under risk and decision-making under uncertainty, with the Allais paradox and the Ellsberg paradox being a prominent example of each, respectively. In this work, we present the first unified, resource-rational account of these two paradoxes. Specifically, we show that Nobandegani et al.’s (2018) sample-based expected utility model provides a unified, process-level account of the two variants of the Allais paradox (the common-consequence effect and the common-ratio effect) and the Ellsberg paradox. Our work suggests that the broad framework of resource-rationality could permit a unified treatment of decision-making under risk and decision-making under uncertainty, thus approaching a unified account of human decision-making.

A Unified, Resource-Rational Account of the Allais and Ellsberg Paradoxes

Strategic decision-making is chiefly studied in behavioral economics using multi-agent games. Decades of empirical research has revealed that emotions play a crucial role in strategic decision-making, calling into question the “emotionless” homo economicus. In this work, we present a unified process-level account of a broad range of empirical findings on the effect of emotions in Prisoner’s Dilemma and Ultimatum games—the two most studied games in behavioral sciences. Under the empirically well-supported assumption that emotions modulate loss aversion, we show that Nobandegani et al.’s (2018) sample-based expected utility model can account for the effect of emotions on: (i) cooperation rate in Prisoner’s Dilemma, and (ii) the rejection rate of unfair offers in the Ultimatum game. We conclude by discussing the implications of our work for emotion research, and for developing a unified process-level account of the role of emotions in strategic decision-making.

Emotions in Games: Toward a Unified Process-Level Account

Widely considered a cornerstone of human morality, trust shapes many aspects of human social interactions. In this work, we show that artificial intelligence (AI) agents learn to trust ― and rationally adapt the extent to which they trust ― by interacting with their counterparts in the trust game (Berg, Dickhaut, & McCabe, 1995), the canonical task for studying trust in behavioral and brain sciences. Leveraging reinforcement learning (RL) to train our AI agents, we systematically investigate learning trust under various parameterizations of this task and show that trust can arise from pure self-interest. Our work sheds new light on the computational underpinnings of trust, taking us a step closer to developing AI agents whose values are aligned with human values.


AI Agents Learn to Trust

Recent experimental work has provided evidence for pure reasoning in infancy, a capacity permitting flexible integration of multiple sources of information to form rational expectations about novel events (Teglas et al., 2011). However, the neural underpinnings of this capacity have remained elusive. In this work, we present the first ecologically rational, neural-level account of these findings on pure reasoning in human infants. Our work bridges two dominant approaches in computational developmental psychology, i.e. neural-network models and Bayesian modeling, substantiating the view that intuitive physics in infancy might, at least partly, involve heuristics: a set of simple, fast, resource efficient, approximation algorithms that yield sufficiently good results.

VPNeural Network Modeling of Pure Reasoning in Preverbal Infants

We propose and implement a relatively simple computational neural-network model of number comparison. Training on paired comparisons of the integers 1-9 enables the model to efficiently and accurately simulate some fundamental empirical phenomena (distance and ratio effects on accuracy and response time). It also generalizes robustly to more advanced tasks involving multidigit integers, negative numbers, and decimal numbers. The work demonstrates that small neural networks can sometimes efficiently learn a powerful system that exhibits extremely robust generalization to untrained items. Some important alternate models of number comparison are considered to establish a broader context. Several predictions and suggestions are made for future empirical and computational research in this area.

A Neural Model of Number Comparison with Robust Generalization

Recent empirical work has shown that human children are adept at learning and reasoning with probabilities. Here, we model a series of recent experiments investigating the development of school-age children's non-symbolic probability reasoning ability using the Neural Probability Learner and Sampler (NPLS) system. We demonstrate that NPLS can accurately simulate the probability judgments and a temporary interference from incorrectly using pure numerical frequencies in probabilistic tasks in children. We discuss the roles of model parameters in simulating probability matching in children, and their occasional tendency to wrongly select less probable outcomes based on frequencies.

A Computational Model of Children's Learning and Use of Probabilities


**For a full list of posters in this session, click the See All link on the right side of the page.**

Poster Session 1 -  C : Cognitive Science

poster

### 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**

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To review the proceedings for the conference or review the searchable program, please visit the [website](https://cognitivesciencesociety.org/program/).


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.

Poster Session 3 -  C : Cognitive Science

This session has no scheduled virtual poster presentation time, but attendees will be able to comment and ask questions about your work/research asynchronously throughout the conference.<br>

**If you are a virtual poster presenter**, you can leave a message in your poster area chat space to advise times (and time zone) that you will be available either in the chat box, or by setting up your own online meeting room (Zoom, etc.).<br>
**If you are an attendee**, please check each poster's chat box and arrange time with presenter.<br>



Virtual Posters

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)
<br>[**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
<br><br>

#### **CogSci 2023 Program Book**
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CogSci 2023

The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Ardavan Nobandegani

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SHORT BIO

Presentations

A Unified, Resource-Rational Account of the Allais and Ellsberg Paradoxes

Emotions in Games: Toward a Unified Process-Level Account

AI Agents Learn to Trust

VPNeural Network Modeling of Pure Reasoning in Preverbal Infants

A Neural Model of Number Comparison with Robust Generalization

A Computational Model of Children's Learning and Use of Probabilities

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