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People reciprocate another person’s altruistic or spiteful intentions toward themselves over repeated interactions, but it is unclear how such other-regarding intentions are represented in people’s minds and how the dynamics of reciprocity unfold. Recent work formalizes other-regarding intentions as welfare tradeoff ratios (WTR) and demonstrates that people reciprocate by adjusting their own WTR in response to the perceived WTR of another person. However, due to the complexity and inaccuracy of existing WTR measurements, it is still infeasible to study the long-term dynamics of people’s WTR adjustment beyond a few trials. Here we develop an experimental paradigm based on nonlinear continuous decomposed games that precisely reveals to the participant the opponent&#39;s WTR and measures the participant’s WTR in a single trial. We find that participants are sensitive to the opponent’s WTR and adjust their WTR accordingly. This experimental framework enables the fine-grained investigation of the long-term dynamics of people’s intention-based reciprocity.

CogSci 2022

Studying the long-term dynamics of reciprocity based on welfare tradeoff ratios

People reciprocate another person’s altruistic or spiteful intentions toward themselves over repeated interactions, but it is unclear how such other-regarding intentions are represented in people’s minds and how the dynamics of reciprocity unfold. Recent work formalizes other-regarding intentions as welfare tradeoff ratios (WTR) and demonstrates that people reciprocate by adjusting their own WTR in response to the perceived WTR of another person. However, due to the complexity and inaccuracy of existing WTR measurements, it is still infeasible to study the long-term dynamics of people’s WTR adjustment beyond a few trials. Here we develop an experimental paradigm based on nonlinear continuous decomposed games that precisely reveals to the participant the opponent's WTR and measures the participant’s WTR in a single trial. We find that participants are sensitive to the opponent’s WTR and adjust their WTR accordingly. This experimental framework enables the fine-grained investigation of the long-term dynamics of people’s intention-based reciprocity.

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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. 





When considering the location of objects and places, we often take perspectives in reference to ourselves or someone/something else. Using ourselves as a reference is considered using an egocentric reference frame, while using something external as a reference is considered using an allocentric reference frame. Of interest is the similarity of how these reference frames inform our understanding of both spatial and social cognitive processes. Similar to how we understand objects in relation to ourselves or an external reference, mentalizing and theory of mind processes have also been described using reference frames. Whether there is a common mechanism for using reference frames for processing both spatial and social information is unclear. The present study explored this idea with an online study where participants performed both a spatial and social (i.e., mentalizing) perspective taking task, along with questionnaire gauging personality, visualization ability, and anxiety. Participants who were better at taking someone else’s spatial perspective tended to be better at mentalizing. This relationship was not present when taking one’s own spatial perspective or when mentalizing was not necessary. We provide preliminary evidence that reference frames contribute to both spatial and social cognitive processes.

Perspective taking and reference frames for spatial and social cognition

Groups of individuals need to coordinate in many real world
domains. However, coordination failure is common and not
well understood. There are few coordination measurements,
analyses focus on averaged data, and models lack coordination
strategies and clear correspondence to cognitive mechanisms.
Here, we present a thorough analysis of human data from a
difficult coordination scenario and a cognitive model implemented
within the ACT-R cognitive architecture to fit and explain
the data. Data were explored to better understand coordination
strategies and group dynamics. The cognitive model included
pre-game preferences, coordination strategies like signaling,
and other player choice predictions. This work highlights
the need for deeper data explorations and presents challenges
for modeling related to coordination dynamics, strategies,
and how players form beliefs about others.

Understanding and Modeling Coordination in the Minimum Effort Game

Theory of Mind (ToM)– the capacity to attribute mental states to others, plays a central role in social cognition and behaviour. Social categorization (SC) - the classification of people as within or outside of one’s own group- also shapes our evaluation of others, often without our awareness. The features that give rise to social categorizations are often static and can be extracted by looking at human faces. However, little is known about the effect of SC in ToM judgements. The most widely-used instrument to assess ToM, Baron-Cohen’s Reading the Mind in the Eyes (RmE), uses exclusively white faces. We created a new database of high-quality videos and stills of ethnically diverse actors depicting 93 different mental states, suitable to study cross-cultural ToM, to be validated using a) the classic RmE four-alternative forced-choice paradigm and b) a task where raters locate each face within a 2-D space of valence and arousal.

Transcultural mindreading: Building a new database of Facial Expressions with actors of diverse ethnicities to assess social cognition.

A previous study used the Antarjami gaming framework to determine the OCEAN personality traits. In this study, a reinforcement learning agent is being compared to its human counterpart through various levels in the game. A Deep-Q Network(DQN) is proposed here for playing the Antarjami game autonomously. The DQN takes as input the image of the game screen and decides the moves/actions it wants to play in the game. A collection of DQNs having uniform architectures is used, where each DQN is trained on a particular level of the game. The starting position of each player within a game level is random in the Antarjami framework, therefore the training of the reinforcement learning agent is agnostic to any bias about the initial positions of the players. The work shows how an RL-Agent gathers scores in a greedy fashion irrespective of any psychological inclinations.

A Study on Relative Performance of an Reinforcement Learning Agent and Human in a Psychometric Assessment Game

One of the ways to make reinforcement learning (RL) more ef-
ficient is by utilizing human advice. Because human advice is
expensive, the central question in advice-based reinforcement
learning is, how to decide in which states the agent should ask
for advice. To approach this challenge, various advice strate-
gies have been proposed. Although all of these strategies dis-
tribute advice more efficiently than naive strategies (such as
choosing random states), they rely solely on the agent’s inter-
nal representation of the task (the action-value function, the
policy, etc.) and therefore, are rather inefficient when this rep-
resentation is not accurate, in particular, in the early stages of
the learning process. To address this weakness, we propose an
approach to advice-based RL, in which the human’s role is not
limited to giving advice in chosen states, but also includes hint-
ing apriori (before the learning procedure) which sub-domains
of the state space require more advice. Specifically, we sug-
gest different ways to improve any given advice strategy by
utilizing the concept of critical states: states in which it is very
important to choose the correct action. Finally, we present ex-
periments in 2 environments that validate the efficiency of our
approach.

Criticality-Based Advice in Reinforcement Learning

Deep learning (DL) makes it possible to automate the work of theorists, developers, and programmers in creating intelligent agents. At the same time, it has limitations related to the statistical nature of the method, which largely ignores available domain knowledge and requires large training datasets. These limitations become a barrier in the field of modeling and characterization of human social-emotional behavior. Biologically inspired cognitive architectures (BICA) are based on facts from cognitive and brain sciences and therefore can be successful here, although at the great cost of intellectual human labor. The challenge therefore is to integrate the two approaches, combining their strengths and compensating for their weaknesses. Here a particular form of such integration is presented, which involves a scaffolding of DL by BICA. Experimental paradigms include a virtual registrar, a virtual partner dance, a virtual clownery, and more. This work was supported by the Russian Science Foundation Grant #22-11-00213.

Socially emotional intelligent agents based on BICA and deep learning

We follow up on recent work demonstrating clear advantages of lexical-to-sublexical feedback in the TRACE model of spoken word recognition. The prior work compared accuracy and recognition times in TRACE with feedback on or off as progressively
more noise was added to inputs. Recognition times were faster with feedback at every level of noise, and there was an accuracy advantage for feedback with noise added to inputs. However, a recent article claims that those results must be an artifact of
converting activations to response probabilities, because feedback could only reinforce the “status quo.” That is, the claim is that given noisy inputs, feedback must reinforce all inputs equally, whether driven by signal or noise. We demonstrate that the feedback advantage replicates with raw activations. We also demonstrate that lexical feedback selectively reinforces lexically-coherent input patterns – that is, signal over noise –
and explain how that behavior emerges naturally in interactive activation.

How Feedback in Interactive Activation Improves Perception

Deep reinforcement learning agents such as AlphaZero have achieved superhuman strength in complex combinatorial games. By contrast, the cognitive science of planning has mostly focused on simple tasks for experimental and computational tractability. Using a board game that strikes a balance between complexity and tractability, we find that AlphaZero agents improve in value function quality and planning depth through learning, similar to human in previous modeling work. In addition, these metrics reflect causal contributions to AlphaZero's playing strength. Yet the strongest contributor is the policy quality. The decrease in policy entropy also drives the increase in planning depth. The contribution of planning depth to performance is lessened in late training. These results contribute to a joint understanding of machine and human planning, providing an interpretable way of understanding the learning and strength of AlphaZero, while generating novel hypothesis on human planning.

Comparing Machine and Human Learning in a Planning Task of Intermediate Complexity

Language reflects our construal of the world which is grounded in everyday sensorimotor experience. Thus, when we experience a particular world including our real physical world, linguistic explanations about it induce particular ways to see it which sometimes influences our behaviors. This study explores such effects of language, especially when the world is virtually presented on a head-mounted display (HMD). We showed university student participants an on-campus view, which was familiar to them but captured from a height of three meters, via a HMD with either of the following two linguistic instructions, “You gradually grow huge.” and “You gradually float up in the air.” Then, they were asked to produce some physical actions while wearing the HMD and answer questions on their bodily images and imagination levels. We discuss a possible interrelationship between language, visual perception, imagination and bodily action, including how they facilitate or restrict each other.

Our construal and bodily actions are impacted by changes in language and virtual viewpoints

Subway systems in large cities witness high volumes of commuter traffic, with crowded coaches and limited seats. In such scenarios, commuters often carefully position themselves in strategic locations with the aim of maximizing their chances of getting a place to sit. While user behavior in subways around the world have been the focus of multiple studies in the past, these everyday acts of ‘optimal decision making’ is of particular interest to the cognitive scientist. This paper inquires into commuter positioning choices in simulated subway coaches, within the framework of Bayesian probabilistic modelling. Data on preferred standing positions were collected across 20 subjects for 30 co-passenger configurations, through an interactive computer game. A generative model based on a Bayesian network involving three key spatial parameters was constructed, and used for inferring preferred positions conditioned on the specific configurations. The model was able to accurately simulate the quick and intuitive decisions made by the players under constraints of time, and also effectively capture noise in responses across subjects

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Studying the long-term dynamics of reciprocity based on welfare tradeoff ratios

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