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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&#39;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.

CogSci 2022

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

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

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AAMAS 2020

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

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.

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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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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. 





Human communication usually exhibits two fundamental and essential characteristics under environmental pressure, efficiency, i.e., using less communication frequency to achieve comparable performances in cooperation; and robustness, i.e., maintaining a relative performance when communicating in a complicated environment. Since a critical goal of designing artificial agents is making them human-like in many scenarios. How artificial agents could learn a human-like communication mechanisms in terms of efficiency and robustness is a long-existing problem which has not yet been solved. Reinforcement Learning, due to its trail-and-error paradigm, provides a promising framework in solving the above research problem. With reinforcement learning, this paper develops architectures that help agents learn an efficient and robust communication, and carries out extensive experiments which uncover that artificial agents are cognitively capable to learn such human-like communication protocol in various environments (tasks).

Achieving Consensus to Learn an Efficient and Robust Communication via Reinforcement Learning

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

Where would you stand on the subway? A Bayesian framework for modeling commuter positioning choices in simulated subway coaches

Reasoners consider some differences in geometric features as more similar than other differences in geometric features. For example, humans and other animals consider horizontal mirror images more similar to each other than vertical mirror images or objects that have different orientations around a point in the vertical plane. Previous theories that explain sensitivity to geometry in terms of Euclidean principles cannot account for these categorization tendencies because in Euclidean geometry, rotation is an invariant transformation. There is thus no difference between the vertical and horizontal axes in Euclidean geometry. We propose a new theory that can account for these tendencies because it considers the role that gravity plays in how minds categorize geometric objects. Our theory makes new predictions about how reasoners categorize geometric objects in different contexts: 3D contexts with gravity, 3D contexts with gravity where objects were manipulated, and 2D contexts. Five experiments with adults support our theory.

When Newton beats Euclid: intuitive physics underlies sensitivity to geometry

This work analyzes how we can understand science and technology as enactive practices, and how that characterization helps promoting an epistemology that does not rely only over the epistemological processes of science and technology, but rather brings into play other categories that help other types of reflections, such as science and technology in the face of cultural diversity. The idea that cognitive technologies can be understood as scaffoldings for developments and innovations within enactive practices is used and developed, to lead to the understanding that cultural variety plays an essential role in understanding the diversity of practices based on differentiation of the specialization of skills in relation to the media through affordances. This allows proposing a proposal for critical intercultural ethics based and understood from enactive practices.

Intercultural enactive ethics: an approach from science and technology understood as enactive practices

Body perception can be influenced by internal (body satisfaction) and external pressures (social comparison). In this study, 120 young women (18-to-30-year-olds) performed two tasks: they estimated the weight of 15 female body images and they compared their weight to that of the images presented in random order. For each estimation, they provided confidence judgments using a Likert scale. Afterward, they completed the Sociocultural Attitudes Questionnaire Towards Appearance (SATAQ-4R) and the Body Shape Questionnaire (BSQ-21) to evaluate the perceived social pressure on the body image. Estimation accuracy in the first task was predicted by confidence judgments. Estimation accuracy in the weight comparison tasks was predicted by Body Measuring Index (BMI), weight concern, body dissatisfaction, and peer pressure. Results show that when comparing our weight to others, our own weight, how we feel about our body, and the perceived pressure to change it determines how accurately we compare it to other people’s bodies.

How heavy do you think you are? Self-weight estimation is modulated by body dissatisfaction and weight concerns.

Research suggests individual differences in motor skills may be associated with reading ability in school-age children. This meta-analysis explored whether deficits in motor skills are evident in developmental dyslexia, using estimates from 33 studies (k) with 122 effects (m) from dyslexic and age-matched neurotypical samples (N dyslexia = 1248, M age = 11.6 years, range 7.0 to 25.3 years). An overall effect of moderate magnitude, g = –.52 [95% CI –.74; –.29], confirmed that motor skills are impaired in dyslexia. Meta-regression analyses indicated no significant effects of participant age or language. Subgroup analyses revealed significant group differences for tasks with nonlinguistic stimuli (k = 28, m = 83), e.g., pegboard task, g = –.46 [–.71; –.22], or linguistic stimuli (k = 10, m = 39), e.g., word copying task, g = –.66 [–1.09; –.24]. Effects were significant for groups with confirmed dyslexia diagnoses (k = 27, m = 103), g = –.56 [–.81; –.30], but not for groups identified as poor readers, (k = 6, m = 19), g = –.32 [–.93; .28]. Effects were significant for fine motor skills (k = 29, m = 106), g = –.61 [95% CI –.83; –.39], but not for gross motor or composite measures (k = 10, m = 16), g = –.26 [95% CI –.76; .24]. The results suggest that fine motor tasks might help to identify children at risk of dyslexia. Longitudinal research may further elucidate relations between motor and reading skills.

Dyslexia and Motor Skills: A Meta-Analysis

As virtual reality devices get more widespread, investigating how humans explore omnidirectional panoramic scenes has become a topic of interest. Several computational models now exist for predicting visual saliency in such environments. However, the existing literature largely ignores auditory cues while estimating saliency maps. To pave the road for more capable models, here, we ask how scene attributes and sound categories influence visual exploration. Considering a dataset of omnidirectional videos of dynamic scenes and eye fixations collected with and without sound, we analyzed how different semantic factors affect eye movement behavior; whether there are significant differences across human, animal, or music sounds present in the videos, or for indoor or outdoor scenes. Analyses revealed that the regulatory effects of audio in human fixations significantly increase in indoor scenes or if the audio is of music but decrease in outdoor scenes or if the audio is of animal or human sounds.

How scene attributes and sound influence visual exploration of omnidirectional panoramic scenes

Object location and identity interact with each other asymmetrically in adults: Participants tend to judge two sequentially presented objects as more similar when they appear in the same location compared to in different locations (a phenomenon referred to as Spatial Congruency Bias). Yet no comparable identity congruency bias is found, suggesting that location is special in object-binding. To investigate whether such a special role of location is inherent in our visual system or acquired during development, we conducted experiments in both 5-year-old children and adults performing location-judgment and identity-judgment tasks. The study replicated asymmetric bias in adults by finding that location biased their identity judgments, but not vice versa. However, symmetric bias was found in children, suggesting fused processing and reciprocal influence between location and identity in early childhood. The results indicate that location maintains its influence across development, whereas object identity’s influence on location gets pruned away during development.

Differential patterns of object-location binding in children and adults: Pruning the special role of location

Here, we test whether the perception of liquids is based on abstract, physics-based representations. To do so, we use a novel behavioral task that requires generalization across scene configurations. We created 30 animations of liquids flowing through and interacting with surfaces and objects. These animations came from six qualitatively different scene configurations and five viscosity levels. During the experiment, participants viewed two liquid animations (side-by-side), each from a different scene configuration, and they rated the similarity of underlying viscosities. We found that not only average similarity ratings are linearly predicted based on true viscosity differences, but also the identity of the scene configuration pairs was uncorrelated to similarity ratings. This successful generalization in human behavior suggests an underlying abstract, physics-based representation of liquids. We also present a new probabilistic model of liquid perception that accounts for behavioral ratings, quantitatively supporting our hypothesis.

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Achieving Consensus to Learn an Efficient and Robust Communication via Reinforcement Learning

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