Recognizing behaviors and the internal state of the participants

Kerr, Wesley; Cohen, Paul R.

doi:10.1109/devlrn.2010.5578868

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…But in order to experimentally test whether the model-derived space can account for the perception of physical and social events, we need a large number of animations in which movements of simple shapes reflect a variety of complex events in both physical and social domains. Prior work has typically created such stimuli using manually-designed interactions (Gao et al, 2009(Gao et al, , 2010Gordon & Roemmele, 2014;Isik et al, 2017), simulations of rule-based behavior (Kerr & Cohen, 2010;Pantelis et al, 2014;Sano et al, 2020), or trajectories extracted from human activities in aerial videos (Shu et al, 2018). However, these methods of stimulus generation are unable to produce a large set of animations depicting rich behaviors and showing violations of physical and social constraints in a continuous and controlled manner.…”

Section: Continuous Spectrum Increasing Complexitymentioning

confidence: 99%

A unified psychological space for human perception of physical and social events

Shu

Peng

Zhu

et al. 2021

Cognitive Psychology

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One of the great feats of human perception is the generation of quick impressions of both physical and social events based on sparse displays of motion trajectories. Here we aim to provide a unified theory that captures the interconnections between perception of physical and social events. A simulation-based approach is used to generate a variety of animations depicting rich behavioral patterns. Human experiments used these animations to reveal that perception of dynamic stimuli undergoes a gradual transition from physical to social events. A learning-based computational framework is proposed to account for human judgments. The model learns to identify latent forces by inferring a family of potential functions capturing physical laws, and value functions describing the goals of agents. The model projects new animations into a sociophysical space with two psychological dimensions: an intuitive sense of whether physical laws are violated, and an impression of whether an agent possesses intentions to perform goal-directed actions. This derived sociophysical space predicts a meaningful partition between physical and social events, as well as a gradual transition from physical to social perception. The space also predicts human judgments of whether individual objects are lifeless objects in motion, or human agents performing goal-directed actions. These results demonstrate that a theoretical unification based on physical potential functions and goal-related values can account for the human ability to form an immediate impression of physical and social events. This ability provides an important pathway from perception to higher cognition.

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Section: Continuous Spectrum Increasing Complexitymentioning

confidence: 99%

A unified psychological space for human perception of physical and social events

Shu

Peng

Zhu

et al. 2021

Cognitive Psychology

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“…Prior computational approaches to generating explanations using observed movements have focused on ascribing physical causes only (Forbus, Usher, Lovett, Lockwood, & Wetzel, 2008;Siskind, 2003) or have ascribed intentions such as "chasing" or "playing tag" that involve more than one agent (and thus are social). These intentions typically do not suggest the more socially sophisticated ability in which one agent factors the thoughts of another into its plans (Barrett et al, 2005;Blythe, Todd, & Miller, 1999;Crick & Scassellati, 2008;Kerr & Cohen, 2010;Young, Igarashi, & Sharlin, 2008).…”

Section: Research Objectivesmentioning

confidence: 99%

Using modified incremental chart parsing to ascribe intentions to animated geometric figures

et al. 2011

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People spontaneously ascribe intentions on the basis of observed behavior, and research shows that they do this even with simple geometric figures moving in a plane. The latter fact suggests that 2-D animations isolate critical information-object movement-that people use to infer the possible intentions (if any) underlying observed behavior. This article describes an approach to using motion information to model the ascription of intentions to simple figures. Incremental chart parsing is a technique developed in natural-language processing that builds up an understanding as text comes in one word at a time. We modified this technique to develop a system that uses spatiotemporal constraints about simple figures and their observed movements in order to propose candidate intentions or nonagentive causes. Candidates are identified via partial parses using a library of rules, and confidence scores are assigned so that candidates can be ranked. As observations come in, the system revises its candidates and updates the confidence scores. We describe a pilot study demonstrating that people generally perceive a simple animation in a manner consistent with the model.

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“…Whether with real robots such as Crick's, or in simulated worlds such as Kerr and Cohen's CAVE model [35], a fundamental question is how intentions develop within a cognitive architecture. Recently the development and use of intentions has been investigated by ontogenetic developmental robotic (DR) approaches.…”

Section: Intentions In Cognitive-social Robotsmentioning

confidence: 99%