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

Abstract

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.

Artificial Social Intelligence: A Comparative and Holistic View

“…Computationally, efforts towards human-like intelligence can be divided into physical intelligence and social intelligence [10] , analogous to the developmental psychology ideas of intuitive physics and intuitive psychology [8,11] . In the literature, physical intelligence [12−14] has been studied systematically and extensively in AI [15,16] , not only in terms of intuitive physics [17−26] and its applications to challenging AI problems (e.g., in computer vision [27−35] and robotics [36−41] ), but also in terms of more abstract forms of physical knowledge [8,9] -causality [42−49] and problemsolving [7, 49−56] .…”

“…Pointing to a cup may indicate its shape, color, capacity to hold water, or a request for assistance in retrieving some water. Consequently, addressing ASI requires a comprehensive approach; improving specific components of an ASI system would not always result in improved performance [9] .…”

“…Among all the cognitive components of ToM, we concentrate on the intent component and examine the evidence of the development of human intent in greater depth. Since humans can inversely infer the underlying intents of others through social contact and act to fulfill those intents based on their beliefs and desires, intent may be the most crucial component of ToM [9] . In fact, research has shown that humans do not encode the entirety of action details but rather observe and interpret actions in terms of their intentions and store these interpretations for later retrieval [97] .…”

“…The developmental psychology literature indicates that sixmonth-old infants view human actions as goal-directed behavior [98] . By the age of 10 months, infants segment continuous behavior streams into discrete units that correspond to what adults would perceive as distinct goal-directed acts [9,99,100] . After their first birthday, infants begin to comprehend that an agent may explore multiple plans to achieve a goal and choose one based on environmental conditions [101] .…”

“…Typically, intentions are hierarchically arranged across extensive spatiotemporal ranges as a sequence of goals [9] . Infants are already capable of perceiving intentions on multiple levels, including concrete action goals, higher order plans, and collaborative goals [104] .…”