The Cognitive Architecture of Perceived Animacy: Intention, Attention, and Memory

Abstract

Human vision supports social perception by efficiently detecting agents and extracting rich information about their actions, goals, and intentions. Here, we explore the cognitive architecture of perceived animacy by constructing Bayesian models that integrate domain‐specific hypotheses of social agency with domain‐general cognitive constraints on sensory, memory, and attentional processing. Our model posits that perceived animacy combines a bottom–up, feature‐based, parallel search for goal‐directed movements with a top–down selection process for intent inference. The interaction of these architecturally distinct processes makes perceived animacy fast, flexible, and yet cognitively efficient. In the context of chasing, in which a predator (the “wolf”) pursues a prey (the “sheep”), our model addresses the computational challenge of identifying target agents among varying numbers of distractor objects, despite a quadratic increase in the number of possible interactions as more objects appear in a scene. By comparing modeling results with human psychophysics in several studies, we show that the effectiveness and efficiency of human perceived animacy can be explained by a Bayesian ideal observer model with realistic cognitive constraints. These results provide an understanding of perceived animacy at the algorithmic level—how it is achieved by cognitive mechanisms such as attention and working memory, and how it can be integrated with higher‐level reasoning about social agency.

Animacy and the prediction of behaviour

“…Many sensory events can lead to the detection of animacy, e.g., hearing a person speak or a bird sing, smelling a wet dog, or feeling an insect land on one's arm. We will mainly focus on vision because this sense is particularly useful for agent detection and animacy perception from vision has been extensively studied 3,5,6 . For example, neural representations of animate objects in ventral temporal cortex can be related to reaction times on visual animacy categorization tasks [7][8][9] .…”

Attributing Social Meaning to Animated Shapes: A New Experimental Study of Apparent Behavior

“…This would provide a greater understanding of what specific characteristics in the animations, such as speed and perceptual organization, lead individuals to make social judgments. Some past research has sought to identify how these characteristics influence attributions (e.g., Gao, Baker, Tang, Xu, & Tenenbaum, 2019; Rasmussen & Jiang, 2019), but further research using our animations could build on this work.…”

ChatGPT Is More Likely to Be Perceived as Male Than Female

“…Indeed, a large swath of research suggests that we spontaneously anthropomorphize non-human entities (Heberlein & Adolphs, 2004). For instance, we perceive a sense of animacy and intentionality from moving shapes (Gao et al, 2019;Heider & Simmel, 1944), gender from point-light displays (Kozlowski & Cutting, 1977), and biological motion from dots (Johansson, 1973). Therefore, research suggests that ChatGPT may embody human-like attributes.…”