Insight into others’ minds: spatio-temporal representations by intrinsic frame of reference

Abstract

Recent research has seen a growing interest in connections between domains of spatial and social cognition. Much evidence indicates that processes of representing space in distinct frames of reference (FOR) contribute to basic spatial abilities as well as sophisticated social abilities such as tracking other’s intention and belief. Argument remains, however, that belief reasoning in social domain requires an innately dedicated system and cannot be reduced to low-level encoding of spatial relationships. Here we offer an integrated account advocating the critical roles of spatial representations in intrinsic frame of reference. By re-examining the results from a spatial task (Tamborello etal., 2012) and a false-belief task (Onishi and Baillargeon, 2005), we argue that spatial and social abilities share a common origin at the level of spatio-temporal association and predictive learning, where multiple FOR-based representations provide the basic building blocks for efficient and flexible partitioning of the environmental statistics. We also discuss neuroscience evidence supporting these mechanisms. We conclude that FOR-based representations may bridge the conceptual as well as the implementation gaps between the burgeoning fields of social and spatial cognition.

Temporal and spectral profiles of conflict processing among multiple frames of reference

“…Individuals adopt multiple frames of reference (FORs) to represent spatial relationships of objects in complex environments (Levinson, ). Although many approaches are available to classify FORs (Levinson, ), in this study, we adopted a classification system based on psycholinguistic research (Sun & Wang, ) that involves an egocentric FOR (EFOR), intrinsic FOR (IFOR), and allocentric FOR (AFOR). EFOR‐based representations are anchored to the observer and must be updated following the movement of the observer's eye, head, and body coordinates (H. Wang, Johnson, & Zhang, ).…”

“…In AFOR‐based representations, the entire environment, such as a room or city, is considered the reference point. This categorical system may reduce ambiguity in spatial descriptions of the world and promote the understanding of human spatial cognition (Majid, Bowerman, Kita, Haun, & Levinson, ; Mou & McNamara, ; Sun & Wang, ; Tamborello, Sun, & Wang, ). Notably, different people may use different FORs to describe the same spatial situation.…”

“…For example, when describing the location of a cat, one may observe the situation with an EFOR (the cat is to the right of me), an IFOR (the cat is to the left of Jim, another observer in the space), and an AFOR (the cat is in the center of the room). Although both AFOR and IFOR use external anchors, the anchors in AFOR (the room, in this case, is an absolute and fixed anchor) are more stable than those in IFOR (in this case, Jim, who can freely change his location or orientation, is a relative and flexible anchor; Levinson, ; Mou, Fan, McNamara, & Owen, ; Mou & McNamara, ; Sun & Wang, ; Tamborello et al, ; Yamamoto & Philbeck, ).…”

“…The frame of reference‐based map of salience theory states that the human brain represents spatial information simultaneously using multiple FORs. All FORs consist of an FOR map with different saliences, and human performance is determined by the interactions of all relevant FOR‐based representations (Itti & Koch, ; Sun & Wang, ; Tamborello et al, ; H. Wang et al, ; H. Wang, Sun, Johnson, & Yuan, ). If different FORs generate different spatial relationships for a target, then conflicts may occur and require cognitive control for resolution (Chen, Weidner, Weiss, Marshall, & Fink, ; Nan, Li, Sun, Wang, & Liu, ; Sun & Wang, ; Tamborello et al, ).…”

The hot hand belief as an inference of local temporal instability

“…The statistical analyses reported in the correspondence article are in the same line by examining nonstationarity in the players' performance based on local "racks." It has been suggested that temporal instability in the task environment is the driving force for predictive learning, which eventually leads to complex achievements such as thought, language, and intelligence (Hawkins & Blakeslee, 2004;O'Reilly, Munakata, Frank, Hazy, & Contributors, 2012;Sun & Wang, 2013b). In this regard, we argue that computational models of human probabilistic reasoning, such as the hot hand belief, have the potential to capture the properties of human cognition in accordance with the statistical structures in the task environment.…”