Predictive social perception: Towards a unifying framework from action observation to person knowledge

Patric Bach*, Kimberley C. Schenke

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)
5 Downloads (Pure)


Action observation is central to human social interaction. It allows people to derive what mental states drive others' behaviour and coordinate (and compete) effectively with them. Although previous accounts have conceptualised this ability in terms of bottom-up (motoric or conceptual) matching processes, more recent evidence suggests that such mechanisms cannot account for the complexity and uncertainty of the sensory input, even in cases where computations should be much simpler (i.e., low-level vision). It has therefore been argued that perception in general, and social perception in particular, is better described as a process of top–down hypothesis testing. In such models, any assumption about others—their goals, attitudes, and beliefs—is translated into predictions of expected sensory input and compared with incoming stimulation. This allows perception and action to be based on these expectations or—in case of a mismatch—for one's prior assumptions to be revised until they are better aligned with the individual's behaviour. This article will give a (selective) review of recent research from experimental psychology and (social) neuroscience that supports such views, discuss the relevant underlying models, and current gaps in research. In particular, it will argue that much headway can be made when current research on predictive social perception is integrated with classic findings from social psychology, which have already shown striking effects of prior knowledge on the processing of other people's behaviour.

Original languageEnglish
Article numbere12312
Number of pages17
JournalSocial and Personality Psychology Compass
Issue number7
Early online date4 Jul 2017
Publication statusPublished - Jul 2017

Bibliographical note

Funding Information: Economic and Social Research Council, Grant/Award Number: ES/J019178/1


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