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Object Files Can Be Purely Episodic
Author(s) -
Stephen R. Mitroff,
Brian J. Scholl,
Nicholaus S. Noles
Publication year - 2007
Publication title -
perception
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.619
H-Index - 91
eISSN - 1468-4233
pISSN - 0301-0066
DOI - 10.1068/p5804
Subject(s) - object (grammar) , computer science , priming (agriculture) , point (geometry) , face (sociological concept) , artificial intelligence , identity (music) , cognitive neuroscience of visual object recognition , computer vision , term (time) , visual objects , communication , psychology , perception , linguistics , neuroscience , quantum mechanics , philosophy , botany , germination , geometry , mathematics , physics , acoustics , biology
Our ability to track an object as the same persisting entity over time and motion may primarily rely on spatiotemporal representations which encode some, but not all, of an object's features. Previous researchers using the 'object reviewing' paradigm have demonstrated that such representations can store featural information of well-learned stimuli such as letters and words at a highly abstract level. However, it is unknown whether these representations can also store purely episodic information (i.e. information obtained from a single, novel encounter) that does not correspond to pre-existing type-representations in long-term memory. Here, in an object-reviewing experiment with novel face images as stimuli, observers still produced reliable object-specific preview benefits in dynamic displays: a preview of a novel face on a specific object speeded the recognition of that particular face at a later point when it appeared again on the same object compared to when it reappeared on a different object (beyond display-wide priming), even when all objects moved to new positions in the intervening delay. This case study demonstrates that the mid-level visual representations which keep track of persisting identity over time--e.g. 'object files', in one popular framework can store not only abstract types from long-term memory, but also specific tokens from online visual experience.

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