Literature DB >> 29118198

A fast, invariant representation for human action in the visual system.

Leyla Isik1, Andrea Tacchetti1, Tomaso Poggio1.   

Abstract

Humans can effortlessly recognize others' actions in the presence of complex transformations, such as changes in viewpoint. Several studies have located the regions in the brain involved in invariant action recognition; however, the underlying neural computations remain poorly understood. We use magnetoencephalography decoding and a data set of well-controlled, naturalistic videos of five actions (run, walk, jump, eat, drink) performed by different actors at different viewpoints to study the computational steps used to recognize actions across complex transformations. In particular, we ask when the brain discriminates between different actions, and when it does so in a manner that is invariant to changes in 3D viewpoint. We measure the latency difference between invariant and noninvariant action decoding when subjects view full videos as well as form-depleted and motion-depleted stimuli. We were unable to detect a difference in decoding latency or temporal profile between invariant and noninvariant action recognition in full videos. However, when either form or motion information is removed from the stimulus set, we observe a decrease and delay in invariant action decoding. Our results suggest that the brain recognizes actions and builds invariance to complex transformations at the same time and that both form and motion information are crucial for fast, invariant action recognition. NEW & NOTEWORTHY The human brain can quickly recognize actions despite transformations that change their visual appearance. We use neural timing data to uncover the computations underlying this ability. We find that within 200 ms action can be read out of magnetoencephalography data and that this representation is invariant to changes in viewpoint. We find form and motion are needed for this fast action decoding, suggesting that the brain quickly integrates complex spatiotemporal features to form invariant action representations.

Entities:  

Keywords:  action recognition; magnetoencephalography; neural decoding; vision

Mesh:

Year:  2017        PMID: 29118198     DOI: 10.1152/jn.00642.2017

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  6 in total

1.  Behavioral and neural representations en route to intuitive action understanding.

Authors:  Leyla Tarhan; Julian De Freitas; Talia Konkle
Journal:  Neuropsychologia       Date:  2021-10-12       Impact factor: 3.139

2.  Social-affective features drive human representations of observed actions.

Authors:  Diana C Dima; Tyler M Tomita; Christopher J Honey; Leyla Isik
Journal:  Elife       Date:  2022-05-24       Impact factor: 8.713

3.  Invariant recognition drives neural representations of action sequences.

Authors:  Andrea Tacchetti; Leyla Isik; Tomaso Poggio
Journal:  PLoS Comput Biol       Date:  2017-12-18       Impact factor: 4.475

4.  Using enriched semantic event chains to model human action prediction based on (minimal) spatial information.

Authors:  Fatemeh Ziaeetabar; Jennifer Pomp; Stefan Pfeiffer; Nadiya El-Sourani; Ricarda I Schubotz; Minija Tamosiunaite; Florentin Wörgötter
Journal:  PLoS One       Date:  2020-12-28       Impact factor: 3.240

5.  Isolating Action Prediction from Action Integration in the Perception of Social Interactions.

Authors:  Ana Pesquita; Ulysses Bernardet; Bethany E Richards; Ole Jensen; Kimron Shapiro
Journal:  Brain Sci       Date:  2022-03-24

6.  Sociality and interaction envelope organize visual action representations.

Authors:  Leyla Tarhan; Talia Konkle
Journal:  Nat Commun       Date:  2020-06-12       Impact factor: 14.919

  6 in total

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