TY  - JOUR
PY  - 2017//
TI  - Action perception as hypothesis testing
JO  - Cortex
A1  - Donnarumma, Francesco
A1  - Costantini, Marcello
A1  - Ambrosini, Ettore
A1  - Friston, Karl
A1  - Pezzulo, Giovanni
SP  - 45
EP  - 60
VL  - 89
IS  - 
N2  - We present a novel computational model that describes action perception as an active inferential process that combines motor prediction (the reuse of our own motor system to predict perceived movements) and hypothesis testing (the use of eye movements to disambiguate amongst hypotheses). The system uses a generative model of how (arm and hand) actions are performed to generate hypothesis-specific visual predictions, and directs saccades to the most informative places of the visual scene to test these predictions - and underlying hypotheses. We test the model using eye movement data from a human action observation study. In both the human study and our model, saccades are proactive whenever context affords accurate action prediction; but uncertainty induces a more reactive gaze strategy, via tracking the observed movements. Our model offers a novel perspective on action observation that highlights its active nature based on prediction dynamics and hypothesis testing.<br><br>Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.<p />  <p>Language: en</p>
LA  - en
SN  - 0010-9452
UR  - http://dx.doi.org/10.1016/j.cortex.2017.01.016
ID  - ref1
ER  -