Visual perception is one of the most advanced function of human brain. The study of different aspects of human perception currently contributes to machine vision applications. Humans estimate the size of objects to grasp them by perceptual mechanisms. However, the motor system is also able to influence the perception system. Here, we found modifications of object size perception after a reaching and a grasping action in different contextual information. This mechanism can be described by the Bayesian model where action provides the likelihood and this latter is integrated with the expected size (prior) derived from the stored object experience (Forward Dynamic Model). Beyond the action-modulation effect, the knowledge of subsequent action type modulates the perceptual responses shaping them according to relevant information required to recognize and interact with objects. Cognitive architectures can be improved on the basis of these processings in order to amplify relevant features of objects and allow to robot/agent an easy interaction with them.

Behavioral insights on influence of manual action on object size perception

BOSCO, ANNALISA;FATTORI, PATRIZIA
2017

Abstract

Visual perception is one of the most advanced function of human brain. The study of different aspects of human perception currently contributes to machine vision applications. Humans estimate the size of objects to grasp them by perceptual mechanisms. However, the motor system is also able to influence the perception system. Here, we found modifications of object size perception after a reaching and a grasping action in different contextual information. This mechanism can be described by the Bayesian model where action provides the likelihood and this latter is integrated with the expected size (prior) derived from the stored object experience (Forward Dynamic Model). Beyond the action-modulation effect, the knowledge of subsequent action type modulates the perceptual responses shaping them according to relevant information required to recognize and interact with objects. Cognitive architectures can be improved on the basis of these processings in order to amplify relevant features of objects and allow to robot/agent an easy interaction with them.
CEUR Workshop Proceedings
21
24
Bosco, Annalisa; Fattori, Patrizia
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/607601
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