Neural mechanisms for the recognition of biological movements

Abstract

The visual recognition of complex movements and actions is crucial for the survival of many species. It is important not only for communication and recognition at a distance, but also for the learning of complex motor actions by imitation. Movement recognition has been studied in psychophysical, neurophysiological and imaging experiments, and several cortical areas involved in it have been identified. We use a neurophysiologically plausible and quantitative model as a tool for organizing and making sense of the experimental data, despite their growing size and complexity. We review the main experimental findings and discuss possible neural mechanisms, and show that a learning-based, feedforward model provides a neurophysiologically plausible and consistent summary of many key experimental results.

Gender bending: auditory cues affect visual judgements of gender in biological motion displays

“…Certainly that interpretation is consistent with some models of the neural mechanisms mediating biological motion perceptions. For example, Giese and Poggio (2003) have proposed that the perception of biological motion arises via sequences of processes acting Wrst to extract biological motion cues from the visual array, then discriminations of coherent directions of motion are made, and then Wnally more complicated analyses are completed. Those more complicated processes give rise to perceptions of representational properties of an actor, like gender.…”

“…In locating their proposed model onto a possible neural array, Giese and Poggio (2003) suggest that biological motion perception is associated with processes in speciWc cortical locations including the superior temporal sulcus (STS) and parts of the frontal lobe. STS and its posterior aspect in particular has been identiWed as being selectively involved in auditory motion processing (Howard et al 1996), multisensory integration (Blake and ShiVrar 2007;Puce and Perrett 2003) and in visual and auditory biological motion processing (Bidet-Caulet et al 2005).…”

Unstructured human activity detection from RGBD images

“…Other approaches include filtering techniques [29] and sampling of video patches [1]. Hierarchical techniques for activity recognition have been used as well, but these typically focus on neurologically-inspired visual cortex-type models [9,32,23,28]. Often, these authors adhere faithfully to the models of the visual cortex, using motion-direction sensitive "cells" such as Gabor filters in the first layer [11,26].…”

Towards a Competitive Learning Model of Mirror Effects in Yes/No Recognition Memory Tests

“…2,[11][12][13] We further assume that neurons can perform a maximum-like operation (which results in an output signal that approximates the maximum among several input signals) to calculate response citeria/thresholds. This operation has been shown to be approximated by complex cells in the visual cortex of cats and neurons in Area V4 of macaques 22 and demonstrated in a neurophysiologically plausible way for feed-forward models. 23 We also assume that humans are able to distinguish high-strength from low-strength stimuli, yet we do not implement how this might be achieved.…”