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      The Second-Agent Effect: Communicative Gestures Increase the Likelihood of Perceiving a Second Agent

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          Abstract

          Background

          Beyond providing cues about an agent's intention, communicative actions convey information about the presence of a second agent towards whom the action is directed (second-agent information). In two psychophysical studies we investigated whether the perceptual system makes use of this information to infer the presence of a second agent when dealing with impoverished and/or noisy sensory input.

          Methodology/Principal Findings

          Participants observed point-light displays of two agents (A and B) performing separate actions. In the Communicative condition, agent B's action was performed in response to a communicative gesture by agent A. In the Individual condition, agent A's communicative action was replaced with a non-communicative action. Participants performed a simultaneous masking yes-no task, in which they were asked to detect the presence of agent B. In Experiment 1, we investigated whether criterion c was lowered in the Communicative condition compared to the Individual condition, thus reflecting a variation in perceptual expectations. In Experiment 2, we manipulated the congruence between A's communicative gesture and B's response, to ascertain whether the lowering of c in the Communicative condition reflected a truly perceptual effect. Results demonstrate that information extracted from communicative gestures influences the concurrent processing of biological motion by prompting perception of a second agent (second-agent effect).

          Conclusions/Significance

          We propose that this finding is best explained within a Bayesian framework, which gives a powerful rationale for the pervasive role of prior expectations in visual perception.

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          Most cited references30

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          Object perception as Bayesian inference.

          We perceive the shapes and material properties of objects quickly and reliably despite the complexity and objective ambiguities of natural images. Typical images are highly complex because they consist of many objects embedded in background clutter. Moreover, the image features of an object are extremely variable and ambiguous owing to the effects of projection, occlusion, background clutter, and illumination. The very success of everyday vision implies neural mechanisms, yet to be understood, that discount irrelevant information and organize ambiguous or noisy local image features into objects and surfaces. Recent work in Bayesian theories of visual perception has shown how complexity may be managed and ambiguity resolved through the task-dependent, probabilistic integration of prior object knowledge with image features.
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            Noise characteristics and prior expectations in human visual speed perception.

            Human visual speed perception is qualitatively consistent with a Bayesian observer that optimally combines noisy measurements with a prior preference for lower speeds. Quantitative validation of this model, however, is difficult because the precise noise characteristics and prior expectations are unknown. Here, we present an augmented observer model that accounts for the variability of subjective responses in a speed discrimination task. This allowed us to infer the shape of the prior probability as well as the internal noise characteristics directly from psychophysical data. For all subjects, we found that the fitted model provides an accurate description of the data across a wide range of stimulus parameters. The inferred prior distribution shows significantly heavier tails than a Gaussian, and the amplitude of the internal noise is approximately proportional to stimulus speed and depends inversely on stimulus contrast. The framework is general and should prove applicable to other experiments and perceptual modalities.
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              Vision as Bayesian inference: analysis by synthesis?

              We argue that the study of human vision should be aimed at determining how humans perform natural tasks with natural images. Attempts to understand the phenomenology of vision from artificial stimuli, although worthwhile as a starting point, can lead to faulty generalizations about visual systems, because of the enormous complexity of natural images. Dealing with this complexity is daunting, but Bayesian inference on structured probability distributions offers the ability to design theories of vision that can deal with the complexity of natural images, and that use 'analysis by synthesis' strategies with intriguing similarities to the brain. We examine these strategies using recent examples from computer vision, and outline some important implications for cognitive science.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2011
                28 July 2011
                : 6
                : 7
                : e22650
                Affiliations
                [1 ]Center for Cognitive Science, Department of Psychology, University of Torino, Torino, Italy
                [2 ]Laboratory of Experimental Psychology, Department of Psychology, Katholieke Universiteit Leuven, Leuven, Belgium
                Royal Holloway, University of London, United Kingdom
                Author notes

                Conceived and designed the experiments: VM MDG BGB KV CB. Performed the experiments: VM. Analyzed the data: VM MDG CB. Wrote the paper: VM CB MDG KV BGB.

                Article
                PONE-D-11-06407
                10.1371/journal.pone.0022650
                3145660
                21829472
                741d4004-4b43-4178-ba46-829d96a3ad37
                Manera et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 7 April 2011
                : 27 June 2011
                Page count
                Pages: 7
                Categories
                Research Article
                Biology
                Neuroscience
                Sensory Perception
                Psychophysics
                Behavioral Neuroscience
                Cognitive Neuroscience
                Medicine
                Mental Health
                Psychology
                Cognitive Psychology
                Experimental Psychology
                Psychophysics
                Sensory Perception
                Neurology
                Cognitive Neurology
                Social and Behavioral Sciences
                Psychology
                Cognitive Psychology
                Experimental Psychology
                Psychophysics
                Sensory Perception

                Uncategorized
                Uncategorized

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