Dataset Information

A hierarchical model of social perception: Psychophysical evidence suggests late rather than early integration of visual information from facial expression and body posture.

ABSTRACT: Facial expressions are one of the most important sources of information about another's emotional states. More recently, other cues such as body posture have been shown to influence how facial expressions are perceived. It has been argued that this biasing effect is underpinned by an early integration of visual information from facial expression and body posture. Here, we replicate this biasing effect, but, using a psychophysical procedure, show that adaptation to facial expressions is unaffected by body context. The integration of face and body information therefore occurs downstream of the sites of adaptation, known to be localised in high-level visual areas of the temporal lobe. Contrary to previous research, our findings thus provide direct evidence for late integration of information from facial expression and body posture. They are consistent with a hierarchical model of social perception, in which social signals from different sources are initially processed independently and in parallel by specialised visual mechanisms. Integration of these different inputs in later stages of the visual system then supports the emergence of the integrated whole-person percept that is consciously experienced.

SUBMITTER: Teufel C

PROVIDER: S-EPMC6420341 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

A hierarchical model of social perception: Psychophysical evidence suggests late rather than early integration of visual information from facial expression and body posture.

Teufel Christoph C Westlake Meryl F MF Fletcher Paul C PC von dem Hagen Elisabeth E

Cognition 20190123

Facial expressions are one of the most important sources of information about another's emotional states. More recently, other cues such as body posture have been shown to influence how facial expressions are perceived. It has been argued that this biasing effect is underpinned by an early integration of visual information from facial expression and body posture. Here, we replicate this biasing effect, but, using a psychophysical procedure, show that adaptation to facial expressions is unaffecte ...[more]

PMID: 30684782

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Project description:In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.

Dataset Information

A hierarchical model of social perception: Psychophysical evidence suggests late rather than early integration of visual information from facial expression and body posture.

Publications

A hierarchical model of social perception: Psychophysical evidence suggests late rather than early integration of visual information from facial expression and body posture.

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