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A time-based account of the perception of odor objects and valences.

ABSTRACT: Is human odor perception guided by memory or emotion? Object-centered accounts predict that recognition of unique odor qualities precedes valence decoding. Valence-centered accounts predict the opposite: that stimulus-driven valence responses precede and guide identification. In a speeded response time study, participants smelled paired odors, presented sequentially, and indicated whether the second odor in each pair belonged to the same category as the first (object evaluation task) or whether the second odor was more pleasant than the first (valence evaluation task). Object evaluation was faster and more accurate than valence evaluation. In a complementary experiment, participants performed an identification task, in which they indicated whether an odor matched the previously presented word label. Responses were quicker for odors preceded by semantically matching, rather than nonmatching, word labels, but results showed no evidence of interference from valence on nonmatching trials. These results are in accordance with object-centered accounts of odor perception.

SUBMITTER: Olofsson JK

PROVIDER: S-EPMC3660998 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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A time-based account of the perception of odor objects and valences.

Olofsson Jonas K JK Bowman Nicholas E NE Khatibi Katherine K Gottfried Jay A JA

Psychological science 20120906 10

Is human odor perception guided by memory or emotion? Object-centered accounts predict that recognition of unique odor qualities precedes valence decoding. Valence-centered accounts predict the opposite: that stimulus-driven valence responses precede and guide identification. In a speeded response time study, participants smelled paired odors, presented sequentially, and indicated whether the second odor in each pair belonged to the same category as the first (object evaluation task) or whether ...[more]

PMID: 22961773

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Project description:In bistable vision, subjective perception wavers between two interpretations of a constant ambiguous stimulus. This dissociation between conscious perception and sensory stimulation has motivated various empirical studies on the neural correlates of bistable perception, but the neurocomputational mechanism behind endogenous perceptual transitions has remained elusive. Here, we recurred to a generic Bayesian framework of predictive coding and devised a model that casts endogenous perceptual transitions as a consequence of prediction errors emerging from residual evidence for the suppressed percept. Data simulations revealed close similarities between the model's predictions and key temporal characteristics of perceptual bistability, indicating that the model was able to reproduce bistable perception. Fitting the predictive coding model to behavioural data from an fMRI-experiment on bistable perception, we found a correlation across participants between the model parameter encoding perceptual stabilization and the behaviourally measured frequency of perceptual transitions, corroborating that the model successfully accounted for participants' perception. Formal model comparison with established models of bistable perception based on mutual inhibition and adaptation, noise or a combination of adaptation and noise was used for the validation of the predictive coding model against the established models. Most importantly, model-based analyses of the fMRI data revealed that prediction error time-courses derived from the predictive coding model correlated with neural signal time-courses in bilateral inferior frontal gyri and anterior insulae. Voxel-wise model selection indicated a superiority of the predictive coding model over conventional analysis approaches in explaining neural activity in these frontal areas, suggesting that frontal cortex encodes prediction errors that mediate endogenous perceptual transitions in bistable perception. Taken together, our current work provides a theoretical framework that allows for the analysis of behavioural and neural data using a predictive coding perspective on bistable perception. In this, our approach posits a crucial role of prediction error signalling for the resolution of perceptual ambiguities.

Dataset Information

A time-based account of the perception of odor objects and valences.

Publications

A time-based account of the perception of odor objects and valences.

Similar Datasets

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