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Successful decoding of famous faces in the fusiform face area.

Axelrod V, Yovel G - PLoS ONE (2015)

Bottom Line: We found that face-identity could be discriminated above chance level only in the fusiform face area.Our results corroborate the role of the fusiform face area in face recognition.Future studies are needed to further explore the role of the more recently discovered anterior face-selective areas in face recognition.

View Article: PubMed Central - PubMed

Affiliation: School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel.

ABSTRACT
What are the neural mechanisms of face recognition? It is believed that the network of face-selective areas, which spans the occipital, temporal, and frontal cortices, is important in face recognition. A number of previous studies indeed reported that face identity could be discriminated based on patterns of multivoxel activity in the fusiform face area and the anterior temporal lobe. However, given the difficulty in localizing the face-selective area in the anterior temporal lobe, its role in face recognition is still unknown. Furthermore, previous studies limited their analysis to occipito-temporal regions without testing identity decoding in more anterior face-selective regions, such as the amygdala and prefrontal cortex. In the current high-resolution functional Magnetic Resonance Imaging study, we systematically examined the decoding of the identity of famous faces in the temporo-frontal network of face-selective and adjacent non-face-selective regions. A special focus has been put on the face-area in the anterior temporal lobe, which was reliably localized using an optimized scanning protocol. We found that face-identity could be discriminated above chance level only in the fusiform face area. Our results corroborate the role of the fusiform face area in face recognition. Future studies are needed to further explore the role of the more recently discovered anterior face-selective areas in face recognition.

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Region of Interest discrimination analysis for cups.(A) Average percent signal change for two cup types in the face-selective areas and non-face selective collateral sulcus area. Error bars denote standard error of the mean. (B) Classification rates between two cup types in face and non-face selective regions. The black line indicates a chance level of 50%. The error bars denote the standard error of the mean.
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pone.0117126.g006: Region of Interest discrimination analysis for cups.(A) Average percent signal change for two cup types in the face-selective areas and non-face selective collateral sulcus area. Error bars denote standard error of the mean. (B) Classification rates between two cup types in face and non-face selective regions. The black line indicates a chance level of 50%. The error bars denote the standard error of the mean.

Mentions: For the univariate analysis, we estimated GLM model (HRF boxcar function) with four regressors: face identities 1,2 and cup types 1,2. This model was used to calculate percent signal change for each condition (for each face identity or cup type) within the predefined ROIs (Fig. 3A and Fig. 6A). Time courses were extracted for each of four regressors (identity 1,2 and cup type 1,2). Block plateau values (from TR = 4 to TR = 10 from block onset) were averaged and submitted to paired t-test analysis (SPSS 17). Time courses were extracted using the MarsBaR region of interest toolbox for SPM [46].


Successful decoding of famous faces in the fusiform face area.

Axelrod V, Yovel G - PLoS ONE (2015)

Region of Interest discrimination analysis for cups.(A) Average percent signal change for two cup types in the face-selective areas and non-face selective collateral sulcus area. Error bars denote standard error of the mean. (B) Classification rates between two cup types in face and non-face selective regions. The black line indicates a chance level of 50%. The error bars denote the standard error of the mean.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4340964&req=5

pone.0117126.g006: Region of Interest discrimination analysis for cups.(A) Average percent signal change for two cup types in the face-selective areas and non-face selective collateral sulcus area. Error bars denote standard error of the mean. (B) Classification rates between two cup types in face and non-face selective regions. The black line indicates a chance level of 50%. The error bars denote the standard error of the mean.
Mentions: For the univariate analysis, we estimated GLM model (HRF boxcar function) with four regressors: face identities 1,2 and cup types 1,2. This model was used to calculate percent signal change for each condition (for each face identity or cup type) within the predefined ROIs (Fig. 3A and Fig. 6A). Time courses were extracted for each of four regressors (identity 1,2 and cup type 1,2). Block plateau values (from TR = 4 to TR = 10 from block onset) were averaged and submitted to paired t-test analysis (SPSS 17). Time courses were extracted using the MarsBaR region of interest toolbox for SPM [46].

Bottom Line: We found that face-identity could be discriminated above chance level only in the fusiform face area.Our results corroborate the role of the fusiform face area in face recognition.Future studies are needed to further explore the role of the more recently discovered anterior face-selective areas in face recognition.

View Article: PubMed Central - PubMed

Affiliation: School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel.

ABSTRACT
What are the neural mechanisms of face recognition? It is believed that the network of face-selective areas, which spans the occipital, temporal, and frontal cortices, is important in face recognition. A number of previous studies indeed reported that face identity could be discriminated based on patterns of multivoxel activity in the fusiform face area and the anterior temporal lobe. However, given the difficulty in localizing the face-selective area in the anterior temporal lobe, its role in face recognition is still unknown. Furthermore, previous studies limited their analysis to occipito-temporal regions without testing identity decoding in more anterior face-selective regions, such as the amygdala and prefrontal cortex. In the current high-resolution functional Magnetic Resonance Imaging study, we systematically examined the decoding of the identity of famous faces in the temporo-frontal network of face-selective and adjacent non-face-selective regions. A special focus has been put on the face-area in the anterior temporal lobe, which was reliably localized using an optimized scanning protocol. We found that face-identity could be discriminated above chance level only in the fusiform face area. Our results corroborate the role of the fusiform face area in face recognition. Future studies are needed to further explore the role of the more recently discovered anterior face-selective areas in face recognition.

Show MeSH