Hamid Karimi Rouzbahani

Correction: Neural signatures of vigilance decrements predict behavioural errors before they occur

Publisher: eLife Sciences Publications, Ltd

Date: 08-08-2023

DOI: 10.7554/ELIFE.91529

Neural signatures of vigilance decrements predict behavioural errors before they occur

Publisher: eLife Sciences Publications, Ltd

Date: 08-04-2021

DOI: 10.7554/ELIFE.60563

Abstract: There are many monitoring environments, such as railway control, in which lapses of attention can have tragic consequences. Problematically, sustained monitoring for rare targets is difficult, with more misses and longer reaction times over time. What changes in the brain underpin these ‘vigilance decrements’? We designed a multiple-object monitoring (MOM) paradigm to examine how the neural representation of information varied with target frequency and time performing the task. Behavioural performance decreased over time for the rare target (monitoring) condition, but not for a frequent target (active) condition. There was subtle evidence of this also in the neural decoding using Magnetoencephalography: for one time-window (of 80ms) coding of critical information declined more during monitoring versus active conditions. We developed new analyses that can predict behavioural errors from the neural data more than a second before they occurred. This facilitates pre-empting behavioural errors due to lapses in attention and provides new insight into the neural correlates of vigilance decrements.

Neural signatures of vigilance decrements predict behavioural errors before they occur

Publisher: Cold Spring Harbor Laboratory

Date: 30-06-2020

DOI: 10.1101/2020.06.29.178970

Abstract: There are many monitoring environments, such as railway control, in which lapses of attention can have tragic consequences. Problematically, sustained monitoring for rare targets is difficult, with more misses and longer reaction times over time. What changes in the brain underpin these “vigilance decrements”? We designed a multiple-object monitoring (MOM) paradigm to examine how the neural representation of information varied with target frequency and time performing the task. Behavioural performance decreased over time for the rare target (monitoring) condition, but not for a frequent target (active) condition. This was mirrored in the neural results: there was weaker coding of critical information during monitoring versus active conditions. We developed new analyses that can predict behavioural errors from the neural data more than a second before they occurred. This paves the way for pre-empting behavioural errors due to lapses in attention and provides new insight into the neural correlates of vigilance decrements.

Three-stage processing of category and variation information by entangled interactive mechanisms of peri-occipital and peri-frontal cortices

Publisher: Cold Spring Harbor Laboratory

Date: 10-01-2018

DOI: 10.1101/189811

Abstract: Invariant object recognition, which refers to the ability of precisely and rapidly recognizing objects in the presence of variations, has been a central question in human vision research. The general consensus is that the ventral and dorsal visual streams are the major processing pathways which undertake category and variation encoding in entangled layers. This overlooks the mounting evidence which support the role of peri-frontal areas in category encoding. These recent studies, however, have left open several aspects of visual processing in peri-frontal areas including whether these areas contributed only in active tasks, whether they interacted with peri-occipital areas or processed information independently and differently. To address these concerns, a passive EEG paradigm was designed in which subjects viewed a set of variation-controlled object images. Using multivariate pattern analysis, noticeable category and variation information were observed in occipital, parietal, temporal and prefrontal areas, supporting their contribution to visual processing. Using task specificity indices, phase and Granger causality analyses, three distinct stages of processing were identified which revealed transfer of information between peri-frontal and peri-occipital areas suggesting their parallel and interactive processing of visual information. A brain-plausible computational model supported the possibility of parallel processing mechanisms in peri-occipital and peri-frontal areas. These findings, while advocating previous results on the role of prefrontal areas in object recognition, extend their contribution from active recognition, in which peri-frontal to peri-occipital feedback mechanisms are activated, to the general case of object and variation processing, which is an integral part of visual processing and play role even during passive viewing.

Three-stage processing of category and variation information by entangled interactive mechanisms of peri-occipital and peri-frontal cortices

Publisher: Springer Science and Business Media LLC

Date: 15-08-2018

DOI: 10.1038/S41598-018-30601-8

Abstract: Object recognition has been a central question in human vision research. The general consensus is that the ventral and dorsal visual streams are the major processing pathways undertaking objects’ category and variation processing. This overlooks mounting evidence supporting the role of peri-frontal areas in category processing. Yet, many aspects of visual processing in peri-frontal areas have remained unattended including whether these areas play role only during active recognition and whether they interact with lower visual areas or process information independently. To address these questions, subjects were presented with a set of variation-controlled object images while their EEG were recorded. Considerable amounts of category and variation information were decodable from occipital, parietal, temporal and prefrontal electrodes. Using information-selectivity indices, phase and Granger causality analyses, three processing stages were identified showing distinct directions of information transaction between peri-frontal and peri-occipital areas suggesting their parallel yet interactive role in visual processing. A brain-plausible model supported the possibility of interactive mechanisms in peri-occipital and peri-frontal areas. These findings, while promoting the role of prefrontal areas in object recognition, extend their contributions from active recognition, in which peri-frontal to peri-occipital pathways are activated by higher cognitive processes, to the general sensory-driven object and variation processing.

Spatiotemporal analysis of category and target-related information processing in the brain during object detection

Publisher: Elsevier BV

Date: 04-2019

DOI: 10.1016/J.BBR.2019.01.025

Abstract: To recognize a target object, the brain implements strategies which involve a combination of externally sensory-driven and internally task-driven mechanisms. While several studies have suggested a role for frontal brain areas in enhancing task-related representations in visual cortices, especially in the lateral-occipital cortex, they have remained silent about the type of information transferred to visual areas. However, the recently developed methods of representational connectivity analysis, allowed us to track the movement of different types of information in the brain. Accordingly, we designed an EEG object detection experiment and inspected the spatiotemporal dynamics of category- and target-related information across the brain. Results showed that the prefrontal area initiated the processing of target-related information. This information was then transferred to posterior brain areas during stimulus presentation probably to facilitate object detection and to direct the decision-making procedure. We also observed that, as compared to category-related information, the target-related information could predict the behavioral performance more accurately, suggesting the dominant representation of internal compared to external information in brain signals. These results provided new evidence about the role of prefrontal cortices in the processing of task-related information in the brain during object detection.

Spatiotemporal Analysis of Category and Target-related Information Processing in the Brain during Object Detection

Publisher: Cold Spring Harbor Laboratory

Date: 04-07-2018

DOI: 10.1101/361642

Abstract: To recognize a target object, the brain implements strategies which involve a combination of externally sensory-driven and internally task-driven mechanisms. While several studies have suggested a role for frontal brain areas in enhancing task-related representations in visual cortices, especially the lateral-occipital cortex, they remained silent about the type of information transferred to visual areas. However, the recently developed method of representational causality analysis, allowed us to track the movement of different types of information in the brain. Accordingly, we designed an EEG object detection experiment and evaluated the spatiotemporal dynamics of category- and target-related information across the brain using. Results showed that the prefrontal area initiated the processing of target-related information. This information was then transferred to posterior brain areas during stimulus presentation to facilitate object detection and to direct the decision-making procedure. We also observed that, as compared to category-related information, the target-related information could predict the behavioral detection performance more accurately, suggesting the dominant representation of internal compared to external information in brain signals. These results provided new evidence about the role of prefrontal cortices in the processing of task-related information the brain during object detection.

Evidence for multiscale multiplexed representation of visual features in EEG

Publisher: Cold Spring Harbor Laboratory

Date: 10-07-2023

DOI: 10.1101/2023.07.09.548296

Abstract: Distinct neural processes are often encoded across distinct time scales of neural activations. However, it has remained unclear if this multiscale coding strategy is also implemented for separate features of the same process. One difficulty is that the conventional methods of time scale analysis provide imperfect estimations of time scales when several components are active during a single process. Developing a novel decoding-based time scale estimation method, we detected distinct time scales for simultaneously present features of visual stimuli in electroencephalography. We observed that orientation and colour of grating stimuli were encoded in shorter whereas the spatial frequency and contrast of those stimuli were encoded in longer time scales. The conventional autocorrelation-based estimation of time scale was unable to detect these distinguishable time scales. These results provide new evidence for a multiscale multiplexed neural code in the human visual system and introduces a flexible method for estimating neural time scales.

Perceptual difficulty modulates the direction of information flow in familiar face recognition

Publisher: Cold Spring Harbor Laboratory

Date: 11-08-2020

DOI: 10.1101/2020.08.10.245241

Abstract: Humans are fast and accurate when they recognize familiar faces. Previous neurophysiological studies have shown enhanced representations for the dichotomy of familiar vs. unfamiliar faces. As familiarity is a spectrum, however, any neural correlate should reflect graded representations for more vs. less familiar faces along the spectrum. By systematically varying familiarity across stimuli, we show a neural familiarity spectrum using electroencephalography. We then evaluated the spatiotemporal dynamics of familiar face recognition across the brain. Specifically, we developed a novel informational connectivity method to test whether peri-frontal brain areas contribute to familiar face recognition. Results showed that feed-forward flow dominates for the most familiar faces and top-down flow was only dominant when sensory evidence was insufficient to support face recognition. These results demonstrate that perceptual difficulty and the level of familiarity influence the neural representation of familiar faces and the degree to which peri-frontal neural networks contribute to familiar face recognition.

Hard-wired feed-forward visual mechanisms of the brain compensate for affine variations in object recognition

Publisher: Elsevier BV

Date: 05-2017

DOI: 10.1016/J.NEUROSCIENCE.2017.02.050

Abstract: Humans perform object recognition effortlessly and accurately. However, it is unknown how the visual system copes with variations in objects' appearance and the environmental conditions. Previous studies have suggested that affine variations such as size and position are compensated for in the feed-forward sweep of visual information processing while feedback signals are needed for precise recognition when encountering non-affine variations such as pose and lighting. Yet, no empirical data exist to support this suggestion. We systematically investigated the impact of the above-mentioned affine and non-affine variations on the categorization performance of the feed-forward mechanisms of the human brain. For that purpose, we designed a backward-masking behavioral categorization paradigm as well as a passive viewing EEG recording experiment. On a set of varying stimuli, we found that the feed-forward visual pathways contributed more dominantly to the compensation of variations in size and position compared to lighting and pose. This was reflected in both the litude and the latency of the category separability indices obtained from the EEG signals. Using a feed-forward computational model of the ventral visual stream, we also confirmed a more dominant role for the feed-forward visual mechanisms of the brain in the compensation of affine variations. Taken together, our experimental results support the theory that non-affine variations such as pose and lighting may need top-down feedback information from higher areas such as IT and PFC for precise object recognition.

Caveats and nuances of model-based and model-free representational connectivity analysis

Publisher: Frontiers Media SA

Date: 10-03-2022

DOI: 10.3389/FNINS.2022.755988

Abstract: Brain connectivity analyses have conventionally relied on statistical relationship between one-dimensional summaries of activation in different brain areas. However, summarizing activation patterns within each area to a single dimension ignores the potential statistical dependencies between their multi-dimensional activity patterns. Representational Connectivity Analyses (RCA) is a method that quantifies the relationship between multi-dimensional patterns of activity without reducing the dimensionality of the data. We consider two variants of RCA. In model-free RCA, the goal is to quantify the shared information for two brain regions. In model-based RCA, one tests whether two regions have shared information about a specific aspect of the stimuli/task, as defined by a model. However, this is a new approach and the potential caveats of model-free and model-based RCA are still understudied. We first explain how model-based RCA detects connectivity through the lens of models, and then present three scenarios where model-based and model-free RCA give discrepant results. These conflicting results complicate the interpretation of functional connectivity. We highlight the challenges in three scenarios: complex intermediate models, common patterns across regions, and transformation of representational structure across brain regions. The article is accompanied by scripts ( osf.io/3nxfa/ ) that reproduce the results. In each case, we suggest potential ways to mitigate the difficulties caused by inconsistent results. The results of this study shed light on some understudied aspects of RCA, and allow researchers to use the method more effectively.

Temporal Variabilities Provide Additional Category-Related Information in Object Category Decoding: A Systematic Comparison of Informative EEG Features

Publisher: MIT Press - Journals

Date: 30-08-2021

DOI: 10.1162/NECO_A_01436

Abstract: How does the human brain encode visual object categories? Our understanding of this has advanced substantially with the development of multivariate decoding analyses. However, conventional electroencephalography (EEG) decoding predominantly uses the mean neural activation within the analysis window to extract category information. Such temporal averaging overlooks the within-trial neural variability that is suggested to provide an additional channel for the encoding of information about the complexity and uncertainty of the sensory input. The richness of temporal variabilities, however, has not been systematically compared with the conventional mean activity. Here we compare the information content of 31 variability-sensitive features against the mean of activity, using three independent highly varied data sets. In whole-trial decoding, the classical event-related potential (ERP) components of P2a and P2b provided information comparable to those provided by original magnitude data (OMD) and wavelet coefficients (WC), the two most informative variability-sensitive features. In time-resolved decoding, the OMD and WC outperformed all the other features (including the mean), which were sensitive to limited and specific aspects of temporal variabilities, such as their phase or frequency. The information was more pronounced in the theta frequency band, previously suggested to support feedforward visual processing. We concluded that the brain might encode the information in multiple aspects of neural variabilities simultaneously such as phase, litude, and frequency rather than mean per se. In our active categorization data set, we found that more effective decoding of the neural codes corresponds to better prediction of behavioral performance. Therefore, the incorporation of temporal variabilities in time-resolved decoding can provide additional category information and improved prediction of behavior.

Examining the generalizability of research findings from archival data

Publisher: Proceedings of the National Academy of Sciences

Date: 19-07-2201

DOI: 10.1073/PNAS.2120377119

Abstract: This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new s les.

Caveats and nuances of model-based and model-free representational connectivity analysis

Publisher: Cold Spring Harbor Laboratory

Date: 11-08-2021

DOI: 10.1101/2021.08.10.455841

Abstract: Brain connectivity analyses have conventionally relied on statistical relationship between one-dimensional summaries of activation in different brain areas. However, summarising activation patterns within each area to a single dimension ignores the potential statistical dependencies between their multi-dimensional activity patterns. Representational Connectivity Analyses (RCA) is a method that quantifies the relationship between multi-dimensional patterns of activity without reducing the dimensionality of the data. We consider two variants of RCA. In model-free RCA, the goal is to quantify the shared information for two brain regions. In model-based RCA, one tests whether two regions have shared information about a specific aspect of the stimuli/task, as defined by a model. However, this is a new approach and the potential caveats of model-free and model-based RCA are still understudied. We first explain how model-based RCA detects connectivity through the lens of models, and then present three scenarios where model-based and model-free RCA give discrepant results. These conflicting results complicate the interpretation of functional connectivity. We highlight the challenges in three scenarios: complex intermediate models, common patterns across regions and transformation of representational structure across brain regions. The paper is accompanied by scripts that reproduce the results. In each case, we suggest potential ways to mitigate the difficulties caused by inconsistent results. The results of this study shed light on some understudied aspects of RCA, and allow researchers to use the method more effectively.

Temporal variabilities provide additional category-related information in object category decoding: a systematic comparison of informative EEG features

Publisher: Cold Spring Harbor Laboratory

Date: 03-09-2020

DOI: 10.1101/2020.09.02.279042

Abstract: How does the human brain encode visual object categories? Our understanding of this has advanced substantially with the development of multivariate decoding analyses. However, conventional electroencephalography (EEG) decoding predominantly use the “mean” neural activation within the analysis window to extract category information. Such temporal averaging overlooks the within-trial neural variability which is suggested to provide an additional channel for the encoding of information about the complexity and uncertainty of the sensory input. The richness of temporal variabilities, however, has not been systematically compared with the conventional “mean” activity. Here we compare the information content of 31 variability-sensitive features against the “mean” of activity, using three independent highly-varied datasets. In whole-trial decoding, the classical event-related potential (ERP) components of “P2a” and “P2b” provided information comparable to those provided by “Original Magnitude Data (OMD)” and “Wavelet Coefficients (WC)”, the two most informative variability-sensitive features. In time-resolved decoding, the “OMD” and “WC” outperformed all the other features (including “mean”), which were sensitive to limited and specific aspects of temporal variabilities, such as their phase or frequency. The information was more pronounced in Theta frequency band, previously suggested to support feed-forward visual processing. We concluded that the brain might encode the information in multiple aspects of neural variabilities simultaneously e.g. phase, litude and frequency rather than “mean” per se. In our active categorization dataset, we found that more effective decoding of the neural codes corresponds to better prediction of behavioral performance. Therefore, the incorporation of temporal variabilities in time-resolved decoding can provide additional category information and improved prediction of behavior.

#EEGManyLabs: Investigating the replicability of influential EEG experiments

Publisher: Elsevier BV

Date: 11-2021

DOI: 10.1016/J.CORTEX.2021.03.013

Abstract: There is growing awareness across the neuroscience community that the replicability of findings about the relationship between brain activity and cognitive phenomena can be improved by conducting studies with high statistical power that adhere to well-defined and standardised analysis pipelines. Inspired by recent efforts from the psychological sciences, and with the desire to examine some of the foundational findings using electroencephalography (EEG), we have launched #EEGManyLabs, a large-scale international collaborative replication effort. Since its discovery in the early 20th century, EEG has had a profound influence on our understanding of human cognition, but there is limited evidence on the replicability of some of the most highly cited discoveries. After a systematic search and selection process, we have identified 27 of the most influential and continually cited studies in the field. We plan to directly test the replicability of key findings from 20 of these studies in teams of at least three independent laboratories. The design and protocol of each replication effort will be submitted as a Registered Report and peer-reviewed prior to data collection. Prediction markets, open to all EEG researchers, will be used as a forecasting tool to examine which findings the community expects to replicate. This project will update our confidence in some of the most influential EEG findings and generate a large open access database that can be used to inform future research practices. Finally, through this international effort, we hope to create a cultural shift towards inclusive, high-powered multi-laboratory collaborations.

Average activity, but not variability, is the dominant factor in the representation of object categories in the brain

Publisher: Elsevier BV

Date: 03-2017

DOI: 10.1016/J.NEUROSCIENCE.2017.01.002

Abstract: To categorize the perceived objects, brain utilizes a broad set of its resources and encoding strategies. Yet, it remains elusive how the category information is encoded in the brain. While many classical studies have sought the category information in the across-trial-averaged activity of neurons/neural populations, several recent studies have observed category information also in the within-trial correlated variability of activities between neural populations (i.e. dependent variability). Moreover, other studies have observed that independent variability of activity, which is the variability of the measured neural activity without any influence from correlated variability with other neurons opulations, could also be modulated for improved categorization. However, it was unknown how important each of the three factors (i.e. average activity, dependent and independent variability of activities) was in category encoding. Therefore, we designed an EEG experiment in which human subjects viewed a set of object exemplars from four categories. Using a computational model, we evaluated the contribution of each factor separately in category encoding. Results showed that the average activity played a significant role while the independent variability, although effective, contributed moderately to the category encoding. The inter-channel dependent variability showed an ignorable effect on the encoding. We also investigated the role of those factors in the encoding of variations which showed similar effects. These results imply that the brain, rather than variability, seems to use the average activity to convey information on the category of the perceived objects.

Informative Neural Codes to Separate Object Categories

Publisher: Cold Spring Harbor Laboratory

Date: 06-12-2020

DOI: 10.1101/2020.12.04.409789

Abstract: In order to develop object recognition algorithms, which can approach human-level recognition performance, researchers have been studying how the human brain performs recognition in the past five decades. This has already in-spired AI-based object recognition algorithms, such as convolutional neural networks, which are among the most successful object recognition platforms today and can approach human performance in specific tasks. However, it is not yet clearly known how recorded brain activations convey information about object category processing. One main obstacle has been the lack of large feature sets, to evaluate the information contents of multiple aspects of neural activations. Here, we compared the information contents of a large set of 25 features, extracted from time series of electroencephalography (EEG) recorded from human participants doing an object recognition task. We could characterize the most informative aspects of brain activations about object categories. Among the evaluated features, event-related potential (ERP) components of N1 and P2a were among the most informative features with the highest information in the Theta frequency bands. Upon limiting the analysis time window, we observed more information for features detecting temporally informative patterns in the signals. The results of this study can constrain previous theories about how the brain codes object category information.

Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward vision models

Publisher: Springer Science and Business Media LLC

Date: 31-10-2017

DOI: 10.1038/S41598-017-13756-8

Abstract: One key ability of human brain is invariant object recognition, which refers to rapid and accurate recognition of objects in the presence of variations such as size, rotation and position. Despite decades of research into the topic, it remains unknown how the brain constructs invariant representations of objects. Providing brain-plausible object representations and reaching human-level accuracy in recognition, hierarchical models of human vision have suggested that, human brain implements similar feed-forward operations to obtain invariant representations. However, conducting two psychophysical object recognition experiments on humans with systematically controlled variations of objects, we observed that humans relied on specific (diagnostic) object regions for accurate recognition which remained relatively consistent (invariant) across variations but feed-forward feature-extraction models selected view-specific (non-invariant) features across variations. This suggests that models can develop different strategies, but reach human-level recognition performance. Moreover, human in iduals largely disagreed on their diagnostic features and flexibly shifted their feature extraction strategy from view-invariant to view-specific when objects became more similar. This implies that, even in rapid object recognition, rather than a set of feed-forward mechanisms which extract diagnostic features from objects in a hard-wired fashion, the bottom-up visual pathways receive, through top-down connections, task-related information possibly processed in prefrontal cortex.

Dissociable Contribution of Extrastriate Responses to Representational Enhancement of Gaze Targets

Publisher: MIT Press - Journals

Date: 18-08-2021

DOI: 10.1162/JOCN_A_01750

Abstract: Before saccadic eye movements, our perception of the saccade targets is enhanced. Changes in the visual representation of saccade targets, which presumably underlie this perceptual benefit, emerge even before the eye begins to move. This perisaccadic enhancement has been shown to involve changes in the response magnitude, selectivity, and reliability of visual neurons. In this study, we quantified multiple aspects of perisaccadic changes in the neural response, including gain, feature tuning, contrast response function, reliability, and correlated activity between neurons. We then assessed the contributions of these various perisaccadic modulations to the population's enhanced perisaccadic representation of saccade targets. We found a partial dissociation between the motor information, carried entirely by gain changes, and visual information, which depended on all three types of modulation. These findings expand our understanding of the perisaccadic enhancement of visual representations and further support the existence of multiple sources of motor modulation and visual enhancement within extrastriate visual cortex.

Generalisability of epileptiform patterns across time and patients

Publisher: Cold Spring Harbor Laboratory

Date: 09-2023

DOI: 10.1101/2023.08.29.23294708

When the whole is less than the sum of its parts: maximum object category information and behavioral prediction in multiscale activation patterns

Publisher: Cold Spring Harbor Laboratory

Date: 14-07-2021

DOI: 10.1101/2021.07.14.452389

Abstract: Neural codes are reflected in complex, temporally and spatially specific patterns of activation. One popular approach to decode neural codes in electroencephalography (EEG) is multivariate decoding. This approach examines the discriminability of activity patterns across experimental conditions to test if EEG contains information about those conditions. However, conventional decoding analyses ignore aspects of neural activity which are informative. Specifically, EEG data can be decomposed into a large number of mathematically distinct features (e.g., entropy, Fourier and Wavelet coefficients) which can reflect different aspects of neural activity. We previously compared 30 such features of EEG data, and found that visual category, and participant behavior, can be more accurately predicted using multiscale spatiotemporally sensitive Wavelet coefficients than mean litude (Karimi-Rouzbahani et al., 2021b). Here, we considered that even this larger set of features may only partially capture the underlying neural code, because the brain could use a combination of encoding protocols within a single trial which is not reflected in any one mathematical feature alone. To check, we combined those mathematical features using state-of-the-art supervised and unsupervised feature selection procedures (n = 17). Across 3 datasets, we compared decoding of visual object category between these 17 sets of combined features, and between combined and in idual features. Object category could be robustly decoded using the combined features from all of the 17 algorithms. However, the combination of features, which were equalized in dimension to the in idual features, were outperformed in most of the time points by the most informative in idual feature (Wavelet coefficients). Moreover, the Wavelet coefficients also explained the behavioral performance more accurately than the combined features. These results suggest that a single but multiscale encoding protocol may capture the neural code better than any combination of features. Our findings put new constraints on the models of neural information encoding in EEG.

Perceptual difficulty modulates the direction of information flow in familiar face recognition

Publisher: Elsevier BV

Date: 06-2021

DOI: 10.1016/J.NEUROIMAGE.2021.117896

Abstract: Humans are fast and accurate when they recognize familiar faces. Previous neurophysiological studies have shown enhanced representations for the dichotomy of familiar vs. unfamiliar faces. As familiarity is a spectrum, however, any neural correlate should reflect graded representations for more vs. less familiar faces along the spectrum. By systematically varying familiarity across stimuli, we show a neural familiarity spectrum using electroencephalography. We then evaluated the spatiotemporal dynamics of familiar face recognition across the brain. Specifically, we developed a novel informational connectivity method to test whether peri-frontal brain areas contribute to familiar face recognition. Results showed that feed-forward flow dominates for the most familiar faces and top-down flow was only dominant when sensory evidence was insufficient to support face recognition. These results demonstrate that perceptual difficulty and the level of familiarity influence the neural representation of familiar faces and the degree to which peri-frontal neural networks contribute to familiar face recognition.

Institute For Research In Fondumental Sciences (IPM)

Location: Iran (Islamic Republic of)

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Iran University Of Science And Technology

Location: Iran (Islamic Republic of)

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Mater Research

Location: Australia

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Shahid Rajaee Teacher Training University

Location: Iran (Islamic Republic of)

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University Of Cambridge

Location: United Kingdom of Great Britain and Northern Ireland

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Macquarie University

Location: Australia

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Discovery Early Career Researcher Award - Grant ID: DE230100608

Start Date: 12-2023

End Date: 12-2029

Amount: $457,810.00

Funder: Australian Research Council