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

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.

Informative Neural Codes to Separate Object Categories

“…This was different to the other features, which were all extracted from the whole trial time span (0 to 1000 ms). These results and previous time-resolved studies (Kaneshiro et al, 2015;Karimi-Rouzbahani, 2018;Karimi-Rouzbahani et al, 2017b;Karimi-Rouzbahani et al, 2020;Karimi-Rouzbahani et al, 2020a) suggest that the object category information might be more accessible in specific time spans from around 50 ms to 300 ms in the EEG signals. However, it has not been shown how (if at all), other features of the EEG signals (i.e.…”

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

“…One critical question for cognitive neuroscience has been whether (if at all) neuroimaging data can explain behavior (Williams et al, 2007;Ritchie et al, 2015;Woolgar et al, 2019;Karimi-Rouzbahani et al, 2019;Karimi-Rouzbahani et al, 2021a). We extended this question by asking whether more optimal decoding of object category information, can lead to better prediction of behavioral performance.…”

Neural signatures of vigilance decrements predict behavioural errors before they occur

“…Our connectivity method follows the recent major recent shift in literature from univariate to multivariate informational connectivity analyses ( Goddard et al, 2016 ; Karimi-Rouzbahani et al, 2017 ; Anzellotti and Coutanche, 2018 ; Goddard et al, 2019 ; Kietzmann et al, 2019 ; Karimi-Rouzbahani et al, 2019 ; Basti et al, 2020 ; Karimi-Rouzbahani et al, 2021 ). This is in contrast with the majority of neuroimaging studies using univariate connectivity analyses which can miss existing connectivity across areas when encountering low-amplitude activity on individual sensors ( Anzellotti and Coutanche, 2018 ; Basti et al, 2020 ).…”

“…This is in contrast with the majority of neuroimaging studies using univariate connectivity analyses which can miss existing connectivity across areas when encountering low-amplitude activity on individual sensors ( Anzellotti and Coutanche, 2018 ; Basti et al, 2020 ). Informational connectivity, on the other hand, is measured either through calculating the correlation between temporally resolved patterns of decoding accuracies across a pair of areas ( Coutanche and Thompson-Schill, 2013 ) or the correlation between representational dissimilarity matrices (RDMs) obtained from a pair of areas ( Kietzmann et al, 2019 ; Goddard et al, 2016 ; Goddard et al, 2019 ; Karimi-Rouzbahani et al, 2019 ; Karimi-Rouzbahani et al, 2021 ). Either one measures how much similarity in information coding there is between two brain areas across conditions, which is interpreted as reflecting their potential informational connectivity, and is less affected by absolute activity values compared to conventional univariate connectivity measures ( Anzellotti and Coutanche, 2018 ).…”

“…First, it reflects the similarity of the way a pair of brain areas encode ‘ distance ’ information during the whole trial. This means that we could not use the component of time in the evaluation of our connectivity as we have implemented elsewhere ( Karimi-Rouzbahani et al, 2019 ; Karimi-Rouzbahani et al, 2021 ). Second, rather than a simple correlation of magnitudes of decoding accuracy between two regions of interest, our connectivity measure reflects a correlation of the patterns of decoding accuracies across conditions (i.e., distances here).…”