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

Remezani

et al. 2020

Preprint

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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.

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Remezani

et al. 2020

Preprint

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“…Critically, as opposed to the Brain-Computer Interface (BCI) community, where the goal of feature extraction is to maximize the decoding accuracy, in cognitive neuroscience the goal is to find better neural correlates for the behavioral effect under study (Hebart and Baker, 2018;Williams et al, 2007;Jacobs et al, 2009;Woolgar et al, 2019;Karimi-Rouzbahani et al, 2021a;Karimi-Rouzbahani et al, 2021b). Specifically, a given feature is arguably only informative if it predicts behavior.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome these issues, we proposed a new approach using medium-sized (50ms) sliding windows at each time step (5ms apart). The 50ms time window makes a compromise between concatenating the whole time window, which in theory allows any feature to be used at the expense of temporal resolution, and decoding in a time resolved fashion at each time point separately, which might lose temporal patterns of activity (Karimi-Rouzbahani et al, 2021b). Within each window, we quantify multiple different mathematical features of the continuous data.…”

Section: Introductionmentioning

confidence: 99%

“…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 amplitude (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.…”

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confidence: 99%

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

2021

Preprint

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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 amplitude (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 individual 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 individual features, were outperformed in most of the time points by the most informative individual 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.

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

Henson

et al. 2021

Preprint

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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.