Biology subjects

Caldara, R.

Publications and source records attributed to Caldara, R..

6 recordsLinked to original sources

bioRxiv2024

Cross-validating the electrophysiological markers of early face categorization

Human face categorization has been widely studied using electroencephalogram (EEG) and event related potentials (ERP). Within this context, the N170 ERP component has emerged as the earliest and most robust neural marker of face categorization, documented in thousands of studies conducted over nearly three decades. However, in the last decade there has been a surge in research using the fast periodic visual stimulation (FPVS) methodology to investigate face categorization. FPVS studies have consistently reported robust bilateral face categorization responses over the occipitotemporal cortex with a right hemispheric dominance, closely mirroring the N170 scalp topography. Yet, the question remains whether the neural response elicited in FPVS can be considered a proxy for the N170 or if it might be driven by different components. To address this issue, we recorded the electrophysiological signals of human observers who viewed natural images of faces and non-face objects during FPVS and three different ERP paradigms. We quantified the FPVS response in the frequency domain and extracted ERP visual components, including the P1, N170 and P2 in response to face stimuli, from both the ERP paradigms as well as the time domain of the FPVS response. Our results revealed little relationship between any single ERP component and the FPVS frequency response. Across methodologies, only the peak-to-peak differences between N170 and P2 components significantly and consistently explained the FPVS frequency response. Our data show that the FPVS frequency response is not singularly contingent on any isolated ERP component, such as the N170, but rather reflects a later complex neural integration. These findings raise crucial methodological and theoretical considerations on the relationship between FPVS and ERP responses, urging caution when interpreting the neurofunctional role of both electrophysiological signals. While both markers are indicative of human face categorization, they appear to capture different stages in this cognitive process.

neuroscience↗

bioRxiv2022

Neural computations in prosopagnosia

We aimed to identify neural computations underlying the loss of face identification ability by modelling the brain activity of brain-lesioned patient PS, a well-documented case of acquired pure prosopagnosia. We collected a large dataset of high-density electrophysiological (EEG) recordings from PS and neurotypicals while they completed a one-back task on a stream of face, object, animal and scene images. We found reduced neural decoding of face identity around the N170 window in PS, and conjointly revealed normal non-face identification in this patient. We used Representational Similarity Analysis (RSA) to correlate human EEG representations with those of deep neural network (DNN) models of vision and caption-level semantics, offering a window into the neural computations at play in patient PSs deficits. Brain representational dissimilarity matrices (RDMs) were computed for each participant at 4 ms steps using cross-validated classifiers. PSs brain RDMs showed significant reliability across sessions, indicating meaningful measurements of brain representations with RSA even in the presence of significant lesions. Crucially, computational analyses were able to reveal PSs representational deficits in high-level visual and semantic brain computations. Such multi-modal data-driven characterisations of prosopagnosia highlight the complex nature of processes contributing to face recognition in the human brain. HighlightsO_LIWe assess the neural computations in the prosopagnosic patient PS using EEG, RSA, and deep neural networks C_LIO_LINeural dynamics of brain-lesioned PS are reliably captured using RSA C_LIO_LINeural decoding shows normal evidence for non-face individuation in PS C_LIO_LINeural decoding shows abnormal neural evidence for face individuation in PS C_LIO_LIPS shows impaired high-level visual and semantic neural computations C_LI

neuroscience↗

bioRxiv2022

The neural code behind real-world recognition abilities

Why are some individuals better at recognising faces? Uncovering the neural mechanisms supporting face recognition ability has proven elusive. To tackle this challenge, we used a multi-modal data-driven approach combining neuroimaging, computational modelling, and behavioural tests. We recorded the high-density electroencephalographic brain activity of individuals with extraordinary face recognition abilities--super-recognisers--and typical recognisers in response to diverse visual stimuli. Using multivariate pattern analyses, we decoded face recognition abilities from 1 second of brain activity with up to 80% accuracy. To better understand the mechanisms subtending this decoding, we compared computations in the brains of our participants with those in artificial neural network models of vision and semantics, as well as with those involved in human judgments of shape and meaning similarity. Compared to typical recognisers, we found stronger associations between early brain computations of super-recognisers and mid-level computations of vision models as well as shape similarity judgments. Moreover, we found stronger associations between late brain representations of super-recognisers and computations of the artificial semantic model as well as meaning similarity judgments. Overall, these results indicate that important individual variations in brain processing, including neural computations extending beyond purely visual processes, support differences in face recognition abilities. They provide the first empirical evidence for an association between semantic computations and face recognition abilities. We believe that such multi-modal data-driven approaches will likely play a critical role in further revealing the complex nature of idiosyncratic face recognition in the human brain. SignificanceThe ability to robustly recognise faces is crucial to our success as social beings. Yet, we still know little about the brain mechanisms allowing some individuals to excel at face recognition. This study builds on a sizeable neural dataset measuring the brain activity of individuals with extraordinary face recognition abilities--super-recognisers--to tackle this challenge. Using state-of-the-art computational methods, we show robust prediction of face recognition abilities in single individuals from a mere second of brain activity, and revealed specific brain computations supporting individual differences in face recognition ability. Doing so, we provide direct empirical evidence for an association between semantic computations and face recognition abilities in the human brain--a key component of prominent face recognition models.

neuroscience↗

bioRxiv2019

In pursuit of visual attention: SSVEP frequency-tagging moving targets

Previous research has shown that visual attention does not always exactly follow gaze direction, leading to the concepts of overt and covert attention. However, it is not yet clear how such covert shifts of visual attention to peripheral regions impact the processing of the targets we directly foveate as they move in our visual field. The current study utilised the co-registration of eye-position and EEG recordings while participants tracked moving targets that were embedded with a 30 Hz frequency tag in a Steady State Visually Evoked Potentials (SSVEP) paradigm. When the task required attention to be divided between the moving target (overt attention) and a peripheral region where a second target might appear (covert attention), the SSVEPs elicited by the tracked target at the 30 Hz frequency band were significantly lower than when participants did not have to covertly monitor for a second target. Our findings suggest that neural responses of overt attention are reduced when attention is divided between covert and overt areas. This neural evidence is in line with theoretical accounts describing attention as a pool of finite resources, such as the perceptual load theory. Altogether, these results have practical implications for many real-world situations where covert shifts of attention may reduce visual processing of objects even when they are directly being tracked with the eyes.

neuroscience↗

bioRxiv2018

Neural representations of faces are tuned to eye movements

Eye movements provide a functional signature of how human vision is achieved. Many recent studies have reported idiosyncratic visual sampling strategies during face recognition. Whether these inter-individual differences are mirrored by idiosyncratic neural responses has not been investigated yet. Here, we tracked observers eye movements during face recognition; additionally, we obtained an objective index of neural face discrimination through EEG that was recorded while subjects fixated different facial information.\n\nAcross all observers, we found that those facial features that were fixated longer during face recognition elicited stronger neural face discrimination responses. This relationship occurred independently of inter-individual differences in fixation biases. Our data show that eye movements play a functional role during face processing by providing the neural system with information that is diagnostic to a specific observer. The effective processing of face identity involves idiosyncratic, rather than universal representations.

neuroscience↗

bioRxiv2018

A decisional space account of saccadic reaction times towards personally familiar faces

Manual and saccadic reaction times (SRTs) have been used to determine the minimum time required for different types of visual categorizations. Such studies have demonstrated that faces can be detected within natural scenes within as little as 100ms (Crouzet, Kirchner & Thorpe, 2010), while increasingly complex decisions require longer processing times (Besson, Barragan-Jason, Thorpe, Fabre-Thorpe, Puma et al., 2017). Following the notion that facial representations stored in memory facilitate perceptual processing (Ramon & Gobbini, 2018), a recent study reported 180ms as the fastest speed at which \"familiar face detection\" based on expressed choice saccades (Visconti di Ollegio Castello & Gobbini, 2015). At first glance, these findings seem incompatible with the earliest neural markers of familiarity reported in electrophysiological studies (Barragan-Jason, Cauchoix & Barbeau, 2015; Caharel, Ramon & Rossion, 2014; Huang, Wu, Hu, Wang, Ding & Qu et al., 2017), which should temporally precede any overtly observed behavioral (oculomotor or manual) categorization. Here, we reason that this apparent discrepancy could be accounted for in terms of decisional space constraints, which modulate both manual RTs observed for different levels of visual processing (Besson et al., 2017), as well as saccadic RTs (SRTs) in both healthy observers and neurological patients (Ramon, in press; Ramon, Sokhn, Lao & Caldara, in press). In the present study, over 70 observers completed three different SRT experiments in which decisional space was manipulated through task demands and stimulus probability. Subjects performed a gender categorization task, or one of two familiar face \"recognition\" tasks, which differed with respect to the number of personally familiar identities presented (3 vs. 7). We observe an inverse relationship between visual categorization proficiency and decisional space. Observers were most accurate for categorization of gender, which could be achieved in as little as 140ms. Categorization of highly predictable targets was more error-prone and required an additional ~100ms processing time. Our findings add to increasing evidence that pre-activation of identity-information can modulate early visual processing in a top-down manner. They also emphasize the importance of considering procedural aspects as well as terminology when aiming to characterize cognitive processes.

animal behavior and cognition↗