The Neural Dynamics of Familiar Face Recognition

Ambrus, Géza Gergely; Kaiser, Daniel; Cichy, Radoslaw Martin; Kovács, Gyula

doi:10.1093/cercor/bhz010

Cited by 35 publications

(82 citation statements)

References 78 publications

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“…Coding of identities is usually found later. Although earliest results are found from ~100 ms onwards (Ambrus, Kaiser, Cichy, & Kovács, 2019;Vida, Nestor, Plaut, & Behrmann, 2017), higher-level identity representations surpassing low-level visual information (Vida et al, 2017), as well as sensitivity to familiar faces (Schweinberger & Neumann, 2016), are found later, at around 250 ms, and differentiation of same sex identities only after 400 ms (Ambrus et al, 2019).…”

Section: Introductionmentioning

confidence: 90%

Spatio-temporal dynamics of face perception

Muukkonen

Ölander

Numminen

et al. 2019

Preprint

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The temporal and spatial neural processing of faces has been investigated rigorously, but few studies have unified these dimensions to reveal the spatio-temporal dynamics postulated by the models of face processing. We used support vector machine decoding and representational similarity analysis to combine information from different locations (fMRI), time windows (EEG), and theoretical models. By correlating information matrices derived from pairwise classifications of neural responses to different facial expressions (neutral, happy, fearful, angry), we found early EEG time windows (starting around 130 ms) to match fMRI data from early visual cortex (EVC), and later time windows (starting around 190ms) to match data from occipital and fusiform face areas (OFA/FFA) and posterior superior temporal sulcus (pSTS).According to model comparisons, the EEG classifications were based more on low-level visual features than expression intensities or categories. In fMRI, the model comparisons revealed change along the processing hierarchy, from low-level visual feature coding in EVC to coding of intensity of expressions in the right pSTS. The results highlight the importance of a multimodal approach for understanding the functional roles of different brain regions in face processing.

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Section: Introductionmentioning

confidence: 90%

Spatio-temporal dynamics of face perception

Muukkonen

Ölander

Numminen

et al. 2019

Preprint

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“…Or do they partly reflect idiosyncratic preferences, that is an individual person's unique aesthetic preference for particular faces? To resolve this question, we performed an analysis where we modelled neural RDMs as a function of individual yes/no responses and attractiveness ratings, while controlling for the database ratings using partial correlation analysis [43][44][45] (Figure 2c). We found that individual attractiveness judgments still significantly predicted cortical representations, both when considering yes/no responses (from the 150-200ms time bin; peaking at 600-650ms, peak t [22]=3.87, p<0.001, pcorr=0.002) and when considering attractiveness ratings (from the 150-200ms time bin; peaking at 500-550ms, peak t [22]=3.90, p<0.001, pcorr=0.012).…”

Section: Early Representations Of Facial Attractiveness Reflect Indivmentioning

confidence: 99%

“…To establish correspondences between the neural RDM and a specific predictor RDM while controlling for other RDMs (see below), we used partial correlations [43][44][45]. All correlations were Fisher-transformed before entering them into statistical analyses.…”

Section: Tracking Neural Representations Of Facial Attractivenessmentioning

confidence: 99%

Tracking cortical representations of facial attractiveness using time-resolved representational similarity analysis

Kaiser

Nyga

2020

Preprint

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When we see a face, we rapidly form an impression of its attractiveness. Here, we investigated how rapidly representations of facial attractiveness emerge in the human brain. In an EEG experiment, participants viewed 100 face photographs and rated them for their attractiveness. Using time-resolved representational similarity analysis on the EEG data, we reveal representations of facial attractiveness after 150-200ms of cortical processing. Interestingly, we show that these representations are related to individual participants' personal attractiveness judgments, suggesting that already early perceptual representations of facial attractiveness convey idiosyncratic attractiveness preferences. Further, we show that these early representations are genuinely related to attractiveness, as they are neither explained by other high-level face attributes, such as face sex or age, nor by features extracted by an artificial deep neural network model of face processing. Together, our results demonstrate early, individually specific, and genuine representations of facial attractiveness, which may underlie fast attractiveness judgments. IntroductionWhen we see a face, we almost immediately can tell whether we find it attractive or not [1]. Beyond such first impressions, facial attractiveness affects people's everyday lives in fundamental ways: for instance, an attractive face grants advantages in various aspects, such as increased success in dating [2,3], receiving help more often [4,5], and being more successful on the job market [6,7].Given these varied effects of facial attractiveness, a large body of research has focused on understanding the factors that make a face attractive. One line of research has tried to establish objective physical markers of facial attractiveness [8,9], revealing that faces are more attractive when they are more similar to the average [10,11], more symmetric [11][12][13], or have favourable sexual characteristics [14]. This research has led into advances in computer vision, providing algorithms that can predict how attractive humans will find a particular face [15,16]. However, there is considerable agreement that there is also a subjective component to facial attractiveness, with responses varying substantially between observers [17][18][19]. Indeed, our impression of facial attractiveness may depend on both: an objectively attractive physical composition of visual features and an idiosyncratic appreciation of these features.How does the brain achieve the transition from physical stimulus properties into an individual representation of facial attractiveness? To answer this question, previous studies have used event-related potentials (ERPs) obtained from EEG recordings to investigate when brain responses to more or less attractive faces differ.Although in many such studies facial attractiveness influences multiple ERP components, a key question is when facial attractiveness is first represented in EEG signals. The answers to this question are quite mixed. Some studies highlight relatively early ER...

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“…Moreover, in a crucial departure from previous efforts to compare the informational requirements of different types of face recognition across distinct task contexts and face presentations, the current thresholds pertain to categorisation functions elicited in parallel by the same face encounters. This notion of concurrence has gained recent traction in the face processing literature, with a number of recent studies using multivariate methods to probe the overlapping time-courses of various types of face categorisation reflected within the same neural response (i.e., decoding "which information is available when", (12)(13)(14)(15)). Here we took a very different approach, capturing differential responses to faces among objects regardless of exact timing (e.g., early/fast vs. late/slow).…”

Section: Discussionmentioning

confidence: 99%

“…Recently, however, a new wave of face research has emerged aimed at elucidating how the brain extracts information along different face dimensions concurrently (12)(13)(14)(15). In contrast to second-order comparisons of face functions (i.e., contrasts across different tasks/face encounters), this approach investigates the different levels of categorisation reflected in the exact same neural response elicited by a given face encounter, often by applying multivariate pattern analysis (MVPA) techniques to high temporal resolution electro/magneto-encephalographic data (EEG, MEG) (16).…”

Section: Main Textmentioning

confidence: 99%

Critical information thresholds underlying concurrent face recognition functions

Quek

Rossion

Liu‐Shuang³

2020

Preprint

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Humans rapidly and automatically recognise faces on multiple different levels, yet little is known about how the brain achieves these manifold categorisations concurrently. We bring a new perspective to this emerging issue by probing the relative informational dependencies of two of the most important aspects of human face processing: categorisation of the stimulus as a face (generic face recognition) and categorisation of its familiarity (familiar face recognition). Recording electrophysiological responses to a large set of natural images progressively increasing in image duration (Expt. 1) or spatial frequency content (Expt. 2), we contrasted critical sensory thresholds for these recognition functions as driven by the same face encounters. Across both manipulations, individual observer thresholds were consistently lower for distinguishing faces from other objects than for distinguishing familiar from unfamiliar faces. Moreover, familiar face recognition displayed marked inter-individual variability compared to generic face recognition, with no systematic relationship evident between the two thresholds. Scalp activation was also more strongly right-lateralised at the generic face recognition threshold than at the familiar face recognition threshold. These results suggest that high-level recognition of a face as a face arises based on minimal sensory input (i.e., very brief exposures/coarse resolutions), predominantly in right hemisphere regions. In contrast, the amount of additional sensory evidence required to access face familiarity is highly idiosyncratic and recruits wider neural networks. These findings underscore the neurofunctional distinctions between these two recognition functions, and constitute an important step forward in understanding how the human brain recognises various dimensions of a face in parallel.Significance StatementThe relational dynamics between different aspects of face recognition are not yet well understood. We report relative informational dependencies for two concurrent, ecologically relevant face recognition functions: distinguishing faces from objects, and recognising people we know. Our electrophysiological data show that for a given face encounter, the human brain requires less sensory input to categorise that stimulus as a face than to recognise whether the face is familiar. Moreover, where sensory thresholds for distinguishing faces from objects are remarkably consistent across observers, they vary widely for familiar face recognition. These findings shed new light on the multifaceted nature of human face recognition by painting a more comprehensive picture of the concurrent evidence accumulation processes initiated by seeing a face.

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The Neural Dynamics of Familiar Face Recognition

Cited by 35 publications

References 78 publications

Spatio-temporal dynamics of face perception

Spatio-temporal dynamics of face perception

Tracking cortical representations of facial attractiveness using time-resolved representational similarity analysis

Critical information thresholds underlying concurrent face recognition functions

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