The brain of the beholder: honouring individual representational idiosyncrasies

Charest, Ian; Kriegeskorte, Nikolaus

doi:10.1080/23273798.2014.1002505

Cited by 24 publications

(20 citation statements)

References 76 publications

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“…Our findings therefore suggest that at the N170 processing stage individual features are weighted in idiosyncratic ways to give rise to an individual representation of facial attractiveness. This idea is in line with recent work showing that representations in object-selective visual cortex are best modelled by each person's idiosyncratic judgments about the objects' semantic similarities 65,66 , showcasing that even perceptual brain representations can have an idiosyncratic component. Clearly, additional work needs to be done to map out this idiosyncratic component in facial attractiveness representations.…”

Section: Discussionsupporting

confidence: 85%

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

Kaiser

Nyga

2020

Sci Rep

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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 150–200 ms 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.

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Section: Discussionsupporting

confidence: 85%

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

Kaiser

Nyga

2020

Sci Rep

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“…Note that RSA is open in principle to integration of any kind of additional information, such as data from different species [57] and behaviour [61]. In particular, it is well suited for the investigation of subject-specific idiosyncrasies in brain function beyond the group average [62,63]. By allowing the combination of behavioural measures with measures of neural data in a common framework, it assesses individual differences in a way unlikely to be affected by subject-specific differences unrelated to activity of the nervous system relevant for behaviour.…”

Section: A Tripartite Approach To Tackle Current Methodological Challmentioning

confidence: 99%

Resolving the neural dynamics of visual and auditory scene processing in the human brain: a methodological approach

Cichy

Teng

2017

Phil. Trans. R. Soc. B

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In natural environments, visual and auditory stimulation elicit responses across a large set of brain regions in a fraction of a second, yielding representations of the multimodal scene and its properties. The rapid and complex neural dynamics underlying visual and auditory information processing pose major challenges to human cognitive neuroscience. Brain signals measured non-invasively are inherently noisy, the format of neural representations is unknown, and transformations between representations are complex and often nonlinear. Further, no single non-invasive brain measurement technique provides a spatio-temporally integrated view. In this opinion piece, we argue that progress can be made by a concerted effort based on three pillars of recent methodological development: (i) sensitive analysis techniques such as decoding and cross-classification, (ii) complex computational modelling using models such as deep neural networks, and (iii) integration across imaging methods (magnetoencephalography/electroencephalography, functional magnetic resonance imaging) and models, e.g. using representational similarity analysis. We showcase two recent efforts that have been undertaken in this spirit and provide novel results about visual and auditory scene analysis. Finally, we discuss the limits of this perspective and sketch a concrete roadmap for future research.This article is part of the themed issue ‘Auditory and visual scene analysis’.

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“…Different stimuli are represented in the brain along a number of dimensions, and representational geometry denotes the arrangement of stimuli in that space and the distances between them [191]. While representational geometry and representational similarity analysis have so far been exploited mostly to assess different models of brain function [192–194], they can also be leveraged to compare representational geometry across individual subjects[195,196]. Representational geometry can be derived from movie or story data, as long as the events of interest are properly labeled [189].…”

Section: Figurementioning

confidence: 99%

Building a Science of Individual Differences from fMRI

Dubois

Adolphs

2016

Trends in Cognitive Sciences

592

482

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To date, fMRI research has been concerned primarily with evincing generic principles of brain function through averaging data from multiple subjects. Given rapid developments in both hardware and analysis tools, the field is now poised to study fMRI-derived measures in individual subjects, and to relate these to psychological traits or genetic variations. We discuss issues of validity and reliability that arise when the focus shifts to individual subjects and that are widely applicable across imaging modalities. We also emphasize that individual assessment of neural function with fMRI presents specific challenges and necessitates careful consideration of anatomical and vascular between-subject variability, sources of within-subject variability, and statistical power.

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The brain of the beholder: honouring individual representational idiosyncrasies

Cited by 24 publications

References 76 publications

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

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

Resolving the neural dynamics of visual and auditory scene processing in the human brain: a methodological approach

Building a Science of Individual Differences from fMRI

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