Face recognition ability is manifest in early dynamic decoding of face-orientation selectivity—Evidence from multi-variate pattern analysis of the neural response

Mares, Inês; Ewing, Louise; Papasavva, Michael; Ducrocq, Emmanuel; Smith, Fraser; Smith, Marie L.

doi:10.1016/j.cortex.2022.11.004

Cited by 3 publications

(5 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sample size was calculated for correlations between 0.3 and 0.5, based on the range of effect sizes observed in a previous study from our group that investigated the neural links (i.e. using ERP and decoding measures) of face expertise 38 . For these effect sizes and a power of 80% (considering p < 0.05), between 29 and 84 participants were required 40 .…”

Section: Methodsmentioning

confidence: 99%

“…Data was downsampled to 250 Hz, and linear support vector machine (SVM) classifiers were trained for each time sample, based on single trials, using all posterior electrodes (O1/2, P7/8, P3/4, Pz, TP7/8, PO7/8, POz, Oz, PO3/4, P5/6, P1/2, TP9/10). These sites were chosen because previous work has shown that these electrodes contain the most informative signal for visual tasks (see 38 , 51 ). We focused on two binary comparisons where participants saw stimuli from the same visual category (i.e., faces or cars) but face expectation was manipulated (i.e.…”

Section: Methodsmentioning

confidence: 99%

“…A classifier using the true labelling was also included in the distribution of results as one of the possible outcomes. Significance at the individual level was calculated as the proportion of the null distribution that was greater than or equal to the accuracy obtained with correct labels, with significance considered when actual decoding is greater than or equal to 95% of the null distribution (FDR corrected, see 38 , 54 , 55 ). In line with previous research studies applying these methods 38 , 39 , 56 we extracted three metrics: sustainability of decoding—defined as the percentage of significant decoding in a given time-window, peak decoding—defined as the maximal positive peak in a given time window and peak decoding latency—defined as the time-point of the maximal positive peak decoding in a given time-window.…”

Section: Methodsmentioning

confidence: 99%

“…Significance at the individual level was calculated as the proportion of the null distribution that was greater than or equal to the accuracy obtained with correct labels, with significance considered when actual decoding is greater than or equal to 95% of the null distribution (FDR corrected, see 38 , 54 , 55 ). In line with previous research studies applying these methods 38 , 39 , 56 we extracted three metrics: sustainability of decoding—defined as the percentage of significant decoding in a given time-window, peak decoding—defined as the maximal positive peak in a given time window and peak decoding latency—defined as the time-point of the maximal positive peak decoding in a given time-window. These metrics aim to evaluate at the individual level the neural differentiation between the key contrasts as measured through decoding accuracy, both in terms of extent of decoding across the selected time window (60–500 ms), and maximal decoding.…”

Section: Methodsmentioning

confidence: 99%

“…While analysis of specific ERP components can be theoretically informative, it is necessarily limited to a discrete, often short timeframe and a localized region of interest: a few select electrodes. Multivariate pattern analysis (MVPA) permits more comprehensive and thorough exploration of the neural underpinnings of a given phenomenon 38 , 39 . We predict that MVPA will highlight significant differential decoding of the neural response, over visual posterior areas, to exactly the same visual input when there is high vs low face expectation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Effects of expectation on face perception and its association with expertise

Mares,

Smith,

Goddard

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

Perceptual decisions are derived from the combination of priors and sensorial input. While priors are broadly understood to reflect experience/expertise developed over one’s lifetime, the role of perceptual expertise at the individual level has seldom been directly explored. Here, we manipulate probabilistic information associated with a high and low expertise category (faces and cars respectively), while assessing individual level of expertise with each category. 67 participants learned the probabilistic association between a color cue and each target category (face/car) in a behavioural categorization task. Neural activity (EEG) was then recorded in a similar paradigm in the same participants featuring the previously learned contingencies without the explicit task. Behaviourally, perception of the higher expertise category (faces) was modulated by expectation. Specifically, we observed facilitatory and interference effects when targets were correctly or incorrectly expected, which were also associated with independently measured individual levels of face expertise. Multivariate pattern analysis of the EEG signal revealed clear effects of expectation from 100 ms post stimulus, with significant decoding of the neural response to expected vs. not stimuli, when viewing identical images. Latency of peak decoding when participants saw faces was directly associated with individual level facilitation effects in the behavioural task. The current results not only provide time sensitive evidence of expectation effects on early perception but highlight the role of higher-level expertise on forming priors.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%