Early ERPs to faces and objects are driven by phase, not amplitude spectrum information: Evidence from parametric, test-retest, single-subject analyses

Abstract

One major challenge in determining how the brain categorizes objects is to tease apart the contribution of low-level and high-level visual properties to behavioral and brain imaging data. So far, studies using stimuli with equated amplitude spectra have shown that the visual system relies mostly on localized information, such as edges and contours, carried by phase information. However, some researchers have argued that some event-related potentials (ERP) and blood-oxygen-level-dependent (BOLD) categorical differences could be driven by nonlocalized information contained in the amplitude spectrum. The goal of this study was to provide the first systematic quantification of the contribution of phase and amplitude spectra to early ERPs to faces and objects. We conducted two experiments in which we recorded electroencephalograms (EEG) from eight subjects, in two sessions each. In the first experiment, participants viewed images of faces and houses containing original or scrambled phase spectra combined with original, averaged, or swapped amplitude spectra. In the second experiment, we parametrically manipulated image phase and amplitude in 10% intervals. We performed a range of analyses including detailed single-subject general linear modeling of ERP data, test-retest reliability, and unique variance analyses. Our results suggest that early ERPs to faces and objects are due to phase information, with almost no contribution from the amplitude spectrum. Importantly, our results should not be used to justify uncontrolled stimuli; to the contrary, our results emphasize the need for stimulus control (including the amplitude spectrum), parametric designs, and systematic data analyses, of which we have seen far too little in ERP vision research.

Early ERPs to faces: aging, luminance, and individual differences

“…Contrast sensitivity loss under photopic light conditions has been observed in particular for intermediate and high spatial frequencies—above 2 cycles/degree of visual angle (Owsley et al, 1983). In our stimuli, 90% of the total power was contained within the low to intermediate spatial frequency range (Bieniek et al, 2012, Figure 1)—below 20 cycles/image, which for our image size of 9° of visual angle is equivalent to ~2.2 cycles/degree. This suggests that most of the spatial frequency content of our images is below the range that is typically affected by aging.…”

“…To model EEG data we used a general linear model (GLM) across trials, at all-time points and all electrodes. We controlled for multiple comparisons using a bootstrap spatial-temporal clustering technique (Pernet et al, 2011; Rousselet et al, 2011; Bieniek et al, 2012). …”

Converging evidence for [coronal] underspecification in English-speaking adults

“…The sum of F-values inside each spatio-temporal cluster was computed and the maximum sums were kept. Then, the maximum sums across electrodes were sorted to obtain a 95 th percentile threshold to which actual F-values were compared (Bieniek, Pernet, & Rousselet, 2012; Pernet et al, 2011; Rousselet & Pernet, 2011; Salvia et al, 2014). …”

The Neural Dynamics of Face Detection in the Wild Revealed by MVPA

“…Minimum RTs across trials were computed using 10 ms sliding time bins ( 2 test, p Ͻ 0.01; Rousselet et al, 2003). Across participants, to allow for lower statistical power than with across-trial data since there were fewer trials, we used 30 ms time bins and a Fisher's exact test ( p Ͻ 0.01; Barragan-Jason et al, 2012Besson et al, 2012). Minimum RTs were estimated by considering the onset of the first significant bin followed by at least 60 ms of significance (Barragan-Jason et al, 2012;Besson et al, 2012).…”