Abstract
The contribution of colour to rapid categorization of natural images is debated. Here, the effect of colour on face categorization was examined using a recently validated paradigm (fast periodic visual stimulation) for measuring high-level face categorization responses with natural images (Rossion et al., 2015, J Vis). High-density electroencephalogram (EEG) was recorded during presentations of 50-s sequences of object images sinusoidally contrast-modulated at F = 12.0 Hz (i.e., 83-ms stimulus-onset asynchrony). Face images were embedded in the sequence at a fixed interval of F/9 (1.33 Hz). There were four conditions: 1) full-colour images; 2) greyscale images; 3) and 4) phase-scrambled images from Conditions 1 and 2, respectively, making faces and objects unrecognizable. Observers' task differed across two experiments: 20 observers responded to random colour changes of a fixation cross ("colour task"); another 20 observers responded when the fixation cross changed to a square ("shape task"). In both experiments, with natural images, selective responses to faces were recorded at 1.33 Hz and harmonics (2.67 Hz, etc.) over occipito-temporal areas, with right-hemisphere dominance; this response was absent with scrambled images. Importantly, in the shape task, face-categorization response (sum of all-channel-averaged responses at significant harmonics) was 22% stronger with natural images in colour than in greyscale (p = 0.025), indicating a substantial advantage from image colour information; this colour advantage was not significant in the colour task (p = 0.94). Behavioural analysis revealed that observers performing the colour task responded 20 ms slower when the natural images contained colour (p = 0.023), despite hit rates at ceiling (> 95% correct) in all conditions. However, no such response-time differences were found in the shape task (p = 0.71). Thus, the advantage of image colour to face categorization interacts with behaviour, suggesting that colour, when not a distractor, has an automatic contribution to face categorization in natural images.
Meeting abstract presented at VSS 2016