Suppression durations for facial expressions under breaking continuous flash suppression: effects of faces’ low-level image properties

Abstract

Perceptual biases for fearful facial expressions are observed across many studies. According to the low-level, visual-based account of these biases, fear expressions are advantaged in some way due to their image properties, such as low spatial frequency content. However, there is a degree of empirical disagreement regarding the range of spatial frequency information responsible for perceptual biases. Breaking continuous flash suppression (b. CFS) has explored these effects, showing similar biases for detecting fearful facial expressions. Recent findings from a b. CFS study highlight the role of high, rather than low spatial frequency content in determining faces’ visibility. The present study contributes to ongoing discussions regarding the efficacy of b. CFS, and shows that the visibility of facial expressions vary according to how they are normalised for physical contrast and spatially filtered. Findings show that physical contrast normalisation facilitates fear’s detectability under b. CFS more than when normalised for apparent contrast, and that this effect is most pronounced when faces are high frequency filtered. Moreover, normalising faces’ perceived contrast does not guarantee equality between expressions’ visibility under b. CFS. Findings have important implications for the use of contrast normalisation, particularly regarding the extent to which contrast normalisation facilitates fear bias effects.

Saccadic eye movements are deployed faster for salient facial stimuli, but are relatively indifferent to their emotional content

“…Spatially filtered versions of faces were created in MATLAB using a second-order Butterworth filter, with cut-off frequencies of f < 1 cpd for low frequency faces, and f > 6 cpd for high-frequency faces. These cut-offs match those used by [18,23,28]. Each face subtended 5.4 • , such that the spatial amplitude spectrum of each face was either broadband, or was constrained to lower than 5 cycles per face-width (lowpass stimuli) or higher than 33 cycles per face-width (highpass stimuli).…”

“…Given this, we should have expected to observe the same effect in the present broadband condition, but we did not. Moreover, Khalid et al [11] did normalise facial stimuli for contrast, finding no fear bias compared to neutral faces, and Webb and Hibbard [23] found that contrast normalisation of facial stimuli facilitates biases for detecting fear expressions. Notably, these examples come from studies using different experimental parameters, and it is likely that the role of image contrast differs relative to the task at hand.…”

“…Notably, these examples come from studies using different experimental parameters, and it is likely that the role of image contrast differs relative to the task at hand. Recently, there has been an emerging view that contrast normalisation can inadvertently both boost [23,26] and impede [27] [19,23,26], they do suggest that improved performance for fearful faces not normalised for contrast [27] may be due to the fact that these faces, compared to those normalised for physical or apparent contrast, are already advantaged in terms of containing higher contrast.…”

“…Another factor to consider is the role of low-level image properties. Low-level, or image-based accounts of the bias for fearful expressions posit that it is the signal, or low-level image properties, of the expression that grant it prioritisation during visual processing, as opposed to its affective or social relevance [22][23][24]. The role of spatial frequency is common in these discussions, where a distinction is made between the lower frequency ranges that convey crude and coarse representations of a face, and the higher frequency information that depicts expression-related facial lines and nuanced information required for extracting fine facial detail.…”

“…This focus reflects the potential role of specialised threat-processing mechanisms, for example in the amygdala [25], but May 24, 2021 2/22 also the idea that biases may reflect non-specialsed aspects of visual processing, for example the effects of the contrast sensitivity function [17]. Between various paradigms, findings show inconclusive frequency-dependent effects [8], where some studies show biased saccadic responses to low frequency fear expressions [13], while others demonstrate biases for high frequency fear expressions in detection tasks where faces are suppressed from conscious awareness [18,23], or in judgement tasks related to the perceived salience of faces [26]. Systematic investigations of saccadic responses to spatially-filtered facial expressions are rare [13], which is surprising given that this method is theoretically compatible with low-level accounts of the fear bias proposing that the prioritisation of fear expressions is likely to operate via crude and unconscious processing pathways [2,13], functioning primarily to direct the observer towards the source of the threat.…”