High spatial frequency filtered primes hastens happy faces categorization in autistic adults

Coarse information of a visual stimulus is conveyed by Low Spatial Frequencies (LSF) and is thought to be rapidly extracted to generate predictions. This may guide fast recognition with the subsequent integration of fine information, conveyed by High Spatial Frequencies (HSF). In autism, emotional face recognition is challenging, and might be related to alterations in LSF predictive processes. We analyzed the data of 27 autistic and 34 non autistic (NA) adults on an emotional Stroop task (i.e., emotional face with congruent or incongruent emotional word) with spatially filtered primes (HSF vs. LSF). We hypothesized that LSF primes would generate predictions leading to faster categorization of the target face compared to HSF primes, in the NA group but not in autism. Surprisingly, HSF primes led to faster categorization than LSF primes in both groups. Moreover, the advantage of HSF vs. LSF primes was stronger for angry than happy faces in NA, but was stronger for happy than angry faces in autistic participants. Drift diffusion modelling confirmed HSF advantage and showed a longer non-decision time (e.g., encoding) in autism. Despite LSF predictive impairments in autism was not corroborated, our analyses suggest low level processing specificities in autism.