Long term fMRI adaptation depends on adapter response in face-selective cortex

Abstract

Repetition suppression (RS) reflects a neural attenuation during repeated stimulation. We used fMRI and the subsequent memory paradigm to test the predictive coding hypothesis for RS during visual memory processing by investigating the interaction between RS and differences due to memory in category-selective cortex (FFA, pSTS, PPA, and RSC). Fifty-six participants encoded face and house stimuli twice, followed by an immediate and delayed (48 h) recognition memory assessment. Linear Mixed Model analyses with repetition, subsequent recognition performance, and their interaction as fixed effects revealed that absolute RS during encoding interacts with probability of future remembrance in face-selective cortex. This effect was not observed for relative RS, i.e. when controlled for adapter-response. The findings also reveal an association between adapter response and RS, both for short and long term (48h) intervals, after controlling for the mathematical dependence between both measures. These combined findings are challenging for predictive coding models of visual memory and are more compatible with adapter-related and familiarity accounts.

Age Effects in Emotional Memory and Associated Eye Movements

“…The stimuli consisted of 80 pictures of houses, which were selected from our own database and stripped of visual background. In total, 40 (20 male) neutral and 40 (20 female) angry faces were selected from our own validated database and other validated face stimuli databases [ 26 , 27 , 30 ] and stripped of visual background. All stimuli were resized to 400 pixels in height.…”

“…Both recognition phases began with five practice trials with car stimuli, which were included to familiarize the participants with the response procedure. See also Stam et al, 2021 and Figure 2 for a schematic design of the procedure [ 30 ]. During the recognition phase, pictures were presented on a laptop running PRESENTATION ® 19.0 (Neurobehavioral Systems, San Francisco, CA, USA) to control stimulus presentation and response registration.…”

Bridging the Gap: Exploring Neural Mechanisms of Facial Repetition Suppression through EEG and DCNNs

“…Although multiple human fMRI studies (Andics et al, 2013; Ewbank et al, 2016; Grotheer et al, 2014; Grotheer & Kovács, 2014; Kovács et al, 2012, 2013; Larsson & Smith, 2012; Mayrhauser et al, 2014) supported that repetition suppression reflected a reduction in prediction error from predictive coding framework (Friston, 2005; Summerfield et al, 2008), several electrophysiological studies in recent years have challenged this that they supported fatigue mechanism with bottom-up or local adaptation instead of sharpening or sparseness representations and predictive coding hypothesis. However, due to the hardness of collecting human electrophysiological data, there were few human neuroimaging studies (Stam et al, 2021) to confirm these findings. Our current study is the first time to provide novel computational ways to explore the neural mechanism of facial repetition suppression using noninvasive human EEG and DCNNs and give strong evidence to support fatigue mechanism of repetition suppression.…”

“…However, the collection of human electrophysiological data presents substantial challenges, leading to a scarcity of human neuroimaging studies that could corroborate these findings (Stam et al, 2021). Our present study is innovative in that it introduces state-of-the-art computational methods, combining noninvasive human EEG with DCNNs, to delve into the neural underpinnings of facial repetition suppression.…”

Bridging the gap between EEG and DCNNs reveals a fatigue mechanism of facial repetition suppression

“…However, the collection of human electrophysiological data presents substantial challenges, leading to a scarcity of human neuroimaging studies that could corroborate these findings. 65 Our present study is innovative in that it introduces state-of-the-art computational methods, combining noninvasive human EEG with DCNNs, to delve into the neural underpinnings of facial repetition suppression. In doing so, we have provided compelling and robust evidence to support the fatigue mechanism as the driving force behind repetition suppression in the context of facial perception.…”