Abstract
Repetition suppression (RS), the relative lower neural response magnitude to repeated as compared to non-repeated stimuli, is often explained within the predictive coding framework. According to this theory, precise predictions (priors) together with less precise sensory evidences lead to decisions that are determined largely by the predictions and the other way around. In other words, the prediction error, namely the magnitude of RS, should depend on the precision of predictions and sensory inputs. In the current study, we aimed at testing this idea by manipulating the clarity and thereby the precision of the incoming sensory data by adding noise to the images. This resulted in an fMRI adaptation design with repeated or alternating trials showing clear or noisy face stimuli. Our results show a noise effect on the activity in the fusiform face area (FFA), namely less activation for noisy than for clear trials, which supports previous findings. No such effects could be found in OFA or LO. Data also showed reliable RS in the FFA (bilateral) and unilaterally in OFA (right) and LO (left). Interestingly, the noise added to the stimuli did not affect the magnitude of RS in any of the tested cortical areas. This suggests that the clarity of the sensory input is not crucial in determining the magnitude of RS.
Highlights
Repetition suppression (RS), the relative lower neural response magnitude to repeated as compared to non-repeated stimuli, is one of the most studied phenomena of cognitive neurosciences
These studies suggested that stimulus repetitions lead to lower prediction errors and this is manifest in RS while rarely presented, thereby surprising, stimuli lead to higher prediction errors and enhanced neural responses
We found a similar pattern in the fusiform face area (FFA) of the two hemispheres
Summary
Repetition suppression (RS), the relative lower neural response magnitude to repeated as compared to non-repeated stimuli, is one of the most studied phenomena of cognitive neurosciences. RS but other stimulus repetition-related phenomena, such as expectation suppression or surprise-related response elevation, were explained under the framework of predictive coding (Summerfield et al, 2008; Todorovic and de Lange, 2012; Grotheer and Kovacs, 2014; Mayrhauser et al, 2014; Grotheer and Kovács, 2015). This theory states that perception is not determined solely by the incoming stimuli themselves, Repetition Suppression for Noisy Faces but it is modulated by inferential processes (Rao and Ballard, 1999). The role of top-down modulatory effects, such as predictions and expectation, in determining the magnitude of RS is under heavy discussion as of today
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