Long term fMRI adaptation depends on adapter response in face-selective cortex
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...
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Veröffentlicht in: | Communications biology 2021-06, Vol.4 (1), p.712-712, Article 712 |
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Sprache: | eng |
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Zusammenfassung: | 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.
In order to examine underlying mechanisms of repetition suppression, Stam et al conducted an fMRI study in which participants were presented with face and house stimuli followed by an immediate and 48 h-delayed memory task. Their data demonstrated that adapter-related and familiarity account modelling are more likely to be more accurate for visual memory than previous predictive coding models. |
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ISSN: | 2399-3642 2399-3642 |
DOI: | 10.1038/s42003-021-02235-6 |