September 2011
Volume 11, Issue 11
Vision Sciences Society Annual Meeting Abstract  |   September 2011
Probing Perceptual Consequences of Ongoing Activity Variations
Author Affiliations
  • Andreas Kleinschmidt
    INSERM-CEA, NeuroSpin, Gif/Yvette, France
Journal of Vision September 2011, Vol.11, 19. doi:
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      Andreas Kleinschmidt; Probing Perceptual Consequences of Ongoing Activity Variations. Journal of Vision 2011;11(11):19.

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      © ARVO (1962-2015); The Authors (2016-present)

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Recordings of ongoing brain activity show remarkable spontaneous fluctuations such that detecting stimulus-driven responses usually requires multiple repetitions and averaging. We have assessed the functional impact of such fluctuations on evoked neural responses and human perceptual performance. We studied human participants using functional neuroimaging and sparse event-related paradigms with sensory probes that could be either ambiguous with respect to perceptual categories (faces) or peri-liminal for a given feature (visual motion coherence). In both instances, fluctuations in ongoing signal of accordingly specialized brain regions (FFA, hMT+) biased how upcoming stimuli were perceived. Moreover, the relation between evoked and ongoing activity was not simply additive, as previously described in other settings, but showed an interaction with perceptual outcome. This latter observation questions the logic of event-related averaging where responses are thought to be unrelated from the level of pre-stimulus activity. We have further analyzed the functional connotation of the imaging signal by analyzing false alarm trials. Counter the notion of this signal being a proxy of sensory evidence, false alarms were preceded by especially low signal. A theoretical framework that is compatible with our observations comes from the family of predictive coding models, the 'free energy' principle proposed by Karl Friston. Together, our findings illustrate the functional consequences of ongoing activity fluctuations and underline that they should not be left unaccounted for as in the traditional mainstream of data analysis.


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