Electrophysiological mass potentials show complex spectral changes upon neuronal activation. However, it is unknown to what extent these complex band-limited changes are interrelated or, alternatively, reflect separate neuronal processes. To address this question, intracranial electrocorticograms (ECoG) responses were recorded in patients engaged in visuomotor tasks. We found that in the 10- to 100-Hz frequency range there was a significant reduction in the exponent χ of the 1/ fχ component of the spectrum associated with neuronal activation. In a minority of electrodes showing particularly high activations the exponent reduction was associated with specific band-limited power modulations: emergence of a high gamma (80–100 Hz) and a decrease in the alpha (9–12 Hz) peaks. Importantly, the peaks' height was correlated with the 1/ fχ exponent on activation. Control simulation ruled out the possibility that the change in 1/ fχ exponent was a consequence of the analysis procedure. These results reveal a new global, cross-frequency (10–100 Hz) neuronal process reflected in a significant reduction of the power spectrum slope of the ECoG signal.

Vision relies on both specific knowledge of visual attributes, such as object categories, and general brain states, such as those reflecting arousal. We hypothesized that these phenomena independently influence recognition of forthcoming stimuli through distinct processes reflected in spontaneous neural activity. Here, we recorded magnetoencephalographic (MEG) activity in participants ( N = 24) who viewed images of objects presented at recognition threshold. Using multivariate analysis applied to sensor-level activity patterns recorded before stimulus presentation, we identified two neural processes influencing subsequent subjective recognition: a general process, which disregards stimulus category and correlates with pupil size, and a specific process, which facilitates category-specific recognition. The two processes are doubly-dissociable: the general process correlates with changes in criterion but not in sensitivity, whereas the specific process correlates with changes in sensitivity but not in criterion. Our findings reveal distinct mechanisms of how spontaneous neural activity influences perception and provide a framework to integrate previous findings.

Arousal levels perpetually rise and fall spontaneously. How markers of arousal - pupil size and frequency content of brain activity - relate to each other and influence behavior in humans is poorly understood. We simultaneously monitored magnetoencephalography and pupil in healthy volunteers at rest and during a visual perceptual decision-making task. Spontaneously varying pupil size correlates with power of brain activity in most frequency bands across large-scale resting-state cortical networks. Pupil size recorded at prestimulus baseline correlates with subsequent shifts in detection bias (c) and sensitivity (d'). When dissociated from pupil-linked state, prestimulus spectral power of resting state networks still predicts perceptual behavior. Fast spontaneous pupil constriction and dilation correlate with large-scale brain activity as well but not perceptual behavior. Our results illuminate the relation between central and peripheral arousal markers and their respective roles in human perceptual decision-making.

The neural mechanisms underlying conscious recognition remain unclear, particularly the roles played by the prefrontal cortex, deactivated brain areas and subcortical regions. We investigated neural activity during conscious object recognition using 7 Tesla fMRI while human participants viewed object images presented at liminal contrasts. Here, we show both recognized and unrecognized images recruit widely distributed cortical and subcortical regions; however, recognized images elicit enhanced activation of visual, frontoparietal, and subcortical networks and stronger deactivation of the default-mode network. For recognized images, object category information can be decoded from all of the involved cortical networks but not from subcortical regions. Phase-scrambled images trigger strong involvement of inferior frontal junction, anterior cingulate cortex and default-mode network, implicating these regions in inferential processing under increased uncertainty. Our results indicate that content-specific activity in both activated and deactivated cortical networks and non-content-specific subcortical activity support conscious recognition.

An inherent limitation of human visual system research stems from its reliance on highly controlled laboratory conditions. Visual processing in the real world differs substantially from such controlled conditions. In particular, during natural vision, we continuously sample the dynamic environment by variable eye movements that lead to inherent instability of the optical image. The neuronal mechanism by which human perception remains stable under these natural conditions remains unknown. Here, we examined a neural mechanism that may contribute to such stability, i.e., the extent to which neuronal responses remain invariant to oculomotor parameters and viewing conditions. To this end, we introduce an experimental paradigm in which intracranial brain activity, a video of the real-life visual scene, and free oculomotor behavior were simultaneously recorded in human patients. Our results reveal, in high-order visual areas, a remarkable level of neural invariance to the length of eye fixations and lack of evidence for a saccade-related neuronal signature. Thus, neuronal responses, while showing high selectivity to the category of visual images, manifested stable "iconic" dynamics. This property of invariance to fixation onset and duration emerged only in high-order visual representations. In early visual cortex, the fixation onset was accompanied with suppressive neural signal, and duration of neuronal responses was largely determined by the fixation times. These results uncover unique neuronal dynamics in high-order ventral stream visual areas that could play an important role in achieving perceptual stability, despite the drastic changes introduced by oculomotor behavior in real life.

While there is a wealth of knowledge about core object recognition-our ability to recognize clear, high-contrast object images, how the brain accomplishes object recognition tasks under increased uncertainty remains poorly understood. We investigated the spatiotemporal neural dynamics underlying object recognition under increased uncertainty by combining MEG and 7 Tesla fMRI in humans during a threshold-level object recognition task. We observed an early, parallel rise of recognition-related signals across ventral visual and frontoparietal regions that preceded the emergence of category-related information. Recognition-related signals in ventral visual regions were best explained by a two-state representational format whereby brain activity bifurcated for recognized and unrecognized images. By contrast, recognition-related signals in frontoparietal regions exhibited a reduced representational space for recognized images, yet with sharper category information. These results provide a spatiotemporally resolved view of neural activity supporting object recognition under uncertainty, revealing a pattern distinct from that underlying core object recognition.

While there is a wealth of knowledge about core object recognition—our ability to recognize clear, high-contrast object images, how the brain accomplishes object recognition tasks under increased uncertainty remains poorly understood. We investigated the spatiotemporal neural dynamics underlying object recognition under increased uncertainty by combining MEG and 7 Tesla fMRI in humans during a threshold-level object recognition task. We observed an early, parallel rise of recognition-related signals across ventral visual and frontoparietal regions that preceded the emergence of category-related information. Recognition-related signals in ventral visual regions were best explained by a two-state representational format whereby brain activity bifurcated for recognized and unrecognized images. By contrast, recognition-related signals in frontoparietal regions exhibited a reduced representational space for recognized images, yet with sharper category information. These results provide a spatiotemporally resolved view of neural activity supporting object recognition under uncertainty, revealing a pattern distinct from that underlying core object recognition.

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