When two dissimilar stimuli are presented to the eyes, perception alternates between multiple interpretations, a phenomenon dubbed binocular rivalry. Numerous recent imaging studies have attempted to unveil neural substrates underlying multistable perception. However, these studies had a conceptual constraint: access to observers' perceptual state relied on their introspection and active report. Here, we investigated to what extent neural correlates of binocular rivalry in healthy humans are confounded by this subjective measure and by action. We used the optokinetic nystagmus and pupil size to objectively and continuously map perceptual alternations for binocular-rivalry stimuli. Combining these two measures with fMRI allowed us to assess the neural correlates of binocular rivalry time locked to the perceptual alternations in the absence of active report. When observers were asked to actively report their percept, our objective measures matched the report. In this active condition, objective measures and subjective reporting revealed that occipital, parietal, and frontal areas underlie the processing of binocular rivalry, replicating earlier findings. Furthermore, objective measures provided additional statistical power due to their continuous nature. Importantly, when observers passively experienced rivalry without reporting perceptual alternations, a different picture emerged: differential neural activity in frontal areas was absent, whereas activation in occipital and parietal regions persisted. Our results question the popular view of a driving role of frontal areas in the initiation of perceptual alternations during binocular rivalry. Instead, we conclude that frontal areas are associated with active report and introspection rather than with rivalry per se.

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Rivalry is a common tool to probe visual awareness: a constant physical stimulus evokes multiple, distinct perceptual interpretations (“percepts”) that alternate over time. Percepts are typically described as mutually exclusive, suggesting that a discrete (all-or-none) process underlies changes in visual awareness. Here we follow two strategies to address whether rivalry is an all-or-none process: first, we introduce two reflexes as objective measures of rivalry, pupil dilation and optokinetic nystagmus (OKN); second, we use a continuous input device (analog joystick) to allow observers a gradual subjective report. We find that the “reflexes” reflect the percept rather than the physical stimulus. Both reflexes show a gradual dependence on the time relative to perceptual transitions. Similarly, observers' joystick deflections, which are highly correlated with the reflex measures, indicate gradual transitions. Physically simulating wave-like transitions between percepts suggest piece-meal rivalry (i.e., different regions of space belonging to distinct percepts) as one possible explanation for the gradual transitions. Furthermore, the reflexes show that dominance durations depend on whether or not the percept is actively reported. In addition, reflexes respond to transitions with shorter latencies than the subjective report and show an abundance of short dominance durations. This failure to report fast changes in dominance may result from limited access of introspection to rivalry dynamics. In sum, reflexes reveal that rivalry is a gradual process, rivalry's dynamics is modulated by the required action (response mode), and that rapid transitions in perceptual dominance can slip away from awareness.

Declarative memories of personal experiences are a key factor in defining oneself as an individual, which becomes particularly evident when this capability is impaired. Assessing the physiological mechanisms of human declarative memory is typically restricted to patients with specific lesions and requires invasive brain access or functional imaging. We investigated whether the pupil, an accessible physiological measure, can be utilized to probe memories for complex natural visual scenes. During memory encoding, scenes that were later remembered elicited a stronger pupil constriction compared to scenes that were later forgotten. Thus, pupil size predicts success or failure of memory formation. In contrast, novel scenes elicited stronger pupil constriction than familiar scenes during retrieval. When viewing previously memorized scenes, those that were forgotten (misjudged as novel) still elicited stronger pupil constrictions than those correctly judged as familiar. Furthermore, pupil constriction was influenced more strongly if images were judged with high confidence. Thus, we propose that pupil constriction can serve as a marker of novelty. Since stimulus novelty modulates the efficacy of memory formation, our pupil measurements during learning indicate that the later forgotten images were perceived as less novel than the later remembered pictures. Taken together, our data provide evidence that pupil constriction is a physiological correlate of a neural novelty signal during formation and retrieval of declarative memories for complex, natural scenes.

The development of large-scale network models that infer the effective (directed) connectivity among neuronal populations from neuroimaging data represents a key challenge for computational neuroscience. Dynamic causal models (DCMs) of neuroimaging and electrophysiological data are frequently used for inferring effective connectivity but are presently restricted to small graphs (typically up to 10 regions) in order to keep model inversion computationally feasible. Here, we present a novel variant of DCM for functional magnetic resonance imaging (fMRI) data that is suited to assess effective connectivity in large (whole-brain) networks. The approach rests on translating a linear DCM into the frequency domain and reformulating it as a special case of Bayesian linear regression. This paper derives regression DCM (rDCM) in detail and presents a variational Bayesian inversion method that enables extremely fast inference and accelerates model inversion by several orders of magnitude compared to classical DCM. Using both simulated and empirical data, we demonstrate the face validity of rDCM under different settings of signal-to-noise ratio (SNR) and repetition time (TR) of fMRI data. In particular, we assess the potential utility of rDCM as a tool for whole-brain connectomics by challenging it to infer effective connection strengths in a simulated whole-brain network comprising 66 regions and 300 free parameters. Our results indicate that rDCM represents a computationally highly efficient approach with promising potential for inferring whole-brain connectivity from individual fMRI data.