Multistability, perceptual value, and internal foraging

Smelling x as y? On (the impossibility of) multistable perception in the chemical senses

Multistable percepts are intriguing phenomena whereby an ambiguous sensory input can be perceived in one of several qualitatively different ways. In such cases, people can switch their attention to perceive the stimulus in either way, though they typically cannot maintain both interpretations in awareness simultaneously. The abundance of evidence demonstrating multistable perception in the visual and auditory modalities can be contrasted with the scarcity, if not absence, of studies reporting similar phenomena in the chemical senses (primarily olfaction and gustation), prompting an intriguing question about this apparent qualitative difference between the senses. This paper seeks to address this question by first briefly reviewing multistable perceptual phenomena in vision and audition to underscore their defining features. We then assess the limited body of research that has occasionally linked multistability to the chemical senses. While a few studies suggest loose analogies between olfactory perception and visual or auditory multistability, no compelling evidence exists for such phenomena in taste. We argue that this absence is unlikely to be explained by any single factor. Rather, it appears to stem from a confluence of constraints, including the lack of spatio-temporal structure and intrinsic dimensionality in chemosensory stimuli, as well as their distinct evolutionary functions and cognitive framing. Together, these factors may help to explain why multistable perceptual experiences seem not to emerge in the chemical senses.

The Aesthetic Appreciation of Multi-Stable Images

Does the quality that renders multi-stable images fascinating, the sudden perceptual reorganization, the switching from one interpretation into another, also make these images appear beautiful? Or is the aesthetic quality of multi-stable figures unrelated to the ease with which they switch? Across two experiments, we presented multi-stable images and manipulated their perceptual stability. We also presented their unambiguous components in isolation. In the first experiment, this manipulation targeted the inherent stimulus stability through properties like figural size and composition. The second experiment added an instruction for observers to actively control the stability, by attempting to either enhance or prevent perceptual switches as best they could. We found that higher stability was associated with higher liking, positive valence, and lower arousal. This increase in appreciation was mainly driven by inherent stimulus properties. The stability instruction only increased the liking of figures that had been comparatively stable to begin with. We conclude that the fascinating feature of multi-stable images does not contribute to their aesthetic liking. In fact, perceptual switching is detrimental to it. Processing fluency can explain this counterintuitive finding. We also discuss the role of ambiguity in the aesthetic quality of multi-stable images.

Psilocybin increases optimistic engagement over time: computational modelling of behaviour in rats

Psilocybin has shown promise as a novel pharmacological intervention for treatment of depression, where post-acute effects of psilocybin treatment have been associated with increased positive mood and decreased pessimism. Although psilocybin is proving to be effective in clinical trials for treatment of psychiatric disorders, the information processing mechanisms affected by psilocybin are not well understood. Here, we fit active inference and reinforcement learning computational models to a novel two-armed bandit reversal learning task capable of capturing engagement behaviour in rats. The model revealed that after receiving psilocybin, rats achieve more rewards through increased task engagement, mediated by modification of forgetting rates and reduced loss aversion. These findings suggest that psilocybin may afford an optimism bias that arises through altered belief updating, with translational potential for clinical populations characterised by lack of optimism.

Bistable perception, precision and neuromodulation

Bistable perception follows from observing a static, ambiguous, (visual) stimulus with two possible interpretations. Here, we present an active (Bayesian) inference account of bistable perception and posit that perceptual transitions between different interpretations (i.e. inferences) of the same stimulus ensue from specific eye movements that shift the focus to a different visual feature. Formally, these inferences are a consequence of precision control that determines how confident beliefs are and change the frequency with which one can perceive-and alternate between-two distinct percepts. We hypothesized that there are multiple, but distinct, ways in which precision modulation can interact to give rise to a similar frequency of bistable perception. We validated this using numerical simulations of the Necker cube paradigm and demonstrate the multiple routes that underwrite the frequency of perceptual alternation. Our results provide an (enactive) computational account of the intricate precision balance underwriting bistable perception. Importantly, these precision parameters can be considered the computational homologs of particular neurotransmitters-i.e. acetylcholine, noradrenaline, dopamine-that have been previously implicated in controlling bistable perception, providing a computational link between the neurochemistry and perception.

An Accumulating Neural Signal Underlying Binocular Rivalry Dynamics

During binocular rivalry, conflicting images are presented one to each eye and perception alternates stochastically between them. Despite stable percepts between alternations, modeling suggests that neural signals representing the two images change gradually, and that the duration of stable percepts are determined by the time required for these signals to reach a threshold that triggers an alternation. However, direct physiological evidence for such signals has been lacking. Here, we identify a neural signal in the human visual cortex that shows these predicted properties. We measured steady-state visual evoked potentials (SSVEPs) in 84 human participants (62 females, 22 males) who were presented with orthogonal gratings, one to each eye, flickering at different frequencies. Participants indicated their percept while EEG data were collected. The time courses of the SSVEP amplitudes at the two frequencies were then compared across different percept durations, within participants. For all durations, the amplitude of signals corresponding to the suppressed stimulus increased and the amplitude corresponding to the dominant stimulus decreased throughout the percept. Critically, longer percepts were characterized by more gradual increases in the suppressed signal and more gradual decreases of the dominant signal. Changes in signals were similar and rapid at the end of all percepts, presumably reflecting perceptual transitions. These features of the SSVEP time courses are well predicted by a model in which perceptual transitions are produced by the accumulation of noisy signals. Identification of this signal underlying binocular rivalry should allow strong tests of neural models of rivalry, bistable perception, and neural suppression.SIGNIFICANCE STATEMENT During binocular rivalry, two conflicting images are presented to the two eyes and perception alternates between them, with switches occurring at seemingly random times. Rivalry is an important and longstanding model system in neuroscience, used for understanding neural suppression, intrinsic neural dynamics, and even the neural correlates of consciousness. All models of rivalry propose that it depends on gradually changing neural activity that on reaching some threshold triggers the perceptual switches. This manuscript reports the first physiological measurement of neural signals with that set of properties in human participants. The signals, measured with EEG in human observers, closely match the predictions of recent models of rivalry, and should pave the way for much future work.

The role of conflict processing in multisensory perception: behavioural and electroencephalography evidence

To form coherent multisensory perceptual representations, the brain must solve a causal inference problem: to decide if two sensory cues originated from the same event and should be combined, or if they came from different events and should be processed independently. According to current models of multisensory integration, during this process, the integrated (common cause) and segregated (different causes) internal perceptual models are entertained. In the present study, we propose that the causal inference process involves competition between these alternative perceptual models that engages the brain mechanisms of conflict processing. To test this hypothesis, we conducted two experiments, measuring reaction times (RTs) and electroencephalography, using an audiovisual ventriloquist illusion paradigm with varying degrees of intersensory disparities. Consistent with our hypotheses, incongruent trials led to slower RTs and higher fronto-medial theta power, both indicative of conflict. We also predicted that intermediate disparities would yield slower RTs and higher theta power when compared to congruent stimuli and to large disparities, owing to the steeper competition between causal models. Although this prediction was only validated in the RT study, both experiments displayed the anticipated trend. In conclusion, our findings suggest a potential involvement of the conflict mechanisms in multisensory integration of spatial information. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

Are Deep Neural Networks Adequate Behavioral Models of Human Visual Perception?

Deep neural networks (DNNs) are machine learning algorithms that have revolutionized computer vision due to their remarkable successes in tasks like object classification and segmentation. The success of DNNs as computer vision algorithms has led to the suggestion that DNNs may also be good models of human visual perception. In this article, we review evidence regarding current DNNs as adequate behavioral models of human core object recognition. To this end, we argue that it is important to distinguish between statistical tools and computational models and to understand model quality as a multidimensional concept in which clarity about modeling goals is key. Reviewing a large number of psychophysical and computational explorations of core object recognition performance in humans and DNNs, we argue that DNNs are highly valuable scientific tools but that, as of today, DNNs should only be regarded as promising-but not yet adequate-computational models of human core object recognition behavior. On the way, we dispel several myths surrounding DNNs in vision science.

Feedback information sharing in the human brain reflects bistable perception in the absence of report

In the search for the neural basis of conscious experience, perception and the cognitive processes associated with reporting perception are typically confounded as neural activity is recorded while participants explicitly report what they experience. Here, we present a novel way to disentangle perception from report using eye movement analysis techniques based on convolutional neural networks and neurodynamical analyses based on information theory. We use a bistable visual stimulus that instantiates two well-known properties of conscious perception: integration and differentiation. At any given moment, observers either perceive the stimulus as one integrated unitary object or as two differentiated objects that are clearly distinct from each other. Using electroencephalography, we show that measures of integration and differentiation based on information theory closely follow participants' perceptual experience of those contents when switches were reported. We observed increased information integration between anterior to posterior electrodes (front to back) prior to a switch to the integrated percept, and higher information differentiation of anterior signals leading up to reporting the differentiated percept. Crucially, information integration was closely linked to perception and even observed in a no-report condition when perceptual transitions were inferred from eye movements alone. In contrast, the link between neural differentiation and perception was observed solely in the active report condition. Our results, therefore, suggest that perception and the processes associated with report require distinct amounts of anterior-posterior network communication and anterior information differentiation. While front-to-back directed information is associated with changes in the content of perception when viewing bistable visual stimuli, regardless of report, frontal information differentiation was absent in the no-report condition and therefore is not directly linked to perception per se.

A circuit mechanism linking past and future learning through shifts in perception

Long-term memory formation is energetically costly. Neural mechanisms that guide an animal to identify fruitful associations therefore have important survival benefits. Here, we elucidate a circuit mechanism in Lymnaea, which enables past memory to shape new memory formation through changes in perception. Specifically, strong classical conditioning drives a positive shift in perception that facilitates the robust learning of a subsequent and otherwise ineffective weak association. Circuit dissection approaches reveal the neural control network responsible, characterized by a mutual inhibition motif. This both sets perceptual state and acts as the master controller for gating new learning. Pharmacological circuit manipulation in vivo fully substitutes for strong paradigm learning, shifting the network into a more receptive state to enable subsequent weak paradigm learning. Thus, perceptual change provides a conduit to link past and future memory storage. We propose that this mechanism alerts animals to learning-rich periods, lowering the threshold for new memory acquisition.