If perception is probabilistic, why does it not seem probabilistic?

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.

Hierarchical motion perception as causal inference

Motion can only be defined relative to a reference frame; yet it remains unclear which reference frame guides perception. A century of psychophysical studies has produced conflicting evidence: retinotopic, egocentric, world-centric, or even object-centric. We introduce a hierarchical Bayesian model mapping retinal velocities to perceived velocities. Our model mirrors the structure in the world, in which visual elements move within causally connected reference frames. Friction renders velocities in these reference frames mostly stationary, formalized by an additional delta component (at zero) in the prior. Inverting this model automatically segments visual inputs into groups, groups into supergroups, progressively inferring structured reference frames and "perceives" motion in the appropriate reference frame. Critical model predictions are supported by two experiments, and fitting our model to the data allows us to infer the subjective set of reference frames used by individual observers. Our model provides a quantitative normative justification for key Gestalt principles providing inspiration for building better models of visual processing in general.

The predictive outfielder: a critical test across gravities

Intercepting moving targets is a widespread challenge across many species. In humans, heuristics that use optic variables have excelled in guiding interception, relying on a closed-loop system to couple optic variables directly with direction of locomotion. This contrasts with models that explicitly recover final positions from initial trajectory conditions. However, comparing these different approaches using empirical data is challenging, as they often predict similar locomotion trajectories. We present a model based on optic variables that continuously updates predictions on the landing position in the three-dimensional scene and remaining flight time based on the outfielder's real-time movements. A distinct feature is the model's adaptability to different gravitational accelerations, making its predictions inherently tailored to specific environments. By actively integrating gravity, our model produces trajectory predictions that can be validated against actual paths. To compare our model with previous ones, we conducted experiments within virtual reality, strategically varying simulated gravity and physical size. The variation in gravity resulted in qualitatively distinct predictions between heuristics based solely on optic variables and our model, which incorporates gravity. The empirical trajectories, kinematic patterns and timing responses aligned well with our model's predictions, emphasizing the importance of including environmental constants.

How can we apply decision-making theories to wild animal behavior? Predictions arising from dual process theory and Bayesian decision theory

Our understanding of decision-making processes and cognitive biases is ever increasing, thanks to an accumulation of testable models and a large body of research over the last several decades. The vast majority of this work has been done in humans and laboratory animals because these study subjects and situations allow for tightly controlled experiments. However, it raises questions about how this knowledge can be applied to wild animals in their complex environments. Here, we review two prominent decision-making theories, dual process theory and Bayesian decision theory, to assess the similarities in these approaches and consider how they may apply to wild animals living in heterogenous environments within complicated social groupings. In particular, we wanted to assess when wild animals are likely to respond to a situation with a quick heuristic decision and when they are likely to spend more time and energy on the decision-making process. Based on the literature and evidence from our multi-destination routing experiments on primates, we find that individuals are likely to make quick, heuristic decisions when they encounter routine situations, or signals/cues that accurately predict a certain outcome, or easy problems that experience or evolutionary history has prepared them for. Conversely, effortful decision-making is likely in novel or surprising situations, when signals and cues have unpredictable or uncertain relationships to an outcome, and when problems are computationally complex. Though if problems are overly complex, satisficing via heuristics is likely, to avoid costly mental effort. We present hypotheses for how animals with different socio-ecologies may have to distribute their cognitive effort. Finally, we examine the conservation implications and potential cognitive overload for animals experiencing increasingly novel situations caused by current human-induced rapid environmental change.

Hierarchical motion perception as causal inference

Since motion can only be defined relative to a reference frame, which reference frame guides perception? A century of psychophysical studies has produced conflicting evidence: retinotopic, egocentric, world-centric, or even object-centric. We introduce a hierarchical Bayesian model mapping retinal velocities to perceived velocities. Our model mirrors the structure in the world, in which visual elements move within causally connected reference frames. Friction renders velocities in these reference frames mostly stationary, formalized by an additional delta component (at zero) in the prior. Inverting this model automatically segments visual inputs into groups, groups into supergroups, etc. and "perceives" motion in the appropriate reference frame. Critical model predictions are supported by two new experiments, and fitting our model to the data allows us to infer the subjective set of reference frames used by individual observers. Our model provides a quantitative normative justification for key Gestalt principles providing inspiration for building better models of visual processing in general.

Perceptual transitions between object rigidity and non-rigidity: Competition and cooperation among motion energy, feature tracking, and shape-based priors

Why do moving objects appear rigid when projected retinal images are deformed non-rigidly? We used rotating rigid objects that can appear rigid or non-rigid to test whether shape features contribute to rigidity perception. When two circular rings were rigidly linked at an angle and jointly rotated at moderate speeds, observers reported that the rings wobbled and were not linked rigidly, but rigid rotation was reported at slow speeds. When gaps, paint, or vertices were added, the rings appeared rigidly rotating even at moderate speeds. At high speeds, all configurations appeared non-rigid. Salient features thus contribute to rigidity at slow and moderate speeds but not at high speeds. Simulated responses of arrays of motion-energy cells showed that motion flow vectors are predominantly orthogonal to the contours of the rings, not parallel to the rotation direction. A convolutional neural network trained to distinguish flow patterns for wobbling versus rotation gave a high probability of wobbling for the motion-energy flows. However, the convolutional neural network gave high probabilities of rotation for motion flows generated by tracking features with arrays of MT pattern-motion cells and corner detectors. In addition, circular rings can appear to spin and roll despite the absence of any sensory evidence, and this illusion is prevented by vertices, gaps, and painted segments, showing the effects of rotational symmetry and shape. Combining convolutional neural network outputs that give greater weight to motion energy at fast speeds and to feature tracking at slow speeds, with the shape-based priors for wobbling and rolling, explained rigid and non-rigid percepts across shapes and speeds (R2 = 0.95). The results demonstrate how cooperation and competition between different neuronal classes lead to specific states of visual perception and to transitions between the states.

How to get rich from inflation

We seem to have rich experience across our visual field. Yet we are surprisingly poor at tasks involving the periphery and low spatial attention. Recently, Lau and collaborators have argued that a phenomenon known as "subjective inflation" allows us to reconcile these phenomena. I show inflation is consistent with multiple interpretations, with starkly different consequences for richness and for theories of consciousness more broadly. What's more, we have only weak reasons favouring any of these interpretations over the others. I provisionally argue for an interpretation on which subjective experience is genuinely rich, but (in peripheral/unattended areas) unreliable as a guide to the external world. The main challenge for this view is that it appears to imply that experience in the periphery is not just unreliable but unstable. However, I argue that this consequence, while initially appearing unintuitive, is in fact plausible.

Bayesian encoding and decoding as distinct perspectives on neural coding

The Bayesian brain hypothesis is one of the most influential ideas in neuroscience. However, unstated differences in how Bayesian ideas are operationalized make it difficult to draw general conclusions about how Bayesian computations map onto neural circuits. Here, we identify one such unstated difference: some theories ask how neural circuits could recover information about the world from sensory neural activity (Bayesian decoding), whereas others ask how neural circuits could implement inference in an internal model (Bayesian encoding). These two approaches require profoundly different assumptions and lead to different interpretations of empirical data. We contrast them in terms of motivations, empirical support and relationship to neural data. We also use a simple model to argue that encoding and decoding models are complementary rather than competing. Appreciating the distinction between Bayesian encoding and Bayesian decoding will help to organize future work and enable stronger empirical tests about the nature of inference in the brain.

Perceptual Transitions between Object Rigidity & Non-rigidity: Competition and cooperation between motion-energy, feature-tracking and shape-based priors

Why do moving objects appear rigid when projected retinal images are deformed non-rigidly? We used rotating rigid objects that can appear rigid or non-rigid to test whether shape features contribute to rigidity perception. When two circular rings were rigidly linked at an angle and jointly rotated at moderate speeds, observers reported that the rings wobbled and were not linked rigidly but rigid rotation was reported at slow speeds. When gaps, paint or vertices were added, the rings appeared rigidly rotating even at moderate speeds. At high speeds, all configurations appeared non-rigid. Salient features thus contribute to rigidity at slow and moderate speeds, but not at high speeds. Simulated responses of arrays of motion-energy cells showed that motion flow vectors are predominantly orthogonal to the contours of the rings, not parallel to the rotation direction. A convolutional neural network trained to distinguish flow patterns for wobbling versus rotation, gave a high probability of wobbling for the motion-energy flows. However, the CNN gave high probabilities of rotation for motion flows generated by tracking features with arrays of MT pattern-motion cells and corner detectors. In addition, circular rings can appear to spin and roll despite the absence of any sensory evidence, and this illusion is prevented by vertices, gaps, and painted segments, showing the effects of rotational symmetry and shape. Combining CNN outputs that give greater weight to motion energy at fast speeds and to feature tracking at slow, with the shape-based priors for wobbling and rolling, explained rigid and nonrigid percepts across shapes and speeds (R2=0.95). The results demonstrate how cooperation and competition between different neuronal classes leads to specific states of visual perception and to transitions between the states.

Taking Conceptual Issues Really Seriously: One Next Step for the Cognitive Science of Consciousness

In this letter we focus on the cognitive science of consciousness. The general message is that, while this interdisciplinary area has made much progress in recent years, there is a tendency of downplaying conceptual issues, and therefore underestimating the difficulties of various problems. We briefly focus on a few prominent examples only, due to the space limit, but the general message should be clear: this recent tendency can be problematic for the progress of the consciousness branch of cognitive sciences.

Perception is rich and probabilistic

When we see a stimulus, e.g. a star-shaped object, our intuition is that we should perceive a single, coherent percept (even if it is inaccurate). But the neural processes that support perception are complex and probabilistic. Simple lines cause orientation-selective neurons across a population to fire in a probabilistic-like manner. Does probabilistic neural firing lead to non-probabilistic perception, or are the representations behind perception richer and more complex than intuition would suggest? To test this, we briefly presented a complex shape and had participants report the correct shape from a set of options. Rather than reporting a single value, we used a paradigm designed to encourage to directly report a representation over shape space—participants placed a series of Gaussian bets. We found that participants could report more than point-estimates of shape. The spread of responses was correlated with accuracy, suggesting that participants can convey a notion of relative imprecision. Critically, as participants placed more bets, the mean of responses show increased precision. The later bets were systematically biased towards the target rather than haphazardly placed around bet 1. These findings strongly indicate that participants were aware of more than just a point-estimate; Perceptual representations are rich and likely probabilistic.

The clear and not so clear signatures of perceptual reality in the Bayesian brain

In a Bayesian brain, every perceptual decision will take into account internal priors as well as new incoming evidence. A reality monitoring system-eventually providing the agent us with a subjective sense of reality avoids us them being confused about whether our experience is perceptual or imagined. Yet not all confusions we experience mean that we wonder wonder whether we may be imagining: some confused experiences feel clearly perceptual but still feel not right. What happens in such confused perceptions, and can the Bayesian brain explain this kind of confusion? In this paper, we offer a characterisation of perceptual confusion and argue that it requires our subjective sense of reality to be a composite of several subjective markers, including a categorical one that can clearly identify an experience as perceptual and connecting us to reality. Our composite account makes new predictions regarding the robustness, the non-linear development and the possible breakdowns of the sense of reality in perception.

A Unifying Perspective on Perception and Cognition Through Linguistic Representations of Emotion

This article will provide a unifying perspective on perception and cognition via the route of linguistic representations of emotion. Linguistic representations of emotions provide a fertile ground for explorations into the nature and form of integration of perception and cognition because emotion has facets of both perceptual and cognitive processes. In particular, this article shows that certain types of linguistic representations of emotion allow for the integration of perception and cognition through a series of steps and operations in cognitive systems, whereas certain other linguistic representations of emotion are not so representationally structured as to permit the unity of perception and cognition. It turns out that the types of linguistic representations of emotion that readily permit the desired unity of perception and cognition are exactly those that are linguistically encoded emotive representations of everyday objects, events, and things around us. It is these ordinary objects, events and things that provide the scaffolding for task-dependent or goal-oriented activities of cognitive systems including autonomous systems. In this way, cognitive systems can be saliently tuned to the outer world by being motivated and also subtly governed by emotion-driven representations. This helps not only tie together perceptual and cognitive processes via the interface between language and emotive representations, but also reveal the limits of emotive representations in amalgamating perceptual and cognitive processes in cognitive systems.

Semantic Expectation Effects on Object Detection: Using Figure Assignment to Elucidate Mechanisms

Recent evidence suggesting that object detection is improved following valid rather than invalid labels implies that semantics influence object detection. It is not clear, however, whether the results index object detection or feature detection. Further, because control conditions were absent and labels and objects were repeated multiple times, the mechanisms are unknown. We assessed object detection via figure assignment, whereby objects are segmented from backgrounds. Masked bipartite displays depicting a portion of a mono-oriented object (a familiar configuration) on one side of a central border were shown once only for 90 or 100 ms. Familiar configuration is a figural prior. Accurate detection was indexed by reports of an object on the familiar configuration side of the border. Compared to control experiments without labels, valid labels improved accuracy and reduced response times (RTs) more for upright than inverted objects (Studies 1 and 2). Invalid labels denoting different superordinate-level objects (DSC; Study 1) or same superordinate-level objects (SSC; Study 2) reduced accuracy for upright displays only. Orientation dependency indicates that effects are mediated by activated object representations rather than features which are invariant over orientation. Following invalid SSC labels (Study 2), accurate detection RTs were longer than control for both orientations, implicating conflict between semantic representations that had to be resolved before object detection. These results demonstrate that object detection is not just affected by semantics, it entails semantics.

What kind of empirical evidence is needed for probabilistic mental representations? An example from visual perception

Recent accounts of perception and cognition propose that the brain represents information probabilistically. While this assumption is common, empirical support for such probabilistic representations in perception has recently been criticized. Here, we evaluate these criticisms and present an account based on a recently developed psychophysical methodology, Feature Distribution Learning (FDL), which provides promising evidence for probabilistic representations by avoiding these criticisms. The method uses priming and role-reversal effects in visual search. Observers' search times reveal the structure of perceptual representations, in which the probability distribution of distractor features is encoded. We explain how FDL results provide evidence for a stronger notion of representation that relies on structural correspondence between stimulus uncertainty and perceptual representations, rather than a mere co-variation between the two. Moreover, such an account allows us to demonstrate what kind of empirical evidence is needed to support probabilistic representations as posited in current probabilistic Bayesian theories of perception.

Predictive brain: Addressing the level of representation by reviewing perceptual hysteresis

In recent years, the idea that the prediction of sensory input is one of the major computational goals of the nervous system led to the development of several large-scale theories of brain functioning, such as different versions of the Bayesian approach to brain functions, predictive coding theories of cognition and the Free-energy principle. During the years, various empirical phenomena have been re-interpreted within such frames, and have been considered as consequences of predictive processing. Here we focus on perceptual hysteresis, or serial dependence, as an exemplary case. We unravel a potential gap in the predictive frameworks and raise the idea that alternative explanations of this effect can solve this issue, as they address the type of cognitive and neural representations involved.

An Integrated World Modeling Theory (IWMT) of Consciousness: Combining Integrated Information and Global Neuronal Workspace Theories With the Free Energy Principle and Active Inference Framework; Toward Solving the Hard Problem and Characterizing Agentic Causation

The Free Energy Principle and Active Inference Framework (FEP-AI) begins with the understanding that persisting systems must regulate environmental exchanges and prevent entropic accumulation. In FEP-AI, minds and brains are predictive controllers for autonomous systems, where action-driven perception is realized as probabilistic inference. Integrated Information Theory (IIT) begins with considering the preconditions for a system to intrinsically exist, as well as axioms regarding the nature of consciousness. IIT has produced controversy because of its surprising entailments: quasi-panpsychism; subjectivity without referents or dynamics; and the possibility of fully-intelligent-yet-unconscious brain simulations. Here, I describe how these controversies might be resolved by integrating IIT with FEP-AI, where integrated information only entails consciousness for systems with perspectival reference frames capable of generating models with spatial, temporal, and causal coherence for self and world. Without that connection with external reality, systems could have arbitrarily high amounts of integrated information, but nonetheless would not entail subjective experience. I further describe how an integration of these frameworks may contribute to their evolution as unified systems theories and models of emergent causation. Then, inspired by both Global Neuronal Workspace Theory (GNWT) and the Harmonic Brain Modes framework, I describe how streams of consciousness may emerge as an evolving generation of sensorimotor predictions, with the precise composition of experiences depending on the integration abilities of synchronous complexes as self-organizing harmonic modes (SOHMs). These integrating dynamics may be particularly likely to occur via richly connected subnetworks affording body-centric sources of phenomenal binding and executive control. Along these connectivity backbones, SOHMs are proposed to implement turbo coding via loopy message-passing over predictive (autoencoding) networks, thus generating maximum a posteriori estimates as coherent vectors governing neural evolution, with alpha frequencies generating basic awareness, and cross-frequency phase-coupling within theta frequencies for access consciousness and volitional control. These dynamic cores of integrated information also function as global workspaces, centered on posterior cortices, but capable of being entrained with frontal cortices and interoceptive hierarchies, thus affording agentic causation. Integrated World Modeling Theory (IWMT) represents a synthetic approach to understanding minds that reveals compatibility between leading theories of consciousness, thus enabling inferential synergy.

Bayesian Decision Models: A Primer

To understand decision-making behavior in simple, controlled environments, Bayesian models are often useful. First, optimal behavior is always Bayesian. Second, even when behavior deviates from optimality, the Bayesian approach offers candidate models to account for suboptimalities. Third, a realist interpretation of Bayesian models opens the door to studying the neural representation of uncertainty. In this tutorial, we review the principles of Bayesian models of decision making and then focus on five case studies with exercises. We conclude with reflections and future directions.

Evaluating the neurophysiological evidence for predictive processing as a model of perception

For many years, the dominant theoretical framework guiding research into the neural origins of perceptual experience has been provided by hierarchical feedforward models, in which sensory inputs are passed through a series of increasingly complex feature detectors. However, the long-standing orthodoxy of these accounts has recently been challenged by a radically different set of theories that contend that perception arises from a purely inferential process supported by two distinct classes of neurons: those that transmit predictions about sensory states and those that signal sensory information that deviates from those predictions. Although these predictive processing (PP) models have become increasingly influential in cognitive neuroscience, they are also criticized for lacking the empirical support to justify their status. This limited evidence base partly reflects the considerable methodological challenges that are presented when trying to test the unique predictions of these models. However, a confluence of technological and theoretical advances has prompted a recent surge in human and nonhuman neurophysiological research seeking to fill this empirical gap. Here, we will review this new research and evaluate the degree to which its findings support the key claims of PP.

The cognitive penetrability of perception: A blocked debate and a tentative solution

Despite the extensive body of psychological findings suggesting that cognition influences perception, the debate between defenders and detractors of the cognitive penetrability of perception persists. While detractors demand more strictness in psychological experiments, proponents consider that empirical studies show that cognitive penetrability occurs. These considerations have led some theorists to propose that the debate has reached a dead end. The issue about where perception ends and cognition begins is, I argue, one of the reasons why the debate is cornered. Another reason is the inability of psychological studies to present uncontroversial interpretations of the results obtained. To dive into other kinds of empirical sources is, therefore, required to clarify the debate. In this paper, I explain where the debate is blocked, and suggest that neuroscientific evidence together with the predictive coding account, might decant the discussion on the side of the penetrability thesis.

Perceptual consciousness and cognitive access from the perspective of capacity-unlimited working memory

Theories of consciousness divide over whether perceptual consciousness is rich or sparse in specific representational content and whether it requires cognitive access. These two issues are often treated in tandem because of a shared assumption that the representational capacity of cognitive access is fairly limited. Recent research on working memory challenges this shared assumption. This paper argues that abandoning the assumption undermines post-cue-based 'overflow' arguments, according to which perceptual consciousness is rich and does not require cognitive access. Abandoning it also dissociates the rich/sparse debate from the access question. The paper then explores attempts to reformulate overflow theses in ways that do not require the assumption of limited capacity. Finally, it discusses the problem of relating seemingly non-probabilistic perceptual consciousness to the probabilistic representations posited by the models that challenge conceptions of cognitive access as capacity-limited.This article is part of the theme issue 'Perceptual consciousness and cognitive access'.

Perceptual consciousness and cognitive access: an introduction

The problem of perceptual consciousness-the question of how our subjective experiences (colours as we see them; sounds as we hear them; tastes, etc., as we feel them) could be accounted for in terms of brain processes-is often regarded as the greatest unsolved mystery of our times. In recent literature, one of the most pressing questions in this regard is whether the neural basis of perceptual consciousness is independent of the neural basis of cognitive access mechanisms that make reporting and reflecting on conscious experiences possible. The Theme Issue focuses on this central problem of consciousness research and aims to contribute to the field by critically discussing state-of-the-art empirical findings, identifying methodological problems and proposing novel approaches.This article is part of the theme issue 'Perceptual consciousness and cognitive access'.

A methodological dilemma for investigating consciousness empirically

This paper exposes a methodological dilemma arising for the research program of finding the neural correlate of consciousness (NCC), the minimal set of brain processes sufficient for a particular percept. The main claim is that it is doubtful that the right kind of correlations will ever be obtained because the foregoing conceptual decisions regarding the relations between consciousness, attention, cognitive access, report, and other cognitive functions determine the interpretation of the correlation data that can be obtained. Relying on subjective reports likely leads to confounding the NCC with neural mechanisms for cognitive functions because reports presuppose cognitive access. No-report paradigms are in danger of confounding the NCC with neural mechanisms underlying unconscious processes. So there does not seem to be a way of making sure to have isolated the neural correlate of conscious experience.