Is predictive processing a theory of perceptual consciousness?

Distinguishing Neural Correlates of Prediction Errors on Perceptual Content and Detection of Content

Accounting for why discrimination between different perceptual contents is not always accompanied by conscious detection of that content remains a challenge for predictive processing theories of perception. Here, we test a hypothesis that detection is supported by a distinct inference within generative models of perceptual content. We develop a novel visual perception paradigm that probes such inferences by manipulating both expectations about stimulus content (stimulus identity) and detection of content (stimulus presence). In line with model simulations, we show that both content and detection expectations influence RTs on a categorization task. By combining a no-report version of our task with functional neuroimaging, we reveal that violations of expectations (prediction errors [PEs]) about perceptual content and detection are supported by visual cortex and pFC in qualitatively different ways: Within visual cortex, activity patterns diverge only on trials with a content PE, but within these trials, further divergence is seen for detection PEs. In contrast, within pFC, activity patterns diverge only on trials with a detection PE, but within these trials, further divergence is seen for content PEs. These results suggest rich encoding of both content and detection PEs and highlight a distributed neural basis for inference on content and detection of content in the human brain.

Cognitive Multisensory Rehabilitation, a novel approach for Complex Regional Pain Syndrome: case series

INTRODUCTION

Effective treatment for Complex Regional Pain Syndrome (CRPS), a chronic pain condition, is challenging. Cognitive Multisensory Rehabilitation (CMR) is a cognitive and sensorimotor treatment approach aimed at restoring function that targets cognitive, somatosensory, and multisensory functions through sensory discrimination tasks.

PURPOSE

We aimed to apply CMR to treat CRPS within our clinical practice. Clinically, we have found promising results in reducing pain and other common features of CRPS, such as Body Perception Disturbances (BPD).

METHODS

In this clinical case series, four CRPS patients who participated in a two-week interdisciplinary CRPS rehabilitation program at the National Complex Regional Pain Syndrome Service in Bath, UK received CMR as part of their treatment. A combination of self-reported measures and clinical outcomes were collected pre and post-rehabilitation program.

RESULTS

Functional improvements and a reduction on BPD were observed in all clinical cases.

DISCUSSION

We demonstrate how CMR may improve several CRPS-related features that often hinder rehabilitation in people living with CRPS.

CONCLUSION

Research involving larger cohorts are necessary to provide empirical evidence of the application of CMR in treating CRPS.

Explanatory power by vagueness. Challenges to the strong prior hypothesis on hallucinations exemplified by the Charles-Bonnet-Syndrome

Predictive processing models are often ascribed a certain generality in conceptually unifying the relationships between perception, action, and cognition or the potential to posit a 'grand unified theory' of the mind. The limitations of this unification can be seen when these models are applied to specific cognitive phenomena or phenomenal consciousness. Our article discusses these shortcomings for predictive processing models of hallucinations by the example of the Charles-Bonnet-Syndrome. This case study shows that the current predictive processing account omits essential characteristics of stimulus-independent perception in general, which has critical phenomenological implications. We argue that the most popular predictive processing model of hallucinatory conditions - the strong prior hypothesis - fails to fully account for the characteristics of nonveridical perceptual experiences associated with Charles-Bonnet-Syndrome. To fill this explanatory gap, we propose that the strong prior hypothesis needs to include reality monitoring to apply to more than just veridical percepts.

Laminar specificity of the auditory perceptual awareness negativity: A biophysical modeling study

How perception of sensory stimuli emerges from brain activity is a fundamental question of neuroscience. To date, two disparate lines of research have examined this question. On one hand, human neuroimaging studies have helped us understand the large-scale brain dynamics of perception. On the other hand, work in animal models (mice, typically) has led to fundamental insight into the micro-scale neural circuits underlying perception. However, translating such fundamental insight from animal models to humans has been challenging. Here, using biophysical modeling, we show that the auditory awareness negativity (AAN), an evoked response associated with perception of target sounds in noise, can be accounted for by synaptic input to the supragranular layers of auditory cortex (AC) that is present when target sounds are heard but absent when they are missed. This additional input likely arises from cortico-cortical feedback and/or non-lemniscal thalamic projections and targets the apical dendrites of layer-5 (L5) pyramidal neurons. In turn, this leads to increased local field potential activity, increased spiking activity in L5 pyramidal neurons, and the AAN. The results are consistent with current cellular models of conscious processing and help bridge the gap between the macro and micro levels of perception-related brain activity.

Towards causal mechanisms of consciousness through focused transcranial brain stimulation

Conscious experience represents one of the most elusive problems of empirical science, namely neuroscience. The main objective of empirical studies of consciousness has been to describe the minimal sets of neural events necessary for a specific neuronal state to become consciously experienced. The current state of the art still does not meet this objective but rather consists of highly speculative theories based on correlates of consciousness and an ever-growing list of knowledge gaps. The current state of the art is defined by the limitations of past stimulation techniques and the emphasis on the observational approach. However, looking at the current stimulation technologies that are becoming more accurate, it is time to consider an alternative approach to studying consciousness, which builds on the methodology of causal explanations via causal alterations. The aim of this methodology is to move beyond the correlates of consciousness and focus directly on the mechanisms of consciousness with the help of the currently focused brain stimulation techniques, such as geodesic transcranial electric neuromodulation. This approach not only overcomes the limitations of the correlational methodology but will also become another firm step in the following science of consciousness.

Leveraging conscious and nonconscious learning for efficient AI

Various interpretations of the literature detailing the neural basis of learning have in part led to disagreements concerning how consciousness arises. Further, artificial learning model design has suffered in replicating intelligence as it occurs in the human brain. Here, we present a novel learning model, which we term the "Recommendation Architecture (RA) Model" from prior theoretical works proposed by Coward, using a dual-learning approach featuring both consequence feedback and non-consequence feedback. The RA model is tested on a categorical learning task where no two inputs are the same throughout training and/or testing. We compare this to three consequence feedback only models based on backpropagation and reinforcement learning. Results indicate that the RA model learns novelty more efficiently and can accurately return to prior learning after new learning with less computational resources expenditure. The final results of the study show that consequence feedback as interpretation, not creation, of cortical activity creates a learning style more similar to human learning in terms of resource efficiency. Stable information meanings underlie conscious experiences. The work provided here attempts to link the neural basis of nonconscious and conscious learning while providing early results for a learning protocol more similar to human brains than is currently available.

Bayesian theories of consciousness: a review in search for a minimal unifying model

The goal of the paper is to review existing work on consciousness within the frameworks of Predictive Processing, Active Inference, and Free Energy Principle. The emphasis is put on the role played by the precision and complexity of the internal generative model. In the light of those proposals, these two properties appear to be the minimal necessary components for the emergence of conscious experience-a Minimal Unifying Model of consciousness.

Neural Design Principles for Subjective Experience: Implications for Insects

How subjective experience is realized in nervous systems remains one of the great challenges in the natural sciences. An answer to this question should resolve debate about which animals are capable of subjective experience. We contend that subjective experience of sensory stimuli is dependent on the brain's awareness of its internal neural processing of these stimuli. This premise is supported by empirical evidence demonstrating that disruption to either processing streams or awareness states perturb subjective experience. Given that the brain must predict the nature of sensory stimuli, we reason that conscious awareness is itself dependent on predictions generated by hierarchically organized forward models of the organism's internal sensory processing. The operation of these forward models requires a specialized neural architecture and hence any nervous system lacking this architecture is unable to subjectively experience sensory stimuli. This approach removes difficulties associated with extrapolations from behavioral and brain homologies typically employed in addressing whether an animal can feel. Using nociception as a model sensation, we show here that the Drosophila brain lacks the required internal neural connectivity to implement the computations required of hierarchical forward models. Consequently, we conclude that Drosophila, and those insects with similar neuroanatomy, do not subjectively experience noxious stimuli and therefore cannot feel pain.