Predictive Neuronal Adaptation as a Basis for Consciousness

Being able to correctly predict the future and to adjust own actions accordingly can offer a great survival advantage. In fact, this could be the main reason why brains evolved. Consciousness, the most mysterious feature of brain activity, also seems to be related to predicting the future and detecting surprise: a mismatch between actual and predicted situation. Similarly at a single neuron level, predicting future activity and adapting synaptic inputs accordingly was shown to be the best strategy to maximize the metabolic energy for a neuron. Following on these ideas, here we examined if surprise minimization by single neurons could be a basis for consciousness. First, we showed in simulations that as a neural network learns a new task, then the surprise within neurons (defined as the difference between actual and expected activity) changes similarly to the consciousness of skills in humans. Moreover, implementing adaptation of neuronal activity to minimize surprise at fast time scales (tens of milliseconds) resulted in improved network performance. This improvement is likely because adapting activity based on the internal predictive model allows each neuron to make a more “educated” response to stimuli. Based on those results, we propose that the neuronal predictive adaptation to minimize surprise could be a basic building block of conscious processing. Such adaptation allows neurons to exchange information about own predictions and thus to build more complex predictive models. To be precise, we provide an equation to quantify consciousness as the amount of surprise minus the size of the adaptation error. Since neuronal adaptation can be studied experimentally, this can allow testing directly our hypothesis. Specifically, we postulate that any substance affecting neuronal adaptation will also affect consciousness. Interestingly, our predictive adaptation hypothesis is consistent with multiple ideas presented previously in diverse theories of consciousness, such as global workspace theory, integrated information, attention schema theory, and predictive processing framework. In summary, we present a theoretical, computational, and experimental support for the hypothesis that neuronal adaptation is a possible biological mechanism of conscious processing, and we discuss how this could provide a step toward a unified theory of consciousness.

Conceptualising Four Categories of Behaviours: Implications for Implementation Strategies to Achieve Behaviour Change

Background: Effectiveness of implementation strategies is influenced by the extent to which they are based on appropriate theories concerning the behaviours that the strategies intend to impact. Effectiveness may be limited simply because the strategies are based on theories that are limited in scope or are derived from partially inaccurate assumptions about the behaviours in question. It may therefore be important to combine insights from various theories to cover the range of influences on the behaviours that will be changed.Aim: This article aims to explore concepts, theories and empirical findings from different disciplines to categorise four types of behaviours and discuss the implications for implementation strategies attempting to change these behaviours.Influences on behaviours: Multilevel influences on behaviours are dichotomized into individual-level and collective-level influences, and behaviours that are guided by conscious cognitive processes are distinguished from those that rely on non-conscious processing. Combining the two dimensions (levels and cognitive modes) creates a 2 x 2 conceptual map consisting of four categories of behaviours. Explicitly conceptualising the levels and cognitive modes is crucial because different implementation strategies are required depending on the characteristics of the behaviours involved in the practise that needs to be changed.Conclusion: The 2 x 2 conceptual map can be used to consider and reflect on the nature of the behaviours that need to be changed, thus providing guidance on the type of theory, model or framework that might be most relevant for understanding and facilitating behaviour change.

Hallucinations: A Functional Network Model of How Sensory Representations Become Selected for Conscious Awareness in Schizophrenia

Hallucinations are conscious perception-like experiences that are a common symptom of schizophrenia spectrum disorders (SSD). Current neuroscience evidence suggests several brain areas are involved in the generation of hallucinations including the sensory cortex, insula, putamen, and hippocampus. But how does activity in these regions give rise to aberrant conscious perceptions that seemingly invade ongoing conscious experience? Most existing models assume that sensory representations are sometimes spontaneously activated in the brain, and that these spontaneous activations somehow play a causal role in the generation of hallucinations. Yet, it remains unclear how these representations become selected for conscious processing. No existing theory of hallucinations has specified such a “selection mechanism.” Global Workspace (GW) theorists argue that the brain’s interconnected processors select relevant piece(s) of information for broadcasting to other brain processors, rendering the information accessible to consciousness; this process known as “ignition” is associated with synchronized activity across distributed cortical and subcortical brain regions. Yet, it remains unclear how certain information and representations become selected for conscious processing. While GW theorists maintain that attention plays an important role, they have not delineated a formal “selection mechanism.” This paper specifies a selection mechanism based upon two central hypotheses: (1) a functional network called the “salience network” plays a critical role in selecting sensory representations for conscious broadcast to the GW in normal (healthy) perception; (2) sensory representations become abnormally selected for conscious broadcast to the GW (instead of being filtered out of consciousness) in individuals with SSD that experience hallucinations.

What it is like to be a bit: an integrated information decomposition account of emergent mental phenomena

A central question in neuroscience concerns the relationship between consciousness and its physical substrate. Here, we argue that a richer characterization of consciousness can be obtained by viewing it as constituted of distinct information-theoretic elements. In other words, we propose a shift from quantification of consciousness—viewed as integrated information—to its decomposition. Through this approach, termed Integrated Information Decomposition (ΦID), we lay out a formal argument that whether the consciousness of a given system is an emergent phenomenon depends on its information-theoretic composition—providing a principled answer to the long-standing dispute on the relationship between consciousness and emergence. Furthermore, we show that two organisms may attain the same amount of integrated information, yet differ in their information-theoretic composition. Building on ΦID’s revised understanding of integrated information, termed ΦR, we also introduce the notion of ΦR-ing ratio to quantify how efficiently an entity uses information for conscious processing. A combination of ΦR and ΦR-ing ratio may provide an important way to compare the neural basis of different aspects of consciousness. Decomposition of consciousness enables us to identify qualitatively different ‘modes of consciousness’, establishing a common space for mapping the phenomenology of different conscious states. We outline both theoretical and empirical avenues to carry out such mapping between phenomenology and information-theoretic modes, starting from a central feature of everyday consciousness: selfhood. Overall, ΦID yields rich new ways to explore the relationship between information, consciousness, and its emergence from neural dynamics.

Why intracranial electrical stimulation of the human brain suggests an essential role for prefrontal cortex in conscious processing: a commentary on Raccah et al.

We read with interest the synthesis by Raccah and colleagues on the perturbations of consciousexperience elicited by intracranial electrical stimulation (iES) of the prefrontal cortex (PFC) in awakeneurosurgical patients. The main outcome of the review is the report that iES of the PFC shows fewercausal changes of conscious experience than iES of posterior sensory areas. The authors interpretedthis finding as a challenge to neuroscientific theories of conscious processing that attribute a centralrole to PFC, such a Global Neuronal Workspace Theory (GNWT) and Higher Order Thought theory(HOT). We agree that this anterior vs posterior issue may offer an experimental challenge that thepresent theories of conscious processing have to take up, and we provide here a list of seven majorpoints that begin to specify a GNWT account for the observations compiled by Raccah and colleaguestogether with more recent, unmentioned, data.

On the Relationship Between Intuition, Consciousness and Cognition: In Search of a Unified Concept of Mind

This paper argues that within contemporary neuroscience and cognitive psychology a new theoretical framework is emerging for the integrated consideration of unconscious (intuitive) processing with conscious ones. This framework requires inclusion of new theories of the brain and consciousness dynamics (Baars, Damasio, Dehaene, Friston, Gazzaniga, LeDoux), evolution of the human memory system (Tulving), and theories of procedural, associative and working memory (Baddley, Reber). Evolutionarily old intuitive processing is not unique, and it is an integral component of any cognitive processes. A revolutionary new synthesis combining intuitive and conscious processing may take the form of a mind vector integrating the two.

From non-conscious processing to conscious events: a minimalist approach

The minimalist approach that we develop here is a framework that allows to appreciate how non-conscious processing and conscious contents shape human cognition, broadly defined. It is composed of three simple principles. First, cognitive processes are inherently non-conscious, while their inputs and (interim) outputs may be consciously experienced. Second, non-conscious processes and elements of the cognitive architecture prioritize information for conscious experiences. Third, conscious events are composed of series of conscious contents and non-conscious processes, with increased duration leading to more opportunity for processing. The narrowness of conscious experiences is conceptualized here as a solution to the problem of channeling the plethora of non-conscious processes into action and communication processes that are largely serial. The framework highlights the importance of prioritization for consciousness, and we provide an illustrative review of three main factors that shape prioritization—stimulus strength, motivational relevance and mental accessibility. We further discuss when and how this framework (i) is compatible with previous theories, (ii) enables new understandings of established findings and models, and (iii) generates new predictions and understandings.

Predictive neuronal adaptation as a basis for consciousness.

Being able to correctly predict the future and to adjust own actions accordingly, offers great survival advantage. In fact, this could be the main reason for organisms to evolve their brains. The most mysterious feature of brain activity: consciousness, also seems to be related to predicting the future and detecting surprise: a mismatch between actual and predicted situation. Even at the single neuron level, predicting future activity and adapting synaptic inputs accordingly, is the best strategy to maximize metabolic energy for a neuron. Following on those ideas, here we examine if surprise minimization by single neurons could be a basis for consciousness. First, we show in simulations that as a neural network learns a task, then the surprise within neurons, defined as: difference between actual and expected activity, changes similarly as consciousness of a learned skill in humans. Moreover, implementing adaptation of neuronal activity to minimize surprise at fast time scales (tens of ms), resulted in improved network performance. This improvement is likely due to the fact that adapting activity based on the internal predictive model, allows each neuron for a more “educated” response to stimuli. Based on those results, we propose that: neuronal predictive adaptation to minimize surprise could be a basic building block of conscious processing. This is because, adapting activity toward a predicted level, allows neurons to exchange not only information about stimulus but also about its internal model predictions and thus, to build more complex predictive models. To be precise, we provide an equation to quantify consciousness as the amount of surprise minus the size of the adaptation error. Since neuronal adaptation can be studied experimentally, this allows for directly testing our hypothesis. Specifically, we postulate that any substance affecting neuronal adaptation will also affect consciousness. Interestingly, our predictive adaptation hypothesis is consistent with multiple ideas presented previously in diverse theories of consciousness, such as global workspace theory, integrated information, attention schema theory, and predictive processing framework. In summary, we present a theoretical, computational and experimental support for the hypothesis that neuronal adaptation is a possible biological mechanism of conscious processing, and we discuss how this could provide a step toward a unified theory of consciousness.