scholarly journals The predictive global neuronal workspace: A formal active inference model of visual consciousness

2020 ◽  
pp. 101918 ◽  
Author(s):  
Christopher J. Whyte ◽  
Ryan Smith
Keyword(s):  
Active Inference ◽  
Inference Model ◽  
Visual Consciousness

scholarly journals The Predictive Global Neuronal Workspace: A Formal Active Inference Model of Visual Consciousness

2020 ◽  
Author(s):  
Christopher J. Whyte ◽  
Ryan Smith
Keyword(s):  
Inattentional Blindness ◽  
Signal Strength ◽  
Active Inference ◽  
Neural Basis ◽  
Neuronal Dynamics ◽  
Inference Model ◽  
Architectural Elements ◽  
Sensory Signal ◽  
Visual Consciousness ◽  
The Relationship

AbstractThe global neuronal workspace (GNW) model has inspired over two decades of hypothesis driven research on the neural basis consciousness. However, recent studies have reported findings that are at odds with empirical predictions of the model. Further, the macro-anatomical focus of current GNW research has limited the specificity of predictions afforded by the model. In this paper we present a neurocomputational model – based on Active Inference – that captures central architectural elements of the GNW and is able to address these limitations. The resulting ‘predictive global workspace’ casts neuronal dynamics as approximating Bayesian inference, allowing precise, testable predictions at both the behavioural and neural levels of description. We report simulations demonstrating the model’s ability to reproduce: 1) the electrophysiological and behaviour results observed in previous studies of inattentional blindness; and 2) the previously introduced four-way taxonomy predicted by the GNW, which describes the relationship between consciousness, attention, and sensory signal strength. We then illustrate how our model can reconcile/explain (apparently) conflicting findings, extend the GNW taxonomy to include the influence of prior expectations, and inspire novel paradigms to test associated behavioural and neural predictions.

scholarly journals Locus Coeruleus tracking of prediction errors optimises cognitive flexibility: An Active Inference model

2019 ◽  
Vol 15 (1) ◽  
pp. e1006267 ◽  
Author(s):  
Anna C. Sales ◽  
Karl J. Friston ◽  
Matthew W. Jones ◽  
Anthony E. Pickering ◽  
Rosalyn J. Moran
Keyword(s):  
Locus Coeruleus ◽  
Cognitive Flexibility ◽  
Prediction Errors ◽  
Active Inference ◽  
Inference Model

scholarly journals Regimes of Expectations: An Active Inference Model of Social Conformity and Human Decision Making

2019 ◽  
Vol 10 ◽  
Author(s):  
Axel Constant ◽  
Maxwell J. D. Ramstead ◽  
Samuel P. L. Veissière ◽  
Karl Friston
Keyword(s):  
Decision Making ◽  
Active Inference ◽  
Social Conformity ◽  
Inference Model ◽  
Human Decision

scholarly journals An active inference model of hierarchical action understanding, learning and imitation

2021 ◽  
Author(s):  
Riccardo Proietti ◽  
Giovanni Pezzulo ◽  
Alessia Tessari
Keyword(s):  
Action Observation ◽  
Sampling Strategy ◽  
Action Understanding ◽  
Active Inference ◽  
Inference Model ◽  
Multiple Routes ◽  
Information Sampling ◽  
Hierarchical Representations ◽  
Action Representations ◽  
Oculomotor Activity

We advance a novel computational model of the acquisition of a hierarchical action repertoire and its use for observation, understanding and motor control. The model is grounded in a principled framework to understand brain and cognition: active inference. We exemplify the functioning of the model by presenting four simulations of a tennis learner who observes a teacher performing tennis shots and forms hierarchical representations of the observed actions - including both actions that are already in her repertoire and novel actions - and finally imitates them. Our simulations that show that the agent’s oculomotor activity implements an active information sampling strategy that permits inferring the kinematics aspects of the observed movement, which lie at the lowest level of the action hierarchy. In turn, this low-level kinematic inference supports higher-level inferences about deeper aspects of the observed actions, such as their proximal goals and intentions. Finally, the inferred action representations can steer imitative motor responses, but interfere with the execution of different actions. Taken together, our simulations show that the same hierarchical active inference model provides a unified account of action observation, understanding, learning and imitation. Finally, our model provides a computational rationale to explain the neurobiological underpinnings of visuomotor cognition, including the multiple routes for action understanding in the dorsal and ventral streams and mirror mechanisms.

scholarly journals Active inference through whiskers

2021 ◽  
Author(s):  
Francesco Mannella ◽  
Federico Maggiore ◽  
Manuel Baltieri ◽  
Giovanni Pezzulo
Keyword(s):  
Active Sensing ◽  
Prediction Errors ◽  
Prior Belief ◽  
Active Inference ◽  
Anticipatory Control ◽  
Inference Process ◽  
Inference Model

Rodents use whisking to probe actively their environment and to locate objects in space, hence providing a paradigmatic biological example of active sensing. Numerous studies show that the control of whisking has anticipatory aspects. For example, rodents target their whisker protraction to the distance at which they expect objects, rather than just reacting fast to contacts with unexpected objects. Here we characterize the anticipatory control of whisking in rodents as an active inference process. In this perspective, the rodent is endowed with a prior belief that it will touch something at the end of the whisker protraction, and it continuously modulates its whisking amplitude to minimize (proprioceptive and somatosensory) prediction errors arising from an unexpected whisker-object contact, or from a lack of an expected contact. We will use the model to qualitatively reproduce key empirical findings about the ways rodents modulate their whisker amplitude during exploration and the scanning of (expected or unexpected) objects. Furthermore, we will discuss how the components of active inference model can in principle map to the neurobiological circuits of rodent whisking.

scholarly journals An Active Inference Model of Conscious Access with and without Report

2021 ◽  
Author(s):  
Christopher Whyte ◽  
Jakob Hohwy ◽  
Ryan Smith
Keyword(s):  
Working Memory ◽  
Visual Masking ◽  
Event Related Potentials ◽  
Active Inference ◽  
Inference Model ◽  
Apparent Absence ◽  
Global Workspace ◽  
Cognitive Theories ◽  
Related Potentials ◽  
Cognitive Action

Cognitive theories of consciousness, such as global workspace theory and higher-order theories, posit that frontoparietal circuits play a crucial role in conscious access. However, recent studies using no-report paradigms have posed a challenge to cognitive theories by demonstrating conscious accessibility in the apparent absence of prefrontal cortex (PFC) activation. To address this challenge, this paper presents a computational model of conscious access, based upon active inference, that treats working memory gating as a cognitive action. We simulate a visual masking task and show that late P3b-like event-related potentials (ERPs), and increased PFC activity, are induced by the working memory demands of report. When reporting demands are removed, these late ERPs vanish and PFC activity is reduced. These results therefore reproduce, and potentially explain, results from no-report paradigms. However, even without reporting demands, our model shows that simulated PFC activity on visible stimulus trials still crosses the threshold for reportability – maintaining the link between PFC and conscious access. Therefore, our simulations show that evidence provided by no-report paradigms does not necessarily contradict cognitive theories of consciousness.

scholarly journals Apathy is Associated with Reduced Precision of Prior Beliefs about Action Outcomes

2019 ◽  
Author(s):  
Frank H. Hezemans ◽  
Noham Wolpe ◽  
James B. Rowe
Keyword(s):  
Brain Function ◽  
Active Inference ◽  
Inference Model ◽  
Prior Beliefs ◽  
Physical Effort ◽  
Future Studies ◽  
Visuomotor Task ◽  
Neuropsychiatric Diseases ◽  
Underlying Mechanisms ◽  
Sufficient Precision

ABSTRACTApathy is a debilitating syndrome that is associated with reduced goal-directed behaviour. Although apathy is common and detrimental to prognosis in many neuropsychiatric diseases, its underlying mechanisms remain controversial. We propose a new model of apathy, in the context of Bayesian theories of brain function, whereby actions require predictions of their outcomes to be held with sufficient precision for ‘explaining away’ differences in sensory inputs. In this active inference model, apathy would result from reduced precision of prior beliefs about action outcomes. Healthy adults (N=47) performed a visuomotor task that independently manipulated physical effort and reward, and served to estimate the precision of priors. Participants’ perception of their performance was biased towards the target, which was accounted for by precise prior beliefs about action outcomes. Crucially, prior precision was negatively associated with apathy. The results support a Bayesian account of apathy, that could inform future studies of clinical populations.

scholarly journals Evidence for entropy maximisation in human free choice behaviour

2020 ◽  
Author(s):  
Natalie Rens ◽  
Philipp Schwartenbeck ◽  
Ross Cunnington ◽  
Giovanni Pezzulo
Keyword(s):  
Decision Making ◽  
Free Will ◽  
Free Choice ◽  
Active Inference ◽  
Freedom Of Choice ◽  
Inference Model ◽  
Choice Behaviour ◽  
Self Evaluation ◽  
Utilitarian Value ◽  
Entropy Maximisation

The freedom to choose between options is strongly linked to notions of free will. Accordingly, several studies have shown that individuals demonstrate a preference for choice, or the availability of multiple options, over and above utilitarian value. Yet we lack a decision-making framework that integrates preference for choice with traditional utility maximisation in free choice behaviour. Here we test the predictions of an active inference model of decision-making in which an agent actively seeks states yielding entropy (availability of options) in addition to utility (economic reward). We designed a study in which participants freely navigated a virtual environment consisting of two consecutive choices leading to reward locations in separate rooms. Critically, the choice of one room always led to two final doors while, in the second room, only one door was permissible to choose. This design allowed us to separately determine the influence of utility and entropy on participants' choice behaviour and their self-evaluation of free will. We found that choice behaviour was better predicted by an inference-based model than by expected utility alone, and that both the availability of options and the value of the context positively influenced participants' perceived freedom of choice. Moreover, this consideration of options was apparent in the ongoing motion dynamics as individuals navigated the environment. These results show that free choice behaviour is well explained by an inference-based framework in which both utility and entropy are optimised.

scholarly journals Neurocomputational mechanisms underlying emotional awareness: insights afforded by deep active inference and their potential clinical relevance

2019 ◽  
Author(s):  
Ryan Smith ◽  
Richard D. Lane ◽  
Thomas Parr ◽  
Karl J. Friston
Keyword(s):  
Emotional Awareness ◽  
Self Report ◽  
Coarse Grained ◽  
Active Inference ◽  
Inference Model ◽  
Emotional Processes ◽  
Individual Variations ◽  
Reflective Capacity ◽  
Computational Processes ◽  
Capacity Characteristic

AbstractEmotional awareness (EA) is recognized as clinically relevant to the vulnerability to, and maintenance of, psychiatric disorders. However, the neurocomputational processes that underwrite individual variations remain unclear. In this paper, we describe a deep (active) inference model that reproduces the cognitive-emotional processes and self-report behaviors associated with EA. We then present simulations to illustrate (seven) distinct mechanisms that (either alone or in combination) can produce phenomena – such as somatic misattribution, coarse-grained emotion conceptualization, and constrained reflective capacity – characteristic of low EA. Our simulations suggest that the clinical phenotype of impoverished EA can be reproduced by dissociable computational processes. The possibility that different processes are at work in different individuals suggests that they may benefit from distinct clinical interventions. As active inference makes particular predictions about the underlying neurobiology of such aberrant inference, we also discuss how this type of modelling could be used to design neuroimaging tasks to test predictions and identify which processes operate in different individuals – and provide a principled basis for personalized precision medicine.

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