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 Locus Coeruleus tracking of prediction errors optimises cognitive flexibility: an Active Inference model

2018 ◽  
Author(s):  
Anna C Sales ◽  
Karl J. Friston ◽  
Matthew W. Jones ◽  
Anthony E. Pickering ◽  
Rosalyn J. Moran
Keyword(s):  
Locus Coeruleus ◽  
Prediction Error ◽  
Internal Models ◽  
Prediction Errors ◽  
Active Inference ◽  
Behavioural Flexibility ◽  
Action Prediction ◽  
Belief Updating ◽  
State Action ◽  
The Brain

AbstractThe locus coeruleus (LC) in the pons is the major source of noradrenaline (NA) in the brain. Two modes of LC firing have been associated with distinct cognitive states: changes in tonic rates of firing are correlated with global levels of arousal and behavioural flexibility, whilst phasic LC responses are evoked by salient stimuli. Here, we unify these two modes of firing by modelling the response of the LC as a correlate of a prediction error when inferring states for action planning under Active Inference (AI).We simulate a classic Go/No-go reward learning task and a three-arm foraging task and show that, if LC activity is considered to reflect the magnitude of high level ‘state-action’ prediction errors, then both tonic and phasic modes of firing are emergent features of belief updating. We also demonstrate that when contingencies change, AI agents can update their internal models more quickly by feeding back this state-action prediction error – reflected in LC firing and noradrenaline release – to optimise learning rate, enabling large adjustments over short timescales. We propose that such prediction errors are mediated by cortico-LC connections, whilst ascending input from LC to cortex modulates belief updating in anterior cingulate cortex (ACC).In short, we characterise the LC/ NA system within a general theory of brain function. In doing so, we show that contrasting, behaviour-dependent firing patterns are an emergent property of the LC’s crucial role in translating prediction errors into an optimal mediation between plasticity and stability.Author SummaryThe brain uses sensory information to build internal models and make predictions about the world. When errors of prediction occur, models must be updated to ensure desired outcomes are still achieved. Neuromodulator chemicals provide a possible pathway for triggering such changes in brain state. One such neuromodulator, noradrenaline, originates predominantly from a cluster of neurons in the brainstem – the locus coeruleus (LC) – and plays a key role in behaviour, for instance, in determining the balance between exploiting or exploring the environment.Here we use Active Inference (AI), a mathematical model of perception and action, to formally describe LC function. We propose that LC activity is triggered by errors in prediction and that the subsequent release of noradrenaline alters the rate of learning about the environment. Biologically, this describes an LC-cortex feedback loop promoting behavioural flexibility in times of uncertainty. We model LC output as a simulated animal performs two tasks known to elicit archetypal responses. We find that experimentally observed ‘phasic’ and ‘tonic’ patterns of LC activity emerge naturally, and that modulation of learning rates improves task performance. This provides a simple, unified computational account of noradrenergic computational function within a general model of behaviour.

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 Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 198
Author(s):  
Stephen Fox
Keyword(s):  
Artificial Intelligence ◽  
Quality Management ◽  
Social Organization ◽  
Generative Models ◽  
Industrial Engineering ◽  
Prediction Errors ◽  
Active Inference ◽  
For Profit ◽  
Different Types ◽  
Inference Theory

Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.

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 T136. NEURAL CORRELATES OF ALTERED COGNITIVE FLEXIBILITY AND STABILITY FOR MOTOR CONTROL IN SCHIZOPHRENIA – JUST TWO SIDES OF THE SAME COIN?

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S282-S282
Author(s):  
Rebekka Lencer ◽  
Isabel Standke ◽  
Udo Dannlowski ◽  
Ricarda I Schubotz ◽  
Ima Trempler
Keyword(s):  
Motor Control ◽  
Cognitive Flexibility ◽  
Negative Symptoms ◽  
Region Of Interest ◽  
Brain Regions ◽  
System Level ◽  
Motor Symptoms ◽  
Prediction Errors ◽  
Cognitive Stability ◽  
Imaging Results

Abstract Background It has been suggested that patients with schizophrenia are impaired in the use of prediction error signals resulting in disturbances of motor control. Alterations in fronto-striatal dopamine transmitter systems are regarded to contribute to these deficits. It is unclear whether the use of predictive strategies for motor control may be systematically related to impaired cognitive functions in patients. In healthy subjects, cognitive flexibility has been related to medial prefrontal cortex (PFC) function, while cognitive stability was related to lateral PFC integrity with both brain regions being modulated by striatal activity. Despite these findings, the interplay of cognitive flexibility and stability needed for motor control and its associations to alterations on the brain system level have not been investigated systematically in this patient group. Methods We assessed patients with schizophrenia (N=22) and healthy controls (N=22) on first, detection of relevant unexpected events (cognitive flexibility) and second, distractor resistance to irrelevant prediction errors (cognitive stability) using a serial prediction task including the digits 1-2-3-4. We applied an event-related design in a functional magnetic resonance imaging (fMRI) environment (3T) to explore brain networks underlying cognitive flexibility and stability, respectively. In analyses, the minimum cluster extent was set to k > 20 and corrected using the false discovery rate (FDR) with p < 0.05. Since we were specifically interested in the role of the striatum, we applied small volume correction at p < 0.05 with the minimum cluster extent set to k > 5 in a region of interest analyses. Participants were also assessed on general cognitive function using the Brief Assessment of Cognition in Schizophrenia (BACS) battery and on motor symptoms using the Heidelberg Scale for Neurological Soft Signs (NSS). Results Patients detected less behaviorally relevant events (M(Pat) = 0.57 vs. M(HC) = 0.78, F(41,1) = 16.32, p < 0.001) and ignored less irrelevant events (M(Pat) = 0.87 vs. M(HC) = 0.93, F(41,1) = 11.78, p < 0.001) implying impairments of both cognitive flexibility and stability in patients. Motor symptoms (NSS) and cognitive deficits (BACS) in patients were exclusively related to cognitive flexibility, but not stability. Brain correlates of reduced flexibility in patients were found in a fronto-striato-thalamo network. More specifically, reduced striatal activation in patients was related to weaker event discrimination and reduced detection of unexpected relevant events. Additionally, exploratory follow-up analyses revealed reduced fronto-striato-temporal activation in patients associated with weaker distractor resistance during the stability task. Note, chlorpromazine equivalents as an indicator of antipsychotic dosage as well as positive and negative symptoms were unrelated to measures of cognitive flexibility and stability. Discussion Together, our findings provide evidence for distinctive neurobiological alterations underlying reduced cognitive flexibility and stability in schizophrenia with reduced flexibility being associated with general cognitive and motor impairments. Our main imaging results show reduced activation in a fronto-striato-thalamo network in response to relevant prediction errors in patients, while impaired cognitive stability may be rather related to alterations in a fronto-striato-temporal network. Reduced caudate activation during behavioral relevant events, which was associated with weaker event discrimination and detection of relevant prediction errors in patients, supports a model of striatal gating being essentially impaired in patients.

Active inference and cognitive-emotional interactions in the brain

2015 ◽  
Vol 38 ◽  
Author(s):  
Giovanni Pezzulo ◽  
Laura Barca ◽  
Karl J. Friston
Keyword(s):  
Prediction Errors ◽  
Active Inference ◽  
Bayesian Fusion ◽  
The Brain

AbstractAll organisms must integrate cognition, emotion, and motivation to guide action toward valuable (goal) states, as described by active inference. Within this framework, cognition, emotion, and motivation interact through the (Bayesian) fusion of exteroceptive, proprioceptive, and interoceptive signals, the precision-weighting of prediction errors, and the “affective tuning” of neuronal representations. Crucially, misregulation of these processes may have profound psychopathological consequences.

scholarly journals Firing patterns of serotonin neurons underlying cognitive flexibility

2016 ◽  
Author(s):  
Sara Matias ◽  
Eran Lottem ◽  
Guillaume P. Dugué ◽  
Zachary F. Mainen
Keyword(s):  
Cognitive Flexibility ◽  
Causal Structure ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Firing Patterns ◽  
Learning Rates ◽  
Serotonin Neurons ◽  
Endogenous Role ◽  
Flexible Adaptation

Serotonin is implicated in mood and affective disorders1,2 but growing evidence suggests that its core endogenous role may be to promote flexible adaptation to changes in the causal structure of the environment3–8. This stems from two functions of endogenous serotonin activation: inhibiting learned responses that are not currently adaptive9,10 and driving plasticity to reconfigure them1113. These mirror dual functions of dopamine in invigorating reward-related responses and promoting plasticity that reinforces new ones16,17. However, while dopamine neurons are known to be activated by reward prediction errors18,19, consistent with theories of reinforcement learning, the reported firing patterns of serotonin neurons21–23 do not accord with any existing theories1,24,25. Here, we used long-term photometric recordings in mice to study a genetically-defined population of dorsal raphe serotonin neurons whose activity we could link to normal reversal learning. We found that these neurons are activated by both positive and negative prediction errors, thus reporting the kind of surprise signal proposed to promote learning in conditions of uncertainty26,27. Furthermore, by comparing cue responses of serotonin and dopamine neurons we found differences in learning rates that could explain the importance of serotonin in inhibiting perseverative responding. Together, these findings show how the firing patterns of serotonin neurons support a role in cognitive flexibility and suggest a revised model of dopamine-serotonin opponency with potential clinical implications.

scholarly journals PID Control as a Process of Active Inference with Linear Generative Models

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 257 ◽  
Author(s):  
Manuel Baltieri ◽  
Christopher Buckley
Keyword(s):  
Free Energy ◽  
Biological Systems ◽  
Generative Models ◽  
Pid Controllers ◽  
Mathematical Framework ◽  
Prediction Errors ◽  
Active Inference ◽  
General Process ◽  
Brain Functions ◽  
Theoretical Approaches

In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. In particular, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to offer a unified understanding of life and cognition within a general mathematical framework derived from information and control theory, and statistical mechanics. However, we argue that if the active inference proposal is to be taken as a general process theory for biological systems, it is necessary to understand how it relates to existing control theoretical approaches routinely used to study and explain biological systems. For example, recently, PID (Proportional-Integral-Derivative) control has been shown to be implemented in simple molecular systems and is becoming a popular mechanistic explanation of behaviours such as chemotaxis in bacteria and amoebae, and robust adaptation in biochemical networks. In this work, we will show how PID controllers can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation when using approximate linear generative models of the world. This more general interpretation also provides a new perspective on traditional problems of PID controllers such as parameter tuning as well as the need to balance performances and robustness conditions of a controller. Specifically, we then show how these problems can be understood in terms of the optimisation of the precisions (inverse variances) modulating different prediction errors in the free energy functional.

scholarly journals Interoceptive active inference and self-representation in social anxiety disorder (SAD): exploring the neurocognitive traits of the SAD self

2020 ◽  
Author(s):  
Philip Gerrans ◽  
Ryan J Murray
Keyword(s):  
Anxiety Disorder ◽  
Social Anxiety ◽  
Social Anxiety Disorder ◽  
Anterior Cingulate ◽  
Affective Processing ◽  
Prediction Errors ◽  
Active Inference ◽  
Symptom Profile ◽  
Insula Cortex ◽  
High Level

Abstract This article provides an interoceptive active inference (IAI) account of social anxiety disorder (SAD). Through a neurocognitive framework, we argue that the cognitive and behavioural profile of SAD is best conceived of as a form of maladaptive IAI produced by a negatively biased self-model that cannot reconcile inconsistent tendencies to approach and avoid social interaction. Anticipated future social interactions produce interoceptive prediction error (bodily states of arousal). These interoceptive states are transcribed and experienced as states of distress due to the influence of inconsistent and unstable self-models across a hierarchy of interrelated systems involved in emotional, interoceptive and affective processing. We highlight the role of the insula cortex, in concert with the striatum, amygdala and dorsal anterior cingulate in the generation and reduction of interoceptive prediction errors as well as the resolution of social approach-avoidance conflict. The novelty of our account is a shift in explanatory priority from the representation of the social world in SAD to the representation of the SAD self. In particular, we show how a high-level conceptual self-model of social vulnerability and inadequacy fails to minimize prediction errors produced by a basic drive for social affiliation combined with strong avoidant tendencies. The result is a cascade of interoceptive prediction errors whose attempted minimization through action (i.e. active inference) yields the symptom profile of SAD. We conclude this article by proposing testable hypotheses to further investigate the neurocognitive traits of the SAD self with respect to IAI.

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