scholarly journals Deeply Felt Affect: The Emergence of Valence in Deep Active Inference

2020 ◽  
pp. 1-49
Author(s):  
Casper Hesp ◽  
Ryan Smith ◽  
Thomas Parr ◽  
Micah Allen ◽  
Karl J. Friston ◽  
...  
Keyword(s):  
Affective State ◽  
Emotional Valence ◽  
Face Validity ◽  
Active Inference ◽  
Mental Action ◽  
Sensory Data ◽  
Context Learning ◽  
Domain Generality ◽  
Internal Action ◽  
Action Model

The positive-negative axis of emotional valence has long been recognized as fundamental to adaptive behavior, but its origin and underlying function have largely eluded formal theorizing and computational modeling. Using deep active inference, a hierarchical inference scheme that rests on inverting a model of how sensory data are generated, we develop a principled Bayesian model of emotional valence. This formulation asserts that agents infer their valence state based on the expected precision of their action model—an internal estimate of overall model fitness (“subjective fitness”). This index of subjective fitness can be estimated within any environment and exploits the domain generality of second-order beliefs (beliefs about beliefs). We show how maintaining internal valence representations allows the ensuing affective agent to optimize confidence in action selection preemptively. Valence representations can in turn be optimized by leveraging the (Bayes-optimal) updating term for subjective fitness, which we label affective charge (AC). AC tracks changes in fitness estimates and lends a sign to otherwise unsigned divergences between predictions and outcomes. We simulate the resulting affective inference by subjecting an in silico affective agent to a T-maze paradigm requiring context learning, followed by context reversal. This formulation of affective inference offers a principled account of the link between affect, (mental) action, and implicit metacognition. It characterizes how a deep biological system can infer its affective state and reduce uncertainty about such inferences through internal action (i.e., top-down modulation of priors that underwrite confidence). Thus, we demonstrate the potential of active inference to provide a formal and computationally tractable account of affect. Our demonstration of the face validity and potential utility of this formulation represents the first step within a larger research program. Next, this model can be leveraged to test the hypothesized role of valence by fitting the model to behavioral and neuronal responses.

scholarly journals Deeply Felt Affect: The Emergence of Valence in Deep Active Inference

2019 ◽  
Author(s):  
Casper Hesp ◽  
Ryan Smith ◽  
Thomas Parr ◽  
Micah Allen ◽  
Karl Friston ◽  
...  
Keyword(s):  
Affective State ◽  
Emotional Valence ◽  
Adaptive Behaviour ◽  
Active Inference ◽  
Affective States ◽  
Mental Action ◽  
Sensory Data ◽  
Context Learning ◽  
Domain Generality ◽  
Internal Action

The positive-negative axis of emotional valence has long been recognised as fundamental to adaptive behaviour, but its domain-generality has largely eluded formal theories and modelling. Using deep active inference – a hierarchical inference scheme that rests on inverting a model of how sensory data are generated – we develop a principled Bayesian account of emotional valence. This formulation associates valence with subjective fitness and exploits the domain-generality of second-order beliefs (i.e., beliefs about beliefs). We construct an affective agent that infers its valence state from the expected precision of its phenotype-congruent action model (i.e., subjective fitness) in any given environment. The ensuing affective states then optimise that confidence pre-emptively. The evidence for inferred (i.e., ‘felt’) valenced states depends upon the (Bayes-optimal) updating term for subjective fitness, which we label affective charge (AC). We simulate affective inference in a T-maze paradigm requiring context learning, followed by context reversal. The result is a deep (biologically plausible) agent that infers its affective state and reduces its uncertainty through internal action (i.e., optimises prior beliefs that underwrite confidence). Thus, we demonstrate the potential of active inference in providing a formal and computationally tractable account of the link between affect, (mental) action, and implicit meta-cognition.

scholarly journals Active Inference: A Process Theory

2017 ◽  
Vol 29 (1) ◽  
pp. 1-49 ◽  
Author(s):  
Karl Friston ◽  
Thomas FitzGerald ◽  
Francesco Rigoli ◽  
Philipp Schwartenbeck ◽  
Giovanni Pezzulo
Keyword(s):  
Free Energy ◽  
Gradient Descent ◽  
Cell Activity ◽  
Face Validity ◽  
Process Theory ◽  
Active Inference ◽  
Neuronal Dynamics ◽  
Context Learning ◽  
Repetition Suppression ◽  
Variational Free Energy

This article describes a process theory based on active inference and belief propagation. Starting from the premise that all neuronal processing (and action selection) can be explained by maximizing Bayesian model evidence—or minimizing variational free energy—we ask whether neuronal responses can be described as a gradient descent on variational free energy. Using a standard (Markov decision process) generative model, we derive the neuronal dynamics implicit in this description and reproduce a remarkable range of well-characterized neuronal phenomena. These include repetition suppression, mismatch negativity, violation responses, place-cell activity, phase precession, theta sequences, theta-gamma coupling, evidence accumulation, race-to-bound dynamics, and transfer of dopamine responses. Furthermore, the (approximately Bayes’ optimal) behavior prescribed by these dynamics has a degree of face validity, providing a formal explanation for reward seeking, context learning, and epistemic foraging. Technically, the fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics, which therefore conform to Hamilton’s principle of least action.

Analyzing Emotional Oscillatory Brain Network for Valence and Arousal-Based Emotion Recognition Using EEG Data

2019 ◽  
Vol 18 (04) ◽  
pp. 1359-1378
Author(s):  
Jianzhuo Yan ◽  
Hongzhi Kuai ◽  
Jianhui Chen ◽  
Ning Zhong
Keyword(s):  
Emotion Recognition ◽  
Affective Computing ◽  
Affective State ◽  
Feature Selection Method ◽  
Emotional Valence ◽  
Brain Network ◽  
Previous Method ◽  
Emotional States ◽  
Eeg Data ◽  
Valence And Arousal

Emotion recognition is a highly noteworthy and challenging work in both cognitive science and affective computing. Currently, neurobiology studies have revealed the partially synchronous oscillating phenomenon within brain, which needs to be analyzed from oscillatory synchronization. This combination of oscillations and synchronism is worthy of further exploration to achieve inspiring learning of the emotion recognition models. In this paper, we propose a novel approach of valence and arousal-based emotion recognition using EEG data. First, we construct the emotional oscillatory brain network (EOBN) inspired by the partially synchronous oscillating phenomenon for emotional valence and arousal. And then, a coefficient of variation and Welch’s [Formula: see text]-test based feature selection method is used to identify the core pattern (cEOBN) within EOBN for different emotional dimensions. Finally, an emotional recognition model (ERM) is built by combining cEOBN-inspired information obtained in the above process and different classifiers. The proposed approach can combine oscillation and synchronization characteristics of multi-channel EEG signals for recognizing different emotional states under the valence and arousal dimensions. The cEOBN-based inspired information can effectively reduce the dimensionality of the data. The experimental results show that the previous method can be used to detect affective state at a reasonable level of accuracy.

scholarly journals Uncertainty, epistemics and active inference

2017 ◽  
Vol 14 (136) ◽  
pp. 20170376 ◽  
Author(s):  
Thomas Parr ◽  
Karl J. Friston
Keyword(s):  
Inhibition Of Return ◽  
Biological Systems ◽  
Generative Model ◽  
Active Inference ◽  
Sensory Data ◽  
Optimal Decisions ◽  
Link Type ◽  
The Face ◽  
Principled Explanation ◽  
Visual Foraging

Biological systems—like ourselves—are constantly faced with uncertainty. Despite noisy sensory data, and volatile environments, creatures appear to actively maintain their integrity. To account for this remarkable ability to make optimal decisions in the face of a capricious world, we propose a generative model that represents the beliefs an agent might possess about their own uncertainty. By simulating a noisy and volatile environment, we demonstrate how uncertainty influences optimal epistemic (visual) foraging. In our simulations, saccades were deployed less frequently to regions with a lower sensory precision, while a greater volatility led to a shorter inhibition of return. These simulations illustrate a principled explanation for some cardinal aspects of visual foraging—and allow us to propose a correspondence between the representation of uncertainty and ascending neuromodulatory systems, complementing that suggested by Yu & Dayan (Yu & Dayan 2005 Neuron 46 , 681–692. ( doi:10.1016/j.neuron.2005.04.026 )).

scholarly journals Towards a computational phenomenology of mental action: modelling meta-awareness and attentional control with deep parametric active inference

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Lars Sandved-Smith ◽  
Casper Hesp ◽  
Jérémie Mattout ◽  
Karl Friston ◽  
Antoine Lutz ◽  
...  
Keyword(s):  
Attentional Control ◽  
Formal Model ◽  
Hierarchical Level ◽  
Active Inference ◽  
Mental Action ◽  
Perceptual Object ◽  
Cognitive States ◽  
Policy Selection ◽  
Current Content ◽  
And Control

Abstract Meta-awareness refers to the capacity to explicitly notice the current content of consciousness and has been identified as a key component for the successful control of cognitive states, such as the deliberate direction of attention. This paper proposes a formal model of meta-awareness and attentional control using hierarchical active inference. To do so, we cast mental action as policy selection over higher-level cognitive states and add a further hierarchical level to model meta-awareness states that modulate the expected confidence (precision) in the mapping between observations and hidden cognitive states. We simulate the example of mind-wandering and its regulation during a task involving sustained selective attention on a perceptual object. This provides a computational case study for an inferential architecture that is apt to enable the emergence of these central components of human phenomenology, namely, the ability to access and control cognitive states. We propose that this approach can be generalized to other cognitive states, and hence, this paper provides the first steps towards the development of a computational phenomenology of mental action and more broadly of our ability to monitor and control our own cognitive states. Future steps of this work will focus on fitting the model with qualitative, behavioural, and neural data.

scholarly journals Towards a formal neurophenomenology of metacognition: modelling meta-awareness, mental action, and attentional control with deep active inference

2020 ◽  
Author(s):  
Lars Sandved Smith ◽  
Casper Hesp ◽  
Antoine Lutz ◽  
Jérémie Mattout ◽  
Karl Friston ◽  
...  
Keyword(s):  
Human Condition ◽  
Hierarchical Level ◽  
Metacognitive Skills ◽  
Active Inference ◽  
Mental Action ◽  
Covert Action ◽  
Cognitive States ◽  
Psychiatric Conditions ◽  
And Control ◽  
Monitor And Control

Metacognition refers to the capacity to access, monitor, and control aspects of one’s mental operations and is central to the human condition and experience. Disorders of metacognition are a hallmark of many psychiatric conditions and the training of metacognitive skills is central in education and in many psychotherapies. This paper provides first steps towards the development of a formal neurophenomenology of metacognition. To do so, we leverage the tools of the active inference framework, extending a previous computational model of implicit metacognition by adding a hierarchical level to model explicit (conscious) meta-awareness and the voluntary control of attention through covert action. Using the example of mind-wandering and its regulation in focused attention, we provide a computational proof of principle for an inferential architecture apt to enable the emergence of central components of metacognition: namely, the ability to access, monitor, and control cognitive states.

scholarly journals Generalised free energy and active inference

2019 ◽  
Vol 113 (5-6) ◽  
pp. 495-513 ◽  
Author(s):  
Thomas Parr ◽  
Karl J. Friston
Keyword(s):  
Free Energy ◽  
Generative Model ◽  
Free Energy Function ◽  
Active Inference ◽  
Future Expectations ◽  
Perceptual Inference ◽  
Energy Functionals ◽  
Sensory Data ◽  
The World ◽  
The Brain

Abstract Active inference is an approach to understanding behaviour that rests upon the idea that the brain uses an internal generative model to predict incoming sensory data. The fit between this model and data may be improved in two ways. The brain could optimise probabilistic beliefs about the variables in the generative model (i.e. perceptual inference). Alternatively, by acting on the world, it could change the sensory data, such that they are more consistent with the model. This implies a common objective function (variational free energy) for action and perception that scores the fit between an internal model and the world. We compare two free energy functionals for active inference in the framework of Markov decision processes. One of these is a functional of beliefs (i.e. probability distributions) about states and policies, but a function of observations, while the second is a functional of beliefs about all three. In the former (expected free energy), prior beliefs about outcomes are not part of the generative model (because they are absorbed into the prior over policies). Conversely, in the second (generalised free energy), priors over outcomes become an explicit component of the generative model. When using the free energy function, which is blind to future observations, we equip the generative model with a prior over policies that ensure preferred (i.e. priors over) outcomes are realised. In other words, if we expect to encounter a particular kind of outcome, this lends plausibility to those policies for which this outcome is a consequence. In addition, this formulation ensures that selected policies minimise uncertainty about future outcomes by minimising the free energy expected in the future. When using the free energy functional—that effectively treats future observations as hidden states—we show that policies are inferred or selected that realise prior preferences by minimising the free energy of future expectations. Interestingly, the form of posterior beliefs about policies (and associated belief updating) turns out to be identical under both formulations, but the quantities used to compute them are not.

scholarly journals Active Inference and Learning in the Cerebellum

2016 ◽  
Vol 28 (9) ◽  
pp. 1812-1839 ◽  
Author(s):  
Karl Friston ◽  
Ivan Herreros
Keyword(s):  
Eyeblink Conditioning ◽  
Predictive Coding ◽  
Trace Conditioning ◽  
Face Validity ◽  
Active Inference ◽  
Focal Lesions ◽  
Delay Conditioning ◽  
Neuronal Populations ◽  
Intrinsic Connectivity ◽  
The Face

This letter offers a computational account of Pavlovian conditioning in the cerebellum based on active inference and predictive coding. Using eyeblink conditioning as a canonical paradigm, we formulate a minimal generative model that can account for spontaneous blinking, startle responses, and (delay or trace) conditioning. We then establish the face validity of the model using simulated responses to unconditioned and conditioned stimuli to reproduce the sorts of behavior that are observed empirically. The scheme’s anatomical validity is then addressed by associating variables in the predictive coding scheme with nuclei and neuronal populations to match the (extrinsic and intrinsic) connectivity of the cerebellar (eyeblink conditioning) system. Finally, we try to establish predictive validity by reproducing selective failures of delay conditioning, trace conditioning, and extinction using (simulated and reversible) focal lesions. Although rather metaphorical, the ensuing scheme can account for a remarkable range of anatomical and neurophysiological aspects of cerebellar circuitry—and the specificity of lesion-deficit mappings that have been established experimentally. From a computational perspective, this work shows how conditioning or learning can be formulated in terms of minimizing variational free energy (or maximizing Bayesian model evidence) using exactly the same principles that underlie predictive coding in perception.

Affectivity during social behaviour in a schizophrenic-like rat

2016 ◽  
Vol 33 (S1) ◽  
pp. S101-S102
Author(s):  
L. Kai ◽  
D. Gill ◽  
G. Wegener ◽  
A. Tasker
Keyword(s):  
High Frequency ◽  
Social Behaviour ◽  
Domoic Acid ◽  
Affective State ◽  
Emotional Valence ◽  
Sensitive Period ◽  
Sprague Dawley ◽  
The Social ◽  
Four Levels ◽  
Behavioural Tests

IntroductionRats are social animals that produce high-frequency whistles said to reflect their underlying affective state. Injecting rats with a glutamate agonist (domoic acid) at a sensitive period of brain development, models aspects of schizophrenia. This is known as the neonatal DOM model.AimsWe investigated whether DOM rats display altered social behaviour – as seen in patients with schizophrenia – using their high-frequency whistles as a proxy for the emotional valence of social situations.MethodsWe used 19 male Sprague Dawley rats, injected with either a low-dose of domoic acid or saline at postnatal days 8 to 14. The social behaviour of the rats was investigated at four levels:– anticipation of social interaction;– dyadic encounter;– three-chamber test;– tickling.Tests were carried out at postnatal days 34 to 40 and 50 to 56. Rat whistles were recorded on all days of testing.ResultsIn progress.ConclusionsThe interest in rat whistles as a supplement to traditional behavioural tests has increased. New software allows for detailed qualitative analysis of the whistle subtypes and thus new complexity to their interpretation. This study can help unravel information encoded in the whistles and shed light on the social behaviour of the DOM rat thus investigating it is applicability as a model of schizophrenia.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Within- and Between-Persons Effects of Self-Esteem and Affective State as Antecedents and Consequences of Dysfunctional Behaviors in the Everyday Lives of Patients With Borderline Personality Disorder

2020 ◽  
Vol 8 (3) ◽  
pp. 428-449 ◽  
Author(s):  
Philip S. Santangelo ◽  
Jana Holtmann ◽  
Georg Hosoya ◽  
Martin Bohus ◽  
Tobias D. Kockler ◽  
...  
Keyword(s):  
Borderline Personality Disorder ◽  
Personality Disorder ◽  
Structural Equation ◽  
Affective State ◽  
Emotional Valence ◽  
Self Esteem ◽  
Borderline Personality ◽  
Equation Modeling ◽  
The Everyday ◽  
Antecedents And Consequences

Dysfunctional behaviors are conceptualized as maladaptive affective coping attempts in borderline personality disorder (BPD). The recent benefits-and-barriers model extended the affective function assumption by adding self-esteem as a barrier to engaging in dysfunctional behaviors. Patients with BPD ( N = 119) carried e-diaries to report their current self-esteem, emotional valence, tense arousal, and whether they engaged in dysfunctional behaviors 12 times a day for 4 days. Dynamic structural equation modeling revealed that on the within-person level, high momentary negative affect predicted dysfunctional behaviors, and on the between-person level, low trait self-esteem predicted dysfunctional behaviors. We also found an association between engaging in dysfunctional behaviors and momentary self-esteem and trait levels of valence and tense arousal. Moreover, our results indicate a deterioration of, rather than relief from, negative affective state after dysfunctional behaviors. These findings highlight the importance of emotion-regulation skills and reestablishing a positive self-view as important treatment targets to reduce dysfunctional behaviors in BPD.

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