scholarly journals Mood dynamics are associated with learning and not choice

2019 ◽  
Author(s):  
Bastien Blain ◽  
Robb Rutledge
Keyword(s):  
Affective State ◽  
Computational Modelling ◽  
Risky Choice ◽  
Adaptive Behaviour ◽  
Prediction Errors ◽  
Affective States ◽  
Environmental Statistics ◽  
Probability Prediction ◽  
The Difference ◽  
Choice Tasks

Updating predictions about which stimuli are associated with reward is an important aspect of adaptive behaviour believed to relate to prediction errors, the difference between experienced and predicted outcomes. Behavioural sensitivity to prediction errors flexibly adapts to environmental statistics. Prediction errors also influence affective states during risky choice tasks that do not require learning, but the relationship between emotions and adaptive behaviour is unknown. Here, using computational modelling we found that mood dynamics, like behaviour, are sensitive to learning-relevant model variables (i.e., probability prediction error). Unlike behaviour, mood dynamics are not sensitive to model variables that influence choice (i.e., expected value), and increasing volatility does not reduce how many trials influence affective state. Finally, depressive symptoms reduce overall mood more in volatile than stable environments. Our findings suggest that mood dynamics are selective for variables relevant to adaptive behaviour and suggest a greater role for mood in learning than choice.

scholarly journals Amphetamine disrupts haemodynamic correlates of prediction errors in nucleus accumbens and orbitofrontal cortex

2019 ◽  
Author(s):  
Emilie Werlen ◽  
Soon-Lim Shin ◽  
Francois Gastambide ◽  
Jennifer Francois ◽  
Mark D Tricklebank ◽  
...  
Keyword(s):  
Nucleus Accumbens ◽  
Orbitofrontal Cortex ◽  
Prediction Error ◽  
Learning Task ◽  
Adaptive Behaviour ◽  
Diagnostic Feature ◽  
Prediction Errors ◽  
Guided Learning ◽  
The Difference ◽  
Precise Relationship

AbstractIn an uncertain world, the ability to predict and update the relationships between environmental cues and outcomes is a fundamental element of adaptive behaviour. This type of learning is typically thought to depend on prediction error, the difference between expected and experienced events, and in the reward domain this has been closely linked to mesolimbic dopamine. There is also increasing behavioural and neuroimaging evidence that disruption to this process may be a cross-diagnostic feature of several neuropsychiatric and neurological disorders in which dopamine is dysregulated. However, the precise relationship between haemodynamic measures, dopamine and reward-guided learning remains unclear. To help address this issue, we used a translational technique, oxygen amperometry, to record haemodynamic signals in the nucleus accumbens (NAc) and orbitofrontal cortex (OFC) while freely-moving rats performed a probabilistic Pavlovian learning task. Using a model-based analysis approach to account for individual variations in learning, we found that the oxygen signal in the NAc correlated with a reward prediction error, whereas in the OFC it correlated with an unsigned prediction error or salience signal. Furthermore, an acute dose of amphetamine, creating a hyperdopaminergic state, disrupted rats’ ability to discriminate between cues associated with either a high or a low probability of reward and concomitantly corrupted prediction error signalling. These results demonstrate parallel but distinct prediction error signals in NAc and OFC during learning, both of which are affected by psychostimulant administration. Furthermore, they establish the viability of tracking and manipulating haemodynamic signatures of reward-guided learning observed in human fMRI studies using a proxy signal for BOLD in a freely behaving rodent.

scholarly journals Momentary subjective well-being depends on learning and not reward

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Bastien Blain ◽  
Robb B Rutledge
Keyword(s):  
Prediction Error ◽  
Computational Modelling ◽  
Well Being ◽  
Learning Task ◽  
Adaptive Behaviour ◽  
Subjective Well Being ◽  
Reward Prediction Error ◽  
Reward Prediction ◽  
The Difference ◽  
Momentary Happiness

Subjective well-being or happiness is often associated with wealth. Recent studies suggest that momentary happiness is associated with reward prediction error, the difference between experienced and predicted reward, a key component of adaptive behaviour. We tested subjects in a reinforcement learning task in which reward size and probability were uncorrelated, allowing us to dissociate between the contributions of reward and learning to happiness. Using computational modelling, we found convergent evidence across stable and volatile learning tasks that happiness, like behaviour, is sensitive to learning-relevant variables (i.e. probability prediction error). Unlike behaviour, happiness is not sensitive to learning-irrelevant variables (i.e. reward prediction error). Increasing volatility reduces how many past trials influence behaviour but not happiness. Finally, depressive symptoms reduce happiness more in volatile than stable environments. Our results suggest that how we learn about our world may be more important for how we feel than the rewards we actually receive.

scholarly journals The Predictive Dynamics of Happiness and Well-Being

2021 ◽  
pp. 175407392110638
Author(s):  
Mark Miller ◽  
Erik Rietveld ◽  
Julian Kiverstein
Keyword(s):  
Mental Health ◽  
Well Being ◽  
Predictive Processing ◽  
Prediction Errors ◽  
Affective States ◽  
Human Flourishing ◽  
Making Sense ◽  
The Difference ◽  
Momentary Happiness ◽  
Error Dynamics

We offer an account of mental health and well-being using the predictive processing framework (PPF). According to this framework, the difference between mental health and psychopathology can be located in the goodness of the predictive model as a regulator of action. What is crucial for avoiding the rigid patterns of thinking, feeling and acting associated with psychopathology is the regulation of action based on the valence of affective states. In PPF, valence is modelled as error dynamics—the change in prediction errors over time . Our aim in this paper is to show how error dynamics can account for both momentary happiness and longer term well-being. What will emerge is a new neurocomputational framework for making sense of human flourishing.

scholarly journals Amphetamine disrupts haemodynamic correlates of prediction errors in nucleus accumbens and orbitofrontal cortex

2019 ◽  
Vol 45 (5) ◽  
pp. 793-803 ◽  
Author(s):  
Emilie Werlen ◽  
Soon-Lim Shin ◽  
Francois Gastambide ◽  
Jennifer Francois ◽  
Mark D. Tricklebank ◽  
...  
Keyword(s):  
Nucleus Accumbens ◽  
Orbitofrontal Cortex ◽  
Prediction Error ◽  
Learning Task ◽  
Adaptive Behaviour ◽  
Diagnostic Feature ◽  
Prediction Errors ◽  
Guided Learning ◽  
The Difference ◽  
Precise Relationship

Abstract In an uncertain world, the ability to predict and update the relationships between environmental cues and outcomes is a fundamental element of adaptive behaviour. This type of learning is typically thought to depend on prediction error, the difference between expected and experienced events and in the reward domain that has been closely linked to mesolimbic dopamine. There is also increasing behavioural and neuroimaging evidence that disruption to this process may be a cross-diagnostic feature of several neuropsychiatric and neurological disorders in which dopamine is dysregulated. However, the precise relationship between haemodynamic measures, dopamine and reward-guided learning remains unclear. To help address this issue, we used a translational technique, oxygen amperometry, to record haemodynamic signals in the nucleus accumbens (NAc) and orbitofrontal cortex (OFC), while freely moving rats performed a probabilistic Pavlovian learning task. Using a model-based analysis approach to account for individual variations in learning, we found that the oxygen signal in the NAc correlated with a reward prediction error, whereas in the OFC it correlated with an unsigned prediction error or salience signal. Furthermore, an acute dose of amphetamine, creating a hyperdopaminergic state, disrupted rats’ ability to discriminate between cues associated with either a high or a low probability of reward and concomitantly corrupted prediction error signalling. These results demonstrate parallel but distinct prediction error signals in NAc and OFC during learning, both of which are affected by psychostimulant administration. Furthermore, they establish the viability of tracking and manipulating haemodynamic signatures of reward-guided learning observed in human fMRI studies by using a proxy signal for BOLD in a freely behaving rodent.

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 Towards estimating affective states in Virtual Reality based on behavioral data

2021 ◽  
Author(s):  
Valentin Holzwarth ◽  
Johannes Schneider ◽  
Joshua Handali ◽  
Joy Gisler ◽  
Christian Hirt ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Adaptive Systems ◽  
User Study ◽  
Affective State ◽  
Head Movements ◽  
Affective States ◽  
Physical Demand ◽  
Perceived Performance ◽  
Sequential Administration ◽  
Better Than

AbstractInferring users’ perceptions of Virtual Environments (VEs) is essential for Virtual Reality (VR) research. Traditionally, this is achieved through assessing users’ affective states before and after being exposed to a VE, based on standardized, self-assessment questionnaires. The main disadvantage of questionnaires is their sequential administration, i.e., a user’s affective state is measured asynchronously to its generation within the VE. A synchronous measurement of users’ affective states would be highly favorable, e.g., in the context of adaptive systems. Drawing from nonverbal behavior research, we argue that behavioral measures could be a powerful approach to assess users’ affective states in VR. In this paper, we contribute by providing methods and measures evaluated in a user study involving 42 participants to assess a users’ affective states by measuring head movements during VR exposure. We show that head yaw significantly correlates with presence, mental and physical demand, perceived performance, and system usability. We also exploit the identified relationships for two practical tasks that are based on head yaw: (1) predicting a user’s affective state, and (2) detecting manipulated questionnaire answers, i.e., answers that are possibly non-truthful. We found that affective states can be predicted significantly better than a naive estimate for mental demand, physical demand, perceived performance, and usability. Further, manipulated or non-truthful answers can also be estimated significantly better than by a naive approach. These findings mark an initial step in the development of novel methods to assess user perception of VEs.

scholarly journals The Interpretation of E-Motions in Faces and Bodies Derived from Static Artworks by Individuals with High Functioning Autistic Spectrum

Vision ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 17
Author(s):  
Maria Elisa Della-Torre ◽  
Daniele Zavagno ◽  
Rossana Actis-Grosso
Keyword(s):  
Positive Emotions ◽  
Emotional Experience ◽  
Affective State ◽  
Likert Scale ◽  
Autism Spectrum ◽  
Emotional Expressions ◽  
Affective States ◽  
Dynamic Component ◽  
Specific Emotion ◽  
Similar Range

E-motions are defined as those affective states the expressions of which—conveyed either by static faces or body posture—embody a dynamic component and, consequently, convey a higher sense of dynamicity than other emotional expressions. An experiment is presented, aimed at testing whether e-motions are perceived as such also by individuals with autism spectrum disorders (ASDs), which have been associated with impairments in emotion recognition and in motion perception. To this aim we replicate with ASD individuals a study, originally conducted with typically developed individuals (TDs), in which we showed to both ASD and TD participants 14 bodiless heads and 14 headless bodies taken from eleven static artworks and four drawings. The Experiment was divided into two sessions. In Session 1 participants were asked to freely associate each stimulus to an emotion or an affective state (Task 1, option A); if they were unable to find a specific emotion, the experimenter showed them a list of eight possible emotions (words) and asked them to choose one from such list, that best described the affective state portrayed in the image (Task 1, option B). After their choice, they were asked to rate the intensity of the perceived emotion on a seven point Likert scale (Task 2). In Session 2 participants were requested to evaluate the degree of dynamicity conveyed by each stimulus on a 7 point Likert scale. Results showed that ASDs and TDs shared a similar range of verbal expressions defining emotions; however, ASDs (i) showed an impairment in the ability to spontaneously assign an emotion to a headless body, and (ii) they more frequently used terms denoting negative emotions (for both faces and bodies) as compared to neutral emotions, which in turn were more frequently used by TDs. No difference emerged between the two groups for positive emotions, with happiness being the emotion better recognized in both faces and in bodies. Although overall there are no significant differences between the two groups with respect to the emotions assigned to the images and the degree of perceived dynamicity, the interaction Artwork x Group showed that for some images ASDs assigned a different value than TDs to perceived dynamicity. Moreover, two images were interpreted by ASDs as conveying completely different emotions than those perceived by TDs. Results are discussed in light of the ability of ASDs to resolve ambiguity, and of possible different cognitive styles characterizing the aesthetical/emotional experience.

scholarly journals Effects of Classroom Activities on Affective State—A Comparison of Third and Fifth Graders

2016 ◽  
Vol 6 (2) ◽  
pp. 1 ◽  
Author(s):  
Michael Fartoukh ◽  
Lucile Chanquoy
Keyword(s):  
Academic Performance ◽  
Educational Psychology ◽  
Grade Level ◽  
Fifth Graders ◽  
Affective State ◽  
Third Graders ◽  
Cognitive Resources ◽  
Affective States ◽  
Classroom Activities ◽  
School Day

<p>We analysed the influence of classroom activities on children’s affective states. Children perform many different activities in the course of an ordinary school day, some of which may trigger changes in their affective state and thus in the availability of their cognitive resources and their degree of motivation. To observe the effects of two such activities (listening to a text and performing a dictation) on affective state, according to grade, we asked 39 third graders and 40 fifth graders to specify their affective state at several points in the day. Results showed that this state varied from one activity to another, and was also dependent on grade level. Third graders differed from fifth graders in the feelings elicited by the activities. The possible implications of these findings for the field of educational psychology and children’s academic performance are discussed.</p>

Perception of Risk as Related to Choice in a Two-Dimensional Risk Situation

1978 ◽  
Vol 43 (3_suppl) ◽  
pp. 1059-1062 ◽  
Author(s):  
John W. Dickson
Keyword(s):  
Risky Choice ◽  
Expected Value ◽  
Two Dimensions ◽  
Choice Situation ◽  
Two Dimensional ◽  
Objective Risk ◽  
Subjective Risk ◽  
Risk Situation ◽  
The Difference ◽  
Two Alternatives

A risky choice was created by manipulating two dimensions of risk for 21 managers attending a conference. The first dimension varied risk by altering the difference in expected value between two alternatives of widely differing variance. The second dimension varied the expectancy of achieving a particular outcome. Whereas choice was significantly related to both dimensions of risk, it was not significantly related to estimates of the subjective risk inherent in the choice situation. It appears that subjective risk does not mediate between objective risk and choice.

scholarly journals Intelligent Monitoring of Affective Factors Underlying Sport Performance by Means of Wearable and Mobile Technology

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1202 ◽  
Author(s):  
Carlos Bailon ◽  
Miguel Damas ◽  
Hector Pomares ◽  
Daniel Sanabria ◽  
Pandelis Perakakis ◽  
...  
Keyword(s):  
Wearable Sensors ◽  
Affective State ◽  
Sport Performance ◽  
Affective States ◽  
Complete Evaluation ◽  
Affective Factors ◽  
Physiological Variables ◽  
Sport Events ◽  
The Impact ◽  
The Relationship

The fluctuation of affective states is a contributing factor to sport performance variability. The context surrounding athletes during their daily life and the evolution of their physiological variables beyond sport events are relevant factors, as they modulate the affective state of the subject over time. However, traditional procedures to assess the affective state are limited to self-reported questionnaires within controlled settings, thus removing the impact of the context. This work proposes a multimodal, context-aware platform that combines the data acquired through smartphones and wearable sensors to assess the affective state of the athlete. The platform is aimed at ubiquitously monitoring the fluctuations of affective states during longitudinal studies within naturalistic environments, overcoming the limitations of previous studies and allowing for the complete evaluation of the factors that could modulate the affective state. This system will also facilitate and expedite the analysis of the relationship between affective states and sport performance.

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