scholarly journals Autistic traits are related to worse performance in a volatile reward learning task despite adaptive learning rates

Autism ◽  
2020 ◽  
pp. 136236132096223 ◽  
Author(s):  
Judith Goris ◽  
Massimo Silvetti ◽  
Tom Verguts ◽  
Jan R Wiersema ◽  
Marcel Brass ◽  
...  
Keyword(s):  
Adaptive Learning ◽  
Autistic Traits ◽  
Learning Task ◽  
Low Noise ◽  
Reward Learning ◽  
Optimal Decision ◽  
Noise Condition ◽  
Impaired Performance ◽  
Learning Rates ◽  
Reward Contingencies

Recent theories propose that autism is characterized by an impairment in determining when to learn and when not. We investigated this by estimating learning rate in environments varying in volatility and uncertainty. Specifically, we correlated autistic traits (in 163 neurotypical participants) with modelled learning behaviour during probabilistic reward learning under the following three conditions: a Stationary Low Noise condition with stable reward contingencies, a Volatile condition with changing reward contingencies and a Stationary High Noise condition where reward probabilities for all options were 60%, resulting in an uncertain, noisy environment. Consistent with earlier findings, we found less optimal decision-making in the Volatile condition for participants with more autistic traits. However, we observed no correlations between underlying adjustments in learning rates and autistic traits, suggesting no impairment in updating learning rates in response to volatile versus noisy environments. Exploratory analyses indicated that impaired performance in the Volatile condition in participants with more autistic traits, was specific to trials with reward contingencies opposite to those initially learned, suggesting a primacy bias. We conclude that performance in volatile environments is lower in participants with more autistic traits, but this cannot be unambiguously attributed to difficulties with adjusting learning rates. Lay abstract Recent theories propose that autism is characterized by an impairment in determining when to learn and when not. Here, we investigated this hypothesis by estimating learning rates (i.e. the speed with which one learns) in three different environments that differed in rule stability and uncertainty. We found that neurotypical participants with more autistic traits performed worse in a volatile environment (with unstable rules), as they chose less often for the most rewarding option. Exploratory analyses indicated that performance was specifically worse when reward rules were opposite to those initially learned for participants with more autistic traits. However, there were no differences in the adjustment of learning rates between participants with more versus less autistic traits. Together, these results suggest that performance in volatile environments is lower in participants with more autistic traits, but that this performance difference cannot be unambiguously explained by an impairment in adjusting learning rates.

scholarly journals Autistic traits are related to worse performance in a volatile reward learning task despite adaptive learning rates

2019 ◽  
Author(s):  
Judith Goris ◽  
Massimo Silvetti ◽  
Tom Verguts ◽  
Jan R. Wiersema ◽  
Marcel Brass ◽  
...  
Keyword(s):  
Adaptive Learning ◽  
Autistic Traits ◽  
Autism Spectrum ◽  
Low Noise ◽  
Reward Learning ◽  
Optimal Decision ◽  
Noise Condition ◽  
Impaired Performance ◽  
Learning Rates ◽  
Reward Contingencies

Recent theories propose that autism spectrum disorder (ASD) is characterized by an impairment in determining when to learn and when not. We investigated this by estimating learning rate in environments varying in volatility and uncertainty. Specifically, we correlated autistic traits (in 163 neurotypical participants) with modelled learning behavior during probabilistic reward learning under three conditions: a Stationary Low Noise condition with stable reward contingencies, a Volatile condition with changing reward contingencies, and a Stationary High Noise condition where reward probabilities for all options were 60%, resulting in an uncertain, noisy environment. Consistent with earlier findings, we found less optimal decision-making in the Volatile condition for participants with more autistic traits. However, we observed no correlations between underlying adjustments in learning rates and autistic traits, suggesting no impairment in updating learning rates in response to volatile versus noisy environments. Exploratory analyses indicated that impaired performance in the Volatile condition in participants with more autistic traits, was specific to trials with reward contingencies opposite to those initially learned, suggesting a primacy bias. We conclude that performance in volatile environments is lower in participants with more autistic traits, but this cannot be unambiguously attributed to difficulties with adjusting learning rates.

scholarly journals Monetary feedback modulates performance and electrophysiological indices of belief updating in reward learning

2017 ◽  
Author(s):  
Daniel Bennett ◽  
Karen Sasmita ◽  
Ryan T. Maloney ◽  
Carsten Murawski ◽  
Stefan Bode
Keyword(s):  
Bayesian Inference ◽  
Cognitive Strategies ◽  
Learning Task ◽  
Feedback Condition ◽  
Event Related Potential ◽  
Reward Learning ◽  
Instructive Feedback ◽  
Motivational State ◽  
Belief Updating ◽  
Learning Rates

AbstractBelief updating entails the incorporation of new information about the environment into internal models of the world. Bayesian inference is the statistically optimal strategy for performing belief updating in the presence of uncertainty. An important open question is whether the use of cognitive strategies that implement Bayesian inference is dependent upon motivational state and, if so, how this is reflected in electrophysiological signatures of belief updating in the brain. Here we recorded the electroencephalogram of participants performing a simple reward learning task with both monetary and non-monetary instructive feedback conditions. Our aim was to distinguish the influence of the rewarding properties of feedback on belief updating from the information content of the feedback itself. A Bayesian updating model allowed us to quantify different aspects of belief updating across trials, including the size of belief updates and the uncertainty of beliefs. Faster learning rates were observed in the monetary feedback condition compared to the instructive feedback condition, while belief updates were generally larger, and belief uncertainty smaller, with monetary compared to instructive feedback. Larger amplitudes in the monetary feedback condition were found for three event-related potential components: the P3a, the feedback-related negativity (FRN) and the late positive potential (LPP). These findings suggest that motivational state influences inference strategies in reward learning, and this is reflected in the electrophysiological correlates of belief updating.

scholarly journals Adolescents Adapt More Slowly than Adults to Varying Reward Contingencies

2014 ◽  
Vol 26 (12) ◽  
pp. 2670-2681 ◽  
Author(s):  
Amir Homayoun Javadi ◽  
Dirk H. K. Schmidt ◽  
Michael N. Smolka
Keyword(s):  
Adaptive Learning ◽  
Prediction Error ◽  
Learning Task ◽  
Expected Value ◽  
Reward Processing ◽  
Learning Rate ◽  
Current State ◽  
Adolescents And Adults ◽  
Reward Contingencies ◽  
Age Range

It has been suggested that adolescents process rewards differently from adults, both cognitively and affectively. In an fMRI study we recorded brain BOLD activity of adolescents (age range = 14–15 years) and adults (age range = 20–39 years) to investigate the developmental changes in reward processing and decision-making. In a probabilistic reversal learning task, adolescents and adults adapted to changes in reward contingencies. We used a reinforcement learning model with an adaptive learning rate for each trial to model the adolescents' and adults' behavior. Results showed that adolescents possessed a shallower slope in the sigmoid curve governing the relation between expected value (the value of the expected feedback, +1 and −1 representing rewarding and punishing feedback, respectively) and probability of stay (selecting the same option as in the previous trial). Trial-by-trial change in expected values after being correct or wrong was significantly different between adolescents and adults. These values were closer to certainty for adults. Additionally, absolute value of model-derived prediction error for adolescents was significantly higher after a correct response but a punishing feedback. At the neural level, BOLD correlates of learning rate, expected value, and prediction error did not significantly differ between adolescents and adults. Nor did we see group differences in the prediction error-related BOLD signal for different trial types. Our results indicate that adults seem to behaviorally integrate punishing feedback better than adolescents in their estimation of the current state of the contingencies. On the basis of these results, we argue that adolescents made decisions with less certainty when compared with adults and speculate that adolescents acquired a less accurate knowledge of their current state, that is, of being correct or wrong.

Knowledge Acquired from Learning: New Evidence of Hierarchical Conceptualization

1994 ◽  
Vol 79 (2) ◽  
pp. 975-993 ◽  
Author(s):  
Alberto Montare
Keyword(s):  
Sex Differences ◽  
Knowledge Acquisition ◽  
Reversal Learning ◽  
Concept Formation ◽  
Learning Task ◽  
Learning Rates ◽  
Level 3 ◽  
Level 1 ◽  
New Evidence ◽  
Level 2

Following successful inductive acquisition of procedural cognition of a discrimination-reversal learning task, 50 female and 50 male undergraduates articulated declarative cognizance of knowledge acquired from learning. Tests of four hypotheses showed that (1) increasingly higher levels of declarative cognizance were associated with faster learning rates, (2) six new cases of cognition-without-cognizance were observed, (3) students presumably using secondary signalization learned faster than those presumably using primary signalization, and (4) no sex differences in learning rates or declarative cognizance were observed. The notion that explicit levels of declarative cognizance may represent implicit hierarchical conceptualization comprised of four systems of knowledge acquisition led to the conclusions that primary signalization may account for inductive senscept formation at Level 1 and for inductive percept formation at Level 2, whereas emergent secondary signalization may account for inductive precept formation at Level 3 and for inductive concept formation at Level 4.

scholarly journals Autistic Traits Moderate the Impact of Reward Learning on Social Behaviour

2015 ◽  
Vol 9 (4) ◽  
pp. 471-479 ◽  
Author(s):  
Maria Serena Panasiti ◽  
Ignazio Puzzo ◽  
Bhismadev Chakrabarti
Keyword(s):  
Social Behaviour ◽  
Autistic Traits ◽  
Reward Learning ◽  
The Impact

scholarly journals Smoking as a Common Modulator of Sensory Gating and Reward Learning in Individuals with Psychotic Disorders

2021 ◽  
Vol 11 (12) ◽  
pp. 1581
Author(s):  
Alexis E. Whitton ◽  
Kathryn E. Lewandowski ◽  
Mei-Hua Hall
Keyword(s):  
Bipolar Disorder ◽  
Prediction Error ◽  
Psychotic Disorders ◽  
Predictive Coding ◽  
Sensory Gating ◽  
Event Related Potential ◽  
Reward Learning ◽  
Smoking History ◽  
Impaired Performance ◽  
Psychotic Bipolar Disorder

Motivational and perceptual disturbances co-occur in psychosis and have been linked to aberrations in reward learning and sensory gating, respectively. Although traditionally studied independently, when viewed through a predictive coding framework, these processes can both be linked to dysfunction in striatal dopaminergic prediction error signaling. This study examined whether reward learning and sensory gating are correlated in individuals with psychotic disorders, and whether nicotine—a psychostimulant that amplifies phasic striatal dopamine firing—is a common modulator of these two processes. We recruited 183 patients with psychotic disorders (79 schizophrenia, 104 psychotic bipolar disorder) and 129 controls and assessed reward learning (behavioral probabilistic reward task), sensory gating (P50 event-related potential), and smoking history. Reward learning and sensory gating were correlated across the sample. Smoking influenced reward learning and sensory gating in both patient groups; however, the effects were in opposite directions. Specifically, smoking was associated with improved performance in individuals with schizophrenia but impaired performance in individuals with psychotic bipolar disorder. These findings suggest that reward learning and sensory gating are linked and modulated by smoking. However, disorder-specific associations with smoking suggest that nicotine may expose pathophysiological differences in the architecture and function of prediction error circuitry in these overlapping yet distinct psychotic disorders.

scholarly journals Consolidation of reward memory during sleep does not require dopaminergic activation

2019 ◽  
Author(s):  
M Alizadeh Asfestani ◽  
V Brechtmann ◽  
J Santiago ◽  
J Born ◽  
GB Feld
Keyword(s):  
Learning Task ◽  
Reward Learning ◽  
Nocturnal Sleep ◽  
Double Blind ◽  
Dopaminergic Neurotransmission ◽  
Long Duration ◽  
Differential Retention ◽  
Better Than ◽  
Dopaminergic Activation

AbstractSleep enhances memories, especially, if they are related to future rewards. Although dopamine has been shown to be a key determinant during reward learning, the role of dopaminergic neurotransmission for amplifying reward-related memories during sleep remains unclear. In the present study, we scrutinize the idea that dopamine is needed for the preferential consolidation of rewarded information. We blocked dopaminergic neurotransmission, thereby aiming to wipe out preferential sleep-dependent consolidation of high over low rewarded memories during sleep. Following a double-blind, balanced, crossover design 20 young healthy men received the dopamine d2-like receptor blocker Sulpiride (800 mg) or placebo, after learning a Motivated Learning Task. The task required participants to memorize 80 highly and 80 lowly rewarded pictures. Half of them were presented for a short (750 ms) and a long duration (1500 ms), respectively, which enabled to dissociate effects of reward on sleep-associated consolidation from those of mere encoding depth. Retrieval was tested after a retention interval of 20 h that included 8 h of nocturnal sleep. As expected, at retrieval, highly rewarded memories were remembered better than lowly rewarded memories, under placebo. However, there was no evidence for an effect of blocking dopaminergic neurotransmission with Sulpiride during sleep on this differential retention of rewarded information. This result indicates that dopaminergic activation is not required for the preferential consolidation of reward-associated memory. Rather it appears that dopaminergic activation only tags such memories at encoding for intensified reprocessing during sleep.

scholarly journals Consolidation of Reward Memory during Sleep Does Not Require Dopaminergic Activation

2020 ◽  
Vol 32 (9) ◽  
pp. 1688-1703 ◽  
Author(s):  
Marjan Alizadeh Asfestani ◽  
Valentin Brechtmann ◽  
João Santiago ◽  
Andreas Peter ◽  
Jan Born ◽  
...  
Keyword(s):  
Learning Task ◽  
Reward Learning ◽  
Nocturnal Sleep ◽  
Double Blind ◽  
Dopaminergic Neurotransmission ◽  
Msec Duration ◽  
Differential Retention ◽  
Better Than ◽  
Dopaminergic Activation

Sleep enhances memories, especially if they are related to future rewards. Although dopamine has been shown to be a key determinant during reward learning, the role of dopaminergic neurotransmission for amplifying reward-related memories during sleep remains unclear. In this study, we scrutinize the idea that dopamine is needed for the preferential consolidation of rewarded information. We impaired dopaminergic neurotransmission, thereby aiming to wipe out preferential sleep-dependent consolidation of high- over low-rewarded memories during sleep. Following a double-blind, balanced, crossover design, 17 young healthy men received the dopamine d2-like receptor blocker sulpiride (800 mg) or placebo, after learning a motivated learning task. The task required participants to memorize 80 highly and 80 lowly rewarded pictures. Half of them were presented for a short (750 msec) and a long (1500 msec) duration, respectively, which permitted dissociation of the effects of reward on sleep-associated consolidation from those of mere encoding depth. Retrieval was tested after a retention interval of approximately 22 hr that included 8 hr of nocturnal sleep. As expected, at retrieval, highly rewarded memories were remembered better than lowly rewarded memories, under placebo. However, there was no evidence for an effect of reducing dopaminergic neurotransmission with sulpiride during sleep on this differential retention of rewarded information. This result indicates that dopaminergic activation likely is not required for the preferential consolidation of reward-associated memory. Rather, it appears that dopaminergic activation only tags such memories at encoding for intensified reprocessing during sleep.

scholarly journals Neural signatures of reward and sensory error feedback processing in motor learning

2019 ◽  
Vol 121 (4) ◽  
pp. 1561-1574 ◽  
Author(s):  
Dimitrios J. Palidis ◽  
Joshua G. A. Cashaback ◽  
Paul L. Gribble
Keyword(s):  
Visual Feedback ◽  
Motor Adaptation ◽  
Learning Task ◽  
Reward Learning ◽  
P300 Amplitude ◽  
Error Feedback ◽  
Learning Mechanisms ◽  
Visuomotor Rotation ◽  
Feedback Processing ◽  
Motor Commands

At least two distinct processes have been identified by which motor commands are adapted according to movement-related feedback: reward-based learning and sensory error-based learning. In sensory error-based learning, mappings between sensory targets and motor commands are recalibrated according to sensory error feedback. In reward-based learning, motor commands are associated with subjective value, such that successful actions are reinforced. We designed two tasks to isolate reward- and sensory error-based motor adaptation, and we used electroencephalography in humans to identify and dissociate the neural correlates of reward and sensory error feedback processing. We designed a visuomotor rotation task to isolate sensory error-based learning that was induced by altered visual feedback of hand position. In a reward learning task, we isolated reward-based learning induced by binary reward feedback that was decoupled from the visual target. A fronto-central event-related potential called the feedback-related negativity (FRN) was elicited specifically by binary reward feedback but not sensory error feedback. A more posterior component called the P300 was evoked by feedback in both tasks. In the visuomotor rotation task, P300 amplitude was increased by sensory error induced by perturbed visual feedback and was correlated with learning rate. In the reward learning task, P300 amplitude was increased by reward relative to nonreward and by surprise regardless of feedback valence. We propose that during motor adaptation the FRN specifically reflects a reward-based learning signal whereas the P300 reflects feedback processing that is related to adaptation more generally. NEW & NOTEWORTHY We studied the event-related potentials evoked by feedback stimuli during motor adaptation tasks that isolate reward- and sensory error-based learning mechanisms. We found that the feedback-related negativity was specifically elicited by binary reward feedback, whereas the P300 was observed in both tasks. These results reveal neural processes associated with different learning mechanisms and elucidate which classes of errors, from a computational standpoint, elicit the feedback-related negativity and P300.

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