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Anxious individuals have difficulty learning the causal statistics of aversive environments.

Browning M, Behrens TE, Jocham G, O'Reilly JX, Bishop SJ - Nat. Neurosci. (2015)

Bottom Line: Statistical regularities in the causal structure of the environment enable us to predict the probable outcomes of our actions.We tested this using an aversive learning task manipulating environmental volatility.This was linked to reduced sensitivity of the pupil dilatory response to volatility, potentially indicative of altered norepinephrinergic responsivity to changes in this aspect of environmental information.

View Article: PubMed Central - PubMed

Affiliation: Functional MRI of the Brain Centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford, UK.

ABSTRACT
Statistical regularities in the causal structure of the environment enable us to predict the probable outcomes of our actions. Environments differ in the extent to which action-outcome contingencies are stable or volatile. Difficulty in being able to use this information to optimally update outcome predictions might contribute to the decision-making difficulties seen in anxiety. We tested this using an aversive learning task manipulating environmental volatility. Human participants low in trait anxiety matched updating of their outcome predictions to the volatility of the current environment, as predicted by a Bayesian model. Individuals with high trait anxiety showed less ability to adjust updating of outcome expectancies between stable and volatile environments. This was linked to reduced sensitivity of the pupil dilatory response to volatility, potentially indicative of altered norepinephrinergic responsivity to changes in this aspect of environmental information.

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Estimates of Participants’ Learning Ratesa) Participants’ choices during the stable and volatile blocks of the aversive learning task were fitted with a Rescorla Wagner learning model in which learning rate was allowed to vary. Estimates of individual participants’ learning rates are displayed (circles) separately for the stable and volatile blocks for the two task schedules (Schedule 1= stable task block first, n=15, Schedule 2 = volatile task block first, n=15). A logarithmic scale is used. Black lines display mean (+−SEM) of participant learning rates, grey dotted lines link the learning rates in volatile and stable blocks for each participant. Participants showed higher learning rates in the volatile versus stable blocks regardless of the order in which they were completed, F(1,28)=16.3, p<0.001. b) The relative log learning rate for the volatile versus the stable blocks (i.e. log (LR in volatile block) – log (LR in stable block)) was negatively correlated with participant trait anxiety, r(28)=−0.42, p= 0.02. The black dotted line indicates the degree to which the model of an optimal Bayesian learner (as described by Behrens and colleagues8) adjusted its learning rate. As can be seen, low trait anxious participants altered their learning rates to a similar degree to the Bayesian Learner, with high trait anxious participants showing a reduced adaptation of learning rate between the volatile and stable blocks of the task. Error bars represent the standard deviation of the estimated parameters from the behavioral model for each subject.
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Figure 2: Estimates of Participants’ Learning Ratesa) Participants’ choices during the stable and volatile blocks of the aversive learning task were fitted with a Rescorla Wagner learning model in which learning rate was allowed to vary. Estimates of individual participants’ learning rates are displayed (circles) separately for the stable and volatile blocks for the two task schedules (Schedule 1= stable task block first, n=15, Schedule 2 = volatile task block first, n=15). A logarithmic scale is used. Black lines display mean (+−SEM) of participant learning rates, grey dotted lines link the learning rates in volatile and stable blocks for each participant. Participants showed higher learning rates in the volatile versus stable blocks regardless of the order in which they were completed, F(1,28)=16.3, p<0.001. b) The relative log learning rate for the volatile versus the stable blocks (i.e. log (LR in volatile block) – log (LR in stable block)) was negatively correlated with participant trait anxiety, r(28)=−0.42, p= 0.02. The black dotted line indicates the degree to which the model of an optimal Bayesian learner (as described by Behrens and colleagues8) adjusted its learning rate. As can be seen, low trait anxious participants altered their learning rates to a similar degree to the Bayesian Learner, with high trait anxious participants showing a reduced adaptation of learning rate between the volatile and stable blocks of the task. Error bars represent the standard deviation of the estimated parameters from the behavioral model for each subject.

Mentions: Learning rate ‘α’ reflects the extent to which participants’ choice behaviour is guided by the outcomes of recent actions versus those further back in the individual’s experience. At high learning rates, choice behaviour is strongly controlled by the outcomes of recent actions. More precisely, the difference between the expected and actual outcome on a given trial has a large impact on change in outcome expectancy and hence subsequent choice behaviour. In contrast, at low learning rates, surprising outcomes lead to little change in outcome expectancy and behaviour. In the current study, we estimated participants’ learning rates in the stable and volatile task blocks by fitting a simple Rescorla Wagner learning model to their choices in each task block (see online Methods and Supplementary Modeling Note). We assessed whether participants, as a group, adapted their learning rate in response to the change in environmental volatility between the stable and volatile blocks. Consistent with prior findings for reward8, which we replicated (Fig S2a), participants’ learning rates were higher in the volatile than stable blocks of our aversive learning task, F(1,28)=16.3, p<0.001 (Fig 2a), regardless of the order in which the two blocks were completed, F(1,28)=1.0, p=0.3.


Anxious individuals have difficulty learning the causal statistics of aversive environments.

Browning M, Behrens TE, Jocham G, O'Reilly JX, Bishop SJ - Nat. Neurosci. (2015)

Estimates of Participants’ Learning Ratesa) Participants’ choices during the stable and volatile blocks of the aversive learning task were fitted with a Rescorla Wagner learning model in which learning rate was allowed to vary. Estimates of individual participants’ learning rates are displayed (circles) separately for the stable and volatile blocks for the two task schedules (Schedule 1= stable task block first, n=15, Schedule 2 = volatile task block first, n=15). A logarithmic scale is used. Black lines display mean (+−SEM) of participant learning rates, grey dotted lines link the learning rates in volatile and stable blocks for each participant. Participants showed higher learning rates in the volatile versus stable blocks regardless of the order in which they were completed, F(1,28)=16.3, p<0.001. b) The relative log learning rate for the volatile versus the stable blocks (i.e. log (LR in volatile block) – log (LR in stable block)) was negatively correlated with participant trait anxiety, r(28)=−0.42, p= 0.02. The black dotted line indicates the degree to which the model of an optimal Bayesian learner (as described by Behrens and colleagues8) adjusted its learning rate. As can be seen, low trait anxious participants altered their learning rates to a similar degree to the Bayesian Learner, with high trait anxious participants showing a reduced adaptation of learning rate between the volatile and stable blocks of the task. Error bars represent the standard deviation of the estimated parameters from the behavioral model for each subject.
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Figure 2: Estimates of Participants’ Learning Ratesa) Participants’ choices during the stable and volatile blocks of the aversive learning task were fitted with a Rescorla Wagner learning model in which learning rate was allowed to vary. Estimates of individual participants’ learning rates are displayed (circles) separately for the stable and volatile blocks for the two task schedules (Schedule 1= stable task block first, n=15, Schedule 2 = volatile task block first, n=15). A logarithmic scale is used. Black lines display mean (+−SEM) of participant learning rates, grey dotted lines link the learning rates in volatile and stable blocks for each participant. Participants showed higher learning rates in the volatile versus stable blocks regardless of the order in which they were completed, F(1,28)=16.3, p<0.001. b) The relative log learning rate for the volatile versus the stable blocks (i.e. log (LR in volatile block) – log (LR in stable block)) was negatively correlated with participant trait anxiety, r(28)=−0.42, p= 0.02. The black dotted line indicates the degree to which the model of an optimal Bayesian learner (as described by Behrens and colleagues8) adjusted its learning rate. As can be seen, low trait anxious participants altered their learning rates to a similar degree to the Bayesian Learner, with high trait anxious participants showing a reduced adaptation of learning rate between the volatile and stable blocks of the task. Error bars represent the standard deviation of the estimated parameters from the behavioral model for each subject.
Mentions: Learning rate ‘α’ reflects the extent to which participants’ choice behaviour is guided by the outcomes of recent actions versus those further back in the individual’s experience. At high learning rates, choice behaviour is strongly controlled by the outcomes of recent actions. More precisely, the difference between the expected and actual outcome on a given trial has a large impact on change in outcome expectancy and hence subsequent choice behaviour. In contrast, at low learning rates, surprising outcomes lead to little change in outcome expectancy and behaviour. In the current study, we estimated participants’ learning rates in the stable and volatile task blocks by fitting a simple Rescorla Wagner learning model to their choices in each task block (see online Methods and Supplementary Modeling Note). We assessed whether participants, as a group, adapted their learning rate in response to the change in environmental volatility between the stable and volatile blocks. Consistent with prior findings for reward8, which we replicated (Fig S2a), participants’ learning rates were higher in the volatile than stable blocks of our aversive learning task, F(1,28)=16.3, p<0.001 (Fig 2a), regardless of the order in which the two blocks were completed, F(1,28)=1.0, p=0.3.

Bottom Line: Statistical regularities in the causal structure of the environment enable us to predict the probable outcomes of our actions.We tested this using an aversive learning task manipulating environmental volatility.This was linked to reduced sensitivity of the pupil dilatory response to volatility, potentially indicative of altered norepinephrinergic responsivity to changes in this aspect of environmental information.

View Article: PubMed Central - PubMed

Affiliation: Functional MRI of the Brain Centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford, UK.

ABSTRACT
Statistical regularities in the causal structure of the environment enable us to predict the probable outcomes of our actions. Environments differ in the extent to which action-outcome contingencies are stable or volatile. Difficulty in being able to use this information to optimally update outcome predictions might contribute to the decision-making difficulties seen in anxiety. We tested this using an aversive learning task manipulating environmental volatility. Human participants low in trait anxiety matched updating of their outcome predictions to the volatility of the current environment, as predicted by a Bayesian model. Individuals with high trait anxiety showed less ability to adjust updating of outcome expectancies between stable and volatile environments. This was linked to reduced sensitivity of the pupil dilatory response to volatility, potentially indicative of altered norepinephrinergic responsivity to changes in this aspect of environmental information.

Show MeSH
Related in: MedlinePlus