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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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Post Outcome Pupil Dilation Tracks Both Environmental Volatility and Outcome SurpriseTime courses for the effect of trial-wise estimates of (a) volatility and (b) surprise on pupil dilation following presentation of the outcome. The graphs show the mean across participants (n=28) of the beta weights obtained by regressing post outcome pupil dilation against trial-wise estimates of environmental volatility and outcome surprise. Post outcome pupil dilation was greater for trials where environmental volatility was high, F(1,26)=9.8, p=0.004, and the outcome was surprising, F(1,26)=9.2, p=0.005. Asterisks indicate 1s time bins in which the effect of volatility or surprise on pupil dilation post outcome differed significantly from zero (bonferonni corrected for multiple comparisons, ps corrected <.05). The effect of trial-wise volatility was longer lasting and had a later onset than that of outcome surprise. (c) The degree to which an individual’s pupil tracked volatility (calculated as the mean beta weight across the 6 second post-outcome period) predicted change in learning rate between volatile and stable blocks, r(26)=0.37, p=0.05. (d) The degree to which an individual’s pupil tracked surprise predicted extent of surprise-related choice reaction time slowing on the subsequent trial, r(26)=0.44, p=0.02. Shaded regions in panels a and b represent the standard error of the mean. Error bars in panels c and d represent the standard deviations of the regression coefficients (beta weights) from the pupil analysis and the parameter estimates from the behavioral model for each subject.
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Figure 3: Post Outcome Pupil Dilation Tracks Both Environmental Volatility and Outcome SurpriseTime courses for the effect of trial-wise estimates of (a) volatility and (b) surprise on pupil dilation following presentation of the outcome. The graphs show the mean across participants (n=28) of the beta weights obtained by regressing post outcome pupil dilation against trial-wise estimates of environmental volatility and outcome surprise. Post outcome pupil dilation was greater for trials where environmental volatility was high, F(1,26)=9.8, p=0.004, and the outcome was surprising, F(1,26)=9.2, p=0.005. Asterisks indicate 1s time bins in which the effect of volatility or surprise on pupil dilation post outcome differed significantly from zero (bonferonni corrected for multiple comparisons, ps corrected <.05). The effect of trial-wise volatility was longer lasting and had a later onset than that of outcome surprise. (c) The degree to which an individual’s pupil tracked volatility (calculated as the mean beta weight across the 6 second post-outcome period) predicted change in learning rate between volatile and stable blocks, r(26)=0.37, p=0.05. (d) The degree to which an individual’s pupil tracked surprise predicted extent of surprise-related choice reaction time slowing on the subsequent trial, r(26)=0.44, p=0.02. Shaded regions in panels a and b represent the standard error of the mean. Error bars in panels c and d represent the standard deviations of the regression coefficients (beta weights) from the pupil analysis and the parameter estimates from the behavioral model for each subject.

Mentions: Group-level analyses revealed that trial volatility was significantly associated with an increase in pupil diameter following outcome delivery, F(1, 26)=9.8, p=0.004. Bonferroni corrected one sample t-tests, performed for each time bin, indicated that this effect was significant from 2–5s post outcome, Fig 3a. Outcome surprise was also positively associated with an increase in pupil diameter, F(1, 26)=9.2, p=0.005. The effect of surprise was observed slightly earlier than that of volatility, being significant from 1–3s post outcome, Fig 3b. For each individual, we additionally calculated single trial-wise summary measures of pupil responsivity to volatility and outcome surprise. These were estimated using the mean beta-weight of the surprise and volatility regressors across the whole 6 second post-outcome period. Correlational analyses using these summary measures revealed that participants’ mean pupil response to trial-wise volatility predicted the degree to which they adjusted their learning rate between the volatile and stable blocks of the task, r(26)=0.37, p=0.05, Fig 3c. Additionally, participants’ mean pupil response to outcome surprise predicted the extent to which they showed choice reaction time slowing as a function of the unexpectedness of the previous trial’s outcome, r(26)=0.44, p=0.02, Fig 3d. These pupil dilation parameters may reflect changes in activity within the locus coeruleus norepinephrine system, though additional dopaminergic or cholinergic influences cannot be ruled out. The current findings are in line with the existence of a functional relationship between the alterations in neurotransmission which underlie these pupilometry changes and the mechanisms which enable environmental statistics to be used to guide learning about the causal structure of the environment13–16.


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)

Post Outcome Pupil Dilation Tracks Both Environmental Volatility and Outcome SurpriseTime courses for the effect of trial-wise estimates of (a) volatility and (b) surprise on pupil dilation following presentation of the outcome. The graphs show the mean across participants (n=28) of the beta weights obtained by regressing post outcome pupil dilation against trial-wise estimates of environmental volatility and outcome surprise. Post outcome pupil dilation was greater for trials where environmental volatility was high, F(1,26)=9.8, p=0.004, and the outcome was surprising, F(1,26)=9.2, p=0.005. Asterisks indicate 1s time bins in which the effect of volatility or surprise on pupil dilation post outcome differed significantly from zero (bonferonni corrected for multiple comparisons, ps corrected <.05). The effect of trial-wise volatility was longer lasting and had a later onset than that of outcome surprise. (c) The degree to which an individual’s pupil tracked volatility (calculated as the mean beta weight across the 6 second post-outcome period) predicted change in learning rate between volatile and stable blocks, r(26)=0.37, p=0.05. (d) The degree to which an individual’s pupil tracked surprise predicted extent of surprise-related choice reaction time slowing on the subsequent trial, r(26)=0.44, p=0.02. Shaded regions in panels a and b represent the standard error of the mean. Error bars in panels c and d represent the standard deviations of the regression coefficients (beta weights) from the pupil analysis and the parameter estimates from the behavioral model for each subject.
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Related In: Results  -  Collection

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Figure 3: Post Outcome Pupil Dilation Tracks Both Environmental Volatility and Outcome SurpriseTime courses for the effect of trial-wise estimates of (a) volatility and (b) surprise on pupil dilation following presentation of the outcome. The graphs show the mean across participants (n=28) of the beta weights obtained by regressing post outcome pupil dilation against trial-wise estimates of environmental volatility and outcome surprise. Post outcome pupil dilation was greater for trials where environmental volatility was high, F(1,26)=9.8, p=0.004, and the outcome was surprising, F(1,26)=9.2, p=0.005. Asterisks indicate 1s time bins in which the effect of volatility or surprise on pupil dilation post outcome differed significantly from zero (bonferonni corrected for multiple comparisons, ps corrected <.05). The effect of trial-wise volatility was longer lasting and had a later onset than that of outcome surprise. (c) The degree to which an individual’s pupil tracked volatility (calculated as the mean beta weight across the 6 second post-outcome period) predicted change in learning rate between volatile and stable blocks, r(26)=0.37, p=0.05. (d) The degree to which an individual’s pupil tracked surprise predicted extent of surprise-related choice reaction time slowing on the subsequent trial, r(26)=0.44, p=0.02. Shaded regions in panels a and b represent the standard error of the mean. Error bars in panels c and d represent the standard deviations of the regression coefficients (beta weights) from the pupil analysis and the parameter estimates from the behavioral model for each subject.
Mentions: Group-level analyses revealed that trial volatility was significantly associated with an increase in pupil diameter following outcome delivery, F(1, 26)=9.8, p=0.004. Bonferroni corrected one sample t-tests, performed for each time bin, indicated that this effect was significant from 2–5s post outcome, Fig 3a. Outcome surprise was also positively associated with an increase in pupil diameter, F(1, 26)=9.2, p=0.005. The effect of surprise was observed slightly earlier than that of volatility, being significant from 1–3s post outcome, Fig 3b. For each individual, we additionally calculated single trial-wise summary measures of pupil responsivity to volatility and outcome surprise. These were estimated using the mean beta-weight of the surprise and volatility regressors across the whole 6 second post-outcome period. Correlational analyses using these summary measures revealed that participants’ mean pupil response to trial-wise volatility predicted the degree to which they adjusted their learning rate between the volatile and stable blocks of the task, r(26)=0.37, p=0.05, Fig 3c. Additionally, participants’ mean pupil response to outcome surprise predicted the extent to which they showed choice reaction time slowing as a function of the unexpectedness of the previous trial’s outcome, r(26)=0.44, p=0.02, Fig 3d. These pupil dilation parameters may reflect changes in activity within the locus coeruleus norepinephrine system, though additional dopaminergic or cholinergic influences cannot be ruled out. The current findings are in line with the existence of a functional relationship between the alterations in neurotransmission which underlie these pupilometry changes and the mechanisms which enable environmental statistics to be used to guide learning about the causal structure of the environment13–16.

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