Trait anxiety is associated with hidden state inference during aversive reversal learning

Abstract

Updating beliefs in changing environments can be achieved either by gradually adapting expectations or by identifying a hidden structure composed of separate states, and inferring which state fits observations best. Previous studies have found that a state inference mechanism might be associated with relapse phenomena, such as return of fear, that commonly represent a major obstacle in clinical treatment of anxiety disorders. Here, we tested whether variability in trait anxiety among healthy individuals is associated with a tendency towards inferring a hidden structure of an aversive environment, as opposed to learning gradually from observations. In a Pavlovian probabilistic aversive learning paradigm, participants had to follow changes in cue-associated shock contingencies by providing probability ratings on each trial. In three sessions, the contingencies switched between high and a low levels of shock probability (60/40%, 75/25% or 90/10%). High trait anxiety was associated with steeper behavioral switches after contingency reversals, and more accurate probability ratings overall. To elucidate the computational mechanisms behind these behavioral patterns, we compared a 1-state model, which reflects gradual updating, with a novel state-inference model (n-state). High trait anxiety was associated with improved fit of the state inference model (n-state) compared to the gradual model (1-state) in the session characterized by the largest shock contingency changes (90/10). This finding provides evidence that trait anxiety variations among health adults are associated with tendency to infer hidden causes that generate the observed aversive outcomes. This was particularly evident in environments with larger contingency changes and less outcome uncertainty. This association may contribute to relapse phenomena observed among high trait anxious individuals.

Anxiety as a disorder of uncertainty: implications for understanding maladaptive anxiety, anxious avoidance, and exposure therapy

“…Adaptive learning requires simultaneously assessing the level of different types of uncertainty in one's environment and adjusting learning accordingly. In tasks or blocks with high unexpected uncertainty (20 or fewer trials per block; Brown et al, 2018;Zika et al, 2022 andvolatile blocks in Browning et al, 2015;Gagne et al, 2020), people with anxiety show slightly slower learning overall, but accelerate learning more after very surprising outcomes that indicate obvious changes. Meanwhile, in tasks or conditions with high irreducible uncertainty (75%/25% contingency or less), people with anxiety show a generally higher learning rate, particularly after surprising losses (Homan et al, 2019; stable blocks in Browning et al, 2015;Gagne et al, 2020) --though this effect is less consistent (Norbury et al, 2021;Zika et al, 2022).…”

“…In tasks or blocks with high unexpected uncertainty (20 or fewer trials per block; Brown et al, 2018;Zika et al, 2022 andvolatile blocks in Browning et al, 2015;Gagne et al, 2020), people with anxiety show slightly slower learning overall, but accelerate learning more after very surprising outcomes that indicate obvious changes. Meanwhile, in tasks or conditions with high irreducible uncertainty (75%/25% contingency or less), people with anxiety show a generally higher learning rate, particularly after surprising losses (Homan et al, 2019; stable blocks in Browning et al, 2015;Gagne et al, 2020) --though this effect is less consistent (Norbury et al, 2021;Zika et al, 2022). These patterns suggest that people with anxiety have an impaired ability to discern whether prediction errors result from irreducible uncertainty, requiring a slower learning rate, or true changes in contingencies, requiring a higher learning rate.…”

Blocked training facilitates learning of multiple schemas

“…Another future direction is to improve our explanation of individual differences: Why is it CURRICULUM EFFECTS ON SCHEMA LEARNING 29 that some participants seem to learn in the interleaved condition but others do not? As mentioned earlier, one promising approach would be to estimate individual differences in key parameters governing latent cause inference (e.g., the concentration parameter, α), using a separate set of tasks; we could then see whether these estimated parameters relate to interleaved learning performance in a way that aligns with the predictions of our Bayesian model (for other work exploring individual differences in latent cause parameters, see, e.g., Gershman and Hartley, 2015;Norbury et al, 2022;Zika et al, 2022).…”

Anxiety as a disorder of uncertainty: Implications for understanding maladaptive anxiety, anxious avoidance, and exposure therapy

“…Adaptive learning requires simultaneously assessing the level of different types of uncertainty in one's environment and adjusting learning accordingly. In tasks or blocks with high unexpected uncertainty (20 or fewer trials per block; Brown et al, 2018;Zika et al, 2022 andvolatile blocks in Browning et al, 2015;Gagne et al, 2020), people with anxiety show slightly slower learning overall, but accelerate learning more after very surprising outcomes that indicate obvious changes. Meanwhile, in tasks or conditions with high irreducible uncertainty (75%/25% contingency or less), people with anxiety show a generally higher learning rate, particularly after surprising losses (Homan et al, 2019;Huang et al, 2017;stable blocks in Browning et al, 2015;Gagne et al, 2020) -though this effect is less consistent (Norbury et al, 2021;Zika et al, 2022).…”

“…In tasks or blocks with high unexpected uncertainty (20 or fewer trials per block; Brown et al, 2018;Zika et al, 2022 andvolatile blocks in Browning et al, 2015;Gagne et al, 2020), people with anxiety show slightly slower learning overall, but accelerate learning more after very surprising outcomes that indicate obvious changes. Meanwhile, in tasks or conditions with high irreducible uncertainty (75%/25% contingency or less), people with anxiety show a generally higher learning rate, particularly after surprising losses (Homan et al, 2019;Huang et al, 2017;stable blocks in Browning et al, 2015;Gagne et al, 2020) -though this effect is less consistent (Norbury et al, 2021;Zika et al, 2022). These patterns suggest that people with anxiety have an impaired ability to discern whether prediction errors result from irreducible uncertainty, requiring a slower learning rate, or true changes in contingencies, requiring a higher learning rate.…”