Stress attenuates the flexible updating of aversive value

Abstract

In a dynamic environment, sources of threat or safety can unexpectedly change, requiring the flexible updating of stimulus-outcome associations that promote adaptive behavior. However, aversive contexts in which we are required to update predictions of threat are often marked by stress. Acute stress is thought to reduce behavioral flexibility, yet its influence on the modulation of aversive value has not been well characterized. Given that stress exposure is a prominent risk factor for anxiety and trauma-related disorders marked by persistent, inflexible responses to threat, here we examined how acute stress affects the flexible updating of threat responses. Participants completed an aversive learning task, in which one stimulus was probabilistically associated with an electric shock, while the other stimulus signaled safety. A day later, participants underwent an acute stress or control manipulation before completing a reversal learning task during which the original stimulus-outcome contingencies switched. Skin conductance and neuroendocrine responses provided indices of sympathetic arousal and stress responses, respectively. Despite equivalent initial learning, stressed participants showed marked impairments in reversal learning relative to controls. Additionally, reversal learning deficits across participants were related to heightened levels of alpha-amylase, a marker of noradrenergic activity. Finally, fitting arousal data to a computational reinforcement learning model revealed that stress-induced reversal learning deficits emerged from stress-specific changes in the weight assigned to prediction error signals, disrupting the adaptive adjustment of learning rates. Our findings provide insight into how stress renders individuals less sensitive to changes in aversive reinforcement and have implications for understanding clinical conditions marked by stress-related psychopathology.

Keywords: cognitive flexibility; computational modeling; reversal learning; stress; threat.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

Experimental timeline and procedure for day 1 (acquisition) and day 2 (reversal).

Fig. 2.

Conditioned responses across sessions. Differential SCR (mean CS+ minus mean CS−) divided into four blocks during each learning stage (three trials × block). Both groups showed significantly stronger learning at block 4 of each session relative to block 1. Group differences emerged during the intermediate two blocks of day 2 reversal learning only. Error bars denote SEM.

Fig. 3.

Mean SCR for each CS across sessions. Mean SCR for each CS is depicted for day 1 (acquisition) and day 2 (reversal). The deficit in reversal learning on day 2 was primarily driven by reduced SCR to the CS+. Error bars denote SEM.

Fig. S1.

Mean SCL for each group on day 1 (acquisition) and day 2 (reversal). Groups’ SCL did not differ during either learning phase but decreased overall across sessions (*P < .05).

Fig. 4.

Neuroendocrine data. Mean (A) α-amylase and (B) cortisol concentrations. Samples were attained at baseline and 10 and 20 min after the stress/control task. Error bars denote SEM.

Fig. S2.

Scatterplots depicting the linear relationship between α-amylase change from baseline and the reversal index for each group. Stressed participants showed a negative correlation both 10 and 20 min after the stressor, suggesting higher α-amylase was related to deficits in aversive value updating relative to initial learning. Control participants showed a similar relation to α-amylase before reversal learning, but no relation after.

Fig. 5.

Modeling results. (A) Mean estimated scaling factor (ρ) during the day 2 reversal phase of learning for each group. Stressed participants revealed significant reductions in the weight assigned to PE signals that dynamically adjust learning rates during value updating. (B) Changes in α-amylase relative to baseline were negatively associated with the overall weight assigned to PE signals during value updating.

Fig. S3.

Model-predicted aggregate SCR for both CS+ and CS− using a single PE weight from each subject. In the control group (Left), subjects showed comparable SCRs for both CS+ and CS− related SCRs across learning phases. However, in the stress group (Right), subjects showed impaired learning to the new CS+ due to reduced scaling factors (*P < .05).

Fig. S4.

Model-predicted trial-by-trial SCR using parameters estimated from each subject. The SCR pattern for CS+ and CS− did not differ between groups on day 1 (acquisition: trials 1 to 12). However, on day 2 (reversal: trials 13 to 24), stressed participants’ lower scaling factor led to dampened sensitivity to the changing shock contingencies (more dependent on cached associability value), and thus slower updating of SCRs to the new CS+ compared with the control group. While the scaling factor during reversal would influence the PE weight parameter for both CS, it has a bigger impact on the associability variable (predicted SCR) that is on the rise (CS+ on the second day, gray curve after trial 12) than the one that’s constantly decaying (CS− on the second day, blue curve after trial 12).