The validity of human avoidance paradigms

Prospective intolerance of uncertainty is associated with maladaptive temporal distribution of avoidance responses: An extension of Flores, López, Vervliet, and Cobos (2018)

BACKGROUND AND OBJECTIVES

Excessive maladaptive avoidance has been claimed to be one of the mechanisms through which intolerance of uncertainty (IU) may play its causal role in the development and maintenance of several anxiety and compulsive disorders. Consistently, Flores et al. (2018) found that individuals with higher Prospective IU (P-IU), a specific IU subfactor, display excessive avoidance response repetitions in a free-operant discriminative task to avoid an aversive noise. In the present study we tested the hypothesis that P-IU not only predicts the amount of avoidance responses but also how well the temporal distribution of such responses fits the temporal distribution of threats.

METHODS

Further correlation and hierarchical regression analysis of Flores et al.'s (2018) data served to test this hypothesis. We evaluated two aspects of the temporal distribution of responses: a) for how long participants were performing the responses; b) the behavioral discrimination between threatening and safe time periods.

RESULTS

The results showed that scoring high in P-IU was positively associated with longer periods of time dedicated to avoiding and with worse behavioral discrimination between threatening and safe time periods. Hierarchical regression analyses indicated that later addition of inhibitory intolerance of uncertainty and trait anxiety did not significantly improved the explained variance.

LIMITATIONS

Our results are exclusively based on the use of a low-cost avoidance response, and the present study does not clarify the precise mechanisms that lead high P-IU people to engage in non-optimal avoidance response distribution through time.

CONCLUSIONS

These results suggest that excessive avoidance is also driven by uncertainty of threat timing and highlight the relevance of P-IU as a vulnerability factor for excessive and outspread avoidance behaviors.

Induction of conditioned avoidance via mental imagery

There is a growing interest on how mental imagery may be involved in the onset and maintenance of anxiety-related disorders. Here, we used an experimental design to investigate whether a key symptom across anxiety-related disorders, namely avoidance, can be induced via mental imagery. Healthy participants first learned that one neutral stimulus (A) was associated with a mild electric shock and two other neutral stimuli (B and C) were not. They then learned to cancel the shock when A was presented, by pressing a button on a keyboard ('behavioral avoidance'). Next, they were asked to imagine that stimulus B was followed by the shock (i.e., without actual B or shock presentations; Experiment 1; N = 66) or they were shown B and asked to imagine the shock (i.e., without actual shock presentations; Experiment 2; N = 60). Finally, in the test phase, they were shown each of the three stimuli (without the shock) and given the opportunity to make the avoidance response. Results showed that participants tended to avoid B in the test phase in Experiment 1, even though it had never been presented with the shock but not in Experiment 2. We discuss how the findings may explain the acquisition of avoidance in the presentation of innocuousstimuli across anxiety-related disorders.

Decomposing conditioned avoidance performance with computational models

Avoidance towards innocuous stimuli is a key characteristic across anxiety-related disorders and chronic pain. Insights into the relevant learning processes of avoidance are often gained via laboratory procedures, where individuals learn to avoid stimuli or movements that have been previously associated with an aversive stimulus. Typically, statistical analyses of data gathered with conditioned avoidance procedures include frequency data, for example, the number of times a participant has avoided an aversive stimulus. Here, we argue that further insights into the underlying processes of avoidance behavior could be unraveled using computational models of behavior. We then demonstrate how computational models could be used by reanalysing a previously published avoidance data set and interpreting the key findings. We conclude our article by listing some challenges in the direct application of computational modeling to avoidance data sets.

Negative reinforcement rate and persistent avoidance following response-prevention extinction

Persistent avoidance may be influenced by prior negative reinforcement rate (i.e., how effective the response is at controlling threat). In clinical settings, the effectiveness of extinction-based methods for treating anxiety-related avoidance may be impacted by prior reinforcement rate. Here, we conducted a laboratory-based treatment study to investigate the persistence of avoidance following response-prevention extinction (RPE) when prior avoidance had been differentially effective at cancelling shock. Participants in three negative reinforcement rate groups (100%, 50%, and 0%) completed a validated avoidance conditioning paradigm involving Pavlovian fear extinction, RPE, and re-extinction phases. It was hypothesised that partially reinforced avoidance would lead to diminished resistance to fear extinction following response prevention, compared to continuously- or never-reinforced avoidance. Persistent avoidance was related to prior negative reinforcement rate, with higher rates more resistant to extinction. These findings illustrate the role of reinforcement rate in the persistence of avoidance and may aid understanding of treatment relapse.

Roles of the Amygdala and Basal Forebrain in Defense: a Reply to Luyck Et al. and Implications for Defensive Action

The commentary by Luyck and colleagues on our paper provides many stimulating viewpoints and interpretations of our original study on dissociable responses in the amygdala and bed nucleus of the stria terminalis in threat processing. Here, we reply to some of the points raised and while agreeing with most of the comments also provide some alternative viewpoints. We end by putting forward a research agenda for how to further investigate the roles of these regions in threat processing, with an emphasis on studying their roles in defensive action.