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Avoidance is a key symptom of anxiety disorders. Maladaptive avoidance impairs general functioning acutely and maintains chronic anxiety. A better understanding of the mechanisms that elicit and maintain excessive avoidance might provide opportunities to improve treatment. Here, we discuss pathways through which avoidance might get amplified in the context of anxiety disorders: 1) increased threat appraisal; 2) enhanced threat avoidance tendencies; 3) impaired regulation of avoidance; 4) habitual avoidance; and 5) attempts at increasing psychological distance. Novel strategies for reducing avoidance are considered. These include memory reconsolidation interference, retraining of avoidance tendencies, mindfulness training and habit disruption approaches. Throughout the paper, we highlight a number of suggestions for future research on avoidance and how to achieve lasting behavior change.

Two experiments using rats in a contextual fear memory preparation compared two approaches to reduce conditioned fear: (1) pharmacological reconsolidation blockade and (2) reactivation-plus-extinction training. In Experiment 1, we explored different combinations of reactivation-plus-extinction parameters to reduce conditioned fear and attenuate reacquisition. In Experiment 2, memory reactivation was followed by extinction training or administration of midazolam (MDZ) (vs. vehicle) to reduce conditioned fear and attenuate spontaneous recovery. We found both treatments to be equally effective in both experiments. This study suggests that parameters leading to memory destabilization during reactivation are critical to observe long-lasting effects of MDZ or reactivation plus extinction.

Background: Prominent theoretical accounts of attention‐deficit/hyperactivity‐disorder (ADHD) hypothesize that reinforcement learning deficits underlie symptoms of ADHD. The Dynamic Developmental Theory and the Dopamine Transfer Deficit hypothesis assume impairments in both the acquisition and extinction of behavior, especially when learning occurs under partial (non‐continuous) reinforcement, and subsequently the Partial Reinforcement Extinction Effect (PREE). Few studies have evaluated instrumental learning in ADHD and the results are inconsistent. The current study investigates instrumental learning under partial and continuous reinforcement schedules and subsequent behavioral persistence when reinforcement is withheld (extinction) in children with and without ADHD. Methods: Large well‐defined samples of children with ADHD (n = 93) and typically developing (TD) children (n = 73) completed a simple instrumental learning task. The children completed acquisition under continuous (100%) or partial (20%) reinforcement, followed by a 4‐min extinction phase. Two‐way (diagnosis by condition) ANOVAs evaluated responses needed to reach the learning criterion during acquisition, and target and total responses during extinction. Results: Children with ADHD required more trials to reach criterion compared to TD children under both continuous and partial reinforcement. After partial reinforcement, children with ADHD executed fewer target responses during extinction than TD children. Children with ADHD executed more responses than TD children during extinction, irrespective of learning condition. Conclusions: The findings demonstrate general difficulties in instrumental learning in ADHD, that is, slower learning irrespective of reinforcement schedule. They also show faster extinction following learning under partial reinforcement in those with ADHD, that is, a diminished PREE. Children with ADHD executed more responses during extinction. Results are theoretically important, with clinical implications for understanding and managing learning difficulties in those with ADHD, as they suggest poorer reinforcement learning and lower behavioral persistence. [ABSTRACT FROM AUTHOR]

Objectives: prolonged fasting influences threat and reward processing, two fundamental systems underpinning adaptive behaviors. In animals, overnight fasting sensitizes the mesolimbic-dopaminergic activity governing avoidance, reward, and fearextinction learning. Despite evidence that overnight fasting may also affect reward and fear learning in humans, effects on human avoidance learning have not been studied yet. Here, we examined the effects of 16 h-overnight fasting on instrumental avoidance and relief from threat omission. Methods: to this end, 50 healthy women were randomly assigned to a Fasting (N = 25) or a Re-feeding group (N = 25) and performed an Avoidance-Relief Task. Results: we found that fasting decreases unnecessary avoidance during signaled safety; this effect was mediated via a reduction in relief pleasantness during signaled absence of threat. A fasting-induced reduction in relief was also found during fear extinction learning. Discussion: we conclude that fasting optimizes avoidance and safety learning. Future studies should test whether these effects also hold for anxious individuals. [ABSTRACT FROM AUTHOR]

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with "entirely different" cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge.