Evidence for an Expectancy-Based Theory of Avoidance Behaviour

Declercq, Mieke; Houwer, Jan De; Baeyens, Frank

doi:10.1080/17470210701851214

Cited by 17 publications

(13 citation statements)

References 13 publications

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“…13 One of the most influential theories of avoidance is the 2-factor model of Mowrer 26 suggesting that avoidance behavior is reinforced by fear reduction. Alternative theories have been advanced based on relief due to the nonoccurrence of the punisher that may be intrinsically rewarding, 12 expectancy learning, 6,17,19 negative occasion setting, 7 and action tendencies 2,14 (for a review see Ref. 15).…”

Section: Discussionmentioning

confidence: 99%

Acquisition and extinction of operant pain-related avoidance behavior using a 3 degrees-of-freedom robotic arm

Meulders

Franssen²,

Fonteyne³

2016

Pain

124

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Ample empirical evidence endorses the role of associative learning in pain-related fear acquisition. Nevertheless, research typically focused on self-reported and psychophysiological measures of fear. Avoidance, which is overt behavior preventing the occurrence of an aversive (painful) stimulus, has been largely neglected so far. Therefore, we aimed to fill this gap and developed an operant conditioning procedure for pain-related avoidance behavior. Participants moved their arm to a target location using the HapticMaster (FCS Robotics; Moog Inc, East Aurora, New York), a 3 degrees-of-freedom, force-controlled robotic arm. Three movement trajectories led to the target location. If participants in the Experimental Group took the shortest/easiest trajectory, they always received a painful stimulus (T1 = 100% reinforcement; no resistance). If they deviated from this trajectory, the painful stimulus could be partly or totally prevented (T2 = 50% reinforcement; T3 = 0% reinforcement), but more effort was needed (T2 = moderate resistance and deviation; T3 = strongest resistance and largest deviation). The Yoked Group received the same reinforcement schedule irrespective of their own behavior. During the subsequent extinction phase, no painful stimuli were delivered. Self-reported pain-expectancy and pain-related fear were assessed, and avoidance behavior was operationalized as the maximal distance from the shortest trajectory. During acquisition, the Experimental Group reported more pain-related fear and pain-expectancy to T1 vs T2 vs T3 and deviated more from the shortest trajectory than the Yoked Group. During subsequent extinction, avoidance behavior, self-reported fear, and pain-expectancy decreased significantly, but conditioned differences persisted despite the absence of painful stimuli. To conclude, this operant learning task might provide a valid paradigm to study pain-related avoidance behavior in future studies.

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Section: Discussionmentioning

confidence: 99%

Acquisition and extinction of operant pain-related avoidance behavior using a 3 degrees-of-freedom robotic arm

Meulders

Franssen²,

Fonteyne³

2016

Pain

124

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“…Thus, avoidance learning is assumed to be driven and maintained by feelings of fear. However, cognitive aspects such as expectations are likely to be involved in human avoidance learning as well (see Rescorla and Wagner, 1972 ; Lovibond, 2006 ; Declercq et al, 2008 ).…”

Section: How Does Victim Sensitivity Perpetuate Itself Across Social mentioning

confidence: 99%

Victimization experiences and the stabilization of victim sensitivity

2015

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People reliably differ in the extent to which they are sensitive to being victimized by others. Importantly, “victim sensitivity” predicts how people behave in social dilemma situations: Victim-sensitive individuals are less likely to trust others and more likely to behave uncooperatively—especially in socially uncertain situations. This pattern can be explained with the sensitivity to mean intentions (SeMI) model, according to which victim sensitivity entails a specific and asymmetric sensitivity to contextual cues that are associated with untrustworthiness. Recent research is largely in line with the model’s prediction, but some issues have remained conceptually unresolved so far. For instance, it is unclear why and how victim sensitivity becomes a stable trait and which developmental and cognitive processes are involved in such stabilization. In the present article, we will discuss the psychological processes that contribute to a stabilization of victim sensitivity within persons, both across the life span (“ontogenetic stabilization”) and across social situations (“actual-genetic stabilization”). Our theoretical framework starts from the assumption that experiences of being exploited threaten a basic need, the need to trust. This need is so fundamental that experiences that threaten it receive a considerable amount of attention and trigger strong affective reactions. Associative learning processes can then explain (a) how certain contextual cues (e.g., facial expressions) become conditioned stimuli that elicit equally strong responses, (b) why these contextual untrustworthiness cues receive much more attention than, for instance, trustworthiness cues, and (c) how these cues shape spontaneous social expectations (regarding other people’s intentions). Finally, avoidance learning can explain why these cognitive processes gradually stabilize and become a trait: the trait which is referred to as victim sensitivity.

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“…If A and B are assumed to be independent, the predicted magnitude of an outcome when AB is presented would follow an additive rule similar to that present in elemental associative theory. This independence assumption is, in fact, one of the critical features of several of the most influential rational models of causal induction (Cheng, 1997;Cheng & Novick, 1992;Holyoak & Cheng, 2011b), and a notion that has also been adopted and discussed extensively in contemporary approaches to learning that attribute all associative phenomena as arising from propositional rules (Declercq, De Houwer, & Baeyens, 2008;Lovibond & Shanks, 2002;Mitchell et al, 2009). For example, the influential Power PC theory proposed by Cheng and colleagues (Cheng, 1997;Holyoak & Cheng, 2011) assumes that the joint predictive power of two candidate causes that independently predict an outcome is given by the noisy-OR rule.…”

Section: Methodsmentioning

confidence: 99%

Exploring the role of stimulus similarity on the summation effect in causal learning

Pérez

Martín

Soto

2017

Preprint

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Several contemporary models of associative learning anticipate that the higher responding to a compound of two cues separately trained with a common outcome than to each of the cues alone -a summation effect-is modulated by the similarity between the cues forming the compound. Here, we explored this hypothesis in a series of causal learning experiments with humans. Participants were presented with two visual cues that separately predicted a common outcome and later asked for the outcome predicted by the compound of the two cues. Importantly, the cues’ similarity was varied between groups through changes in shape, spatial position, color, configuration and rotation. In variance with the predictions of these models, we observed similar and strong levels of summation in both groups across all manipulations of similarity (Experiments 1-5). The summation effect was significantly reduced by manipulations intended to impact assumptions about the causal independence of the cues forming the compound, but this reduction was independent of stimulus similarity (Experiment 6). These results are problematic for similarity-based models and can be more readily explained by rational approaches to causal learning.

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Evidence for an Expectancy-Based Theory of Avoidance Behaviour

Cited by 17 publications

References 13 publications

Acquisition and extinction of operant pain-related avoidance behavior using a 3 degrees-of-freedom robotic arm

Acquisition and extinction of operant pain-related avoidance behavior using a 3 degrees-of-freedom robotic arm

Victimization experiences and the stabilization of victim sensitivity

Exploring the role of stimulus similarity on the summation effect in causal learning

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