Momentary and integrative response strategies in causal judgment

Collins, Darrell J.; Shanks, David R.

doi:10.3758/bf03194331

Cited by 81 publications

(125 citation statements)

References 16 publications

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“…In our study, the stimulus valence was measured before and after the entire experiment. Judgments made at the end of a complete experiment tend to be integrative 10 . This means that participants might have collapsed information of both the acquisition and the reduction phase when completing this rating.…”

Section: Discussionmentioning

confidence: 99%

Comparing Counterconditioning and Extinction as Methods to Reduce Fear of Movement-Related Pain

Meulders

Karsdorp

Claes

et al. 2015

The Journal of Pain

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Cognitive-behavioral treatments for chronic pain typically target pain-related fear -exposure in vivo is a common treatment focusing on disconfirming harm expectancy of feared movements. Exposure therapy is tailored on Pavlovian extinction; an alternative fear reduction technique that also alters stimulus valence is counterconditioning. We compared both procedures to reduce pain-related fear using a Voluntary Joystick Movement Paradigm.Participants were randomly allocated to the Counterconditioning or Extinction Group. During fear acquisition, moving the joystick in two directions (CS+) was followed by a painful electrocutaneous stimulus (pain-US), while moving the joystick in two other directions was not (CS-). During fear reduction, one CS+ was extinguished, but another CS+ was still followed by pain in the Extinction Group; in the Counterconditioning Group one CS+ was extinguished and followed by a monetary reward-US, and another CS+ was followed by both USs (pain-US and reward-US). Results indicate that counterconditioning effectively reducespain-related fear but that it does not produce deeper fear reduction than extinction. Adding a reward-US to a painful movement did neither attenuate fear nor the intensity/unpleasantness of the pain itself. Both procedures changed stimulus valence. We contend that changing the affective valence of feared movements might improve fear reduction and may prevent relapse.

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Section: Discussionmentioning

confidence: 99%

Comparing Counterconditioning and Extinction as Methods to Reduce Fear of Movement-Related Pain

Meulders

Karsdorp

Claes

et al. 2015

The Journal of Pain

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“…One explanation for this might relate to the retrospective nature of US expectancy assessment. As pointed out by Collins and Shanks (2002), retrospective judgements are mostly based on an integrative strategy. As such, participants might have judged US expectancies based on an integration of the knowledge they obtained during the whole experiment (Collins & Shanks, 2002).…”

Section: Discussionmentioning

confidence: 99%

“…As pointed out by Collins and Shanks (2002), retrospective judgements are mostly based on an integrative strategy. As such, participants might have judged US expectancies based on an integration of the knowledge they obtained during the whole experiment (Collins & Shanks, 2002). In relation to this, it should be noted that measuring US expectancies were questioned after participants had gone through a subsequent phase (test phase).…”

Section: Discussionmentioning

confidence: 99%

Return of fear after retrospective inferences about the absence of an unconditioned stimulus during extinction

Raes

Houwer

Verschuere

et al. 2011

Behaviour Research and Therapy

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“…Specifically, Dennis and Ahn (2001) found a primacy effect where early information in the learning stage was weighted more heavily in causal strength judgments. On the other hand, Collins and Shanks (2002) found that when intermediate judgments were introduced (causal strength judgments after every 10 learning trials), participants showed recency effects, weighting later information more heavily in their judgments.…”

Section: Order Effectsmentioning

confidence: 95%

“…Most past research on order effects in causality has examined these effects in the context of causal learning (Dennis & Ahn, 2001;Collins & Shanks, 2002;Abbott, Griffiths, et al, 2011). in one scenario, participants were asked about the likelihood that sales of a popular caffeine free soda will increase next year (the effect) given the advertising budget for the soda remains the same (the absent cause) and the soda company lowers the price of the drink (the present cause).…”

Section: A Quantum Framework For Probabilistic Inference 25mentioning

confidence: 99%

A quantum probability framework for human probabilistic inference.

Trueblood

Yearsley

Pothos

2017

Journal of Experimental Psychology: General

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThere is considerable variety in human inference (e.g., a doctor inferring the presence of a disease, a juror inferring the guilt of a defendant, or someone inferring future weight loss based on diet and exercise). As such, people display a wide range of behaviors when making inference judgments.Sometimes, people's judgments appear Bayesian (i.e., normative), but in other cases, judgments deviate from the normative prescription of classical probability theory. How can we combine bothBayesian and non-Bayesian influences in a principled way? We propose a unified explanation of human inference using quantum probability theory. In our approach, we postulate a hierarchy of mental representations, from 'fully' quantum to 'fully' classical, which could be adopted in different situations. In our hierarchy of models, moving from the lowest level to the highest involves changing assumptions about compatibility (i.e., how joint events are represented). Using results from three experiments, we show that our modeling approach explains five key phenomena in human inference including order effects, reciprocity (i.e., the inverse fallacy), memorylessness, violations of the Markov condition, and anti-discounting. As far as we are aware, no existing theory or model can explain all five phenomena. We also explore transitions in our hierarchy, examining how representations change from more quantum to more classical. We show that classical representations provide a better account of data as individuals gain familiarity with a task.We also show that representations vary between individuals, in a way that relates to a simple measure of cognitive style, the Cognitive Reflection Test.Keywords: Human judgment, quantum probability theory, Bayes' rule, order effects, Markov condition A Quantum Framework for Probabilistic Inference 3 A Quantum Probability Framework for Human Probabilistic InferenceEveryday we face situations where we must make inferences about the world around us. For example, a doctor must determine the likelihood that a patient has a disease based on a set of symptoms. A juror must decide the probability that a defendant is guilty after hearing the cases made by the prosecution and defense. Or, maybe you want to judge the likelihood that you will weigh less next month if you start exercising more regularly and you improve your diet. In general, the inference problem involves judging the likelihood of some hypothesis (e.g., presence of a disease, guilt of a defendant, future weight loss) based on a series of evidence (e.g., medicalsymptoms, prosecution and defense cases, changes in your diet and exercise).Bayesian inference is widely accepted as the normative approach to inference. However, decades of research in human judgment and decision-making have suggested that people's judgments often violate the rules of Bayesian inference and classical probability theory (Tversky...

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Momentary and integrative response strategies in causal judgment

Cited by 81 publications

References 16 publications

Comparing Counterconditioning and Extinction as Methods to Reduce Fear of Movement-Related Pain

Comparing Counterconditioning and Extinction as Methods to Reduce Fear of Movement-Related Pain

Return of fear after retrospective inferences about the absence of an unconditioned stimulus during extinction

A quantum probability framework for human probabilistic inference.

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