There are at least two kinds of probability matching: Evidence from a secondary task

Otto, A. Ross; Taylor, Eric; Markman, Arthur B.

doi:10.1016/j.cognition.2010.11.009

Cited by 53 publications

(73 citation statements)

References 17 publications

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“…We are not the first to fail to find effects of cognitive load on sequential binary choice (see also Otto et al, 2011). The inconsistency of findings may indicate that the link between cognitive load and probability maximizing is not particularly robust, but hinges on the specifics of the experimental setup, such as the difficulty of the concurrent task or the outcome probabilities in the choice task (Otto et al, 2011).…”

Section: Discussionmentioning

confidence: 95%

“…Moreover, the potentially pivotal role of strategy implementation effort has remained largely unexplored, which may explain the inconsistent findings on the effects of cognitive load in these settings. Specifically, some studies have demonstrated that probability matching decreases under cognitive load (Wolford et al, 2004), whereas others have failed to find differences in probability matching rates under dual-versus single-task conditions (Otto, Taylor, & Markman, 2011).…”

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confidence: 99%

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Taking the easy way out? Increasing implementation effort reduces probability maximizing under cognitive load

Schulze

Newell

2016

Mem Cogn

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Cognitive load has previously been found to have a positive effect on strategy selection in repeated risky choice. Specifically, whereas inferior probability matching often prevails under single-task conditions, optimal probability maximizing sometimes dominates when a concurrent task competes for cognitive resources. We examined the extent to which this seemingly beneficial effect of increased task demands hinges on the effort required to implement each of the choice strategies. Probability maximizing typically involves a simple repeated response to a single option, whereas probability matching requires choice proportions to be tracked carefully throughout a sequential choice task. Here, we flipped this pattern by introducing a manipulation that made the implementation of maximizing more taxing and, at the same time, allowed decision makers to probability match via a simple repeated response to a single option. The results from two experiments showed that increasing the implementation effort of probability maximizing resulted in decreased adoption rates of this strategy. This was the case both when decision makers simultaneously learned about the outcome probabilities and responded to a dual task (Exp. 1) and when these two aspects were procedurally separated in two distinct stages (Exp. 2). We conclude that the effort involved in implementing a choice strategy is a key factor in shaping repeated choice under uncertainty. Moreover, highlighting the importance of implementation effort casts new light on the sometimes surprising and inconsistent effects of cognitive load that have previously been reported in the literature.

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Section: Discussionmentioning

confidence: 95%

mentioning

confidence: 99%

Taking the easy way out? Increasing implementation effort reduces probability maximizing under cognitive load

Schulze

Newell

2016

Mem Cogn

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“…In fact, probability matching can be the outcome of various strategies (see Otto et al 2011). But even more importantly, both of these explanations could work in concert: Probability matching should be particularly likely if people (1) have a strong perception of illusory patterns and (2) are prepared to-impulsively-bet on them.…”

Section: Summary Of Research Questions and Hypothesesmentioning

confidence: 99%

Betting on Illusory Patterns: Probability Matching in Habitual Gamblers

et al. 2015

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Why do people gamble? A large body of research suggests that cognitive distortions play an important role in pathological gambling. Many of these distortions are specific cases of a more general misperception of randomness, specifically of an illusory perception of patterns in random sequences. In this article, we provide further evidence for the assumption that gamblers are particularly prone to perceiving illusory patterns. In particular, we compared habitual gamblers to a matched sample of community members with regard to how much they exhibit the choice anomaly 'probability matching'. Probability matching describes the tendency to match response proportions to outcome probabilities when predicting binary outcomes. It leads to a lower expected accuracy than the maximizing strategy of predicting the most likely event on each trial. Previous research has shown that an illusory perception of patterns in random sequences fuels probability matching. So does impulsivity, which is also reported to be higher in gamblers. We therefore hypothesized that gamblers will exhibit more probability matching than nongamblers, which was confirmed in a controlled laboratory experiment. Additionally, gamblers scored much lower than community members on the cognitive reflection task, which indicates higher impulsivity. This difference could account for the difference in probability matching between the samples. These results suggest that gamblers are more willing to bet impulsively on perceived illusory patterns.

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“…However, recent work from our labs has shown that a model that assumes a simple win-stay/lose-shift (WSLS) strategy can often characterize behavior in repeated choice decision-making tasks better than do traditional RL models (Otto, Taylor, & Markman, 2011;Worthy, Otto, & Maddox, 2012). The WSLS strategy is fairly straightforward: Participants "stay" by picking the same option on the next trial if they are rewarded (a "win" trial) or switch by picking a different option on the next trial if they are not rewarded (a "loss" trial).…”

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confidence: 99%

Heterogeneity of strategy use in the Iowa gambling task: A comparison of win-stay/lose-shift and reinforcement learning models

2012

Self Cite

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The Iowa gambling task (IGT) has been used in numerous studies, often to examine decision-making performance in different clinical populations. Reinforcement learning (RL) models such as the expectancy valence (EV) model have often been used to characterize choice behavior in this work, and accordingly, parameter differences from these models have been used to examine differences in decision-making processes between different populations. These RL models assume a strategy whereby participants incrementally update the expected rewards for each option and probabilistically select options with higher expected rewards. Here we show that a formal model that assumes a win-stay/lose-shift (WSLS) strategy-which is sensitive only to the outcome of the previous choice-provides the best fit to IGT data from about half of our sample of healthy young adults, and that a prospect valence learning (PVL) model that utilizes a decay reinforcement learning rule provides the best fit to the other half of the data. Further analyses suggested that the better fits of the WSLS model to many participants' data were not due to an enhanced ability of the WSLS model to mimic the RL strategy assumed by the PVL and EV models. These results suggest that WSLS is a common strategy in the IGT and that both heuristic-based and RL-based models should be used to inform decision-making behavior in the IGT and similar choice tasks.

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There are at least two kinds of probability matching: Evidence from a secondary task

Cited by 53 publications

References 17 publications

Taking the easy way out? Increasing implementation effort reduces probability maximizing under cognitive load

Taking the easy way out? Increasing implementation effort reduces probability maximizing under cognitive load

Betting on Illusory Patterns: Probability Matching in Habitual Gamblers

Heterogeneity of strategy use in the Iowa gambling task: A comparison of win-stay/lose-shift and reinforcement learning models

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