Sequential patterns and maximizing.

Peterson, Cameron R.; Ulehla, Z. Joseph

doi:10.1037/h0021597

Cited by 51 publications

(35 citation statements)

References 3 publications

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“…Recent research has applied more descriptive versions of repeated choice problems that disclose the relevant outcome probabilities (and sometimes the outcome-generating process) to participants prior to the task-for example, by asking participants to predict the outcomes of repeated rolls of a fair die (Gal & Baron, 1996;James & Koehler, 2011;Newell & Rakow, 2007;Peterson & Ulehla, 1965). Probability matching is typically observed in both variants, but it tends to occur less when the probabilities are known from the start (Fantino & Esfandiari, 2002) and/or when the generating process can be identified as random (Morse & Runquist, 1960;Peterson & Ulehla, 1965).…”

Section: Discussionmentioning

confidence: 99%

“…Yet this finding is surprising only if one assumes that people really believe the structure of the sequential choice task to be simple and the outcome sequence to be random. If they do not believe that the outcomes are statistically independent-which seems a reasonable assumption, in light of everyday experiences of repeated events (see, e.g., Ayton & Fischer, 2004)-they might attempt to outperform the static maximizing strategy by finding a predictable pattern in the outcome sequence (Gaissmaier & Schooler, 2008;Peterson & Ulehla, 1965). Because any predictable pattern must match the outcome frequencies, probability matching would occur as a by-product of such an elaborate search, rather than as a strategy per se.…”

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confidence: 99%

“…Unturbe and Corominas (2007) found that the complexity of the rules that participants reported to have followed during a sequential choice task was inversely related to probability maximizing behavior (see also McMahon & Scheel, 2010). Moreover, allowing participants to conclusively infer that the outcomegenerating process is random-and thereby encouraging them to accept the true absence of patterns in the outcome sequence-reduces probability matching (Morse & Runquist, 1960;Peterson & Ulehla, 1965).…”

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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: 99%

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confidence: 99%

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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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“…As an example, consider the probabilistic contingency experiment, which has many versions in psychology (Fantino & Esfandiari, 2002;Gal & Baron, 1996;Peterson & Ulehla, 1965;Shanks, Tunney, & McCarthy, 2002;Tversky & Edwards, 1966). In one version, the participant sits in front of two lights (one red and one blue) and is told that he or she is to predict which of the lights will be flashed on each trial and that there will be several dozen such trials (participants are often paid money for correct predictions).…”

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confidence: 99%

Is probability matching smart? Associations between probabilistic choices and cognitive ability

2003

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In three experiments involving over 1,500 university students (n 5 1,557) and two different probabilistic choice tasks, we found that the utility-maximizing strategy of choosing the most probable alternative was not the majority response. In a story problem version of a probabilistic choice task in which participants chose from among five different strategies, the maximizing response and the probabilitymatching response were each selected by a similar number of students (roughly 35% of the sample selected each). In a more continuous, or trial-by-trial,task, the utility-maximizing response was chosen by only one half as many students as the probability-matching response. More important, in both versions of the task, the participants preferring the utility-maximizing response were significantly higher in cognitive ability than were the participants showing a probability-matching tendency. Critiques of the traditional interpretation of probability matching as nonoptimal may well help explain why some humans are drawn to the nonmaximizing behavior of probability matching, but the traditional heuristics and biases interpretation can most easily accommodate the finding that participants high in computational ability are more likely to carry out the rule-based cognitive procedures that lead to maximizing behavior.

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“…Peterson & Ulehla (1965) discouraged search for sequential dependencies by having one group of Ss generate the random sequences of events themselves. These Ss showed a significantly greater proportion of maximizing responses than a control group who responded to the same sequences without prior knowledge of their generating source.…”

Section: Dependent Measuresmentioning

confidence: 99%