Probability matching and strategy availability

Koehler, Derek J.; James, Greta

doi:10.3758/mc.38.6.667

Cited by 71 publications

(75 citation statements)

References 18 publications

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“…That is, those with lower CRT scores showed more probability matching than those with higher CRT scores, and as the proportion of people with lower CRT scores was larger among gamblers, they showed more probability matching on average. This relation between probability matching and CRT mirrors previous findings in the literature (Koehler and James 2010). Importantly, the difference between gamblers and community members with regard to the CRT could not be explained by other demographic variables (sex, age, education) or other measures of cognitive capacity (digit span, symbol task, trail making test).…”

Section: Discussionsupporting

confidence: 73%

“…Koehler and James (2010), for instance, showed that even people who adopted probability matching endorsed maximizing as the better strategy when asked to directly compare the two, and used it more when it was brought to their attention before the task. Also, studies have shown that the maximizing strategy is used more often by students with greater academic experience (Gal and Baron 1996), higher college admission test scores (West and Stanovich 2003), higher working memory capacity (Rakow et al 2010), and higher cognitive reflection task scores (Koehler and James 2010), suggesting that education and cognitive capacity play a role in enhancing the use of a maximizing strategy.…”

Section: Probability Matching and Its Relation To Betting On Illusorymentioning

confidence: 99%

“…Furthermore, we investigated whether gamblers and community members differed more generally with regard to impulsivity, and whether this difference explained potential differences in probability matching. Impulsivity was assessed with the cognitive reflection task (CRT, Frederick 2005), which was previously shown to predict probability matching (Koehler and James 2010).…”

Section: Summary Of Research Questions and Hypothesesmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 73%

Section: Probability Matching and Its Relation To Betting On Illusorymentioning

confidence: 99%

Section: Summary Of Research Questions and Hypothesesmentioning

confidence: 99%

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Betting on Illusory Patterns: Probability Matching in Habitual Gamblers

et al. 2015

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“…(See Newell, Lagnado, &Shanks, 2007, chap.11, andShanks etal., 2002, for further discussion. ) have investigated probability matching in tasks in which the outcomes and their probabilities of occurrence are fully described to participants (e.g., Gal & Baron, 1996;James & Koehler, 2011;Koehler & James, 2009, 2010Newell & Rakow, 2007;West & Stanovich, 2003). The finding that probability matching is common even in these situations is remarkable, given that the described problems provide all of the information necessary for rational responding (i.e., identification of the maximizing strategy as optimal), even before a single choice is made.…”

mentioning

confidence: 99%

“…At least for some people, the maximizing strategy might not come immediately to mind. Koehler and James (2010) gave participants a version of the die problem, but for one group they also provided a "hint" about the possible choice strategies that might be used. Specifically, before making their choices, the matching and maximizing strategies were described and participants were asked which strategy was likely to earn more money.…”

mentioning

confidence: 99%

Probability matching in risky choice: The interplay of feedback and strategy availability

et al. 2012

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Probability matching in sequential decision making is a striking violation of rational choice that has been observed in hundreds of experiments. Recent studies have demonstrated that matching persists even in described tasks in which all the information required for identifying a superior alternative strategy-maximizing-is present before the first choice is made. These studies have also indicated that maximizing increases when (1)the asymmetry in the availability of matching and maximizing strategies is reduced and (2)normatively irrelevant outcome feedback is provided. In the two experiments reported here, we examined the joint influences of these factors, revealing that strategy availability and outcome feedback operate on different time courses. Both behavioral and modeling results showed that while availability of the maximizing strategy increases the choice of maximizing early during the task, feedback appears to act more slowly to erode misconceptions about the task and to reinforce optimal responding. The results illuminate the interplay between "topdown" identification of choice strategies and "bottom-up" discovery of those strategies via feedback. Keywords Probability matching . Maximizing . Decision making . Heuristics . Rational choice theory When faced with a choice between two options, one of which offers a higher probability of receiving a fixed payoff than does the other, a rational agent should always choose the option with the higher payoff probability. For example, if one option delivers one dollar 70 % of the time (and nothing the rest of the time), while the other pays one dollar only 30 % of the time (and otherwise nothing), a rational agent should choose the 70 % option. This is true whether the choice is faced once or repeatedly: The option offering the higher probability of receiving the payoff should always be chosen.Despite the clear superiority of this strategy (referred to as maximizing), participants faced with a series of such choices often show responding closer to probability matching-allocating responses to the two options in proportion to their relative probabilities of occurrence. In other words, people bet on the 30 % option 30 % of the time, and the 70 % option 70 %

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On the Decision to Explore New Alternatives: The Coexistence of Under‐ and Over‐exploration

Teodorescu

Erev

2013

Behavioral Decision Making

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The decision whether to explore new alternatives or to choose from familiar ones is implicit in many of our daily activities. How is this decision made? When will deviation from optimal exploration be observed? The current paper examines exploration decisions in the context of a multi-alternative "decisions from experience" task. In each trial, participants could choose a familiar option (the status quo) or a new alternative (risky exploration). The observed exploration rates were more sensitive to the frequent outcome from choosing new alternatives than to the average outcome. That is, the implicit decision whether to explore a new alternative reflects underweighting of rare events: Over-exploration was documented in "Rare Disasters" environments, and insufficient exploration was evident in "Rare Treasures" environments. In addition, the results reveal a decrease in exploration of new alternatives over time even when it is always optimal and some exploration even when it is never reinforcing. These results can be captured with models that share a distinction between "data collection" and "outcome-driven" decision modes. Under the data collection mode, the decision maker collects information about the environment, to be used in future choices. Under the outcome-driven mode, the decision maker relies on small samples of previous experiences with familiar versus unfamiliar alternatives, before the selection of a specific alternative. The predictive value of a two-parameter "explorative sampler" quantification of these assumptions is demonstrated.

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Probability matching and strategy availability

Cited by 71 publications

References 18 publications

Betting on Illusory Patterns: Probability Matching in Habitual Gamblers

Betting on Illusory Patterns: Probability Matching in Habitual Gamblers

Probability matching in risky choice: The interplay of feedback and strategy availability

On the Decision to Explore New Alternatives: The Coexistence of Under‐ and Over‐exploration

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