Of matchers and maximizers: How competition shapes choice under risk and uncertainty

Schulze, Christin; Ravenzwaaij, Don van; Newell, Ben R.

doi:10.1016/j.cogpsych.2015.03.002

Cited by 23 publications

(31 citation statements)

References 49 publications

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“…The improvements reported by Gershman's (2016) empirical-prior approach in the context of reinforcementlearning modeling are quite fortunate, as they directly address some long-standing challenges in this domain. Reinforcement-learning models are regularly adopted as a way to analyze repeated trial-and-error decisions in psychology and neuroscience (e.g., Schulze, van Ravenzwaaij, & Newell, 2015;Erev & Barron, 2005;Baron & Erev, 2003;Niv et al, 2015;Dayan & Daw, 2008;Chase, Kumar, Eickhoff, & Dombrovski, 2015;Dayan & Balleine, 2002). Despite their prominence, these models have well-documented cases of parameter non-identifiability and sloppiness (e.g., Humphries, Bruno, Karpievitch, & Wotherspoon, 2015;Wetzels et al, 2010; but see, e.g., Ahn et al, 2011Ahn et al, , 2014Steingroever, Wetzels, & Wagenmakers, 2013, for examples of satisfactory parameter identifiability).…”

Section: Overcoming Varieties Of Non-identifiability and Sloppinessmentioning

confidence: 99%

The relative merit of empirical priors in non-identifiable and sloppy models: Applications to models of learning and decision-making

Spektor

Kellen

2018

Psychon Bull Rev

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Formal modeling approaches to cognition provide a principled characterization of observed responses in terms of a set of postulated processes, specifically in terms of parameters that modulate the latter. These model-based characterizations are useful to the extent that there is a clear, one-to-one mapping between parameters and model expectations (identifiability) and that parameters can be recovered from reasonably sized data using a typical experimental design (recoverability). These properties are sometimes not met for certain combinations of model classes and data. One suggestion to improve parameter identifiability and recoverability involves the use of "empirical priors", which constrain parameters according to a previously observed distribution of values. We assessed the efficacy of this proposal using a combination of real and artificial data. Our results showed that a point-estimate variant of the empirical-prior method could not improve parameter recovery systematically. We identified the source of poor parameter recovery in the low information content of the data. As a follow-up step, we developed a fully Bayesian variant of the empirical-prior method and assessed its performance. We find that even such a method that takes the covariance structure of the parameter distributions into account cannot reliably improve parameter recovery. We conclude that researchers should invest additional efforts in improving the informativeness of their experimental designs, as many of the problems associated to impoverished designs cannot be alleviated by modern statistical methods alone.

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Section: Overcoming Varieties Of Non-identifiability and Sloppinessmentioning

confidence: 99%

The relative merit of empirical priors in non-identifiable and sloppy models: Applications to models of learning and decision-making

Spektor

Kellen

2018

Psychon Bull Rev

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“…To assess strategy selection in individual participants toward the end of learning, we classified participants' response proportions in the final trial block as either probability maximizing or probability matching. Participants who selected the dominant color on no less than 95 % of trials in the last block were defined as probability maximizers; participants who allocated their choices within 5 % of the average reward probability of the more probable option (.70 ± .05) were defined as probability matchers (see, e.g., Schulze, van Ravenzwaaij, & Newell, 2015). We carried out three-way chi-square tests to evaluate whether the adoption of probability maximizing and probability matching in the final trial block was associated with the color-key mapping manipulation, contingent on working memory load condition.…”

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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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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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“…2. We classified these individual-level responses as probability matching (allocating choices within 5% of the average outcome probability-i.e., 70%± 5%) or probability maximizing (selecting the dominant color on at least 95% of rolls; see, e.g., Schulze, van Ravenzwaaij, & Newell, 2015). No single group of decision makers probability matched.…”

Section: Resultsmentioning

confidence: 99%

More heads choose better than one: Group decision making can eliminate probability matching

Schulze

Newell

2015

Psychon Bull Rev

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Probability matching is a robust and common failure to adhere to normative predictions in sequential decision making. We show that this choice anomaly is nearly eradicated by gathering individual decision makers into small groups and asking the groups to decide. The group choice advantage emerged both when participants generated responses for an entire sequence of choices without outcome feedback (Exp.1a) and when participants made trial-by-trial predictions with outcome feedback after each decision (Exp.1b). We show that the dramatic improvement observed in group settings stands in stark contrast to a complete lack of effective solitary deliberation. These findings suggest a crucial role of group discussion in alleviating the impact of hasty intuitive responses in tasks better suited to careful deliberation.

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Of matchers and maximizers: How competition shapes choice under risk and uncertainty

Cited by 23 publications

References 49 publications

The relative merit of empirical priors in non-identifiable and sloppy models: Applications to models of learning and decision-making

The relative merit of empirical priors in non-identifiable and sloppy models: Applications to models of learning and decision-making

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

More heads choose better than one: Group decision making can eliminate probability matching

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