Modeling Behavior in a Clinically-Diagnostic Sequential Risk-Taking Task

Wallsten, Thomas S.; Pleskac, Timothy J.; Lejuez, Carl W.

doi:10.1037/e683332011-069

Cited by 60 publications

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“…DMs with lower values of ␤ will be more variable in their overall gambling behavior during a sequential risk-taking task. Departing from the model in Wallsten et al (2005), the response function in Equation 3 also contains a response bias module, h , allowing DMs to have a bias in their response that changes over rounds. Some DMs, as Wallsten et al (2005) report, have an exploratory bias whereby during the first few rounds they continue choosing the play option past the maximizing trial.…”

Section: Responsementioning

confidence: 99%

“…Besides describing behavior during the tasks, the models also show how the populations differ on the underlying cognitive dimensions captured within the models. For example, during the BART, Wallsten et al (2005) found that people who take unhealthy and unsafe risks tend to differ from normal populations in how they evaluate payoffs and the consistency of their responses.What is unclear, however, is the extent to which real-world risk takers systematically differ in the learning process used during the BART. There are two possible roles the learning process could play.…”

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“…One possibility is that DMs use the same learning process regardless of the task environment. This prediction is derived from the counterintuitive finding that DMs mentally model the BART, a nonstationary probabilistic environment, as a stationary or sampling-with-replacement environment (Wallsten et al, 2005). The mental representation in turn affects the specific learning process used.…”

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“…Before the first round of each game, DMs have a prior opinion about the probability of a success, q. The prior beliefs are modeled with beta distributions, f 1 (q), 4 The cognitive model is technically number 3 in Wallsten et al (2005). 5 Because of the nature of the BART, a probability weighting function could not be estimated, and so now weighting function was assumed.…”

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“…First, I introduce the sequential risk-taking paradigm and compare two exemplars from it: the BART and Slovic's (1966) devil task. Next, to derive predictions about how learning may either aid or obstruct in identifying risk takers, I introduce Wallsten et al's (2005) Bayesian sequential risk-taking model (BSR) of the paradigm. I show how the model can be adapted to account for different mental representations of different stochastic environments.…”

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Decision making and learning while taking sequential risks.

Pleskac

2008

Journal of Experimental Psychology: Learning, Memory, and Cogni

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A sequential risk-taking paradigm used to identify real-world risk takers invokes both learning and decision processes. This article expands the paradigm to a larger class of tasks with different stochastic environments and different learning requirements. Generalizing a Bayesian sequential risk-taking model to the larger set of tasks clarifies the roles of learning and decision making during sequential risky choice. Results show that respondents adapt their learning processes and associated mental representations of the task to the stochastic environment. Furthermore, their Bayesian learning processes are shown to interfere with the paradigm's identification of risky drug use, whereas the decision-making process facilitates its diagnosticity. Theoretical implications of the results in terms of both understanding risk-taking behavior and improving risk-taking assessment methods are discussed.Keywords: risk taking, learning, Bayesian, individual differences, cognitive psychometrics Learning and decision making are conceptually linked. Typically only after decision makers (DMs) make a decision do they observe or experience the precise outcome of that decision. For example, only after commuters select a traffic route do they determine its effectiveness, and only after athletes use a steroid do they learn about the precise properties it has on their body. These observations better inform DMs about the precise properties of their choice options and shape their next decision among the same or similar options. Despite this natural association between decision and learning processes, most decision-making theories fail to incorporate or explicate a learning component (e.g., Busemeyer & Townsend, 1993;González-Vallejo, 2002;Kahneman & Tversky, 1979). Yet, how DMs learn from experience has proven an important process in understanding risk-taking behavior. It can, for example, create an aversion toward risky alternatives in the gain domain and an attraction toward risky alternatives in the loss domain-a pattern typically attributed to how DMs evaluate outcomes (Denrell, 2007;March, 1996). The learning process can even produce the opposite pattern (Erev & Barron, 2005;Hertwig, Barron, Weber, & Erev, 2004;Weber, Shafir, & Blais, 2004).Applying theories of decision making to the Balloon Analogue Risk Task (BART; Lejuez et al., 2002) or to the Iowa Gambling Task (Bechara, Damasio, Damasio, & Anderson, 1994) also exposes the necessity of learning. Clinicians use these laboratorybased gambling tasks to study and identify people with specific clinical or neurological deficits. Cognitive models of these tasks reveal that decision and learning processes are necessary to account for choices made by both clinical and normal populations (Busemeyer & Stout, 2002;Wallsten, Pleskac, & Lejuez, 2005). Besides describing behavior during the tasks, the models also show how the populations differ on the underlying cognitive dimensions captured within the models. For example, during the BART, Wallsten et al. (2005) found that people who take un...

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Decision making and learning while taking sequential risks.

Pleskac

2008

Journal of Experimental Psychology: Learning, Memory, and Cogni

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What Can Performance in the IEDS Task Tell Us About Attention Shifting in Clinical Groups?

et al. 2021

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The Intra‐Extra‐dimensional set shift task (IEDS) is a widely used test of learning and attention, believed to be sensitive to aspects of executive function. The task proceeds through a number of stages, and it is generally claimed that patterns of errors across stages can be used to discriminate between reduced attention switching and more general reductions in rates of learning. A number of papers have used the IEDS task to argue for specific attention shifting difficulties in Autism Spectrum Disorder (ASD) and Schizophrenia, however, it remains unclear how well the IEDS really differentiates between reduced attention shifting and other causes of impaired performance. To address this issue, we introduce a simple computational model of performance in the IEDS task, designed to separate the competing effects of attention shifting and general learning rate. We fit the model to data from ASD and comparison individuals matched on age and IQ, as well as to data from four previous studies which used the IEDS task. Model fits do not show consistent evidence for reductions in attention shifting rates in ASD and Schizophrenia. Instead, we find performance is better explained by differences in learning rate, particularly from punishment, which we show correlates with IQ. We, therefore, argue that the IEDS task is not a good measure of attention shifting in clinical groups. Lay Summary The Intra‐Extra‐Dimensional Set shift task (IEDS) is often given to autistic individuals, who tend to make more errors relative to comparison groups. This higher error rate is taken to mean that autistic individuals struggle with attention control. Our computational model of the IEDS shows that the performance of ASD and some other clinical groups can be explained instead by differences in learning rate, rather than differences in attention control.

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Ownership Status and the Representation of Assets of Uncertain Value: The Balloon Endowment Risk Task (BERT)

Walasek

Wright

Rakow

2014

Behavioral Decision Making

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Owners tend to overvalue possessions relative to non‐owners: a phenomenon known as the endowment effect. In three experiments, using markets for goods of uncertain value, we investigated whether this can be partly attributed to misperceiving an asset's profitability or to uncertainty about a good's utility. To test our hypotheses, we devised the Balloon Endowment Risk Task, in which participants can sell or buy their right to participate in the Balloon Analogue Risk Task. Once purchased/retained, a virtual balloon is pumped to accrue money, which is lost if the balloon bursts. Participants first learn about the risky asset (balloon) by observing others playing the Balloon Analogue Risk Task before they enter the market. In Experiment 1, we replicated the endowment effect; yet, owners and non‐owners predicted pumping the same number of times and subsequently did so when given that opportunity. In Experiments 2 and 3, the level of uncertainty about the balloon's profitability was manipulated by modifying the number of bursts that participants viewed in the initial learning stage. When more diagnostic information was provided, making the average burst point easier to estimate and reducing value uncertainty and increasing confidence in valuation, the endowment effect diminished although mean estimates of the average burst point did not differ between owners and non‐owners. Thus, endowment effects were partly attributable to value uncertainty but could not be explained by owners and non‐owners having divergent perceptions of the asset's payoff distribution. Copyright © 2014 John Wiley & Sons, Ltd.

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Modeling Behavior in a Clinically-Diagnostic Sequential Risk-Taking Task

Cited by 60 publications

References 0 publications

Decision making and learning while taking sequential risks.

Decision making and learning while taking sequential risks.

What Can Performance in the IEDS Task Tell Us About Attention Shifting in Clinical Groups?

Ownership Status and the Representation of Assets of Uncertain Value: The Balloon Endowment Risk Task (BERT)

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