Comparison of Decision Learning Models Using the Generalization Criterion Method

Ahn, Woo-Young; Busemeyer, Jerome R.; Wagenmakers, Eric‐Jan; Stout, Julie C.

doi:10.1080/03640210802352992

Cited by 213 publications

(399 citation statements)

References 34 publications

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“…Several updating rules have been proposed in the literature; none has emerged as being generally superior (e.g., Ahn, Busemeyer, Wagenmakers, & Stout, 2008;Steingroever et al, 2013aSteingroever et al, , 2013bYechiam & Busemeyer, 2005). We therefore considered three updating rules that have received support in studies on experience-based decision making.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Unpacking buyer-seller differences in valuation from experience: A cognitive modeling approach

Pachur

Scheibehenne

2017

Psychon Bull Rev

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People often indicate a higher price for an object when they own it (i.e., as sellers) than when they do not (i.e., as buyers)-a phenomenon known as the endowment effect. We develop a cognitive modeling approach to formalize, disentangle, and compare alternative psychological accounts (e.g., loss aversion, loss attention, strategic misrepresentation) of such buyer-seller differences in pricing decisions of monetary lotteries. To also be able to test possible buyer-seller differences in memory and learning, we study pricing decisions from experience, obtained with the sampling paradigm, where people learn about a lottery's payoff distribution from sequential sampling. We first formalize different accounts as models within three computational frameworks (reinforcement learning, instance-based learning theory, and cumulative prospect theory), and then fit the models to empirical selling and buying prices. In Study 1 (a reanalysis of published data with hypothetical decisions), models assuming buyer-seller differences in response bias (implementing a strategicmisrepresentation account) performed best; models assuming buyer-seller differences in choice sensitivity or memory (implementing a loss-attention account) generally fared worst. In a new experiment involving incentivized decisions (Study 2), models assuming buyer-seller differences in both outcome sensitivity (as proposed by a loss-aversion account) and response bias performed best. In both Study 1 and 2, the models implemented in cumulative prospect theory performed best. Model recovery studies validated our cognitive modeling approach, showing that the models can be distinguished rather well. In summary, our analysis supports a loss-aversion account of the endowment effect, but also reveals a substantial contribution of simple response bias. Keywords Computational modeling . Endowment effect . Risky decision making . Decision from experience . Loss aversionThe minimum price at which people are willing to accept to sell an object they own is often higher than the maximum price they are willing to pay for the same object when they do not own it. This systematic difference between buying and selling prices-which can also occur within the same person when taking different perspectives-is known as the endowment effect (Kahneman, Knetsch, & Thaler, 1990, 1991Thaler, 1980; see also Birnbaum & Stegner, 1979). The endowment effect has attracted considerable attention because it contradicts a central tenet of economic theory: According to the Coase theorem, the value of an object should be independent of its initial ownership (Kahneman et al., 1990).There have been several proposals as to which cognitive mechanisms give rise to the endowment effect, or how buyers' and sellers' cognitive mechanisms might differ generally.1 For instance, there may be asymmetries between buyers and sellers in terms of the order in which they search for positive 1 Note that this issue is orthogonal to the question of which aspects of being endowed-such as ownership, expectations...

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Section: Reinforcement Learningmentioning

confidence: 99%

Unpacking buyer-seller differences in valuation from experience: A cognitive modeling approach

Pachur

Scheibehenne

2017

Psychon Bull Rev

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“…Underlying cognitive processes in the IGT were assessed using a mathematical model of the task, the Prospect Valence Learning (PVL) model (Ahn, Busemeyer, Wagenmakers, & Stout, 2008), which has yielded interesting insights into the behaviour in drug-using samples (Fridberg et al, 2010).…”

Section: Psychopathic Personality Traits 135mentioning

confidence: 99%

“…PVL model (Ahn et al, 2008) To examine how participants learned from their choices across the task, and how wins and losses influenced their choice strategy, we applied the PVL model to the data. The PVL model involves four parameters that are associated with different cognitive components of decision-making.…”

Section: Iowa Gambling Task (Igt)mentioning

confidence: 99%

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Psychopathic Personality Traits and Iowa Gambling Task Performance in Incarcerated Offenders

Hughes

Dolan

Trueblood

et al. 2014

Psychiatry, Psychology and Law

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“…Computational models such as the Expectancy-Valence (EV) model (Busemeyer and Stout 2002), the Perseverance Valence Learning (PVL) model, the PVL-Delta model (Ahn et al 2008(Ahn et al , 2011Fridberg et al 2010), and the Value Plus Perseverance (VPP) model (Worthy et al 2013), consider factors such as the attention given to outcome valence (i.e., to wins vs. losses), how the recency of feedback affects future decisions, and how choices are influenced by experience (i.e., to what extent choices are random). Such models assume that the valence experienced on each trial informs a probabilistic choice mechanism, and quantifies outcome expectation and prediction error on an individual trial-by-trial basis, thereby estimating individuals' subjective experiences of the task and task-expectations, rather than objective task outcomes (Yechiam and Busemeyer 2005).…”

mentioning

confidence: 99%

Computational EEG modelling of decision making under ambiguity reveals spatio-temporal dynamics of outcome evaluation

Jollans

Whelan

Venables

et al. 2017

Behavioural Brain Research

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Complex human cognition, such as decision-making under ambiguity, is reflected in dynamic spatio-temporal activity in the brain. Here, we combined event-related potentials (ERPs), with their high temporal resolution and moderate spatial resolution afforded by high-density arrays, with computational modelling of the time course of decision-making and outcome evaluation. We contrasted four computational models (Expectancy-Valence, ProspectValence Learning (PVL), PVL-Delta, and Value Plus Perseverance (VPP) model) and found that the VPP model provided the best post hoc fit. Measures of choice probability generated using the VPP model, as well as objective trial outcomes, were applied as regressors in a general linear model of the EEG signal to create a three-dimensional spatio-temporal characterization of task-related neural dynamics. We observed that outcome valence, outcome magnitude, and VPP choice probability are expressed in distinctly separate components of the ERP. Our findings show, for the first time, model-based analysis of the spatio-temporal dynamics of outcome evaluation in complex human decision-making.

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Comparison of Decision Learning Models Using the Generalization Criterion Method

Cited by 213 publications

References 34 publications

Unpacking buyer-seller differences in valuation from experience: A cognitive modeling approach

Unpacking buyer-seller differences in valuation from experience: A cognitive modeling approach

Psychopathic Personality Traits and Iowa Gambling Task Performance in Incarcerated Offenders

Computational EEG modelling of decision making under ambiguity reveals spatio-temporal dynamics of outcome evaluation

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