A Comparison of Reinforcement Learning Models for the Iowa Gambling Task Using Parameter Space Partitioning

Steingroever, Helen; Wetzels, Ruud; Wagenmakers, Eric‐Jan

doi:10.7771/1932-6246.1150

Cited by 39 publications

(94 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our finding that sleep loss affects the updating parameter on the IGT may therefore only apply when participants are employing the most common strategy, which is captured by the EV model. Finally, in this study we tested only the EV model and did not fit and compare other models, such as the Prospect Valence-Learning (PVL) model (Ahn, Busemeyer, Wagenmakers, & Stout, 2008;Fridberg, Queller, & Ahn, 2010;Steingroever, Wetzels, & Wagenmakers, 2013). It also is possible to fit the EV model by task block.…”

Section: Discussionmentioning

confidence: 97%

Daytime Sleepiness Is Associated With Reduced Integration of Temporally Distant Outcomes on the Iowa Gambling Task

Olson

Weber

Rauch

et al. 2014

Behavioral Sleep Medicine

View full text Add to dashboard Cite

Sleep deprivation is associated with performance decrements on some measures of executive functioning. For instance, sleep deprivation results in altered decision making on the Iowa Gambling Task. However, it is unclear which component processes of the task may be driving the effect. In this study, Iowa Gambling task performance was decomposed using the Expectancy-Valence model. Recent sleep debt and greater daytime sleepiness were associated with higher scores on the updating parameter, which reflects the extent to which recent experiences are emphasized over remote ones. Findings suggest that the effects of insufficient sleep on IGT performance are due to shortening of the time horizon over which decisions are integrated. These findings may have clinical implications in that individuals with sleep problems may not integrate more temporally distant information when making decisions.

show abstract

Section: Discussionmentioning

confidence: 97%

Daytime Sleepiness Is Associated With Reduced Integration of Temporally Distant Outcomes on the Iowa Gambling Task

Olson

Weber

Rauch

et al. 2014

Behavioral Sleep Medicine

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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...

show abstract

“…The IGT is arguably the most popular neuropsychological paradigm to measure decision-making deficits in an experimental context. Part of the data was already reanalyzed elsewhere (i.e., [8], [10][11][12][13][14]) in order to assess basic assumptions underlying the performance of healthy participants on the IGT, and to compare reinforcement-learning models that try to disentangle psychological processes underlying performance on the IGT.…”

Section: Introductionmentioning

confidence: 99%

Data from 617 Healthy Participants Performing the Iowa Gambling Task: A “Many Labs” Collaboration

Steingroever

Fridberg

Horstmann

et al. 2015

Journal of Open Psychology Data

Self Cite

View full text Add to dashboard Cite

This data pool (N = 617) comes from 10 studies assessing performance of healthy participants (i.e., no known neurological impairments) on the Iowa gambling task (IGT)-a task measuring decision making under uncertainty in an experimental context. Participants completed a computerized version of the IGT consisting of 95 -150 trials. The data consist of the choices of each participant on each trial, and the resulting rewards and losses. The data are stored as .rdata, .csv, and .txt files, and can be reused to (1) analyze IGT performance of healthy participants; (2) create a "super control group"; or (3) facilitate model-comparison efforts.

show abstract

A Comparison of Reinforcement Learning Models for the Iowa Gambling Task Using Parameter Space Partitioning

Cited by 39 publications

References 62 publications

Daytime Sleepiness Is Associated With Reduced Integration of Temporally Distant Outcomes on the Iowa Gambling Task

Daytime Sleepiness Is Associated With Reduced Integration of Temporally Distant Outcomes on the Iowa Gambling Task

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

Data from 617 Healthy Participants Performing the Iowa Gambling Task: A “Many Labs” Collaboration

Contact Info

Product

Resources

About