What the future holds and when A description-experience gap in intertemporal choice

Pachur, Thorsten; Pleskac, Timothy J.; Hertwig, Ralph

doi:10.31234/osf.io/grwye

Cited by 6 publications

(6 citation statements)

References 13 publications

(17 reference statements)

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“…Numerous other choice and judgment phenomena have, for several decades, been studied primarily with description-based paradigms, including base-rate neglect, sunk-cost effects, and social and strategic dilemmas (see Fantino & Navarro, 2012). Recently, researchers have begun to examine the possibility of description-experience gaps in other domains, such as temporal discounting (Dai, Pachur, Pleskac, & Hertwig, 2017; Kemel & Travers, 2016), strategic reasoning in social games (Fleischhut, Artinger, Olschewski, Volz, & Hertwig, 2014; Martin, Gonzalez, Juvina, & Lebiere, 2014), consumer choice (Wulff, Hills, & Hertwig, 2015a), medical decisions and reasoning (Armstrong & Spaniol, 2017; Fraenkel, Peters, Tyra, & Oelberg, 2016; Lejarraga, Pachur, Frey, & Hertwig, 2015), and adolescent risk taking (Pollak et al, 2016; Rosenbaum, Venkatraman, Steinberg, & Chein, 2016; van den Bos & Hertwig, 2017).…”

Section: Discussionmentioning

confidence: 99%

A meta-analytic review of two modes of learning and the description-experience gap.

Wulff

Mergenthaler-Canseco

Hertwig

2018

Psychological Bulletin

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243

348

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People can learn about the probabilistic consequences of their actions in two ways: One is by consulting descriptions of an action's consequences and probabilities (e.g., reading up on a medication's side effects). The other is by personally experiencing the probabilistic consequences of an action (e.g., beta testing software). In principle, people taking each route can reach analogous states of knowledge and consequently make analogous decisions. In the last dozen years, however, research has demonstrated systematic discrepancies between description- and experienced-based choices. This description-experience gap has been attributed to factors including reliance on a small set of experience, the impact of recency, and different weighting of probability information in the two decision types. In this meta-analysis focusing on studies using the sampling paradigm of decisions from experience, we evaluated these and other determinants of the decision-experience gap by reference to more than 70,000 choices made by more than 6,000 participants. We found, first, a robust description-experience gap but also a key moderator, namely, problem structure. Second, the largest determinant of the gap was reliance on small samples and the associated sampling error: free to terminate search, individuals explored too little to experience all possible outcomes. Third, the gap persisted when sampling error was basically eliminated, suggesting other determinants. Fourth, the occurrence of recency was contingent on decision makers' autonomy to terminate search, consistent with the notion of optional stopping. Finally, we found indications of different probability weighting in decisions from experience versus decisions from description when the problem structure involved a risky and a safe option. (PsycINFO Database Record

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Section: Discussionmentioning

confidence: 99%

A meta-analytic review of two modes of learning and the description-experience gap.

Wulff

Mergenthaler-Canseco

Hertwig

2018

Psychological Bulletin

Self Cite

243

348

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“…Solving the problem of what creates the diverging factors between CGT and DDT measures of impulsivity is likely to require extensions of empirical paradigms to whittle the difference between tasks down to key factors. For instance, a version of a DDT where the delays before outcomes are actively experienced (early investigations along these lines such as Dai, Pachur, Pleskac, & Hertwig, 2018; Xu, 2018) could help eliminate (or confirm) the description-experience gap as a culprit of the two-factor structure. Likewise, swapping the counter component of the CGT with a deliberate delay selection procedure (where delays are associated with different bets) might bring it closer to a DDT-like procedure.…”

Section: Discussionmentioning

confidence: 99%

Testing the factor structure underlying behavior using joint cognitive models: Impulsivity in delay discounting and Cambridge gambling tasks.

Kvam¹,

Romeu²,

Turner³

et al. 2021

Psychological Methods

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Neurocognitive tasks are frequently used to assess disordered decision making, and cognitive models of these tasks can quantify performance in terms related to decision makers' underlying cognitive processes. In many cases, multiple cognitive models purport to describe similar processes, but it is difficult to evaluate whether they measure the same latent traits or processes. In this article, we develop methods for modeling behavior across multiple tasks by connecting cognitive model parameters to common latent constructs. This approach can be used to assess whether 2 tasks measure the same dimensions of cognition, or actually improve the estimates of cognitive models when there are overlapping cognitive processes between 2 related tasks. The approach is then applied to connecting decision data on 2 behavioral tasks that evaluate clinically relevant deficits, the delay discounting task and Cambridge gambling task, to determine whether they both measure the same dimension of impulsivity. We find that the discounting rate parameters in the models of each task are not closely related, although substance users exhibit more impulsive behavior on both tasks. Instead, temporal discounting on the delay discounting task as quantified by the model is more closely related to externalizing psychopathology like aggression, while temporal discounting on the Cambridge gambling task is related more to response inhibition failures. The methods we develop thus provide a new way to connect behavior across tasks and grant new insights onto the different dimensions of impulsivity and their relation to substance use.

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“…Numerous other choice and judgment phenomena have, for several decades, been studied primarily with description-based paradigms, including base-rate neglect, sunk-cost effects, and social and strategic dilemmas (see Fantino & Navarro, 2012). Recently, researchers have begun to examine the possibility of description-experience gaps in other domains, such as temporal discounting (Dai, Pachur, Pleskac, & Hertwig, 2018;Kemel & Travers, 2016), strategic reasoning in social games (Fleischhut, Artinger, Olschewski, Volz, & Hertwig, 2014;Martin, Gonzalez, Juvina, & Lebiere, 2014), consumer choice (Wulff, Hills, & Hertwig, 2015b), medical decisions and reasoning (Armstrong & Spaniol, 2017;Fraenkel, Peters, Tyra, & Oelberg, 2016), and adolescent risk taking (Pollak et al, 2016;van den Bos & Hertwig, 2017). This means that turning to the process of exploration and learning with the help of experiential paradigms to understand the cognitive processes behind a wide range of important cognitive tasks has just begun.…”

Section: Discussionmentioning

confidence: 99%

Uncovering the Anatomy of Search Without Technology 1

Wulff¹,

Hertwig²

2019

A Handbook of Process Tracing Methods

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We gratefully acknowledge editorial support from Susannah Goss and the editors' helpful comments. ANATOMY OF SEARCH 2Uncovering the Anatomy of Search Without Technology Admittedly, our chapter is a somewhat odd addition in a Handbook of Process-Tracing Methods. We will not revel at the ever growing and ever more sophisticated methods to trace processes nor will we conceive of still another technology. Nevertheless, our concern will be with "information search prior to choice"-the object of desire of what Schulte-Mecklenbeck et al. (2017) called "movement-based (process tracing) such as computer-based information boards, eye tracking, joystick and slider bar, mouse tracking or the tracking of reaching pointing" (p. 443). Our starting point is this observation: Process-tracing methods often focus on cognitive tasks that in themselves are devoid of an explicit and extended episode of search, thus requiring sophisticated technologies and efforts to look through this dimly lit and small window of search.Our chapter will be concerned with a very different way, requiring much less engineering, of laying bare the anatomy of the search process, and in fact, giving search a leading role in behavioral experiments and theories of choice. The cognitive task that we focus on is risky choice, still one of the most important domains for studying the way humans make decisions.Since the early 2000s research on risky choice has (again) begun to study experiential-based paradigms (for reviews see Hertwig & Erev, 2009;Wulff, Mergenthaler Canseco, & Hertwig, 2018)-often in parallel with description-based paradigms. In the experiential paradigms, the search process and its major defining properties-that is, for instance, amount of search and the search policy-unfolds for everyone to see. Experiential paradigms in risky choice and many other lines of research, according to our key argument, represent an alternative to the use of sophisticated process-tracing technologies and auxiliary assumptions necessary to lift the veil and to understand the processes preceding choice. ANATOMY OF SEARCH3 Let us emphasize that our goal in this chapter is not to play process-tracing technologies against experiential paradigms. Barely any aspect of modern life-from technology, science, commerce, literary arts to news media and the World Wide Web-is conceivable without the reading and writing of symbolic descriptions. Therefore, it is important to understand how people search information in environments in which symbolic descriptions of information can be directly perused. Yet, research employing process-tracing technologies may nevertheless benefit greatly from the consideration experiential of paradigms, for instance in parallel to the description-based paradigms, thus enjoying a view on search that is less obscured and requires fewer assumptions.

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What the future holds and when A description-experience gap in intertemporal choice

Abstract: This project is aimed at replicating and extending the results of the project Intertemporal choice under temporal uncertainty.

Cited by 6 publications

References 13 publications

A meta-analytic review of two modes of learning and the description-experience gap.

A meta-analytic review of two modes of learning and the description-experience gap.

Testing the factor structure underlying behavior using joint cognitive models: Impulsivity in delay discounting and Cambridge gambling tasks.

Uncovering the Anatomy of Search Without Technology 1

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