Value certainty in drift-diffusion models of preferential choice.

Lee, Douglas G.; Usher, Marius

doi:10.1037/rev0000329

Cited by 19 publications

(29 citation statements)

References 79 publications

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“…A key output of the BMS is the exceedance probability, which informs about how likely it is that a given model is more frequently implemented across the population of participants (relative to all other models under consideration; (Rigoux et al, 2014;Stephan et al, 2009)). Previous studies have successfully used this approach to fitting and comparing variants of DDM (Feltgen & Daunizeau, 2021;Lee & Hare, 2022;Lee & Usher, 2021;Lopez-Persem et al, 2016).…”

Section: Model 3: Multi-attribute Ddm Plus Expected Value (Maddm+ev)mentioning

confidence: 99%

Risky decisions are influenced by individual attributes as a function of risk preference

Lee¹,

D’Alessandro²,

Iodice³

et al. 2022

Preprint

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It has long been assumed in economic theory that multi-attribute decisions involving several attributes or dimensions – such as probabilities and amounts of money to be earned during risky choices – are resolved by first combining the attributes of each option to form an overall expected value and then comparing the expected values of the alternative options, using a unique evidence accumulation process (integrate-then-compare model). Two plausible alternatives would be: performing multiple comparisons between the individual attributes in parallel and then integrating the results of the comparisons afterwards (compare-then-integrate model); combining the two methods described above (combined model). Distinguishing between these alternative models has been difficult, as they tend to generate similar predictions across a wide range of conditions. Here, we devise a novel method to disambiguate between integrate-then-compare, compare-then-integrate, and combined models of multi-attribute decisions, by orthogonally manipulating the expected value of the options and the saliency of their attributes. Our results, using behavioral measures and drift-diffusion models, provide evidence in favor of the compare-then-integrate and combined models but against the integrate-then-compare model. This suggests that risky decisions are resolved by running in parallel multiple comparisons between the separate attributes – possibly in combination with an additional comparison between the expected values of the options. This result stands in contrast with the assumption of standard economic theory that choices require a unique comparison of expected values and suggests that at the cognitive and neural levels, decision processes might be more distributed than commonly assumed.

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Section: Model 3: Multi-attribute Ddm Plus Expected Value (Maddm+ev)mentioning

confidence: 99%

Risky decisions are influenced by individual attributes as a function of risk preference

Lee¹,

D’Alessandro²,

Iodice³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…BMS provides an exceedance probability that measures how likely it is that a given model is more frequently implemented, relative to all other models under consideration, in the population from which participants were drawn (Rigoux et al, 2014;Stephan et al, 2009). This approach to fitting and comparing variants of DDM has already been successfully demonstrated in previous studies (Feltgen & Daunizeau, 2021;Lee & Usher, 2021;Lopez-Persem et al, 2016). Our VBA-based approach makes use of the concise analytical formulation of mean RT, as opposed to the full distribution of RT.…”

Section: Model Fitting Proceduresmentioning

confidence: 99%

Evidence Accumulates for Individual Attributes during Value-Based Decisions

Lee

Hare

2021

Preprint

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When choosing between different options, we tend to consider specific attribute qualities rather than deliberating over some general sense of the objects’ overall values. The importance of each attribute together with its quality will determine our preference rankings over the available alternatives. Here, we show that the relative importance of the latent attributes within food rewards reliably differs when the items are evaluated in isolation compared to when binary choices are made between them. Specifically, we used standard regression and sequential sampling models to examine six datasets in which participants evaluated, and chose between, multi-attribute snack foods. We show that models that assume that attribute importance remains constant across evaluation and choice contexts fail to reproduce fundamental patterns in the choice data and provide quantitatively worse fits to the choice outcomes, response times, and confidence reports compared to models that allow for attribute importance to vary across preference elicitation methods. Our results provide important evidence that incorporating attribute-level information into computational models helps us to better understand the cognitive processes involved in value-based decision-making.

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“…Specifically, in situations where the accumulating evidence has lower precision, deliberation should persist for longer (cf. (Lee & Usher, 2021)).…”

Section: Computational Advantages Of the Cddmmentioning

confidence: 99%

Evidence or Confidence: What is really monitored during a decision?

Lee

Pezzulo

2021

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Assessing one's confidence in one's choices is of paramount importance to making adaptive decisions, and it is thus no surprise that humans excel in this ability. However, standard models of decision-making, such as the drift-diffusion model (DDM), treat confidence assessment as a post-hoc or parallel process that does not directly influence the choice -- the latter depends only on accumulated evidence. Here, we pursue the alternative hypothesis that what is accumulated during a decision is confidence (that the to-be selected option is the best) rather than raw evidence. Accumulating confidence has the appealing consequence that the decision threshold corresponds to a desired level of confidence for the choice, and that confidence improvements can be traded off against the resources required to secure them. We show that most previous findings on perceptual and value-based decisions traditionally interpreted from an evidence-accumulation perspective can be explained more parsimoniously from our novel confidence-driven perspective. Furthermore, we show that our novel confidence-driven DDM (cDDM) naturally generalizes to any number of decisions -- which is notoriously extemporaneous using traditional DDM or related models. Finally, we discuss future empirical evidence that could be useful in adjudicating between these alternatives.

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Value certainty in drift-diffusion models of preferential choice.

Cited by 19 publications

References 79 publications

Risky decisions are influenced by individual attributes as a function of risk preference

Risky decisions are influenced by individual attributes as a function of risk preference

Evidence Accumulates for Individual Attributes during Value-Based Decisions

Evidence or Confidence: What is really monitored during a decision?

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