An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data

Feltgen, Quentin; Daunizeau, Jean

doi:10.3389/frai.2021.531316

Cited by 9 publications

(6 citation statements)

References 60 publications

(84 reference statements)

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“…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;Lopez-Persem et al, 2016).…”

Section: Computational Model-fitting Proceduresmentioning

confidence: 99%

Value certainty in drift-diffusion models of preferential choice.

Lee¹,

Usher²

2023

Psychological Review

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The drift-diffusion model (DDM) is widely used and broadly accepted for its ability to account for binary choices (in both the perceptual and preferential domains) and for their response times (RT), as a function of the stimulus or the option values. The DDM is built on an evidence accumulation to bound concept, where, in the value domain, a decision maker repeatedly samples the mental representations of the values of the options until satisfied that there is enough evidence in favor of one option over the other. As the signals that drive the evidence are derived from value estimates that are not known with certainty, repeated sequential samples are necessary to average out noise. The classic DDM does not allow for different options to have different levels of variability in their value representations. However, recent studies have shown that decision makers often report levels of certainty regarding value estimates that vary across choice options. There is therefore a need to extend the DDM to include an option-specific value certainty component. We present several such DDM extensions and validate them against empirical data from four previous studies. The data supports best a DDM version in which the drift of the accumulation is based on a sort of signal-to-noise ratio of value for each option (rather than a mere accumulation of samples from the corresponding value distributions). This DDM variant accounts for the positive impact of value certainty on choice consistency and for the negative impact of value certainty on RT.

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Section: Computational Model-fitting Proceduresmentioning

confidence: 99%

Value certainty in drift-diffusion models of preferential choice.

Lee¹,

Usher²

2023

Psychological Review

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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 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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An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data

Cited by 9 publications

References 60 publications

Value certainty in drift-diffusion models of preferential choice.

Value certainty in drift-diffusion models of preferential choice.

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

Evidence Accumulates for Individual Attributes during Value-Based Decisions

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