Evidence Accumulates for Individual Attributes during Value-Based Decisions

Lee, Douglas G.; Hare, Todd A.

doi:10.1101/2021.08.05.455296

Cited by 4 publications

(8 citation statements)

References 94 publications

(158 reference statements)

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“…Our results on both choice outcomes and postdecision confidence ratings are consistent with these findings. Similar results have also been reported for options where the attributes are objectively defined, such as intertemporal choice (monetary amount and delay; Amasino et al, 2019) and risky choice (monetary amount and probability; Lee et al, 2022). In addition, our model simulation and comparison results suggest that the representations that determine an option's subjective value may differ between rating and binary choice tasks.…”

Section: Discussionsupporting

confidence: 84%

Evidence accumulates for individual attributes during value-based decisions.

Lee¹,

Hare²

2023

Decision

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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 options' overall values (OVs). The importance of each attribute together with its quality will determine our preference rankings over the available alternatives. Here, we test the hypothesis that the most prominent class of model for simple decisions-sequential sampling or evidence accumulation to bound-can be bolstered by explicitly incorporating variables related to individual attributes in addition to the standard usage of OV estimates. We examine six data sets in which participants evaluated snack foods both in terms of OV and individual attributes, then choose between pairs of the same snacks and show that only models that explicitly incorporate information about the individual attributes are able to reproduce fundamental patterns in the choice data, such as the influence of attribute disparity on decisions, and such models provide quantitatively better fits to the choice outcomes, response times, and confidence ratings compared to models based on OV alone. Our results provide important evidence that incorporating attribute-level information into computational models help us to better understand the cognitive processes involved in value-based decision-making.

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Section: Discussionsupporting

confidence: 84%

Evidence accumulates for individual attributes during value-based decisions.

Lee¹,

Hare²

2023

Decision

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“…We followed the approach used by Lee and Hare (Lee & Hare, 2022) and considered three main models. Model 1 (expected value DDM or evDDM) is based on comparisons of expected value (EV).…”

Section: Model-based Analysis Using the Drift-diffusion Model (Ddm)mentioning

confidence: 99%

“…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%

“…The question about withinalternative versus within-attribute comparisons has been asked not only with respect to human decision-makers, but also rhesus macaques (Farashahi et al, 2019). A few studies have already used drift-diffusion modeling to provide evidence that participants compare individual attributes during deliberation about intertemporal choices (Dai & Busemeyer, 2014) or that they seem to both compare attributes across options and integrate values within options during value-based decisions (Lee & Hare, 2022;Yang & Krajbich, 2022).…”

Section: Introductionmentioning

confidence: 99%

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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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“…Decisions where no universal objective values existperhaps the most common varietymust rely solely on subjective values. Previous research has established that people often subjectively assess choice options in terms of specific individual value-related attributes (Amasino et al, 2019;Barakchian et al, 2021;Bhatia & Stewart, 2018;Hare et al, 2011;Harris et al, 2018;Lee & Hare, 2022;Luce et al, 2000;Maier et al, 2020;Noguchi & Stewart, 2018;Reeck et al, 2017;Sullivan et al, 2015;Weber & Johnson, 2009). It is assumed that such attribute assessments should aggregate to approximate subjective estimates of overall value, provided that a sufficient set of attributes is considered.…”

Section: Introductionmentioning

confidence: 99%

Value certainty and choice confidence are multidimensional constructs that guide decision-making

Lee¹,

Hare²

2022

Preprint

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The degree of certainty that decision-makers have about their evaluations of available choice alternatives and their confidence about selecting the subjectively best alternative are important factors that affect current and future value-based choices. Assessments of the alternatives in a given choice set are rarely unidimensional; their values are usually derived from a combination of multiple distinct attributes. For example, the taste, texture, quantity, and nutritional content of a snack food may all be considered when determining whether to consume it. Here, we examined how certainty about the levels of individual attributes of an option relates to certainty about the overall value of that option as a whole and/or to confidence in having chosen the subjectively best available option. We found that certainty and confidence are derived from unequally-weighted combinations of attribute certainties rather than simple, equal combinations of all sources of uncertainty. Attributes that matter more in determining choice outcomes are also weighted more in metacognitive evaluations of certainty or confidence. Moreover, we found that the process of deciding between two alternatives leads to refinements in both attribute estimations and the degree of certainty in those estimates. Attributes that are more important in determining choice outcomes are refined more during the decision process in terms of both estimates and certainty. Although certainty and confidence are typically treated as unidimensional, our results indicate that they, like value estimates, are subjective, multidimensional constructs.

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Evidence Accumulates for Individual Attributes during Value-Based Decisions

Cited by 4 publications

References 94 publications

Evidence accumulates for individual attributes during value-based decisions.

Evidence accumulates for individual attributes during value-based decisions.

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

Value certainty and choice confidence are multidimensional constructs that guide decision-making

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