The Multi-attribute Linear Ballistic Accumulator Model of Context Effects in Multi-alternative Choice

Trueblood, Jennifer S.; Brown, Scott; Heathcote, Andrew; Busemeyer, Jerome R.

doi:10.1037/e519682015-026

Cited by 14 publications

(31 citation statements)

References 13 publications

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“…This mechanism relies on range effects, according to which extending the range of values on one attribute dimension reduces perceived differences on that attribute, reducing the attribute's weight in the decision task (see also Wedell & Pettibone, , and Pettibone & Wedell, ). Alternatively, Trueblood et al (in press) have proposed the multi‐attribute linear ballistic accumulator (MLBA) model as an explanation for these effects. In MLBA, attribute weights depend on the similarity between pairs of objects on different attributes.…”

Section: Discussionmentioning

confidence: 99%

“…The asymmetric dominance effect has recently been shown to emerge in judgment tasks. This work either uses perceptual stimuli (Trueblood et al, ) or else uses a multi‐cue design similar to previous choice experiments (Trueblood, ; Trueblood et al, in press). With perceptual stimuli, participants are asked to make judgments of magnitude involving different shapes, with dominated decoys having lower values than the target on dimensions such as width and length (see also Choplin & Hummel, ).…”

Section: Methodsmentioning

confidence: 99%

“…This effect has also recently been established in cue‐based judgment and perceptual judgment tasks. Particularly, Trueblood () and Trueblood et al () found that criminal judgments involving two suspects can be reversed if a third suspect, dominated in cue values by one but not the other of the initial suspects, is added to the consideration set. Likewise, Trueblood et al () found that judgments of relative magnitude of two shapes can be affected if a third shape with smaller length and width than one but not the other of the initial shapes is added to the consideration set (see also Choplin & Hummel, ).…”

Section: Confirmatory Search and Decoy Effectsmentioning

confidence: 99%

“…This research finds that adding or removing irrelevant alternatives—or decoys—from the set of available choice options can alter preferences. Recently, psychologists have begun extending this research to inference and judgment (Choplin & Hummel, ; Maylor & Roberts, ; Trueblood, ; Trueblood, Brown, & Heathcote, in press; Trueblood, Brown, Heathcote, & Busemeyer, ). As in the domain of preferential choice, this work finds that adding or removing irrelevant alternatives from the set of available responses can alter stated beliefs.…”

Section: Introductionmentioning

confidence: 99%

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Confirmatory Search and Asymmetric Dominance

Bhatia

2014

Behavioral Decision Making

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Decision makers use confirmatory search strategies in judgment tasks. As a result of this, their attention towards task-relevant cues is biased in favor of cues supporting available responses. Changing these responses can alter the cues used in the judgment task and, subsequently, alter beliefs. We use this mechanism to predict and explain the emergence of the asymmetric dominance effect in judgment. In four sets of experiments, we document systematic changes in belief, as dominated options are added to the response set. These effects emerge for a number of naturalistic judgment tasks and are mediated by the increased accessibility of decoy-supporting cues. Finally, these effects can be eliminated when the decision maker's attention is drawn towards the cues supporting the non-dominant response.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Confirmatory Search and Decoy Effectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Confirmatory Search and Asymmetric Dominance

Bhatia

2014

Behavioral Decision Making

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“…Although several different variants of EAMs exist, most are able to provide a close quantitative fit to these choice response time distributions observed in a range of different paradigms, and have been shown to qualitatively capture a range of different choice and response time benchmarks (Ratcliff, 1978;Ratcliff & Rouder, 1998;Usher & McClelland, 2001;Ratcliff & Tuerlinckx, 2002;Verdonck & Tuerlinckx, 2014). These models have also served as the basis for extensions to explain the choice response time distributions in more complex decisions, such as categorization (Nosofsky & Palmeri, 1997;Nosofsky, Little, Donkin, & Fific, 2011), multi-attribute choice (Roe, Busemeyer, & Townsend, 2001;Usher & McClelland, 2004;Tsetsos, Usher, & Chater, 2010;Trueblood, Brown, & Heathcote, 2014), absolute identification , choice confidence (Van Zandt & Maldonado-Molina, 2004;Ratcliff & Starns, 2009;Pleskac & Busemeyer, 2010), and stop signal paradigms (Matzke, Love, & Heathcote, 2017;Matzke, Hughes, Badcock, Michie, & Heathcote, 2017). Overall, EAMs are one of the most successful frameworks in the history of cognitive psychology, providing an accurate account of data from a range of rapid decision-making tasks, and serving as a basis for extensions to more complex decisions.…”

Section: Past Successmentioning

confidence: 99%

Evidence Accumulation Models: Current Limitations and Future Directions

Evans¹,

Wagenmakers²

2020

TQMP

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Evidence accumulation models (EAMs) have been the dominant models of speeded decision-making for several decades. These models propose that evidence accumulates for decision alternatives at some rate, until the evidence for one alternative reaches some threshold that triggers a decision. As a theory, EAMs have provided an accurate account of the choice response time distributions in a range of decision-making tasks, and as a measurement tool, EAMs have provided direct insight into how cognitive processes differ between groups and experimental conditions, resulting in EAMs becoming the standard paradigm of speeded decision-making. However, we argue that there are several limitations to how EAMs are currently tested and applied, which have begun to limit their value as a standard paradigm. Specifically, we believe that a theoretical plateau has been reached for the level of explanation that EAMs can provide about the decisionmaking process, and that applications of EAMs have started to become restrictive and of limited value. We provide several recommendations for how researchers can help to overcome these limitations. As a theory, we believe that EAMs can provide further value through being constrained by sources of data beyond the standard choice response time distributions, being extended to the entire decision-making process from encoding to responding, and having the random sources of variability replaced by systematic sources of variability. As a measurement tool, we believe that EAMs can provide further value through being a default method of inference for cognitive psychology in place of mean response time and choice, and being applied to a broader range of empirical questions that better capture individual differences in cognitive processes.

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The best of times and the worst of times are interchangeable.

Hawkins¹,

Marley²,

Heathcote³

et al. 2014

Decision

Self Cite

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We commonly determine the most preferred (best) and least preferred (worst) of a set of options, yet it is unclear whether the two choices are based on the same or different information. We examined best and worst choices using discrete choice tasks, where participants selected either the best option from a set, the worst option, or selected both the best and the worst option. One experiment used perceptual judgments of area, and another used consumer preferences for various attributes of mobile phones. In both domains, we found that the task (best, worst, or best and worst) does not alter the preferences expressed for the best (respectively, the worst) option. We also observed that the choice probabilities were consistent with a single latent dimension -options that were frequently selected as best were infrequently selected as worst, and vice versa -both within and between respondents. A quantitative model of choice and response time provided convergent evidence on those relations, with model variants that assumed an inverse relationship between the estimated parameters for best and worst choices accounting well for the data. We conclude that the diverse types of best and worst choices that we studied can be conceived as opposing ends of a single continuous dimension rather than distinct latent entities. We discuss these results in the light of rather different results for accepting (e.g., purchasing) and rejecting (e.g., not purchasing) options from a set.

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The Multi-attribute Linear Ballistic Accumulator Model of Context Effects in Multi-alternative Choice

Cited by 14 publications

References 13 publications

Confirmatory Search and Asymmetric Dominance

Confirmatory Search and Asymmetric Dominance

Evidence Accumulation Models: Current Limitations and Future Directions

The best of times and the worst of times are interchangeable.

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