Modeling Strategy Switches in Multi-attribute Decision Making

Lee, Michael; Gluck, Kevin A.

doi:10.1007/s42113-020-00092-w

Cited by 9 publications

(8 citation statements)

References 34 publications

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“…The ideas of our approach are not entirely novel. Processtracing has already been extensively used to study people's decision strategies (Payne et al, 1993;Schulte-Mecklenbeck et al, 2011;Schulte-Mecklenbeck et al, 2019) and Bayesian inference has been used to infer which decision strategies are include in individual participants' repertoire (Scheibehenne et al, 2013), when people switch between different decision strategies (Lee & Gluck, 2021), and which strategies people use in economic games Costa-Gomes and Crawford (2006), Crawford (2008), andCosta-Gomes et al (2001). Our method has several advantages.…”

Section: Discussionmentioning

confidence: 99%

“…The methods developed by Lee and Gluck (2021) and Lee et al (2019) are more similar to our method in that they infer which strategy each participant used on each trial of the experiment. The main difference is that these methods were developed for studying multi-cue decisionmaking whereas our method was developed for studying planning.…”

Section: Discussionmentioning

confidence: 99%

“…The method by Lee et al (2019) has the advantage that it uses process-tracing data, verbal reports, and choices whereas our method exclusively relies on the process-tracing data. While our method and Lee et al (2019) analyze the data of each participant individually, the method by Lee and Gluck (2021) additionally performs inference at the group level and constrains inferences about individual participants by the characteristics of the group. Furthermore, the method by Lee and Gluck (2021) additionally infers two aspects of the generative model of strategy sequences from the data, namely the probabilities of possible initial strategies and the probabilities of possible strategy transitions.…”

Section: Discussionmentioning

confidence: 99%

“…While our method and Lee et al (2019) analyze the data of each participant individually, the method by Lee and Gluck (2021) additionally performs inference at the group level and constrains inferences about individual participants by the characteristics of the group. Furthermore, the method by Lee and Gluck (2021) additionally infers two aspects of the generative model of strategy sequences from the data, namely the probabilities of possible initial strategies and the probabilities of possible strategy transitions. The main advance of our method is that it differentiates between a much larger number of different strategies (79 vs. 4).…”

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

A computational process-tracing method for measuring people’s planning strategies and how they change over time

Jain¹,

Callaway²,

Griffiths³

et al. 2022

Behav Res

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One of the most unique and impressive feats of the human mind is its ability to discover and continuously refine its own cognitive strategies. Elucidating the underlying learning and adaptation mechanisms is very difficult because changes in cognitive strategies are not directly observable. One important domain in which strategies and mechanisms are studied is planning. To enable researchers to uncover how people learn how to plan, we offer a tutorial introduction to a recently developed process-tracing paradigm along with a new computational method for measuring the nature and development of a person’s planning strategies from the resulting process-tracing data. Our method allows researchers to reveal experience-driven changes in people’s choice of individual planning operations, planning strategies, strategy types, and the relative contributions of different decision systems. We validate our method on simulated and empirical data. On simulated data, its inferences about the strategies and the relative influence of different decision systems are accurate. When evaluated on human data generated using our process-tracing paradigm, our computational method correctly detects the plasticity-enhancing effect of feedback and the effect of the structure of the environment on people’s planning strategies. Together, these methods can be used to investigate the mechanisms of cognitive plasticity and to elucidate how people acquire complex cognitive skills such as planning and problem-solving. Importantly, our methods can also be used to measure individual differences in cognitive plasticity and examine how different types (pedagogical) interventions affect the acquisition of cognitive skills.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

A computational process-tracing method for measuring people’s planning strategies and how they change over time

Jain¹,

Callaway²,

Griffiths³

et al. 2022

Behav Res

View full text Add to dashboard Cite

show abstract

“…A remaining challenge to establish the RulEx-J model as a more epistemic cognitive model is to test and compare different theoretical conceptualizations of the process mixing. Instead of having a constant mixture of both processes at all times, it might be possible that participants vary the relative proportion of processes between trials or switch between processes over sequences of trials (Lee & Gluck, 2020; Lee et al, 2019), trial-by-trial, or even between stimuli (as assumed by the ATRIUM model; Erickson & Kruschke, 1998). Other mixture processes might also be possible, such as the one proposed by the CX-COM (combining Cue abstraction with eXemplar memory assuming COMpetitive 762 memory retrieval; Albrecht et al, 2019) model.…”

Section: Discussionmentioning

confidence: 99%

Measuring the mixture of rule-based and exemplar-based processes in judgment: A hierarchical Bayesian approach.

Izydorczyk¹,

Bröder²

2023

Decision

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Based on theoretical and empirical considerations, Bröder et al. (2017) proposed the RulEx-J model to quantify the relative contribution of rule-and exemplar-based processes in numerical judgments. In their original article, a least-squares (LS) optimization procedure was used to estimate the model parameters. Despite general evidence for the validity of the model, the authors suggested that a strong bias in favoring the rule module could arise when there is noise in the data. In this article, we present a hierarchical Bayesian implementation of the RulEx-J model with the goal to rectify this problem. In a series of simulation studies, we demonstrate the ability of the hierarchical Bayesian RulEx-J model to recover parameters accurately and to be more robust against noise in the data, compared to an LS estimation routine. One further advantage of the hierarchical Bayesian approach is the direct implementation of hypotheses about group differences in the model structure. A validation experiment as well as reanalyses of two experiments from different labs demonstrate the usefulness of the approach for testing hypotheses about processing differences. Further applications for judgment research are discussed.

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Strategy selection in decisions from givens: Deciding at a glance?

Pachur

2022

Cognitive Psychology

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Modeling Strategy Switches in Multi-attribute Decision Making

Cited by 9 publications

References 34 publications

A computational process-tracing method for measuring people’s planning strategies and how they change over time

A computational process-tracing method for measuring people’s planning strategies and how they change over time

Measuring the mixture of rule-based and exemplar-based processes in judgment: A hierarchical Bayesian approach.

Strategy selection in decisions from givens: Deciding at a glance?

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