Anthony Marley

Goal-based models for discrete choice analysis

Publisher: Elsevier BV

Date: 07-2017

DOI: 10.1016/J.TRB.2017.03.005

The BWS profile case

Publisher: Cambridge University Press

Date: 23-09-2015

DOI: 10.1017/CBO9781107337855.004

Not just noise: A goal pursuit interpretation of stochastic choice.

Publisher: American Psychological Association (APA)

Date: 10-2018

DOI: 10.1037/DEC0000077

The BWS object case

Publisher: Cambridge University Press

Date: 23-09-2015

DOI: 10.1017/CBO9781107337855.003

Dissecting the Random Component of Utility

Publisher: Springer Science and Business Media LLC

Date: 08-2002

DOI: 10.1023/A:1020258402210

Introduction and overview of the book

Publisher: Cambridge University Press

Date: 23-09-2014

DOI: 10.1017/CBO9781107337855.002

An integrated model of choices and response times in absolute identification.

Publisher: American Psychological Association (APA)

Date: 04-2008

DOI: 10.1037/0033-295X.115.2.396

Integrating cognitive process and descriptive models of attitudes and preferences

Publisher: Wiley

Date: 11-10-2014

DOI: 10.1111/COGS.12094

Abstract: Discrete choice experiments—selecting the best and/or worst from a set of options—are increasingly used to provide more efficient and valid measurement of attitudes or preferences than conventional methods such as Likert scales. Discrete choice data have traditionally been analyzed with random utility models that have good measurement properties but provide limited insight into cognitive processes. We extend a well‐established cognitive model, which has successfully explained both choices and response times for simple decision tasks, to complex, multi‐attribute discrete choice data. The fits, and parameters, of the extended model for two sets of choice data (involving patient preferences for dermatology appointments, and consumer attitudes toward mobile phones) agree with those of standard choice models. The extended model also accounts for choice and response time data in a perceptual judgment task designed in a manner analogous to best–worst discrete choice experiments. We conclude that several research fields might benefit from discrete choice experiments, and that the particular accumulator‐based models of decision making used in response time research can also provide process‐level instantiations for random utility models.

Looking forward

Publisher: Cambridge University Press

Date: 23-09-2015

DOI: 10.1017/CBO9781107337855.007

Individuals’ Decisions in the Presence of Multiple Goals

Publisher: Springer Science and Business Media LLC

Date: 11-05-2017

DOI: 10.1007/S40547-017-0071-1

Basic models

Publisher: Cambridge University Press

Date: 23-09-2015

DOI: 10.1017/CBO9781107337855.006

The BWS multi-profile case

Publisher: Cambridge University Press

Date: 23-09-2015

DOI: 10.1017/CBO9781107337855.005

The best of times and the worst of times are interchangeable

Publisher: American Psychological Association (APA)

Date: 07-2014

DOI: 10.1037/DEC0000012

Dissociating speed and accuracy in absolute identification: the effect of unequal stimulus spacing

Publisher: Springer Science and Business Media LLC

Date: 13-08-2008

DOI: 10.1007/S00426-008-0158-2

Abstract: Identification accuracy for sets of perceptually discriminable stimuli ordered on a single dimension (e.g., line length) is remarkably low, indicating a fundamental limit on information processing capacity. This surprising limit has naturally led to a focus on measuring and modeling choice probability in absolute identification research. We show that choice response time (RT) results can enrich our understanding of absolute identification by investigating dissociation between RT and accuracy as a function of stimulus spacing. The dissociation is predicted by the SAMBA model of absolute identification (Brown, Marley, Dockin, & Heathcote, 2008), but cannot easily be accommodated by other theories. We show that SAMBA provides an accurate, parameter free, account of the dissociation that emerges from the architecture of the model and the physical attributes of the stimuli, rather than through numerical adjustment. This violation of the pervasive monotonic relationship between RT and accuracy has implications for model development, which are discussed.

Accumulating advantages: A new conceptualization of rapid multiple choice.

Publisher: American Psychological Association (APA)

Date: 03-2020

DOI: 10.1037/REV0000166

Abstract: Independent racing evidence-accumulator models have proven fruitful in advancing understanding of rapid decisions, mainly in the case of binary choice, where they can be relatively easily estimated and are known to account for a range of benchmark phenomena. Typically, such models assume a one-to-one mapping between accumulators and responses. We explore an alternative independent-race framework where more than one accumulator can be associated with each response, and where a response is triggered when a sufficient number of accumulators associated with that response reach their thresholds. Each accumulator is primarily driven by the difference in evidence supporting one versus another response (i.e., that response's "advantage"), with secondary inputs corresponding to the total evidence for both responses and a constant term. We use Brown and Heathcote's (2008) linear ballistic accumulator (LBA) to instantiate the framework in a mathematically tractable measurement model (i.e., a model whose parameters can be successfully recovered from data). We show this "advantage LBA" model provides a detailed quantitative account of a variety of benchmark binary and multiple choice phenomena that traditional independent accumulator models struggle with in binary choice the effects of additive versus multiplicative changes to input values, and in multiple choice the effects of manipulations of the strength of lure (i.e., nontarget) stimuli and Hick's law. We conclude that the advantage LBA provides a tractable new avenue for understanding the dynamics of decisions among multiple choices. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Best-worst scaling: theory, methods and applications

Publisher: Cambridge University Press

Date: 23-09-2015

DOI: 10.1017/CBO9781107337855

Abstract: Best-worst scaling (BWS) is an extension of the method of paired comparison to multiple choices that asks participants to choose both the most and the least attractive options or features from a set of choices. It is an increasingly popular way for academics and practitioners in social science, business, and other disciplines to study and model choice. This book provides an authoritative and systematic treatment of best-worst scaling, introducing readers to the theory and methods for three broad classes of applications. It uses a variety of case studies to illustrate simple but reliable ways to design, implement, apply, and analyze choice data in specific contexts, and showcases the wide range of potential applications across many different disciplines. Best-worst scaling avoids many rating scale problems and will appeal to those wanting to measure subjective quantities with known measurement properties that can be easily interpreted and applied.

Is absolute identification always relative? Comment on Stewart, Brown, and Chater (2005).

Publisher: American Psychological Association (APA)

Date: 2007

DOI: 10.1037/0033-295X.114.2.528

Probabilistic choice (models) as a result of balancing multiple goals

Publisher: Elsevier BV

Date: 02-2013

DOI: 10.1016/J.JMP.2013.03.003

Generalising the drift rate distribution for linear ballistic accumulators

Publisher: Elsevier BV

Date: 10-2015

DOI: 10.1016/J.JMP.2015.09.002

Sequential preference questions factors influencing completion rates and response times using an online panel

Publisher: Elsevier BV

Date: 09-2013

DOI: 10.1016/J.JOCM.2013.04.009

University Of Pennsylvania

Location: United States of America

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University Of Victoria

Location: Canada

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University Of Birmingham

Location: United Kingdom of Great Britain and Northern Ireland

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Discovery Projects - Grant ID: DP110100234

Start Date: 2011

End Date: 03-2016

Amount: $544,155.00

Funder: Australian Research Council

Linkage - International - Grant ID: LX0348125

Start Date: 11-2003

End Date: 12-2007

Amount: $38,700.00

Funder: Australian Research Council

Choice Foundations: Diagnostic Tools For Individual-level Model Development

Start Date: 2018

End Date: 2020

Funder: Australian Research Council

Improving Choice Models: Multiple Goal Pursuit And Multi-stage Decision Processes

Start Date: 2014

End Date: 2016

Funder: Australian Research Council

Discovery Projects - Grant ID: DP140103966

Start Date: 06-2014

End Date: 12-2017

Amount: $680,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP180101070

Start Date: 05-2018

End Date: 10-2018

Amount: $437,510.00

Funder: Australian Research Council