Literature DB >> 23997275

A Tutorial on Adaptive Design Optimization.

Jay I Myung1, Daniel R Cavagnaro, Mark A Pitt.   

Abstract

Experimentation is ubiquitous in the field of psychology and fundamental to the advancement of its science, and one of the biggest challenges for researchers is designing experiments that can conclusively discriminate the theoretical hypotheses or models under investigation. The recognition of this challenge has led to the development of sophisticated statistical methods that aid in the design of experiments and that are within the reach of everyday experimental scientists. This tutorial paper introduces the reader to an implementable experimentation methodology, dubbed Adaptive Design Optimization, that can help scientists to conduct "smart" experiments that are maximally informative and highly efficient, which in turn should accelerate scientific discovery in psychology and beyond.

Entities:  

Year:  2013        PMID: 23997275      PMCID: PMC3755632          DOI: 10.1016/j.jmp.2013.05.005

Source DB:  PubMed          Journal:  J Math Psychol        ISSN: 0022-2496            Impact factor:   2.223


  21 in total

1.  Bayesian adaptive estimation of psychometric slope and threshold.

Authors:  L L Kontsevich; C W Tyler
Journal:  Vision Res       Date:  1999-08       Impact factor: 1.886

Review 2.  Toward a method of selecting among computational models of cognition.

Authors:  Mark A Pitt; In Jae Myung; Shaobo Zhang
Journal:  Psychol Rev       Date:  2002-07       Impact factor: 8.934

Review 3.  Systems biology: experimental design.

Authors:  Clemens Kreutz; Jens Timmer
Journal:  FEBS J       Date:  2009-02       Impact factor: 5.542

4.  Sequential optimal design of neurophysiology experiments.

Authors:  Jeremy Lewi; Robert Butera; Liam Paninski
Journal:  Neural Comput       Date:  2009-03       Impact factor: 2.026

5.  Functional adaptive sequential testing.

Authors:  Edward Vul; Jacob Bergsma; Donald I A MacLeod
Journal:  Seeing Perceiving       Date:  2010

6.  Adaptive design optimization: a mutual information-based approach to model discrimination in cognitive science.

Authors:  Daniel R Cavagnaro; Jay I Myung; Mark A Pitt; Janne V Kujala
Journal:  Neural Comput       Date:  2010-04       Impact factor: 2.026

7.  Discriminating Among Probability Weighting Functions Using Adaptive Design Optimization.

Authors:  Daniel R Cavagnaro; Mark A Pitt; Richard Gonzalez; Jay I Myung
Journal:  J Risk Uncertain       Date:  2013-12

8.  An adaptive psychophysical method for subject classification.

Authors:  A B Cobo-Lewis
Journal:  Percept Psychophys       Date:  1997-10

9.  Optimal experimental design for model discrimination.

Authors:  Jay I Myung; Mark A Pitt
Journal:  Psychol Rev       Date:  2009-07       Impact factor: 8.934

10.  Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach.

Authors:  Daniel R Cavagnaro; Richard Gonzalez; Jay I Myung; Mark A Pitt
Journal:  Manage Sci       Date:  2013-02       Impact factor: 4.883

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  18 in total

1.  Challenges and promises for translating computational tools into clinical practice.

Authors:  Woo-Young Ahn; Jerome R Busemeyer
Journal:  Curr Opin Behav Sci       Date:  2016-10-01

2.  A hierarchical adaptive approach to optimal experimental design.

Authors:  Woojae Kim; Mark A Pitt; Zhong-Lin Lu; Mark Steyvers; Jay I Myung
Journal:  Neural Comput       Date:  2014-08-22       Impact factor: 2.026

Review 3.  Nonmonotonic Plasticity: How Memory Retrieval Drives Learning.

Authors:  Victoria J H Ritvo; Nicholas B Turk-Browne; Kenneth A Norman
Journal:  Trends Cogn Sci       Date:  2019-07-26       Impact factor: 20.229

Review 4.  A Bayesian perspective on severity: risky predictions and specific hypotheses.

Authors:  Noah van Dongen; Jan Sprenger; Eric-Jan Wagenmakers
Journal:  Psychon Bull Rev       Date:  2022-08-15

5.  On the Functional Form of Temporal Discounting: An Optimized Adaptive Test.

Authors:  Daniel R Cavagnaro; Gabriel J Aranovich; Samuel M McClure; Mark A Pitt; Jay I Myung
Journal:  J Risk Uncertain       Date:  2016-09-13

6.  Alpha Oscillations Are Causally Linked to Inhibitory Abilities in Ageing.

Authors:  Giulia Borghini; Michela Candini; Cristina Filannino; Masud Hussain; Vincent Walsh; Vincenzo Romei; Nahid Zokaei; Marinella Cappelletti
Journal:  J Neurosci       Date:  2018-04-03       Impact factor: 6.167

7.  ADOpy: a python package for adaptive design optimization.

Authors:  Jaeyeong Yang; Mark A Pitt; Woo-Young Ahn; Jay I Myung
Journal:  Behav Res Methods       Date:  2021-04

Review 8.  The Potential of Adaptive Design in Animal Studies.

Authors:  Arshad Majid; Ok-Nam Bae; Jessica Redgrave; Dawn Teare; Ali Ali; Daniel Zemke
Journal:  Int J Mol Sci       Date:  2015-10-12       Impact factor: 5.923

9.  Toward a new application of real-time electrophysiology: online optimization of cognitive neurosciences hypothesis testing.

Authors:  Gaëtan Sanchez; Jean Daunizeau; Emmanuel Maby; Olivier Bertrand; Aline Bompas; Jérémie Mattout
Journal:  Brain Sci       Date:  2014-01-23

10.  Active SAmpling Protocol (ASAP) to Optimize Individual Neurocognitive Hypothesis Testing: A BCI-Inspired Dynamic Experimental Design.

Authors:  Gaëtan Sanchez; Françoise Lecaignard; Anatole Otman; Emmanuel Maby; Jérémie Mattout
Journal:  Front Hum Neurosci       Date:  2016-07-07       Impact factor: 3.169

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