WoMMBAT: A user interface for hierarchical Bayesian estimation of working memory capacity

Richard D. Morey; Candice C. Morey

doi:10.3758/s13428-011-0114-8

WoMMBAT: A user interface for hierarchical Bayesian estimation of working memory capacity

Richard D. Morey^*, Candice C. Morey

^*Corresponding author for this work

School of Philosophy, Psychology and Language Sciences

Research output: Contribution to journal › Article › peer-review

Abstract / Description of output

The change detection paradigm has become an important tool for researchers studying working memory. Change detection is especially useful for studying visual working memory, because recall paradigms are difficult to employ in the visual modality. Pashler (Perception & Psychophysics, 44, 369-378, 1988) and Cowan (Behavioral and Brain Sciences, 24, 87-114, 2001) suggested formulas for estimating working memory capacity from change detection data. Although these formulas have become widely used, Morey (Journal of Mathematical Psychology, 55, 8-24, 2011) showed that the formulas suffer from a number of issues, including inefficient use of information, bias, volatility, uninterpretable parameter estimates, and violation of ANOVA assumptions. Morey presented a hierarchical Bayesian extension of Pashler's and Cowan's basic models that mitigates these issues. Here, we present WoMMBAT (Working Memory Modeling using Bayesian Analysis Techniques) software for fitting Morey's model to data. WoMMBAT has a graphical user interface, is freely available, and is cross-platform, running on Windows, Linux, and Mac operating systems.

Original language	English
Pages (from-to)	1044-1065
Number of pages	22
Journal	Behavior Research Methods
Volume	43
Issue number	4
DOIs	https://doi.org/10.3758/s13428-011-0114-8
Publication status	Published - Dec 2011

Keywords / Materials (for Non-textual outputs)

SHORT-TERM-MEMORY
Working memory
BINDING
Visual short term memory
OBJECTS
STORAGE CAPACITY
COMPLEXITY
NUMBER
VISUAL MEMORY
Bayesian models
MODEL
Bayesian hierarchical models
Multinomial models
Capacity estimates
REGRESSION
Visual change detection

Access to Document

10.3758/s13428-011-0114-8

1 Participation in conference
1 Invited talk

What do asymmetric patterns of interference suggest about modular working memory storage?
Candice Morey (Speaker)
Apr 2013
Activity: Academic talk or presentation types › Invited talk
European Working Memory Symposium
Candice Morey (Speaker)
Sept 2012
Activity: Participating in or organising an event types › Participation in conference

Cite this

@article{3b9d0b1c0e8842dcbac17843744b0d82,

title = "WoMMBAT: A user interface for hierarchical Bayesian estimation of working memory capacity",

abstract = "The change detection paradigm has become an important tool for researchers studying working memory. Change detection is especially useful for studying visual working memory, because recall paradigms are difficult to employ in the visual modality. Pashler (Perception & Psychophysics, 44, 369-378, 1988) and Cowan (Behavioral and Brain Sciences, 24, 87-114, 2001) suggested formulas for estimating working memory capacity from change detection data. Although these formulas have become widely used, Morey (Journal of Mathematical Psychology, 55, 8-24, 2011) showed that the formulas suffer from a number of issues, including inefficient use of information, bias, volatility, uninterpretable parameter estimates, and violation of ANOVA assumptions. Morey presented a hierarchical Bayesian extension of Pashler's and Cowan's basic models that mitigates these issues. Here, we present WoMMBAT (Working Memory Modeling using Bayesian Analysis Techniques) software for fitting Morey's model to data. WoMMBAT has a graphical user interface, is freely available, and is cross-platform, running on Windows, Linux, and Mac operating systems.",

keywords = "SHORT-TERM-MEMORY, Working memory, BINDING, Visual short term memory, OBJECTS, STORAGE CAPACITY, COMPLEXITY, NUMBER, VISUAL MEMORY, Bayesian models, MODEL, Bayesian hierarchical models, Multinomial models, Capacity estimates, REGRESSION, Visual change detection",

author = "Morey, {Richard D.} and Morey, {Candice C.}",

year = "2011",

month = dec,

doi = "10.3758/s13428-011-0114-8",

language = "English",

volume = "43",

pages = "1044--1065",

journal = "Behavior Research Methods",

issn = "1554-351X",

publisher = "Psychonomic Society Inc.",

number = "4",

}

TY - JOUR

T1 - WoMMBAT: A user interface for hierarchical Bayesian estimation of working memory capacity

AU - Morey, Richard D.

AU - Morey, Candice C.

PY - 2011/12

Y1 - 2011/12

N2 - The change detection paradigm has become an important tool for researchers studying working memory. Change detection is especially useful for studying visual working memory, because recall paradigms are difficult to employ in the visual modality. Pashler (Perception & Psychophysics, 44, 369-378, 1988) and Cowan (Behavioral and Brain Sciences, 24, 87-114, 2001) suggested formulas for estimating working memory capacity from change detection data. Although these formulas have become widely used, Morey (Journal of Mathematical Psychology, 55, 8-24, 2011) showed that the formulas suffer from a number of issues, including inefficient use of information, bias, volatility, uninterpretable parameter estimates, and violation of ANOVA assumptions. Morey presented a hierarchical Bayesian extension of Pashler's and Cowan's basic models that mitigates these issues. Here, we present WoMMBAT (Working Memory Modeling using Bayesian Analysis Techniques) software for fitting Morey's model to data. WoMMBAT has a graphical user interface, is freely available, and is cross-platform, running on Windows, Linux, and Mac operating systems.

AB - The change detection paradigm has become an important tool for researchers studying working memory. Change detection is especially useful for studying visual working memory, because recall paradigms are difficult to employ in the visual modality. Pashler (Perception & Psychophysics, 44, 369-378, 1988) and Cowan (Behavioral and Brain Sciences, 24, 87-114, 2001) suggested formulas for estimating working memory capacity from change detection data. Although these formulas have become widely used, Morey (Journal of Mathematical Psychology, 55, 8-24, 2011) showed that the formulas suffer from a number of issues, including inefficient use of information, bias, volatility, uninterpretable parameter estimates, and violation of ANOVA assumptions. Morey presented a hierarchical Bayesian extension of Pashler's and Cowan's basic models that mitigates these issues. Here, we present WoMMBAT (Working Memory Modeling using Bayesian Analysis Techniques) software for fitting Morey's model to data. WoMMBAT has a graphical user interface, is freely available, and is cross-platform, running on Windows, Linux, and Mac operating systems.

KW - SHORT-TERM-MEMORY

KW - Working memory

KW - BINDING

KW - Visual short term memory

KW - OBJECTS

KW - STORAGE CAPACITY

KW - COMPLEXITY

KW - NUMBER

KW - VISUAL MEMORY

KW - Bayesian models

KW - MODEL

KW - Bayesian hierarchical models

KW - Multinomial models

KW - Capacity estimates

KW - REGRESSION

KW - Visual change detection

U2 - 10.3758/s13428-011-0114-8

DO - 10.3758/s13428-011-0114-8

M3 - Article

SN - 1554-351X

VL - 43

SP - 1044

EP - 1065

JO - Behavior Research Methods

JF - Behavior Research Methods

IS - 4

ER -

WoMMBAT: A user interface for hierarchical Bayesian estimation of working memory capacity

Abstract / Description of output

Keywords / Materials (for Non-textual outputs)

Access to Document

Fingerprint

Activities

What do asymmetric patterns of interference suggest about modular working memory storage?

European Working Memory Symposium

Cite this