flankr: EPS presentation

flankr: An R Package Implementing Computational Models of Attentional

Selectivity

Jim Grangej.a.grange@keele.ac.uk

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Eriksen & Eriksen (1974)

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Flanker Task

• Response times are slower to incongruent trials compared to congruent– The “congruency effect”

• Attentional selectivity improves with processing time (Gratton et al., 1998)– Evidence for this gathered using so-called

Conditional Accuracy Functions (CAFs)

Improvement of Attentional Selectivity

• Continuous Improvement of attentional selectivity– Shrinking attentional spotlight reduces the effect

of flankers on response selection as processing time progresses (Heitz & Engle, 2007; White et al., 2011)

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e.g., Heitz & Engle (2007)

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e.g., Heitz & Engle (2007)

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e.g., Heitz & Engle (2007)

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e.g., Heitz & Engle (2007)

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e.g., Heitz & Engle (2007)

Improvement of Attentional Selectivity

• Discrete Improvement of attentional selectivity– Attentional selectivity rather poor in a first stage

of processing, but switches to a focussed processing mode at discrete time-point (Huebner et al., 2010).

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e.g., Huebner et al. (2010)

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e.g., Huebner et al. (2010)

Prop

ortio

n Co

rrec

t

Probability of entering second stage increases with processing timePr

opor

tion

Corr

ect

Improvement of Attentional Selectivity

• Two competing theories for improvement of attentional selectivity:– Continuous improvement– Discrete improvement

• These accounts are hard to disambiguate at the behavioural level– Both predict the observed improvement of

attentional selectivity with time

Computational Implementations

• Computational models are advantageous for model comparison– Precise, quantitative (cf., verbal models), model

predictions can be directly compared to observed data

– Statistical competitive model comparison techniques can be used to select best-fitting model

Behavior Research Methods, in press

Dual-Stage, Two-Phase Model

(Huebner et al., 2010)

Shrinking Spotlight Model (White et

al., 2011)

The Models

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Correct Response Boundary

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Correct Response Boundary

Error Response Boundary

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Early Attentional Selection

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Late Attentional Selection

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Time

Time

Time

Time

Overview of flankr

flankr

• flankr is a package which extends R statistics, written with C++ and R– Hence the “r” on flankr…– R is a free statistical programming language

flankr

• You do NOT need to know R to use flankr– The paper is written with an R-novice in mind

flankr

• You do NOT need to know R to use flankr– The paper is written with an R-novice in mind

www.r-project.org

flankr

• You do NOT need to know R to use flankr– The paper is written with an R-novice in mind

www.rstudio.com

flankr

• You do NOT need to know R to use flankr– The paper is written with an R-novice in mind

www.rstudio.com

www.github.com/JimGrange/flankr

Overview of flankr

• Simulate data from the DSTP and SSP models– Useful for exploring model characteristics

• Fit DSTP and SSP model to user data– Fit to congruent & incongruent trials– Fit group data or individual subjects– Multiple parameter optimisation methods

supported• Plot model fits to user data• Model comparison via statistical tests• Bootstrapping & Jack-knifing of model fits

Overview of flankr

• Simulate data from the DSTP and SSP models– Useful for exploring model characteristics

• Fit DSTP and SSP model to user data– Fit to congruent & incongruent trials– Fit group data or individual subjects– Multiple parameter optimisation methods

supported• Plot model fits to user data• Model comparison via statistical tests• Bootstrapping & Jack-knifing of model fits

Simulating Data

Simulating Data

Simulating Data

Simulating Data

Simulating Data

Simulating Data

Overview of flankr

• Simulate data from the DSTP and SSP models– Useful for exploring model characteristics

• Fit DSTP and SSP model to user data– Fit to congruent & incongruent trials– Fit group data or individual subjects– Multiple parameter optimisation methods

supported• Plot model fits to user data• Model comparison via statistical tests• Bootstrapping & Jack-knifing of model fits

Overview of flankr

• Simulate data from the DSTP and SSP models– Useful for exploring model characteristics

• Fit DSTP and SSP model to user data– Fit to congruent & incongruent trials– Fit group data or individual subjects– Multiple parameter optimisation methods

supported• Plot model fits to user data• Model comparison via statistical tests• Bootstrapping & Jack-knifing of model fits

Fitting Empirical Data

Warning Signal

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Fitting Empirical Data

Cumulative Distribution Function

Conditional AccuracyFunction

Fitting Empirical Data

Fitting Empirical Data

Fitting Empirical Data

Fitting Empirical Data

Overview of flankr

• Simulate data from the DSTP and SSP models– Useful for exploring model characteristics

• Fit DSTP and SSP model to user data– Fit to congruent & incongruent trials– Fit group data or individual subjects– Multiple parameter optimisation methods

supported• Plot model fits to user data• Model comparison via statistical tests• Bootstrapping & Jack-knifing of model fits

Overview of flankr

• Simulate data from the DSTP and SSP models– Useful for exploring model characteristics

• Fit DSTP and SSP model to user data– Fit to congruent & incongruent trials– Fit group data or individual subjects– Multiple parameter optimisation methods

supported• Plot model fits to user data• Model comparison via statistical tests• Bootstrapping & Jack-knifing of model fits

Overview of flankr

• Simulate data from the DSTP and SSP models– Useful for exploring model characteristics

• Fit DSTP and SSP model to user data– Fit to congruent & incongruent trials– Fit group data or individual subjects– Multiple parameter optimisation methods

supported• Plot model fits to user data• Model comparison via statistical tests• Bootstrapping & Jack-knifing of model fits

Overview of flankr

• Simulate data from the DSTP and SSP models– Useful for exploring model characteristics

• Fit DSTP and SSP model to user data– Fit to congruent & incongruent trials– Fit group data or individual subjects– Multiple parameter optimisation methods

supported• Plot model fits to user data• Model comparison via statistical tests• Bootstrapping & Jack-knifing of model fits

Model Comparison

• Fit DSTP model to data– Get bBIC_DSTP

• Fit SSP model to data– Get bBIC_SSP

• Fit with the lowest bBIC is to be preferred– Parameters are penalised via M, so simpler

models are preferred, all else equal…

Overview of flankr

• Simulate data from the DSTP and SSP models– Useful for exploring model characteristics

• Fit DSTP and SSP model to user data– Fit to congruent & incongruent trials– Fit group data or individual subjects– Multiple parameter optimisation methods

supported• Plot model fits to user data• Model comparison via statistical tests• Bootstrapping & Jack-knifing of model fits

Overview of flankr

• Simulate data from the DSTP and SSP models– Useful for exploring model characteristics

• Fit DSTP and SSP model to user data– Fit to congruent & incongruent trials– Fit group data or individual subjects– Multiple parameter optimisation methods

supported• Plot model fits to user data• Model comparison via statistical tests• Bootstrapping & Jack-knifing of model fits

Bootstrapping

• Often, fits to individual subjects are too noisy• Group fits are therefore preferred when trial

numbers are low

• How to examine differences of parameter values between experimental conditions?– We only have one set of parameter values for

each condition

Bootstrapping

Best Model Parameters (Condition

A)

Sim. 1

simDSTP Fit 1

fitDSTP

Best Model Parameters (Condition

A)

Sim. 1

simDSTP Fit 1

fitDSTP

Sim. 2

Sim. 3

Sim. N

Fit 2

Fit 3

Fit N

Best Model Parameters (Condition

A)

Sim. 1

simDSTP Fit 1

fitDSTP

Sim. 2

Sim. 3

Sim. N

Fit 2

Fit 3

Fit N

Current Work

• Due to ability to simulate data from each model, flankr can be used for detailed model comparison studies

• Current work examining model mimicry– The extent to which each model makes unique

predictions of data

Model Mimicry

• If models make unique predictions, then data simulated from one model should be better fit by that generating model

DSTP DSTP Data

DSTP bBIC

SSPbBIC

DSTP Generated Data

DSTP Generated Data

DSTP Model Preferred

DSTP Generated Data

SSP Model Preferred

DSTP Generated Data

Model Mimicry(Both models fit

equally well)

Model Mimicry

• 1,000 data sets simulated for each model• Each data set then fit by each model & plotted

on landscape

DSTP DSTP Data

DSTP bBIC

SSPbBIC

DSTP Generating Model

56%

44%

SSP Generating Model

74%

26%

Model Mimicry

• The DSTP model generates data that is equally well fit by the SSP model– Some degree of model mimicry

• The SSP model generates relatively unique data that the DSTP model cannot predict– But SSP model not as well fit to human data,

generally

Model Mimicry

• More diagnostic data might be required to establish the dynamics of attentional selectivity

Incon.Con.

LEFT RIGHT

CongruentIncongruent

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Thank You!

A copy of these slides will be available on my website:

www.jimgrange.wordpress.com