flankr: eps presentation
TRANSCRIPT
flankr: An R Package Implementing Computational Models of Attentional
Selectivity
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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