flankr: EPS presentation
Transcript of flankr: EPS presentation
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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)
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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)
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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)
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Prop
ortio
n Co
rrec
t
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Probability of entering second stage increases with processing timePr
opor
tion
Corr
ect
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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
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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
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Behavior Research Methods, in press
Dual-Stage, Two-Phase Model
(Huebner et al., 2010)
Shrinking Spotlight Model (White et
al., 2011)
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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
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Time
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Time
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Time
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Overview of flankr
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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
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flankr
• You do NOT need to know R to use flankr– The paper is written with an R-novice in mind
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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
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flankr
• You do NOT need to know R to use flankr– The paper is written with an R-novice in mind
www.rstudio.com
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flankr
• You do NOT need to know R to use flankr– The paper is written with an R-novice in mind
www.rstudio.com
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www.github.com/JimGrange/flankr
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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
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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
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Simulating Data
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Simulating Data
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Simulating Data
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Simulating Data
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Simulating Data
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Simulating Data
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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
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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
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Fitting Empirical Data
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Warning Signal
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Fitting Empirical Data
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Cumulative Distribution Function
Conditional AccuracyFunction
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Fitting Empirical Data
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Fitting Empirical Data
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Fitting Empirical Data
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Fitting Empirical Data
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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
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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
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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
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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
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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…
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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
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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
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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
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Bootstrapping
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Best Model Parameters (Condition
A)
Sim. 1
simDSTP Fit 1
fitDSTP
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Best Model Parameters (Condition
A)
Sim. 1
simDSTP Fit 1
fitDSTP
Sim. 2
Sim. 3
Sim. N
Fit 2
Fit 3
Fit N
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Best Model Parameters (Condition
A)
Sim. 1
simDSTP Fit 1
fitDSTP
Sim. 2
Sim. 3
Sim. N
Fit 2
Fit 3
Fit N
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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
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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
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DSTP Generated Data
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DSTP Generated Data
DSTP Model Preferred
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DSTP Generated Data
SSP Model Preferred
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DSTP Generated Data
Model Mimicry(Both models fit
equally well)
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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
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DSTP Generating Model
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56%
44%
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SSP Generating Model
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74%
26%
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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
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Model Mimicry
• More diagnostic data might be required to establish the dynamics of attentional selectivity
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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