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The Effect of Episodic Retrieval on Inhibition in Task Switching
Jim Grange, Agnieszka Kowalczyk, & Rory O’Loughlin
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A Problem of Control
• Humans live in a rich, multi-task environment
• Goal-directed behaviour requires selecting the most relevant stimulus to act upon
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A Problem of Control
• Stimulus selection is only half the battle:– Stimuli are often multivalent
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A Problem of Control
• When stimuli are multivalent, we must be able to select the relevant task to perform
• We must also be able to maintain that operation once selected so task-irrelevant operations do not intrude
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A Problem of Control
• We must also be able to maintain that task once selected so task-irrelevant intrusions do not occur
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A Problem of Control
• We must also be able to switch away from this task when our goals change
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Stability-Flexibility Dilemma
• Task representations must be stable so task-irrelevant intrusions do not occur
• Task representations must be flexible so that they can be removed when goals change
• How is this tension resolved?
Goschke (2000)
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Task Switching
Grange & Houghton (2009, 2010); Houghton et al. (2009)
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How is Task Switching Achieved?
• A possible solution:
– Activate task-relevant representations when they are required
– Inhibit task-irrelevant representations when they are no longer required
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Inhibition in Task Switching
A B A
Time
Mayr & Keele (2000)
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Inhibition in Task Switching
A B A
Time
Mayr & Keele (2000)
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Inhibition in Task Switching
A B A
Time
Mayr & Keele (2000)
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Inhibition in Task Switching
A B A
Time
Mayr & Keele (2000)
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Inhibition in Task Switching
A B AC B A
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Inhibition in Task Switching
A B AC B A
Backward Inhibition (BI) = RT(ABA) – RT(CBA)“N–2 repetition cost”
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Inhibition in Task Switching
• Why is this effect important?
– Provides insight into how cognitive control during task switching is achieved
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Inhibition in Task Switching
• Why is this effect important?
– Can be used to investigate inhibition using different approaches:• Clinical• Neuropsychological• Neuroscience • Individual Differences
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Inhibition in Task Switching
• Why is this effect important?
– Many “inhibition” effects can be explained without appeal to inhibitory mechanisms• e.g., negative priming, Stroop performance
– N-2 repetition cost is—to date—robust against these alternative explanations
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Episodic Retrieval Account
• A key non-inhibitory account that can explain a lot of “inhibitory-type” effects
• Automatic cue-based retrieval of episodic traces of previous task experience
– Retrieval facilitates performance if it matches current task demands
– Retrieval interferes with performance if it mis-matches current task demands
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“Bottom Left!”
Time
MATCH!
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“Bottom Left!”
Time
MISMATCH!
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Episodic Retrieval Account
• Explains the n-2 repetition cost by interference during episodic retrieval rather than inhibition
Time
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EpisodicMatch
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N-2 Repetition Facilitation
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Episodic Mismatch
N-2 Repetition Facilitation
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N-2 Repetition Cost
N-2 Repetition Facilitation
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Episodic Retrieval Prediction
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Mayr’s (2002) Results
Error bars denote +/- 1 SE
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Mayr (2002)
• Episodic retrieval cannot explain n-2 repetition cost in task switching– Remains a strong marker of inhibition
• It is not clear, though, whether episodic retrieval has any modulatory effect
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Mayr (2002)
• Numerical trend for smaller costs for episodic matches
• F(1, 38) = 1.3, p=.26
• Can’t accept a null!
Error bars denote +/- 1 SE
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Mayr (2002)
• Bayesian analysis of this interaction (BF01 = 0.315) suggests null ~ 3 times more likely
• This only provides “anecdotal” support for null (Schoenbrodt et al., 2016)
Error bars denote +/- 1 SE
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The Present Study
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The Present Study
• Replicate key aspects of Mayr’s (2002) design
• Used sequential Bayesian analysis to collect compelling data
– We only stopped data collection once we had “substantial” support for one hypothesis over the other
– (i.e., whether episodic retrieval does or does not modulate the n-2 repetition cost)
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Sequential Bayesian Analysis
• Conduct Bayesian t-test after every participant– N-2 repetition cost (resp. rep.) Vs. – N-2 repetition cost (resp. switch)
• Bayes Factor– Degree of support for one model (i.e., hypothesis)
compared to another model, given the data observed– BF10 of 10 means alternative is 10 times more likely
than null, given the data– BF10 of 0.1 means null is 10 times more likely than
alternative, given the data
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Sequential Bayesian Analysis
• Stop data collection when the Bayes factor is either:
– Greater than 6 (strong support for alternative)
– Less than 1/6 (strong support for null)
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Method
• N = 76• Replication of Mayr’s
design• 4 blocks of 120 trials• Task chosen randomly
(no repetitions)• Stimulus location
chosen randomly
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Results• Sequence: F(1, 75) = 94.14, p < .001, η2
G = .018
• Response Rep.:F(1, 75) = 18.21, p < .001, η2
G = .004
• Interaction: F(1, 75) = 9.60, p < .01, η2
G = .001
Error bars denote +/- 1 SE
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Results• Bayes Factor:• BF10 = 9.97
• Model of different n-2 repetition costs for response repetition and switch is 10 times more likely than a null model
Error bars denote +/- 1 SE
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Discussion
• N-2 repetition cost is modulated by episodic retrieval
– When retrieval parameters match current task demands, the n-2 repetition cost is reduced
– Almost halves the cost (!)
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Conclusions
• We have provided evidence for (at least) a modulatory role of episodic retrieval during task switching
• The n–2 repetition cost in task switching is (at least) a contaminated measure– Task-specific inhibition plus– Episodic interference / facilitation
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Conclusions
• Researchers needs to be cognisant of this issue when using this effect as a “pure” measure of inhibition
– Used to investigate inhibition using different approaches:• Clinical• Neuropsychological• Neuroscience • Individual Differences
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Thank You!
A copy of these slides will be available on our lab’s website:
www.jimgrange.wordpress.com
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Prior Robustness Check
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Progression of Bayes Factor
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