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The Effect of Episodic Retrieval on
Inhibition in Task Switching
Jim Grange, Agnieszka Kowalczyk, &
Rory O’Loughlin
A Problem of Control
• Humans live in a rich, multi-task environment
• Goal-directed behaviour requires selecting the
most relevant stimulus to act upon
A Problem of Control
• Stimulus selection is only half the battle:
– Stimuli are often multivalent
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
A Problem of Control
• We must also be able to maintain that task
once selected so task-irrelevant intrusions do
not occur
A Problem of Control
• We must also be able to switch away from
this task when our goals change
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)
Task Switching
Grange & Houghton (2009, 2010); Houghton et al. (2009)
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
Inhibition in Task Switching
A B A
Time
Mayr & Keele (2000)
Inhibition in Task Switching
A B A
Time
Mayr & Keele (2000)
Inhibition in Task Switching
A B A
Time
Mayr & Keele (2000)
Inhibition in Task Switching
A B A
Time
Mayr & Keele (2000)
Inhibition in Task Switching
A B A
C B A
Inhibition in Task Switching
A B A
C B A
Backward Inhibition (BI) = RT(ABA) – RT(CBA)
“N–2 repetition cost”
Inhibition in Task Switching
• Why is this effect important?
– Provides insight into how cognitive control during
task switching is achieved
Inhibition in Task Switching
• Why is this effect important?
– Can be used to investigate inhibition using
different approaches:
• Clinical
• Neuropsychological
• Neuroscience
• Individual Differences
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
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
“Bottom Left!”
Time
MATCH!
“Bottom Left!”
Time
MISMATCH!
Episodic Retrieval Account
• Explains the n-2 repetition cost by
interference during episodic retrieval rather
than inhibition
Time
Episodic
Match
N-2 Repetition
Facilitation
Episodic
Mismatch
N-2 Repetition
Facilitation
N-2 Repetition
Cost
N-2 Repetition
Facilitation
Episodic Retrieval Prediction
Mayr’s (2002) Results
Error bars denote +/- 1 SE
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
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
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
The Present Study
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)
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
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)
Method
• N = 76
• Replication of Mayr’s
design
• 4 blocks of 120 trials
• Task chosen randomly
(no repetitions)
• Stimulus location
chosen randomly
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
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
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 (!)
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
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
Thank You!
A copy of these slides will be available
on our lab’s website:
www.jimgrange.wordpress.com
Prior Robustness Check
Progression of Bayes Factor
The effect of episodic retrieval on inhibition in task switching

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The effect of episodic retrieval on inhibition in task switching

  • 1. The Effect of Episodic Retrieval on Inhibition in Task Switching Jim Grange, Agnieszka Kowalczyk, & Rory O’Loughlin
  • 2. A Problem of Control • Humans live in a rich, multi-task environment • Goal-directed behaviour requires selecting the most relevant stimulus to act upon
  • 3. A Problem of Control • Stimulus selection is only half the battle: – Stimuli are often multivalent
  • 4. 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
  • 5. A Problem of Control • We must also be able to maintain that task once selected so task-irrelevant intrusions do not occur
  • 6. A Problem of Control • We must also be able to switch away from this task when our goals change
  • 7. 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)
  • 8. Task Switching Grange & Houghton (2009, 2010); Houghton et al. (2009)
  • 9. 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
  • 10. Inhibition in Task Switching A B A Time Mayr & Keele (2000)
  • 11. Inhibition in Task Switching A B A Time Mayr & Keele (2000)
  • 12. Inhibition in Task Switching A B A Time Mayr & Keele (2000)
  • 13. Inhibition in Task Switching A B A Time Mayr & Keele (2000)
  • 14. Inhibition in Task Switching A B A C B A
  • 15. Inhibition in Task Switching A B A C B A Backward Inhibition (BI) = RT(ABA) – RT(CBA) “N–2 repetition cost”
  • 16. Inhibition in Task Switching • Why is this effect important? – Provides insight into how cognitive control during task switching is achieved
  • 17. Inhibition in Task Switching • Why is this effect important? – Can be used to investigate inhibition using different approaches: • Clinical • Neuropsychological • Neuroscience • Individual Differences
  • 18. 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
  • 19. 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
  • 22. Episodic Retrieval Account • Explains the n-2 repetition cost by interference during episodic retrieval rather than inhibition Time
  • 23.
  • 24.
  • 25.
  • 31. Mayr’s (2002) Results Error bars denote +/- 1 SE
  • 32. 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
  • 33. 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
  • 34. 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
  • 36. 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)
  • 37. 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
  • 38. 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)
  • 39. Method • N = 76 • Replication of Mayr’s design • 4 blocks of 120 trials • Task chosen randomly (no repetitions) • Stimulus location chosen randomly
  • 40. 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
  • 41. 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
  • 42. 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 (!)
  • 43. 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
  • 44. 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
  • 45. Thank You! A copy of these slides will be available on our lab’s website: www.jimgrange.wordpress.com