Inhibitory Control in Task Switching

Inhibitory Control in Task Switching

Jim Grangewww.jimgrange.wordpress.com.

http://www.jimgrange.wordpress.com/

My Research Programme

1. Cognitive control, with particular focus on inhibitory control

2. Application & development of computational cognitive models

3. Issues surrounding replication in psychological science

A Problem of Control

• Humans live in a rich, multi-task environment

• Goal-directed behaviour requires selecting the most relevant stimulus to act upon


• Stimulus selection is only half the battle:– Stimuli are often multivalent


• 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


• We must also be able to maintain that task once selected so task-irrelevant intrusions do not occur


• 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

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)


A B A

Time

Mayr & Keele (2000)


A B A

Time

Mayr & Keele (2000)


A B A

Time

Mayr & Keele (2000)


A B AC B A


A B AC B A

Backward Inhibition (BI) = RT(ABA) – RT(CBA)“N–2 repetition cost”


• 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



– Can be used to investigate inhibition using different approaches:• Healthy Ageing• Clinical• Neuropsychological• Neuroscience • Individual Differences

Assessing What is Inhibited

What is inhibited?

• Mayr & Keele (2000) suggested the whole task-set becomes inhibited

– “...configuration of perceptual, attentional, mnemonic, and motor processes critical for a particular task goal.” (p.5).

What is inhibited?

• More parsimonious to assume inhibition is more selective

• Only those aspects of the trial structure that generates inter-trial conflict should be inhibited

Cue — Target — Response

What is inhibited?

• Cue-related processes (Houghton et al., 2009)– In order to perform the correct task, participants

must rely on task representations in working memory

– Old representations become inhibited

• If cue provides exogenous support for target selection, less reliance on WM representation– Therefore, reduced observable inhibition?

Effects of Cuing on Inhibition





Error bars denote +/- 1 Standard Error around the mean

Iconic Cues Word Cues Abstract Cues

-10

0

10

20

30

40

50

60

70

80N

–2 R

epeti

tion

cost

(ms)


Grange & Houghton (2010a)

• Used a negative transfer paradigm– Participants become practiced with arbitrary cue-

target pairings– Halfway through the experiment, cue-target

pairings are switched

• Cues and targets remain constant throughout the experiment– Thus, difficulty of cue processing is manipulated

independent of cue and target sets



Pre-Switch Post-Switch600

620

640

660

680

700

720

740

760

780

800

ABA

CBA

Reac

tion

Tim

e (m

s)

*

*

Error bars +/- 1 SE around mean

20ms

55ms

Sig. interaction:

F(1,31) = 6.39, p<.01

Practice & the N–2 Repetition Cost

Practice & Inhibition

• Is inhibition only required when the tasks are relatively novel?– Or is inhibition a core architectural process?

• Previous studies have examined the effect of practice on the switch cost– Slower RTs for task switch vs. repetition trials


Stoet & Snyder (2007).


• Two reasons to predict a reduction of inhibition with practice:

– 1) Gradual automisation of cue-based formation of task representation / retrieval of target information from LTM

– 2) Predictions from a computational model

1) Automisation of Cue-Based Preparation

• More inhibition required if cue-based preparation is more difficult– As practice progresses, cue-based retrieval of

target pairing should become automised (e.g., Logan, 1988)







– With practice, abstract cues behave like meaningful cues

2) Predictions from a Computational Model

• Grange, Juvina, & Houghton (2013) modelled inhibition in task switching using ACT-R

• Task-sets represented as “chunks” of information in declarative memory (DM)– “A `Square’ cue is associated with a bordered

target”


• When a retrieval request is made, the most active chunk is retrieved and acted upon


• When a retrieval request is made, the most active chunk is retrieved and acted upon– Base Level Activation • reflects recency and frequency of practice


• When a retrieval request is made, the most active chunk is retrieved and acted upon– Base Level Activation • reflects recency and frequency of practice

– Short-term inhibition


• Also makes the prediction that n-2 repetition cost should decrease with practice…

Error bars +/- 1 SE around mean

Cost per 120 Trials

Assessing the Reliability of the N–2 Repetition Cost

Back to the Beginning…


– Can be used to investigate inhibition using different approaches:• Healthy Ageing• Clinical• Neuropsychological• Neuroscience • Individual Differences

Whitmer & Banich (2007)

Mayr et al. (2006)

Model Successes

Grange & Juvina (2015)

Model Successes


Model Successes


How Reliable is the N–2 Repetition Cost?

Kowalczyk & Grange (in press)

• 72 participants completed three task switching paradigms



Target Detection Paradigm



Visual Judgement Paradigm



Numerical Judgement Paradigm

ParityMagnitude Form

37 two


Paradigm

Mea

n N

–2 R

epeti

tion

Cost

(ms)


• To assess reliability, we used a form of split-half reliability

– Repeated, random, splitting of data

Subject 1

Target Detection

S1 TDv.1

S1 TDv.2

N–2 cost V.1

N–2 cost V.2

r

r1

Subject 1

Target Detection

S1 TDv.1

S1 TDv.2

N–2 cost V.1

N–2 cost V.2

r

r1…

……

……

……

…..

r500

How reliable is the n–2 repetition cost?

NOT VERY!

Assessing Non-Inhibitory Accounts of N–2 Repetition Cost

Logan’s “Inhibitophiles vs. Inhibitophobes”

InhibitoPHILES InhibitoPHOBES

InhibitoSCEPTIC

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

EpisodicMatch

N-2 Repetition Facilitation

Episodic Mismatch


N-2 Repetition Cost


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!


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)


Grange et al. (under review)

• 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)


• 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


• Stop data collection when the Bayes factor is either:

– Greater than 6 (strong support for alternative)

– Less than 1/6 (strong support for null)


• 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


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


Experiment 2

Cue–Task Transparency




F(1, 65) = 8.88, p<.001



BF10 = 7.89


• N-2 repetition cost is modulated by episodic retrieval

– When retrieval parameters match current task demands, the n-2 repetition cost is drastically reduced

– Important if we wish to use this cost as a marker of inhibition


• The n–2 repetition cost in task switching is (at least) a contaminated measure– Task-specific inhibition plus– Episodic interference / facilitation

• Researchers needs to be cognisant of this issue when using this effect as a “pure” measure of inhibition

Conclusion

• N-2 repetition cost is a promising tool to investigate cognitive inhibition, BUT

– We need to work on its reliability– We need to appreciate it’s a contaminated

measure– We need to develop richer computational accounts– We need to re-visit “application” work with the

above in mind

Thank You!

A copy of these slides will be available on our lab’s website:

www.jimgrange.wordpress.com

http://www.jimgrange.wordpress.com/

Science

Inhibitory Control in Task Switching