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The use of computational modeling for mapping the mindMarieke K. van Vugt1, [email protected]
1 Dept of Artificial Intelligence, University of Groningen, The Netherlands
Modeling to disentangle effect of meditation on cognition Modeling to predict new effects of meditation on cognition
• Detailed description of the cognitive process under study (Mehlhorn et al., 2012)• Verbal descriptions often ambiguous
Why use modeling?
• Decomposing a cognitive task into crucial cog-nitive operations
• Defining it in equations or algorithms• Simulating the task on a computer• Matching parameters of the model to observed
data• Changes in parameters indicate specific cogni-
tive mechanisms
What is cognitive modeling?
Parameters:drift rate quality of informa-
tion (inverse of men-tal noise)
decision threshold response conserva-tiveness
Ter non-decision timestarting point bias
The drift diffusion model of decision making
van Vugt & Jha (2011)
• 29 retreatants at Shambhala Mountain Center (ages 21–70)• One month - 6–10 hrs per day• Week 1 & 2: focus on breath• Week 3 & 4: widen focus and compassion• 29 age- and education-matched controls without meditation training tested one
month apart
mean RT var RT
Why these changes?
→ Modeling!
DDM shows reduction in perceptual noise
Interaction between time and group: p = 0.04 (non-parametric ANOVA)
DDM shows reduction in perceptual noise
data: Lutz et al. (2009)variability in drift rate → fluctuations of attention
Decreased drift variability in dichotic listening
cong inc neut cong inc neut0
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v
MedContr
T1 T2
data: van den Hurk et al. (2010; submitted)
Increased drift in attention network task
Meditation decreases mental noise(More specific conclusions)
Conclusions
Can we simulate this on a computer?
A conceptual model of meditation
• Forces you to be precise• Connection to Western theories of cognition• Make predictions for transfer to cognitive tasks
Why make a model of a meditating computer?
• ACT-R is a cognitive architecture• Models cognition as a computer algorithm• Consists of modules reflecting cognitive re-
sources:– visual/aural: perception– goal (ACC): keeping a goal in mind– declarative (frontal): declarative memory
store– imaginal (parietal): working memory focus– motor/speech: produce responses– procedural (basal ganglia): proceduralizing
sequences
Introducing ACT-R cognitive architecture
• Start with meditation instruction → put focus on goal “meditating”• Competition with a distracting “thought pump” process• How could it regain focus? Ideas?
Outcome measuresFraction of time spent on the breathLength of distracted episodesStrength of productions (reflecting e.g., habits)Contents of distraction (pos vs neg memories)
Outline of the meditating model
production
visual
aural-location
goal
imaginal-action
temporal
imaginal
vocal
retrieval
aural
manual
visual-location
production
visual
aural-location
goal
imaginal-action
temporal
imaginal
vocal
retrieval
aural
manual
visual-location
production
visual
aural-location
goal
imaginal-action
temporal
imaginal
vocal
retrieval
aural
manual
visual-location
production
visual
aural-location
goal
imaginal-action
temporal
imaginal
vocal
retrieval
aural
manual
visual-location
production
visual
aural-location
goal
imaginal-action
temporal
imaginal
vocal
retrieval
aural
manual
visual-location
production
visual
aural-location
goal
imaginal-action
temporal
imaginal
vocal
retrieval
aural
manual
visual-location
production
visual
aural-location
goal
imaginal-action
temporal
imaginal
vocal
retrieval
aural
manual
visual-location
production
visual
aural-location
goal
imaginal-action
temporal
imaginal
vocal
retrieval
aural
manual
visual-location
production
visual
aural-location
goal
imaginal-action
temporal
imaginal
vocal
retrieval
aural
manual
visual-location
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production
recall-next
start-thought-pump
recall-next
start-thought-pump
recall-next
start-thought-pump
visual
aural-location
goal
imaginal-action
temporal
imaginal itemitem0
itemitem13
itemitem14
vocal
retrieval itemitem5
itemitem3
itemitem5
itemitem5
aural
manual
visual-location
Modeling the thought pump
• Development of a computational model ofmeditation
• Aim: comparing meditation model to taskmodels
• First: verify predictions for transfer to at-tentional blink
• Next: make predictions for untested tasks(using Acttransfer - Taatgen, in press)
• Ultimately: better understand why medi-tation helps people
0.6
0.7
0.8
0.9
2 4 8lag
T2|T
1 (%
)
meditation
FA
OM
experience
low exp
high exp
van Vugt & Slagter (in preparation)
Conclusions
