Modeling meditation ?!

Marieke van Vugt

Modeling framework

• ACT-R: adaptive control of thought – rational (John Anderson, CMU, 1994)

ACT-R

• Visual module = simulated eyes• Aural module = simulated ears• Motor module = simulated hands• Speech module = simulated speech• Imaginal module = simulated scratch pad• Declarative module = memory• Procedural module = keeps track of goals– Fires production rules

Declarative memory

• Contains facts• Retrieval speeddepends on activation• Conscious

knowledge

(add-dm (b ISA count-order first 1 second 2) (c ISA count-order first 2 second 3) (d ISA count-order first 3 second 4) (e ISA count-order first 4 second 5) (f ISA count-order first 5 second 6) (first-goal ISA count-from start 2 end 4))

Production• A 50-ms step of cognition• IF-THEN rules• A serial bottleneck – Only one production

at a time can “fire”• Associated with basal ganglia• Reflects proceduralknowledge

Example production(P counting-example

=goal>isa countstate incrementingnumber =num1 =retrieval> isa count-orderfirst =num1 second =num2

==>=goal> number =num2 +retrieval> isa count-orderfirst =num2

)

English Description If the goal chunk is of the type count the state slot has the value incrementing there is a number we will call =num1 and the chunk in the retrieval buffer is of type count-order the first slot has the value =num1 and the second slot has a value we will call =num2 Thenchange the goal to continue counting from =num2 and request a retrieval of a count-order chunk to find the number that follows =num2

Imaginal module• Reflects internal imagery• Also called “problem state” or “working

memory”• Holds intermediate outcomes of computations

or representation of task– E.g., solving an equation– 3x + 4 = 8– Imaginal: 3x = 12

• Takes 200 ms

Visual modules• Reflects visual attention• Contains what and where distinction– What = visual buffer– Where = visual-location buffer

• Contains objects with features• Attention can move from one object to thenext• Requires 65 ms to shiftattention or encode

Modeling counting

• How do you count from 2 to 4?

– Retrieve the first goal: count-from start 2 end 4– Retrieve a bit of information about the number

that comes after 2 (first 2 second 3)– If we haven’t yet reached the final count (4), then

retrieve another bit of information (first 3 second 4)

– When we have reached the final count, stop

What model output looks like> (run 1) 0.000 GOAL SET-BUFFER-CHUNK GOAL FIRST-GOAL REQUESTED NIL 0.000 PROCEDURAL CONFLICT-RESOLUTION 0.000 PROCEDURAL PRODUCTION-SELECTED START 0.000 PROCEDURAL BUFFER-READ-ACTION GOAL 0.050 PROCEDURAL PRODUCTION-FIRED START 0.050 PROCEDURAL MOD-BUFFER-CHUNK GOAL 0.050 PROCEDURAL MODULE-REQUEST RETRIEVAL 0.050 PROCEDURAL CLEAR-BUFFER RETRIEVAL 0.050 DECLARATIVE START-RETRIEVAL 0.050 PROCEDURAL CONFLICT-RESOLUTION 0.100 DECLARATIVE RETRIEVED-CHUNK C 0.100 DECLARATIVE SET-BUFFER-CHUNK RETRIEVAL C 0.100 PROCEDURAL CONFLICT-RESOLUTION 0.100 PROCEDURAL PRODUCTION-SELECTED INCREMENT 0.100 PROCEDURAL BUFFER-READ-ACTION GOAL 0.100 PROCEDURAL BUFFER-READ-ACTION RETRIEVAL 0.150 PROCEDURAL PRODUCTION-FIRED INCREMENT 0.150 PROCEDURAL MOD-BUFFER-CHUNK GOAL 0.150 PROCEDURAL MODULE-REQUEST RETRIEVAL 0.150 PROCEDURAL CLEAR-BUFFER RETRIEVAL 0.150 DECLARATIVE START-RETRIEVAL 0.150 PROCEDURAL CONFLICT-RESOLUTION 0.200 DECLARATIVE RETRIEVED-CHUNK D 0.200 DECLARATIVE SET-BUFFER-CHUNK RETRIEVAL D 0.200 PROCEDURAL CONFLICT-RESOLUTION 0.200 PROCEDURAL PRODUCTION-SELECTED INCREMENT 0.200 PROCEDURAL BUFFER-READ-ACTION GOAL 0.200 PROCEDURAL BUFFER-READ-ACTION RETRIEVAL 0.250 PROCEDURAL PRODUCTION-FIRED INCREMENT

(c ISA count-order first 2 second 3) (d ISA count-order first 3 second 4)

How would you model meditation?

• Choose type of meditation• Go through instructions• What happens at every moment in time?

• You can use:– Visual– Motor– Imaginal (working memory)– Retrieval (episodic memory)– Production/goals

Starting point: a box-and-arrow model

Vago & Silbersweig (2012)

13

paying attention thought pump

14

Focused Attention (FA) Meditation Model

15

Crucial mechanisms:

• Becoming distracted: – we forget to checking our goal– goal decays in memory – competing goal of distraction takes over

• Returning to meditation– Retrieving the memory of „I want to meditate“– Easier to return to meditation when the thoughts

are not very captivating

Testing the model: mind-wandering

• During meditation, no behavior• But: crucial component of model is thought-

pump• Scientists started measuring thought pump in

simple, boring tasks• More errors, more response time variability

when distracted

Task

• Press button when “O” appears, but not when “Q”• Many more “O” than “Q”• Only one stimulus every 3 seconds• When in thought pump, model does not retrieve

stimulus-response mapping

Predicted data

Conclusions

• Cognitive architecture which simulates a complete cognitive process with simulated eyes, ears, memory etc.

• Example model of counting• First attempts at modeling meditation

(shamatha with support)