1. Modeling meditation ?!
Marieke van Vugt

2. Modeling framework
ACT-R: adaptive control of thought – rational (John Anderson, CMU, 1994)

3. 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

4. Declarative memory Contains facts Retrieval speed
(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))
Contains facts
Retrieval speed
depends on activation
Conscious knowledge

5. 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 procedural
knowledge

6. Example production
(P counting-example =goal> isa count state incrementing number =num1 =retrieval> isa count-order first =num1 second =num2 ==> number =num2 +retrieval> first =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 Then change the goal to continue counting from =num2 and request a retrieval of a count-order chunk to find the number that follows =num2

7. 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

8. 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 the
next
Requires 65 ms to shift
attention or encode

9. 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
We start by

10. 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)

11. 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

12. Starting point: a box-and-arrow model
Now here is what I did. I started with a published box-and-arrow model.
Vago & Silbersweig (2012)

13. paying attention thought pump
This was my simplification: there is a competition between an attentive process and a “thought pump”

14. Focused Attention (FA) Meditation Model
Model also evokes many questions: how do we really become distracted?

15. Crucial mechanisms: Becoming distracted: Returning to meditation
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
In addition to this, there can be different types

16. 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

17. 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

18. Predicted data

19. 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)