Modeling and Neuroscience (or ACT-R and fMRI)

Modeling and Neuroscience (or ACT-R and fMRI)

Jon M. FinchamJon M. Fincham

Carnegie Mellon University, Pittsburgh, [email protected]

Jon M. Fincham ACT-R PGSS 2001

Overview

MotivationMotivation Task SpecificsTask Specifics Modeling SpecificsModeling Specifics Experiment ResultsExperiment Results ImplicationsImplications


“Neuroscience” issues Where does x take place?Where does x take place? What does circuit x do? What does circuit x do? How is x computed?How is x computed?

“Modeling” issues How is x computed? How is x computed? Where does x take place?Where does x take place? What circuit participates in x?What circuit participates in x?


Modeling & fMRI Issues Computational cognitive modeling provides rich Computational cognitive modeling provides rich

predictions of behavior over time. Can we use the predictions of behavior over time. Can we use the richness of a cognitive model to drive fMRI data richness of a cognitive model to drive fMRI data analysis and if so how do we do it?analysis and if so how do we do it?

How can we use fMRI results to guide How can we use fMRI results to guide development of specific cognitive models and development of specific cognitive models and ACT-R theory in generalACT-R theory in general


The Task: Tower of Hanoi (of course)

The 5-disk Tower of Hanoi (TOH) task is behaviorally rich planning task

The subgoaling strategy involves varying numbers of planning steps at each move while progressing toward the goal state

ACT-R cognitive model nicely captures behavioral data


Task Summary: Pre-scan practice

21 pseudo-random problems, classic interface, explicit subgoal posting, mousing

21 pseudo-random problems, grid interface, explicit subgoal posting, mousing

7 problems, grid interface, secondary task, no subgoal posting, 3 button response

Memorize single goal state, 10 simple practice problems


TOH Classic Interface


TOH Grid Interface


The Subgoaling Strategy

1. Select largest out of place disk in current context and destination peg.

2. If direct move, do it and goto step 1. Otherwise, set subgoal to make move

3. If next largest disk blocks destination, select it and other peg & go to step 2.

4. If next largest disk blocks source, select it and other peg & go to step 2.


TOH 3-tower example move sequence

Plan 3 move sequence (3-C, 2-B, 1-C)

Plan 1 move sequence (2-B)

Plan 1 move sequence (1-B)

Plan 2 move sequence (2-C, 1-A)

Plan 1 move sequence (2-C)

Plan 1 move sequence (1-C)

Plan 1 move sequence (3-C) Goal State


The Task: TOH in the magnet

One full volume (25 slices) every 4 secondsOne full volume (25 slices) every 4 seconds 16 seconds per move = 4 scans per move16 seconds per move = 4 scans per move 12 20-23 move problems, about 6 minutes 12 20-23 move problems, about 6 minutes

eacheach


Behavioral Results

1_1c_s 2_2b 3_1b 4_3c 5_1a_s 6_2c 7_1c 8_big1500

2000

2500

3000

3500

4000

4500

3-disk Tower Move Sequence

move_type


Behavioral Results

Individual Performace on 3-disk Tower

0

1000

2000

3000

4000

5000

6000

m1 m2 m3 m4 m5 m6 m7 m8

Move

s1

s2

s3

s4

s5

s6

s7

s8


What do we want to see?

How does the brain handle goal processing?How does the brain handle goal processing? Which brain areas are differentially Which brain areas are differentially

responsive to goal setting operations?responsive to goal setting operations? Are there identifiable circuits that Are there identifiable circuits that

collectively implement manipulation of collectively implement manipulation of goals?goals?


Terminology

BOLD - Blood Oxygenation Level BOLD - Blood Oxygenation Level Dependent response (aka hemodynamic Dependent response (aka hemodynamic response)response)

MR - magnetic resonance, signal measured MR - magnetic resonance, signal measured in the magnetin the magnet

Voxel - approximately cube “point” within Voxel - approximately cube “point” within the brainthe brain


Where do we begin?

Run model over problem set, collecting Run model over problem set, collecting goal setting event timestampsgoal setting event timestamps

Use goal setting timestamps to generate an Use goal setting timestamps to generate an ideal BOLD-like timeseriesideal BOLD-like timeseries


ACTR(t) Events and Time Series


BOLD Response CharacteristicsAdditivity of BOLD Response

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 5 10 15 20 25

Time

Cumulative

Response 1

Response 2

Response 3


Identifying a responsive voxel

Model MR signal as a function of the ACT-R Model MR signal as a function of the ACT-R generated time seriesgenerated time series

MR(t) = BMR(t) = B00 + B + B11*trial(t) + B*trial(t) + B22*ACTR(t) + *ACTR(t) + (t)(t) Ignore error trials and immediate successorsIgnore error trials and immediate successors

Run regression for every one of the 25x64x64 Run regression for every one of the 25x64x64 voxelsvoxels

Result is a beta map for each regressorResult is a beta map for each regressor


Group Analysis

Morph each brain into a reference brainMorph each brain into a reference brain

Voxel-wise 2-tailed t-test of HVoxel-wise 2-tailed t-test of H00: B: B22 = 0 across = 0 across

subjectssubjects


Analysis Summary

Within subject voxel-wise regression of MR Within subject voxel-wise regression of MR signal against ACT-R generated time seriessignal against ACT-R generated time series MR(t) = BMR(t) = B00 + B + B11*trial(t) + B*trial(t) + B22*ACTR(t) + *ACTR(t) + (t)(t) Ignore error trials and immediate successorsIgnore error trials and immediate successors

Voxel-wise 2-tailed t-test of HVoxel-wise 2-tailed t-test of H00: B: B22 = 0 across = 0 across

subjectssubjects Threshold at p<0.0005 and contiguity of 8 voxelsThreshold at p<0.0005 and contiguity of 8 voxels


TOH Activation Map (p < 0.0005, contiguity = 8)

R L


Premotor & Parietal activity increase parametrically with number of planning steps


Prefrontal - Basal Ganglia - Thalamic Circuit


PFC -Basal Ganglia -Thalamus

Striatum = Pattern Striatum = Pattern Matching & conflict Matching & conflict resolution?resolution?

Result gates thalamus Result gates thalamus to update buffers?to update buffers?

Cortex

Thalamus

GP Striatum


Summary of findings so far... Move planning activity in parietal and premotor Move planning activity in parietal and premotor

areas varies parametrically with number of areas varies parametrically with number of planning stepsplanning steps

PFC-Basal Ganglia-Thalamic circuit does not vary PFC-Basal Ganglia-Thalamic circuit does not vary parametrically with number of planning steps but parametrically with number of planning steps but shows significant BOLD response during high shows significant BOLD response during high planning moves onlyplanning moves only

Suggests PFC becomes engaged when sequencing Suggests PFC becomes engaged when sequencing of multiple moves is requiredof multiple moves is required


What can we conclude about the model? Subjects are bypassing subgoaling Subjects are bypassing subgoaling

procedure for 2-tower subproblemsprocedure for 2-tower subproblems Setting a goal “move disk 1 to opposite of Setting a goal “move disk 1 to opposite of

where disk 2 goes” where disk 2 goes”

Now we can use GLM model comparison Now we can use GLM model comparison techniques to confirm best fitting models...techniques to confirm best fitting models...


What can we conclude about ACT-R? Nothing…….yet.Nothing…….yet. Goal manipulation does seem to predict Goal manipulation does seem to predict

brain activity in the “right” places, butbrain activity in the “right” places, but Need to run other studies in different Need to run other studies in different

domains (and different models) to gain domains (and different models) to gain confidence in our label of “goal processing” confidence in our label of “goal processing” circuitrycircuitry


What have we learned so far?

Applying cognitive modeling to the Applying cognitive modeling to the neuroimaging domain is feasible: models can neuroimaging domain is feasible: models can inform analysisinform analysis

fMRI data can inform models fMRI data can inform models fMRI data can inform architecturefMRI data can inform architecture Symbiotic relationship exists between Symbiotic relationship exists between

modeling and fMRImodeling and fMRI What else?What else?


What else can we examine?

+goal>, +retrieval>, +visual>, +aural>, +goal>, +retrieval>, +visual>, +aural>, +manual>, +manual>,

Number of elements in goalNumber of elements in goal Number of full buffersNumber of full buffers


Thank you!

Modeling and Neuroscience (or ACT-R and fMRI)

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