Loading in 5 sec....

ACT-R/S: Extending ACT-R to make big predictions PowerPoint Presentation

ACT-R/S: Extending ACT-R to make big predictions

- By
**amish** - Follow User

- 104 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about ' ACT-R/S: Extending ACT-R to make big predictions ' - amish

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

### ACT-R/S: Extending ACT-R to make big predictions

Christian Schunn, Tony Harrison,

Xioahui Kong, Lelyn Saner,

Melanie Shoup, Mike Knepp, …

University of Pittsburgh

Approach

Combine functional analysis

- Computational level (Marr); Knowledge level (Newell); Rational level (Anderson)
with neuroscience understanding

- most elaborated about gross structure
to build a spatial cognitive architecture for problem solving

Need for 3 Systems

- Computational Considerations
- Some tasks need to ignore size, orientation, location
- Some tasks need highly metric 3D part reps

Need for 3 Systems

- Computational Considerations
- Some tasks need to ignore size, orientation, location
- Some tasks need highly metric 3D part reps
- Some tasks need relative 3D locations of blob objects

- object identification

Configural

- navigation

Manipulative

- grasping & tracking

ACT-R/S: Three Visiospatial SystemsTraditional “what” system

Traditional “where” system

Visual Representation

Manipulative Representation

Configural Representation

Allocentric vs. egocentric representations

- All ACT-R/S representations are inherently egocentric representations
=> Allocentric view points must be inferred (computed)

- Q:
- What about data suggestive of allocentric representations?

Configural System

Representation

Configural Buffer

Path

Integrator

Triangle-T1

Triangle-TN

• Vectors

• Identity-tag

• Vectors

• Identity-tag

Circle-TN

Circle-T1

+

• Vectors

• Identity-tag

• Vectors

• Identity-tag

Circ-Tri-T1

Circ-Tri-TN

• Triangle-ID

• Circle-ID

• delta-heading

• delta-pitch

• triangle-range

• circle-range

• Triangle-ID

• Circle-ID

• delta-heading

• delta-pitch

• triangle-range

• circle-range

from Muller, 1984

“Place-cells”- Pyramidal cells in rodent hippocampus (CA1/CA3)
- Fires maximally w/r rodent’s location - regardless of orientation
- Span many modalities (aural, olfactory, visual, haptic & vestibular)
- Stable across time
- Plot cell-firing rate across space

“Place-cells”(the not-so pretty picture)

- Cell firing within a rat is also correlated with:
- Goal (Shapiro & Eichenbaum, 1999)
- Direction of travel (O’Keefe, 1999)
- Duration in the environment (Ludvig, 1999)
- Relative configuration of landmarks (Tanila, Shapiro & Eichenbaum, 1997; Fenton, Csizmadia, & Muller, 2000)

from Burgess, Jackson, Hartley & O’Keefe 2000

• Configural representation (vectors) supports lowest level navigation - but defines an infinite set of locations

• Configural relationship (between two) establishes a unique location in space

ACT-R/S and “Place-cells”Circ-Tri-TN level navigation - but defines an infinite set of locations

Circle-TN

• Triangle-ID

• Circle-ID

• delta-heading

• delta-pitch

• triangle-range

• circle-range

• Vectors

• Identity-tag

Triangle-TN

• Vectors

• Identity-tag

Egocentric RepresentationAllocentric InterpetationForaging Model level navigation - but defines an infinite set of locations

- Virtual rat searching for food
- Square environment with each wall as a landmark (obstacle free)
- When no food is available, rat free roams or returns to previously successful location
- Food is placed semi-randomly to force rat to cover the entire environment multiple times
- Record activation across time and space for preselected configural-relationships
- (Add Guasssian noise)

“Single-Chunk” Recording level navigation - but defines an infinite set of locations

• Stable fields are a function

of regularities in the

learned attending pattern.

• Multiple passes through

same region will reactivate

configural relation chunk.

• Multi-modal peaks likewise

influenced by goal (same

landmarks, different order).

What about humans? level navigation - but defines an infinite set of locations

- Small scale orientation and navigation data typically reports egocentric representations
- Diwadkar & McNamara, 1997; Roskos-Ewoldsen, McNamara, Shelton, & Carr, 1998; Shelton & McNamara, 1997

- One famous counter-example
- Mou & McNamara, 2002

Mou & McNamara (2002) level navigation - but defines an infinite set of locations

E

- Subjects study a view of objects from 315 deg.
- Study it as if from intrinsic axis (0 deg)
- A-B
- C-D-E
- F-G

- Testing asks subjects to imagine:
- Standing at X
- Look at Y
- Point to Z

- Plot pointing error as function of imagined heading (X-Y)
- 0, 90, 180, 270 much lower error!

B

D

F

A

C

E

315º

View position

0º

Zero parameter level navigation - but defines an infinite set of locationsegocentric prediction

- The hierarchical task analysis of training and testing
- But extra boost from encoding configuration chunks (egocentric vectors as in ACT-R/S)

- Count number of times any specific chunk will be accessed
- Compute probability of successful retrieval of chunks (location, facing, pointing), using basic ACT-R chunk learning and retrieval functions, default parameters, delay of 10 minutes

Modeling Frames of Reference level navigation - but defines an infinite set of locations

- Data (Exp 1)

- Zero parameter prediction
- Playing with noise parameter(s) and retrieval threshold () improve absolute fit (RMSE)
- All (reasonable) parameter values produce similar qualitative fit

More data level navigation - but defines an infinite set of locations

- Having mats on the floor which emphasize allocentric frame of reference
- No effect (as predicted)

- Square vs. round room
- No effect (as predicted)

- Training order from ego vs. allocentric orientation
- Big effect (as predicted)

Training Order level navigation - but defines an infinite set of locations

Mou & McNamara (2002) Exp 2

“Allocentric”

“Egocentric”

Data

Model

r=.62

r=.85

Download Presentation

Connecting to Server..