# Presentation: 185693

## Aging a Cognitive Model

- Brad Best, Christian Lebiere & Lila Laux
- Micro Analysis & Design

## Why Age a Cognitive Model?

- Baby boomers are aging and the population age is rising steadily.
- Due to age-related changes in abilities, the population is becoming more and more diverse.
- It is important to understand how age-related changes in cognitive and perceptual abilities affect performance of many tasks.
- Cognitive modeling (CM) offers a cost effective way to predict how aging will affect performance on any task.
- Existing cognitive models can be “aged” with little effort
- The results of CM can guide the development of systems and devices that “age well.”
- CM can alert us to design problems before implementation by exposing problem areas.

**Notes:**
Because of the diversity of the aging population and the fact that different systems within the individual age at different rates, older populations are much more diverse and different from each other than their younger cohorts – this is true from about age 30 on

## The Boomer Bulge Grows Older

**Notes:**
The rest of the world is experiencing a similar aging phenomenon, with some countries like Japan experiencing an even more profound aging over the next 30 years.  With increasing age there is decreasing perceptual ability in all areas (hearing, vision, touch, smell, etc.) as well as changes in cognitive functioning (slowed reaction  time, longer time to learn, more interference form distracters, etc.)  The many possible combinations of abilities that result from these changes, which occur independently and differentially in different individuals, create a population in which members are increasingly more different from each other and from younger cohorts, and creates many “sub populations”, which makes user testing very costly and difficult.  Modeling allows you to consider all combinations of levels of performance to predict how a person who shows specific declines could perform or to study how we might expect a population of people with diminishing abilities to perform.

## How Can We Age a Cognitive Model?

- We would like an “aged” model to account for cognitive effects of aging such as:
- Decline in working memory
- Decrease in processing speed
- Consistency of aging effect across models is important
- Solution: Apply model-independent parameters to existing models
- Parameters determined by prior research, cross-task validation, literature search

## ACT-R: An Explicit Computational Theory of Cognition

- Modeling individual differences in ACT-R
- The ACT-R Cognitive Architecture
- Assumptions, features and knowledge representation
- Modeling individual differences in working memory
- Modeling individual differences in processing speed
- Application to the Study of Aging
- Task, model and results
- GPS Task, model and results
- Potential Applications

## ACT-R Architecture

- Integrated Cognitive Architecture
- Develop high-fidelity models of cognition for a broad range of tasks
- Modular architecture maps to functional neuroimaging areas
- Embodiment at perceptual-motor level

- Environment

- Productions
- (Basal Ganglia)

- Retrieval Buffer (VLPFC)

- Matching (Striatum)

- Selection (Pallidum)

- Execution (Thalamus)

- Goal Buffer (DLPFC)

- Visual Buffer (Parietal)

- Manual Buffer (Motor)

- Manual Module (Motor/Cerebellum)

- Visual Module       (Occipital/etc)

- Intentional Module      (not identified)

- Declarative Module      (Temporal/Hippocampus)

## Assumptions and Features

**Symbolic level:** *sequential* execution of atomic steps of cognition
**Subsymbolic level:** *massively parallel* pattern-matching in modules implementing procedural, declarative or perceptual/motor functions
**Embodied cognition:** augment the cognition modules with perceptual/motor modules acting as a *principled interface* between cognition and the environment
**Parallelism and bottlenecks:** each module operates *sequentially* but in *parallel* with others and communicate through *restricted* buffer channels
**Workload and situation awareness:** *grounded* in cognitive mechanisms
**Individual differences and behavioral moderators:** modeled through *constrained variations* of continuous-valued architectural parameters

## Knowledge Representation

- Procedural-declarative distinction
- Declarative: facts as **chunks**
- Set of *small* number of elements
- Can be arranged hierarchically
- Procedural: skills as **production rules**
*Basic* condition-action pairs
- Tests current buffers state
- Conflict resolution selection
- Transformations of buffers
- Triggers actions in modules

**Add-Column**
- IF the goal is to add n1 and n2
-      and n1+n2=n3
- THEN write n3 in that column

-    336
- + 848
-        4

## Working Memory

- Provides the resources needed to retrieve and maintain information during cognitive processing
- Working memory capacity as source activation W

- Working memory capacity is limited
- W = 1 evenly divided (Anderson, Reder, & Lebiere, 1996)
- Working memory limits vary across individuals
- W varies over individuals (Lovett, Reder, & Lebiere, 1999)

## Modeling Age-Related Differences in Working Memory

- Varying W captures differences in individuals’ working memory performance
- W can be used to predict performance across tasks
- W is a workable measure of working memory capacity - architecture allows its application to any given ACT-R model to generate predictions of the impact of working memory differences on any task
- ACT-R can be fruitfully applied to the study of individual differences on complex interactive tasks

## Memory Task

- Mental Arithmetic (Salthouse & Coon 1994) Study 2
- The task is to compute complex arithmetic expressions
- Complexity of expression (0 to 7 operations) and degree of nesting (sequential vs. hierarchical) are manipulated
- Performance depends on both long-term arithmetic knowledge and short-term memory of partial results
- Response time increases linearly with number of operations
- Slope of increase is significantly higher for hierarchical expressions
- Slope is higher for older adults for both conditions

## Cognitive Model

- Methodology: model developed to solve the task
- A dozen simple production rules are required to deal with complex arithmetic expressions including parentheses
- Knowledge of arithmetic facts is extracted from the lifetime simulation of cognitive arithmetic (Lebiere, 1998)
- All architectural parameters left at their default values
- W was varied to reflect differences between groups
- Results are direct, constrained prediction of the architecture applied to the task through the most direct model without representation or parameter manipulation (other than W)

## Results

- RT increases linearly with complexity
- Slope is significantly higher for hierarchical problems
- Slope is higher for older adults in both conditions

## Executive Function: Production Matching and Execution

- Mapping age-related changes in executive function in ACT-R is less obvious than in the case of working memory
- Procedural Speed
- Pure “executive processing”
- Visual Speed
- Visuo-motor and perceptual aspects
- Manual Speed
- Physical
- Some combination of these may be useful

## Effects of Processing Speed on an Approach and Landing Piloting Task

- Complex task with interactions between pilot, instruments, controls, and environment
- One useful measure of model performance is percentage of correct runs as a function of
- Procedural Speed
- Visual Speed
- Manual Speed
- By varying these parameters, the effect of different cognitive and motor abilities can be predicted

## Sensitivity to Procedural Speed

- Because of the central nature of productions, performance degrades very sharply with speed of cycle
- Fortunately, there is a safety factor of 2 before degradation occurs

## Sensitivity to Visual Speed

- Performance is very sensitive to speed of visual shifts
- Improving by factor of 2 yields perfect performance
- Impairments lead to rapid deterioration

## Sensitivity to Manual Speed

- Performance is highly sensitive to speed of manual operations
- Supports division of labor between PF and PNF

## Applying Variation of Processing Speed to Existing Model of GPS Task

- Drive from Micro Analysis & Design, at 4949 Pearl East Circle, to The Foundry, at 1109 Walnut Street
- Entering the destination
- Similar to previously modeled scenarios using ACT-R, but not modeled yet for this scenario
- Driving according to GPS instructions
- Interacts with level of knowledge user has of destination and streets and landmarks along the way
- Construct Model of Normal Performance for Completing Error Analysis
- No original intention to model aging

## Route Map: Follow Black Line

## Task Interface: Garmin GPS

## Modes
Map Page
Next Turn Page
Refresh
Blank for ~2-3 seconds
Really
Information Presented
Map
Text describing next turn direction and street
Distance and Time to go
Direction Arrow

- Modes
- Map Page
- Next Turn Page
- Refresh
- Blank for ~2-3 seconds
- Really
- Information Presented
- Map
- Text describing next turn direction and street
- Distance and Time to go
- Direction Arrow

- GPS Interface

## The GPS Enforces Waypoint to Waypoint Navigation

- This imposes regularity on the task
- From Start to Destination
- Pearl E Circle to Pearl St, Left on Pearl St
- Pearl St to 28th, Left on 28th
- 28th to Canyon, Right on Canyon
- Canyon to 11th, Right on 11th
- 11th to Walnut, Right on Walnut
- Walnut to the Foundry, Arriving on Left

## Basic Model

- Starts with goal of going to Foundry
- Attends to GPS for directions for next turn
- Controls direction and speed of car
- Reads street signs
- Completes six turns (including destination)
- Initial model was error free with exception of final turn, for which insufficient warning was given by GPS in many cases

## Aging the Basic Model

- Basic model uses larger grain size than typical ACT-R model (non-standard usage)
- Single cycle includes perceptual recognition, retrieval, decision, and motor action
- “procedural speed”, an ACT-R cycle time, includes procedural, visual, and motor speed in this case
- Processing time for one cycle assumed ~600ms for Basic model
- Age model by changing ONLY processing speed
- Explored model performance using 400ms and 800ms for procedural speed
- Factor of 2 with “normal” model in center of range
- No code changes

## Results of Simulation

## Discussion of GPS Task Results

- The “aged” model of the GPS task failed to reach the final destination in the majority of the simulated runs (90%)
- The “normal” model, when it did fail, only failed on the last turn
- The “aged” model failed on turns 1, 3, 5, and 6 – turns 2 and 4 allow earlier notice of the turn direction
- The “aged” model often misses information during a run, even during correct performance, but the simulation allows multiple chances to get back on track

## Conclusions

- Our approach of modeling aging is identical to that used to model individual differences
- constrained variations of principled parameters in integrated cognitive architecture
- Differences between age groups can be captured by the same parameter variations as used to account for individual differences
- Memory and speed of executive processing can be changed for existing models by changing single parameters
- Aging an existing model ***requires no coding***
- Applications:
- Predicting difficulties with system design before implementation
- Suggesting remedies to problems through model inspectability