Flashcards in Comp. Models of the Mind II b Deck (25):

1

## Explain Bonini's paradox!

### as a model of a complex system becomes more complete, it becomes less understandable (as hard to understand as real world system)

2

## What do we want to ask ourselves, when we validate a model?

### How adequately does the model reflect the aspects of the real world it has been designed to model?

3

## Six factors of the multidimensional utility criterion?

###
- parsimony

- effectiveness (explicit procedures for deriving predictions)

- broad generality (models based on general cognitive theories also reduce the irrelevant specification problem)

- accuracy and ease of falsification

- surprise! (interesting and counterintuitive behavior)

- coverage of variety of data and different knowledge

4

## Three actions to show how adequately a model reflects the aspects of the real world:

###
- explicate how much a model constrains the data to befitted

- report data variability: verify real world data agrees also with outcomes ruled out by the model

- show there are plausible results the model cannot fit

5

## Give an example of process analysis!

### Marr's Levels of Explanations of Complex Systems

6

## Marr's analysis has ____________ three levels.

### Marr's analysis has AT LEAST three levels.

7

## Steps in cognitive modeling according to Busemeyer & Diederich (2010) - step 1/6

### REFORMULATE assumptions of conceptual theoretical framework into more rigorous mathematical/computer language form

8

## Steps in cognitive modeling according to Busemeyer & Diederich (2010) - step 2/6

###
Additional detailed AD HOC ASSUMTPIONS to COMPLETE the model: required for precise quantitative predictions

(e.g. selection of feature definitions)

9

## Steps in cognitive modeling according to Busemeyer & Diederich (2010) - step 3/6

###
PARAMETER ESTIMATION from observed data

(e.g. weight coefficient)

10

## Steps in cognitive modeling according to Busemeyer & Diederich (2010) - step 4/6

### COMPARISON of predictions of competing models

11

## Steps in cognitive modeling according to Busemeyer & Diederich (2010) - step 5/6

### EMPIRICAL TESTS, aiming for parameter-free tests

12

## Steps in cognitive modeling according to Busemeyer & Diederich (2010) - step 6/6

### REFORMULATE THEORETICAL framework and construct new models

13

## What is there to say about: Steps in cognitive modeling according to Busemeyer & Diederich (2010) - step 1/6

### - Use of basic cognitive principles of the conceptual theory for model construction

14

## What is there to say about: Steps in cognitive modeling according to Busemeyer & Diederich (2010) - step 2/6

### - Number of ad hoc assumptions should be minimised

15

## What is there to say about: Steps in cognitive modeling according to Busemeyer & Diederich (2010) - step 3/6

### - Ideal: Parameter-free models

16

## What is there to say about: Steps in cognitive modeling according to Busemeyer & Diederich (2010) - step 4/6

###
- Question whether model CAN fit data is MEANINGLESS!

- Which model provides a better representation wrt. specific aspects of target

17

## What is there to say about: Steps in cognitive modeling according to Busemeyer & Diederich (2010) - step 5/6

###
- Experimental conditions leading to opposite qualitative or ordinal predictions from competing models for any parameter settings (e.g. different categorization)

- Alternative: quantitative tests: magnitude of prediction errors

18

## Why are Marr's levels of analysis so important?

### Importance of clearly identifying/delineating/distinguishing the DOMAIN of a model

19

## The three levels of Marr:

###
- Competence / Computational Theory

- Representation and Algorithm

- Hardware Implementation

20

## The aims/questions of the three levels of Marr:

###
- WHAT is the GOAL of the computation, WHY is it appropriate, and what is the logic of the strategy by which it can be carried out?

- HOW can this computation be implemented? In particular, what is the REPRESENTATION for the input and output and what is the ALGORITHM for the transformation?

- How can the representation and the algorithm be REALISED PHYSICALLY?

21

## The what + why (= computational theory) of the check register:

###
What: arithmetic, addition (independent of particular representation)

Why: addition meets purposeful constraints (e.g. zero element, commutativity)

22

## The how (representation and algorithm) of the check register:

###
- Addition: Same representation of numbers for inputs and outputs (or: bar-code -> total sum in numbers)

- Wide choice of representations

- Choice of algorithm often depends on representation

- Quality of algorithm considered if multiple algorithms per representation possible

23

## The physical realisation of the check register:

### - (Mis)match of algorithmic styles and computational substrates (e.g. parallelism on single-processor architecture)

24

## Name 7 limitations of GOMS!

###
- models apply to skilled users only

- only NGOMSL accounts for (restricted) learning

- no account for recall after period of disuse

- no account for slips even skilled users make

- focus on motor components rather than on cognitive processes

- task selection itself is not addressed

- no modelling of fatigue or individual differences

25