Comp. Models of the Mind II b Flashcards Preview

Mei:CogSci Semester 2 > Comp. Models of the Mind II b > Flashcards

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

Name three advantages of GOMS!

- prediction of human performance with reasonable accuracy
- widely used for qualitative and quantitative task analysis
- framework for fitting of specialised models