Comp. Models of the Mind II b

Question 1

Explain Bonini’s paradox!

Answer 1

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

Question 2

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

Answer 2

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

Question 3

Six factors of the multidimensional utility criterion?

Answer 3

• 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

Question 4

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

Answer 4

• 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

Question 5

Give an example of process analysis!

Answer 5

Marr’s Levels of Explanations of Complex Systems

Question 6

Marr’s analysis has ____________ three levels.

Answer 6

Marr’s analysis has AT LEAST three levels.

Question 7

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

Answer 7

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

Question 8

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

Answer 8

Additional detailed AD HOC ASSUMTPIONS to COMPLETE the model: required for precise quantitative predictions
(e.g. selection of feature definitions)

Question 9

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

Answer 9

PARAMETER ESTIMATION from observed data

e.g. weight coefficient

Question 10

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

Answer 10

COMPARISON of predictions of competing models

Question 11

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

Answer 11

EMPIRICAL TESTS, aiming for parameter-free tests

Question 12

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

Answer 12

REFORMULATE THEORETICAL framework and construct new models

Question 13

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

Answer 13

• Use of basic cognitive principles of the conceptual theory for model construction

Question 14

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

Answer 14

• Number of ad hoc assumptions should be minimised

Question 15

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

Answer 15

• Ideal: Parameter-free models

Question 16

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

Answer 16

• Question whether model CAN fit data is MEANINGLESS!

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

Question 17

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

Answer 17

• 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

Question 18

Why are Marr’s levels of analysis so important?

Answer 18

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

Question 19

The three levels of Marr:

Answer 19

• Competence / Computational Theory
• Representation and Algorithm
• Hardware Implementation

Question 20

The aims/questions of the three levels of Marr:

Answer 20

• 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?

Question 21

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

Answer 21

What: arithmetic, addition (independent of particular representation)
Why: addition meets purposeful constraints (e.g. zero element, commutativity)

Question 22

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

Answer 22

• 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

Question 23

The physical realisation of the check register:

Answer 23

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

Question 24

Name 7 limitations of GOMS!

Answer 24

• 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

Question 25

Name three advantages of GOMS!

Answer 25

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