Computational Modelling

Question 1

Why Model?

Answer 1

Provides a framework for interpreting data
Select a model based on quantitative and intellectual judgement
Instantiation of a quantitative model ensures all assumptions of a theory are identified and tested

Question 2

What is a Model?

Answer 2

Abstract framework that captures the structure of data

Simpler version of what you’re trying to explain

Question 3

Levels of Analysis (Marr, 1982)

Answer 3

• Computational Level: what the system does/why
• Representational Level: how does it do that, what processes build the representations
• Physical System Level: how the system is physically realized e.g. biological vision: which neural structures build the visual system

Question 4

Model Classification (Lewandowsky & Farrell, 2011)

Answer 4

• Data description models: describes relationship between variables
• Process characterization models: peek inside ‘black box’, neutral to implementations of the processes they characterize
• Process explanation models: up-close view of ‘black box’, try to implement how the processes occur

Question 5

Expectations of Models

Answer 5

Bring out relationships between variables that would not otherwise have been realised; explore the implications of human behaviour

Question 6

Limitations of Models

Answer 6

• Need to be falsifiable but not false

- Need to have verisimilitude (partial truth value)