Session 1: Background

• Definition of formalized (computational) cognitive models
• Criteria for evaluating models before data collection

Session 2: Introduction to the math

• Modeling Choices
• Modeling other process measures
• Comparing models
• Euclidian Diagnostic Task Selection (EDTS)
• Multiple-Measure Maximum Likelihood Strategy Classification Method (MM-ML)

Session 3: Exercises

• I show you how to apply EDTS
• You will apply MMML to a dataset from our lab

• What are formalized (computationl) cognitive process models?
• Why are process models (in general) better models?
• What are good, what are bad models?
• What does a typical process model look like?

Choices between options

• Decision to vaccinate or not
• Decision for a risky bet or save bet
• Decision between products

Judgments about “objects”

• Judgment about the subjective intensity of a stimulus
• Judgment abput the value of an object
• Social judgments

Biological measures

• Heart rate (excitation)
• Pupil dilatation (level of processing)

• Behaviourism
• Cognitive psychology

A model has a high level of universality if it applies to many situations.

Model A says something about situation 1 and 2, model B says only something about sitation 1. Model A has a higher level of universality.

Example

„If a child is frustrated, then it reacts aggressively.“

The degree of precision increases with the number of potential observations that falsify the model.

Example

• Nominal: “If a child plays ego-shooter games, it reacts more aggressively.”
• Ordinal: “A child who plays more often ego-shooter games reacts more often aggressively.”
• Quantitative-functional: “The relation between frequency of playing ego-shooter games is positive linearly related to aggressive behaviour.”

Determine the empirical content of the following five hypotheses

1. In my refrigerator is Coke.
2. In my refrigerator are two bottles of Coke.
3. In my refrigerator is Coke or no Coke.
4. In my refrigerator and in my neighbour’s refrigerator are two bottles of Coke.
5. In my refrigerator and in my neighbour’s refrigerator are three bottles of Coke.

Model components: Properties of a situation (e.g., stimuli) and properties of a person (e.g. information processing style), etc. (everything that can be measured)

Behaviour: Choices, judgmens, etc. (everything that can be measured)

\[\text{model components(situation, person)} \rightarrow \text{model output(behaviour)}\]

Arrow = Mathematical function that describes how model input (options of a function) results into model output (output of a function)

Models that measure cognitive variables

• Signal detection theory that measures discriminability d’
• Rasch-model measuring person’s ability \(\theta\)
• Multinomial models that measure memory

(Process-)models that describe how information is processed

• Anderson: ACT-R model of cognition
• Eliasmith: Modelling a brain
• Krajbich: Evidence accumulation models
• Network-model