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Additional resources


Pictue reference

Plan for Today

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

First Session: Leading Questions

  • 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?

Model and Data

Lewandowsky & Farrell. (2011). Computational modeling in cognition.

Model and Data

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)

Models in Psychology

  • Behaviourism
  • Cognitive psychology

Model of a Process Model

Miller & Page. (2007). Complex adaptive systems.

Properties of Good Models

(LSE library, No known Copyright Restrictions)

a-posteriori (i.e., after data has been collected)

Principle of falsification


a-priori (i.e., before data is collected)

Empirical content:

  • Level of universality
  • Degree of precision

Popper. (1982). Logik der Forschung.

Glöckner & Betsch. (2011). The empirical content of theories in judgment and decision making. JDM, 6, 711-721.

Jekel. (2019). Empirical content as a criterion for evaluating models. CP, 20, 273-275.

Empirical Content: Universality

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.


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

Empirical Content: Precision

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


  • 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.”

Bröder. (2011). Versuchsplanung und experimentelles Praktikum

Empirical Content: Precision

Roberts & Pashler. (2000). How persuasive is a good fit? […]. PR, 107, 358-367.

Degree of Precision and Prediction

Gigerenzer & Brighton. (2009). Homo heuristicus […]. TiCS, 1, 107-143.

\[y = b_0 + b_1 \times x + b_2 \times x^2 + \ldots + b_z \times x^z + \epsilon \]

Exercise: Empirical Content

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.

Formalized (Computational) Models

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)

Formalized Cognitive Models

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

Example: Formalized Cognitive Models

Krajbich & Rangel (2011). Multialternative drift-diffusion model […]. PNAS, 108, 13852-13857.

Advantages of Formalized versus Verbal Models