Additional resources
References
Pictue reference
Additional resources
References
Pictue reference
Session 1: Background
Session 2: Introduction to the math
Session 3: Exercises
Lewandowsky & Farrell. (2011). Computational modeling in cognition.
Choices between options
Judgments about “objects”
Biological measures
Miller & Page. (2007). Complex adaptive systems.
(LSE library, No known Copyright Restrictions)
a-posteriori (i.e., after data has been collected)
Principle of falsification
Corroboration
a-priori (i.e., before data is collected)
Empirical content:
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.
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
Bröder. (2011). Versuchsplanung und experimentelles Praktikum
Roberts & Pashler. (2000). How persuasive is a good fit? […]. PR, 107, 358-367.
Gigerenzer & Brighton. (2009). Homo heuristicus […]. TiCS, 1, 107-143.
\[y = b_0 + b_1 \times x + b_2 \times x^2 + \ldots + b_z \times x^z + \epsilon \]
Determine the empirical content of the following five hypotheses
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
(Process-)models that describe how information is processed
Krajbich & Rangel (2011). Multialternative drift-diffusion model […]. PNAS, 108, 13852-13857.