Cognitive Psychology, 2015-11-06

Module: 
Cognitive Psychology
Examiner: 
Frank Jäkel
Assessor: 
Steffen Waterkamp
Date: 
Fri, 2015-11-06

Courses: Cognitive Psychology + PMPC

Protocol symbols:

* Main question
^ Intermediate question during explaining main question
-> Own answers
-} Thoughts/remarks about own answers
[_] Reference to specific feedback that was given to the specified section/question. See below before the book references.

Unimportant note: I was an hour too early. Fortunately this was not a problem because I had to catch my train with direction Lutter[*] ! :) I got a 1.3.

You can choose between English and German as the examination language. But it does not matter, if you mix the languages - it is primarily used to decide in which language the questions are asked.

In an earlier meeting Prof. Jäkel told me that I would be examined 20 minutes about basics of cognitive psychology [CP] and 10 minutes about PMPC. He limited the topics to perception, memory and problem solving for CP basics and to basics of probability theory and signal detection for PMPC. He also gave explicit references what to learn. See the 'book references' section below for more details. I used "Cognitive Psychology by Goldstein" as learning material for CP.

== Perception

* Introductory question was about Gestalt laws.
-> Hint (not given by the examiner): There are more than 7. And "Perceptual Organization" is not one of them. [1] It is more a general idea on which the actual Gestalt laws are based on.
I listed all the other laws I knew and explained the law of good continuation in more detail.

* What is unconscious inference?
^ Why "unconscious"?
^ Why "inference"?

== Memory

* Explain partial report method
-> Sensory memory related. Compare with whole report and delayed partial report method.
^ Difference between "sensory memory" and "iconic memory" [2]

* Why do we differentiate between STM and LTM?
-} In the the beginning I did not know where he wanted to go with this question.
^ Explain serial position curve.
^ How to manipulate primacy and recency effect
-> Recency effect: Delay retrieval, use distraction before retrieval.
-} I did not know how to manipulate the primacy effect :(

== Problem Solving

* Do you know an experiment referring problem solving with experts/novices?
-> chess experiment, structural vs surface features, semantic knowledge leads attention, fictional chess positions have bad performance for both groups
^ Another reason why experts can store more?
-} correct answer is "chunking", did not know that at this point

== PMPC

* Explain Bayes rule
-> write down Bayes rules, explain terms (posterior, prior, likelihood, evidence)
^ Derive rule
-> Derive analytically [3]
^ Explain semantically (standard application example, some medical test)
^ Derive using probability tree
-} Forgot how to do that :( [4]

* Explain ROC
-> P(H) vs P(FA), explain Hit, Miss, False Alarm, Correct Rejection
-> Explain how to create ROC, using sliding threshold
-> Equal-variance gaussian case example
^ Draw threshold into example, show Hit-rate and FA-area

== Feedback

[0] Books teach concepts/models first and show example data later. Real life is different. You have the data and have to come up with the models. I answered questions how the books taught me. He would have preferred the second way but had the understanding why this was the case.

[1] My mistakes were ignored since it was intro question. :) Everyone is nervous in the beginning.

[2] People with an mathematical background often do not want to learn terms by heart. Terms are important as well.

[3/4] He was sad I did not know how to derive Bayes rule with the probability tree. This was the main thing I should have learned in PMPC. But was not as grave since I was able to derive it analytically.

[*] Lutter, no article. Amazing freshmen event. Predicting the progression using the last years as reference, this description is subject to change.

------------- Book references --------------

Cognitive Psychology Basics

*** Perception
Chapter 3 in Cognitive Psychology by Goldstein
Chapter 2 in Cognition by Sternberg & Sternberg
*** Memory
Chapter 5-7 in Cognitive Psychology by Goldstein
Chapter 5-6 in Cognition by Sternberg & Sternberg
*** Problem Solving
Chapter 12 in Cognitive Psychology by Goldstein
Chapter 12 in Cognition by Sternberg & Sternberg

PMPC

*** Grundlagen der Wahrscheinlichkeitstheorie und Bayes
(Unabhängigkeit, bedingte Wahrscheinlichkeiten, Summenregel,
Bayes'sche Regel, Wahrscheinlichkeitsverteilungen,
Dichten). Brauchbare Referenzen dafür sind die Einführungskapitel
z.B. in
Mosteller, F.; Rourke, R. E. & Thomas, G. B. Probability with
statistical applications Addison-Wesley, 1970
Berry, D. A. Statistics. A Bayesian Perspective. Duxbury Resource
Center, 1995
Kruschke, J. K. Doing Bayesian Data Analysis Academic Press, 2011
*** Signalentdeckungstheorie
Falls Sie neben der Artikel, die es auf Stud.IP gibt, noch andere
Einführungen lesen wollen, können Sie in diese Bücher schauen:
Wickens, T. D. Elementary Signal Detection Theory Oxford
University Press, 2002
Macmillan, N. A. & Creelman, C. D. Detection Theory: A User's
Guide Cambridge University Press, 1991
Coombs, C. H.; Dawes, R. M. & Tversky, A. Mathematische
Psychologie: Eine Einführung 6 Theorie der Erkennbarkeit von
Signalen Beltz, 1975, 197-237