PSY 8150: Cognitive Psychology

Spring 2020

Course Information

Course Description

This is a graduate-level course providing a broad examination of topics in cognition, including perception, attention, memory, language, decision making, and other cognitive processes, with an emphasis on contemporary theories and experimental research. We will study these topics through a combination of discussion and hands-on activities. Readings will consist of original research articles that illustrate core theoretical concepts and major debates in the field. Assignments and class activities are designed to solidify knowledge of these concepts and provide opportunities to practice scientific skills.


Through this course, you will gain the following knowledge and skills:

Course Materials

All reading material will be posted on the course website. There is no required textbook for the course, but if you would like an introductory textbook for cognitive psychology, please let me know. I have a few books that you are welcome to borrow (first come, first serve).

Course Requirements


Course assessments will focus on using what you have learned to demonstrate your skills in making oral and written arguments supported by evidence, which are key skills for every scientist. Specifically, (1) two take-home exams and a paper (60% of the grade) will allow you to demonstrate your ability to formulate written arguments about the debates and topics we cover, and support your arguments with evidence; and (2) class participation and discussion (40% of the grade) will allow you to demonstrate your ability to make oral arguments and take an informed stance on a debate. Please contact me if you have any concerns about meeting the requirements of these assessments.

Assessment Points Grade percentage
Take-home midterm 100 pts 20%
Take-home final 100 pts 20%
Paper 100 pts 20%
Class discussion experts 100 pts 20%
Class participation 100 pts 20%

Grading Scale

A-...90.00-93.32% A...>93.32%
B-...80.00-83.32% B...83.33-86.66% B+...86.67-89.99%
C-...70.00-73.32% C...73.33-76.66% C+...76.67-79.99%
D-...60.00-63.32% D...63.33-66.66% D+...66.67-69.99%

Paper and exams

The goal of the exams and the paper is to demonstrate your understanding of the concepts covered in class, apply the scientific principles you have learned, and take a stance on current debates in the field, backing up your claims with evidence. The paper will consist of a literature review and experiment proposal. I am happy to provide comments on drafts of the paper during the semester, but please allow at least two weeks for me to give you comments (i.e., no comments for drafts submitted within two weeks of the paper deadline). Additional details about the exams and paper will be provided in class.

Participation and discussion experts

Discussing research with other scholars is an important skill for a scientist. Therefore, you and your fellow students will get the most out of the course if you attend every class. Please arrive on time and contribute to class discussions and activities. If you must miss class, I will be happy to meet with you to go over any material you missed. Just send me a message. Many of our class periods will be devoted to discussion of current research and debates in the field, centered around one or more journal articles on the topic. Each discussion will be led by myself and several students in the class. You will have an opportunity to select your discussion topics on the first day of class. You do not need to meet with the other discussion leaders before class (but you are welcome to if you want!). Instead, each student should be prepared to contribute to the discussion of the article, understanding the hypotheses put forth, the approach used, and the results, as well as how the data fit with one or more broader theories in cognitive psychology. Each discussion leader should contribute to presenting their thoughts and guiding the discussion in class. Everyone else should participate in the discussion as well. It's okay if there are things in the article you don't understand—that's part of the reason we discuss the material together. I am also happy to meet with you before class to answer any questions. If you must miss class for some reason on a day you are scheduled to lead discussion, please meet with me to make up the work.

Course Policies

Standard Villanova Course Policies

Course-specific Policies

Course Schedule

Date Topic Main Readings Extra Readings Deadlines/Notes
1/14 Introduction/Scheduling Schedule
1/16 The past, present, and future of cognitive psychology Clark (2013), Target article only (pp. 1—21)
1/21 Computational Models: Associative learning, Bayesian models, connectionism Courville et al. (2006) Rescorla (1988)
1/23 Grant writing
1/28 Categories: Fuzzy logical models, gradient vs. discrete representations Dale et al. (2007) Spivey & Dale (2006)
1/30 Manuscript writing Using LaTeX
2/4 Low-level vision: Color perception, synesthesia Beeli et al. (2007),
Simner (2006)
Eagleman & Goodale (2009)
2/6 Resopnsible conduct of research: Peer review, authorship Fine & Kurdek (1993),
APA Science Student Council (2006)
2/11 High-level vision: Scene perception, gaze control Ferreira et al. (2009),
Richardson et al. (2009)
Henderson (2003)
2/13 Giving presentations Papers for class discussion:
Triesch et al. (2003)
Hollingworth et al. (2001)
Spivey & Geng (2001)
2/18 Top-down effects in perception McClelland et al. (2006),
McQueen et al. (2006)
Class activity
2/20 Methodology: Eye-tracking Chambers et al. (2004)
Allopenna et al. (1998)
2/25 Language and meaning Apfelbaum et al. (2011)
Elman (2004)
McClelland & Rogers (2003)
word2vec: Demo
word2vec: Full model
2/27 Catch-up Midterm due
3/2 Spring Break
3/4 Spring Break
3/10 Sensory memory, working memory Zhang & Luck (2008),
Bays & Husain (2008)
Alvarez & Cavanagh (2004),
Luck & Vogel (2013)
3/12 Methodology: EEG/ERP technique
3/17 Long-term memory, false memories Clancy et al. (2002),
Frenda et al. (2013)
Zhu et al. (2013)
3/19 Resopnsible conduct of research: Data ownership, mentor-mentee responsibilities
3/24 Data ownership (continued), Data analysis in Excel Example data for analysis
3/26 Sparse vs. detailed information in long-term memory Konkle et al. (2010)
3/31 Expertise Xu et al. (2005),
Scott et al. (2005)
Rossian et al. (2002),
Bentin et al. (2002)
4/2 Data analysis/graphs in R Example data and scripts,
Introductory script
4/7 Heuristics, decision making, mental models Sterman & Booth Sweeney (2007) Yousif et al. (2019)
4/9 Easter Break
4/14 Metacognition, learning and memory Bennett et al. (2018),
Mueller & Oppenheimer (2014)
4/16 Individual meetings to discuss paper
4/21 Theory of mind, imitation Meltzoff & Moore (1977),
Jones (2012)
Tomasello et al. (2003),
Povinelli & Vonk (2003)
4/23 Optimality, Bayesian models revisited Griffiths & Tenenbaum (2006),
Marcus & Davis (2013)
Bowers & Davis (2012a),
Griffiths et al. (2012),
Bowers & Davis (2012b),
Jones & Love (2011)
Final paper due
4/28 Embodied cognition Proffitt et al. (2003),
Durgin et al. (2009)
Barsalou et al. (2003),
Mahon & Caramazza (2008),
Proffitt (2006)
4/30 Catch-up
5/4 Final exam due