Computational Cognition (830:547, Prof. Feldman, Fall 2018)

Theme. Modern cognitive science begins with the premise that the brain is essentially a very special kind of computer. This course will explore the following questions: What does this mean, exactly? Why do we think it? Is it true? And if it is true, so what?

Along the way, the course will introduce some of the foundational ideas that underlie the computational study of the mind. The emphasis will be on fundamental mathematical and conceptual elements of computational models of the mind, as well as historical controversies about how the computational structure of cognition is best understood.

Weekly assignment. Each week, each student will be expected to send me a single question or comment (at least a sentence, at most a long paragraph) on each of that week’s readings by e-mail (jacob@ruccs.rutgers.edu). These do not have to be eloquent pronouncements or summaries. Rather, they should be casual but thoughtful remarks or queries concerning something that struck you or puzzled you.

Graded assignments. Graded assignments will consist of approximately four short (4-5 page) written assignments, on topics to be announced. In some cases, these will be thought experiments; others will be polemical essays concerning a theoretical, meta-theoretical, conceptual, or formal issue discussed in class. The goal of these assignments is to develop clarity of argument while building awareness of the theoretical ideas covered in class. Essays will be due at irregular intervals to be detailed in class.   

 

Schedule of topics and readings (tentative)

Week 0: Introduction

   • Braitenberg, V. (1984): Getting around; Fear and aggression; Love; Values and special tastes; Logic; Selection, the impersonal engineer; Concepts; Space, things and movements; Shapes; Getting ideas; Rules and regularities. From Vehicles: Experiments in Synthetic Psychology (excerpt). Cambridge, MA: MIT Press

   • Miller, G. (2003): The cognitive revolution: a historical perspective. Trends in Cognitive Sciences, 7(3), 141–144.

Week 1: Computation

    • Turing, A (1950): Computing machinery and intelligence. Mind, 59, 433–460.

    • Sipser (1997): Decidability. Chapter 3 of: Introduction to the theory of computation. Boston: PWS Publishing, 1997

         - Recommended: Sipser Chs. 0,1,2.

Week 2: Competence and performance

     • Marr, D. (1982): The philosophy and the approach. From Vision: A computational investigation into the human representation and processing of visual information. San Francisco: Freeman.

     • Anderson, J. (1990): Introduction. From The Adaptive Character of Thought. Hillsdale, NJ: Erlbaum.

     • Jonas, E. & Kording, K. (2017) Could a neuroscientist understand a microprocessor? PLoS Computational Biology. 13(1): e1005268, 1-24.

Week 3: Complexity

    • Garey, M. R. and Johnson, D. S. (1979): Computers, complexity, and intractability. From Computers and intractability: A guide to the theory of NP-completeness. New York: W.H.Freeman.

    • Feldman, J. (2016). The simplicity principle in perception and cognition. WIREs Cognitive Science, 7, 330–340.

Week 4: Heuristics and rationality

     • Tversky, A, Kahneman, D (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185, 4157:1124-31.

     • Todd, P. M. & Gigerenzer, G. (2012): What Is Ecological Rationality? From Ecological Rationality: Intelligence in the World (Todd, P. M., Gigerenzer, G. and the ABC Research Group). Oxford: Oxford University Press.

Week 5: Bayesian models I

     • Feldman, J. (2015) Bayesian models of perceptual organization. From Handbook of Perceptual Organization (J. Wagemans, ed.), 1008-1026. Oxford: Oxford University Press.

     • Oaksford, M. and Chater, N. (2009): Precis of Bayesian Rationality: The Probabilistic Approach to Human Reasoning. Behavioral and Brain Sciences, 32, 69–120.

Week 6: Connectionism I

      • Rumelhart, D. E. and McClelland, J. L. (1986) PDP models and general issues in cognitive science. In Parallel Distributed Processing (D. Rumelhart, J. L. McClelland, and the PDP Research Group). Cambridge: M.I.T. Press.

      • Minsky, M. and Papert, S. (1988) Epilogue. From Perceptrons: an introduction to computational geometry: expanded edition. Cambridge: M.I.T. Press (original edition: 1969).

Week 7: Production systems

      • Anderson, J. R. and Lebiere, C. (2003): The Newell Test for a theory of cognition. Behavioral and Brain Sciences, 26, 1-53. 

Week 8: Bayesian models II

      • McClelland, J. L., Botvinick, M. M., Noelle, D. C., Plaut, D. C., Rogers, T. T., Seidenberg, M. S., and Smith, L. B. (2010) Letting structure emerge: connectionist and dynamical systems approaches to cognition. Trends in Cognitive Sciences, 14(8), 348–356.

      • Griffiths, T. L., Chater, N., Kemp, C. Perfors, A. and Tenenbaum, J. B. (2010) Probabilistic models of cognition: exploring representations and inductive biases. Trends in Cognitive Sciences, 14(8), 357-64.

Week 9: Causal models

      • Pearl, J. (2000). Epilogue: The art and science of cause and effect. From Causality. Cambridge: Cambridge University Press.

      • Glymour, C. (2003). Learning, prediction and causal Bayes nets. Trends in Cognitive Sciences 7(1), 43-48.

Week 10: Connectionism II

      • LeCun, Y, Bengio, Y & Hinton, G. (2015) Deep learning. Nature, 521, 436-444.

Week 11: Probabilistic computational neuroscience

      • Pouget, A. and Zemel, R. S. (2011). Population codes. From Bayesian brain: probabilistic approaches to neural coding (Doya, K., Ishii, S., Pouget, A. and Rao, R. P. N., eds.). Cambridge, MA: MIT Press.

 

 

 

 

 

Psychology 521

Instructor: Jacob Feldman (website, email)

Teaching assistant: Serena De Stefani

Time and place: Thursdays 12pm - 3pm, Psychology 101

Overview: This course is designed to introduce psychology students to the basic ideas and techniques underlying the design of psychological experiments and the statistical analysis of data. Topics will include:

    • Basic experimental design

    • Probability and probability distributions

    • The logic of conventional null hypothesis significance testing (NHST)

    • Basic NHST techniques such as correlation, regression, t-tests, and analysis of variance

    • The logic of Bayesian inference

    • Basic Bayesian (and approximately Bayesian) techniques including Bayes factors, AIC and BIC. 

Primary texts:

    Gravetter, F. J. and Wallnau, L. B. (2007) Statistics for the behavioral sciences (any edition*). Wadsworth.

        *Any edition should suffice, though chapter numbers differ among editions, so be careful. Chapter numbers below are based on the 7th edition.

     McElreath, R. (2016): Statistcal rethinking: A Bayesian Course with Examples in R and Stan.  

Additional readings: There will be several additional readings, available on the Sakai site and linked below.

Software practicum: Serena will lead weekly sessions (during class time) focusing on the statistical package R. 

Assignments and other graded exercises: Links to problem sets will be added below as they are assigned. 

Announcements will be sent via Sakai. 

Schedule of topics and readings:  

  Topic: Read: Assignment:
Week 1 Introduction: Experimental design G&W chs. 1, 2; McElreath ch. 1  
Week 2 Descriptive statistics and distributions G&W chs. 3, 4; Cohen 1994  
Week 3 Probability; Bayesian inference G&W chs. 5, 6; McElreath ch. 2  
Week 4 Sampling distributions G&W chs. 7, 8; Wagenmakers 2007  
Week 5 Null hypothesis significance testing G&W chs. 9,10; McElreath ch. 3  
Week 6 t-tests G&W ch. 11; Gallistel 2009  
Week 7  One-way analysis of variance G&W chs. 12*, 13 (Introduction to ANOVA); McElreath ch. 4  
Week 8 Multivariate analysis of variance G&W ch. 14 (repeated measures), 15 (Two-factor ANOVA);   
Week 9 Correlation and regression G&W ch. 16 (correlation); 17 (Introduction to regression); McElreath ch. 5  
Week 10  Chi-square etc. G&W ch. 18 (Chi-square); 19 (Binomial)  
Week 11  Bayes factors, AIC and BIC 

McElreath Ch. 6; Masson 2010

 
Week 12 Presentations    
Week 13  Presentations    

 

 

Cognition (305:03, Prof. Feldman, Fall 2015)

Course: Cognition (Psychology 305:03, Index number 18047)       

Professor: Jacob Feldman, A125, Busch Psychology Annex (email, website) and possibly guest lecturers.

Class time and place: Mondays and Thursdays, 10:20am—11:40am, Tillett 226

Readings: There is no textbook for this course. Readings are posted below, with links to Sakai. Additional readings will be assigned over the course of the semester and posted on Sakai. 

Course webpage: http://ruccs.rutgers.edu/jacob-teaching/20-personal-sites/jacob-feldman/191-cognition-305-03-prof-feldman-fall-2015

Sakai page: Click here

      Additional readings will be posted in Resources on the Sakai site, and linked below. 

Professor's office hours: Thursdays, 12-1pm, A125, Room Psychology (Busch) Addition. Best to email me if you plan to visit.

Teaching Assistant: Serena De Stefani. Office hours: Wednesdays 10-11am, A134, Psychology (Busch) Addition.

 

Exams and grading

  1. (25% of grade) On-line quizzes: Sakai quizzes will be given weekly.
    Quizzes are 30 minutes long, and will be available on-line Fridays 7am - 7pm.
  2. (25% of grade) Midterm: Monday, Oct. 19, in class
  3. (10% of grade) Written assignments: Two brief essays (5% each) will be assigned over the course of the term. 
  4. (35% of grade) Final exam: Wednesday, Dec. 16, 12-3pm.

 

Policies 

“Graduation insurance.” If this is your last semester and you need to pass this course in order to graduate on time, email me before the midterm. If you don’t ask before the midterm, “graduation insurance” will not be offered. 

Final grades are final. Once grades are assigned after the final exam, grade changes will not be considered. Don’t even ask. If you are having trouble with the material before that, email me or come to office hours. 

Academic integrity. Cheating and other violations of academic integrity will not be tolerated in this course. The university’s policy on academic integrity can be foundhere.

Weather alerts. If there is bad weather (e.g., snow or threat of snow), class may be canceled, even if the university remains open. If so, I will send an email to the entire class. So if you suspect bad weather please check your email before coming to class. 

 

Schedule of topics (dates approximate)

Date

 

Topic

 

Read

Sept. 3   Welcome    
Sept. 8 (TUES!), 10   The mind as a machine   Whitman, Ch. 1
Sept. 14   NO CLASS   -
Sept. 17, 21, 24   Roots of cognitive psychology   Turing articlePinker, The Blank Slate, Ch. 1
Sept. 28, Oct. 1   Neuroscience   Friedenberg & Silverman, ch. 6Quiroga et al. article
Oct. 5, Oct. 8   Perception   Goldstein, ch. 5
Oct. 12, 15   Attention   Revlin, ch. 2
Oct. 19   MIDTERM    
Oct. 22, 26, 29   Memory, Knowledge   Kellogg, ch. 4Kellogg, ch. 5
Nov. 2, 5   Concepts and Categories   Smith: Categorization
Nov, 9, 12   Language   Reed, ch. 10Wynne: Aping Language
Nov. 16, 19   Language (cont.)   -  
Nov. 23   Reasoning & Decision Making   Sternberg & Sternberg, ch. 12
Nov. 26   Thanksgiving; NO CLASS   -  
Nov. 30, Dec. 3, 7   Reasoning & Decision Making (cont.)   -
Dec. 10   Review Session    

Final: Wed., Dec. 16, 12-3pm (35% of grade)

 

Rules for on-line quizzes

1. Quizzes are open-book. You may consult your class notes, textbook, or other non-class sources. 

2. However, your answers must be your own. You must not communicate in any way, shape or form with other humans during the period the quiz is open. No other students may be in the room with you when you work on the quiz. 

3. You may not share the questions with anyone else after seeing them. That includes any method of communication: verbal, text, phone, semaphore, smoke signals, etc. Once you know the questions, keep them to yourself. 

4. Once you open the quiz, you have 45 minutes to finish.

 

Cognition (305:02, Prof. Feldman, Spring 2017)

Course: Cognition (Psychology 305:02, Index number 14788)                                                                 

Professor: Jacob Feldman, A125, Busch Psychology Annex (email, website) and possibly guest lecturers.

Class time and place: Mondays and Thursdays, 10:20am—11:40am, SEC 118

Readings: There is no textbook for this course. Readings are posted below, with links to Sakai. Additional readings may be assigned over the course of the semester and posted on Sakai. 

Sakai page:  link

      Additional readings will be posted in Resources on the Sakai site, and linked below. 

Professor's office hours: Mondays 2-3pm, Room A125, Room Psychology (Busch) Addition. Best to email me if you plan to visit.

Teaching Assistant: Serena De Stefani. Office hours: Tuesdays 9-10am, Psych A134.

Exams and grading

  1. (25% of grade) On-line quizzes: Sakai quizzes will be given weekly.
    Quizzes are 45 minutes long, and will be available on-line Fridays 7am - 8pm. (See rules below.)
  2. (25% of grade) Midterm: Thursday, March 2, in class
  3. (10% of grade) Written assignments: Two brief essays (5% each) will be assigned over the course of the term. 
  4. (35% of grade) Final exam: Monday, May 8, 8-11am, SEC 118. The final exam is cumulative. All material from the entire class is fair game, although the exam will emphasize the material after the midterm. Students arriving after 8:45am will not be allowed to take the exam. 

 

Policies 

Slides will be posted after each class, but not before, and are often incomprehensible if you weren't at class. Regular attendance is essential to doing well in this course!

“Graduation insurance.” If this is your last semester and you need to pass this course in order to graduate on time, email me before the midterm. If you don’t ask before the midterm, “graduation insurance” will not be offered. 

Final grades are final. Once grades are assigned after the final exam, grade changes will not be considered. Don’t even ask. If you are having trouble with the material before that, email me or come to office hours. 

Academic integrity. Cheating and other violations of academic integrity will not be tolerated in this course. The university’s policy on academic integrity can be found here.

Weather alerts. If there is bad weather (e.g., snow or threat of snow), class may be canceled, even if the university remains open. If so, I will send an email to the entire class. So if you suspect bad weather please check your email before coming to class. 

 

Schedule of topics (dates approximate)

Date

 

Topic

 

Read

Jan. 19   Welcome    
Jan. 23, 26   The mind as a machine   Whitman, Ch. 1
Jan. 30, Feb. 2   Roots of cognitive psychology   Turing (1950); Pinker, The Blank Slate, Ch. 1
Feb. 6, 9   Neuroscience   Friedenberg & Silverman, ch. 6; Quiroga et al. article
Feb. 13, 16, 20   Perception   Goldstein, ch. 5
Feb. 23, 27   Attention   Revlin, ch. 2
Mar. 2   MIDTERM    
Mar. 6, 9   Memory   Kellogg, ch. 4; Henry Molaison
Mar. 13, 16    - Spring break -     
Mar. 20, 23   Knowledge   Kellogg, ch. 5
Mar. 27, 30   Concepts and Categories   Smith: Categorization
Apr. 3, 6   Language   Reed, ch. 10; Wynne: Aping Language
Apr. 10, 13   Language (cont.)   -  
Apr. 17, 20   Reasoning & Decision Making   Sternberg & Sternberg, ch. 12
Apr. 24, 27   Reasoning & Decision Making (cont.)   -
May 1   Review Session    

Final: Final exam: Monday, May 8, 8-11am, SEC 118 (35% of grade)

 

Rules for on-line quizzes

1. Quizzes are open-book. You may consult your class notes, textbook, or other non-class sources. 

2. However, your answers must be your own. You must not communicate in any way, shape or form with other humans during the period the quiz is open. No other students may be in the room with you when you work on the quiz. 

3. You may not share the questions with anyone else after seeing them. That includes any method of communication: verbal, text, phone, semaphore, smoke signals, etc. Once you know the questions, keep them to yourself. 

4. Once you open the quiz, you have 45 minutes to finish.

5. Makeups are available for documented medical problems or work conflicts only. You must let me know about the problem before the quiz closes

 

Frequently Asked Questions 

Q: Which is more important, the lectures or the readings?

A: The lectures are my best attempt to explain the material I think is important. The readings supplements the lectures. They are both fair game for exams, but the lectures are better guide to what will be on the exams. But be advised that if you do not do the reading you wilil not do well in this class.

 

Q. Do you post the slides?

A. Yes. I post the lecture slides after class, but not before. However the slides are sometimes very sketchy, and you will not really be able to understand them if you haven’t attended class. 

 

Q: Do I have to come to class?

A. You are an adult; make your own choices. But if you don’t come to class, you have little chance of getting better than a D. You can’t realistically do well in this class without attending regularly. I take attendance once in a while so I have a sense of who’s showing up.

 

Q. Do I have to do the reading?

A. You an adult; make your own choices. But if you don’t do the reading, you will not do very well in this class.

 

Q: Will [insert topic, fact, phenomenon] be on the exam?

A. Maybe. I try to test ideas I think are important. Most questions on exams are about “big ideas.” But naturally the questions range in importance from broad concepts to narrower facts and terms. Everything taught in the course is fair game. But the more important it is, the more likely it is to be on the exams. The lectures are my best attempt to explain what I think is important.

 

Q. Any other rules I should know about?

A. No cell phones visible in class. Really. Computers and tablets are ok for note-taking only. Facebooking, texting, instagramming, watching Netflix, etc are distracting and disrespectful to the other students. 

 

Q: Is the final exam cumulative?

A. Yes. The midterm will cover everything from the beginning of the course until the midterm. The final exam will cover everything from the beginning of the course until the end of the course, drawing heavily from material from the second half of the course. .

The concept of “concepts” in cognitive science aka CONCEPTS

Cognitive Science 310 (01:185:310:01, #17872)

Prof. Jacob Feldman

Fall, 2015

“What is a concept, that the brain may know it, and the brain, that it may know a concept?”
[Apologies to Warren McCulloch]

What is a concept? An idea, an insight, a theory? An abstraction from experience, or an innate belief? The way our mind organizes the world, including the mental categories with which we represent it, is a central idea in cognitive science. This course aims to introduce students to the study of concepts from a broad interdisciplinary point of view, surveying how concepts are understood in Psychology, Philosophy, Computer Science, and Neuroscience. The emphasis is on both the diversity of conceptions of “concepts” in these disciplines, as well as on core principles they all share. 

 

Class coordinates

Where: Frelinghuysen Hall (CAC), Room A6

When: Tuesdays and Fridays, 9:50am - 11:10am

Professor’s office hours: Psychology, Busch, Room A125. Thursdays 1-2pm.

TA: EJ Green. Office hours: Fridays, 12-1pm, Dept. of Philosophy (106 Somerset St., 5th floor) room 529.

 

Schedule of topics and reading assignments

Week:

Topic

Read:

Week 1 Introduction: concepts Murphy, Ch. 1; Seriès & Sprevak
  Note: NO CLASS on Tuesday Sept. 9 (Rutgers Monday schedule)
Week 2 Induction and concept learning Murphy Ch. 2; Sober, 2009;Sloman & Lagnado, 2005
Week 3 Classical vs. fuzzy models of categories Murphy Ch. 3; Medin & Smith, 1984Fodor & Lepore, 1996
Week 4 Simplicity and its discontents Murphy Ch. 4; Murphy & Medin, 1985Feldman, 2003
Weeks 5-7 Philosophy: What is knowledge? Murphy, Chs. 7, 8; Fodor
  — MIDTERM: Tuesday, Oct. 20, in class —
Weeks 8-10 Machine learning: how can you generalize from data? Murphy, Chs. 5, 12; Shepard; Jakel et al.
Weeks 11-13 Neuroscience: How does the brain represent abstractions? Murphy 9, 10; Quiroga et al..
Weeks 14 Putting it all together Murphy, Ch. 13

 

Grading

25%: Midterm. In class, closed book. Tuesday, Oct. 20

25%: Essays. A number of short (2-page) essays will be assigned at irregular intervals throughout the course. 

40%: Weekly reading responses (in Assignments on Sakai). 

10%: Class participation. (Criteria determined by the whim of the professor. Basically, show up and try to participate in class discussions.)

There is no final exam in this course. 

 

Text and readings

Primary Text: 

Supplementary readings (Preliminary list; more will be added later):

  • Murphy, G. L., and Medin, D. L. (1985). The role of theories in conceptual coherence. Psychological Review, 92, 289-316.
  • Seriès, P and Sprevak, M. (2015) From intelligent machines to the human brain. In Massimi, M., Carmel, D., Clark, A., Lavelle, J. S., Peacock, J., Prtichard, D., Richmond, A., Seriès, P, Smith, K. and Sprevak, M (eds.)Philosophy and the sciences for everyone. Abingdon, Oxon: Routledge. 
  • Sloman, S.A., & Lagnado, D. (2005). The problem of induction. In R. Morrison and K. Holyoak (Eds.). Cambridge Handbook of Thinking & Reasoning, New York: Cambridge University Press, pp. 95-116. 
  • Sober, E. (2009) Justified belief and Hume’s problem of induction. From Core questions in philosophy. Pearson.
  • Feldman, J. (2003) The simplicity principle in human concept learning. Current Directions in Psychological Science, 12(6), 227–232. 
  • Medin, D. & Smith, E. E. (1984) Concepts and Concept Formation. Annual Review of Psychology, 35, 113-138.
  • Fodor, J. (1996): The pet fish and the red herring: why concepts aren’t prototypes. Cognition, 58, 243—276. 
  • Shepard, R. (1987): Towards a universal law of generalization. Science, 237, 1317—1323. 
  • Jakel, F., Scholkopf, B., and Wichmann, F. (2008) Generalization and similarity in exemplar models of categorization: Insights from machine learning. Psychonomic Bulletin and Review, 15(2), 25—271. 
  • Quiroga, R. Q., Fried, I. and Koch, C. (2013). Brain cells for grandmother. Scientific American, 308, 30-35. 

 

Course policies

“Graduation insurance.” If this is your last semester and you need to pass this course in order to graduate on time, email me before the midterm. If you don’t ask before the midterm, “graduation insurance” will not be offered. 

Final grades are final. Once grades are assigned after the final exam, grade changes will not be considered. Don’t even ask. If you are having trouble with the material before that, email me or come to office hours. 

Academic integrity. Cheating and other violations of academic integrity will not be tolerated in this course. The university’s policy on academic integrity can be found here

Weather alerts. If there is bad weather (e.g., snow or threat of snow), class may be canceled, even if the university remains open. If so, I will send an email to the entire class via Sakai. So if you suspect bad weather, please check your email before coming to class. 

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