PSYC 80103 Cognitive Technologies Spring 2018

Overview

This is the course website for PSYC 80103, Cognitive Technologies: From Theory and Data to Application, Spring 2018. Check this site for regular updates to course information.

Professor: Dr. Matthew Crump

  • Lecture Friday, Time: 11:45am-1:45pm, Room: 6114 @ GC
  • Office: 4307 James Hall
  • Email: mcrump@brooklyn.cuny.edu
  • Office hours: by appointment
# Lecture Date Topic Assignment
1 2-Feb Overview tbd
2 9-Feb Speeding Reading  
3 16-Feb Instance Theory  
4 23-Feb Semantic Models: LSA & BEAGLE  
5 2-Mar Semantic Models: Applications  
6 9-Mar Literature Review  
7 16-Mar Computational Classification Techniques  
8 23-Mar Object Recognition, Decoding Brain states  
9 W,11-Apr Brain Training  
10 13-Apr Appifying Education  
11 20-Apr Lie Detection  
12 27-Apr Augmented Reality, Smart Spaces  
13 4-May Cognition on Drugs  
14 11-May Big Data  
15 TBD Final Paper due  

Required Readings

NOTE: Check Blackboard for a link to most of these papers. I’ve put them all into a Zotero repository (.rdf file). If you have not already done so, download Zotero. Then, download the .rdf file from blackboard and load it from Zotero. All of the .pdfs should now appear in a new Zotero folder.

1. Overview

2. Speeding Reading

Written language is a technology people have developed to communicate ideas. What are the cognitive limits to the process of reading? Can they be expanded with new technology?

  • Rayner, K., Schotter, E. R., Masson, M. E., Potter, M. C., & Treiman, R. (2016). So Much to Read, So Little Time How Do We Read, and Can Speed Reading Help? Psychological Science in the Public Interest, 17, 4–34.

  • Balota, D. A. (2016). Speed Reading: You Can’t Always Get What You Want, but Can You Sometimes Get What You Need? Psychological Science in the Public Interest, 17, 1–3.

  • Benedetto, S., Carbone, A., Pedrotti, M., Le Fevre, K., Bey, L. A. Y., & Baccino, T. (2015). Rapid serial visual presentation in reading: The case of Spritz. Computers in Human Behavior, 45, 352–358.

3. Instance Theory

We look at cognitive theories of memory proposing the instance (episodic representation) as a fundamental unit of cognition. This will set the stage for our following discussions of computational techniques for pattern classification that have been applied to solving problems in numerous domains.

- Jacoby, L. L., & Brooks, L. R. (1984). Nonanalytic cognition: Memory, perception, and concept learning. Psychology of learning and motivation, 18, 1-47.
- Kolers, P. A., & Roediger, H. L. (1984). Procedures of mind. Journal of Verbal Learning and Verbal Behavior, 23, 425–449.
- Hintzman, D. (1986). Schema abstraction in a multiple-trace memory model. Psychological Review, 93, 411–428.

4. Semantic Models: LSA and BEAGLE

Relatively simple learning models trained on large corpuses of texts (e.g., digitized books) can become sensitive to the semantic meaning of words. These models project words into a geometric space, such that the distance between words represents their semantic similarity. The models offer both a theory of human semantics, and technology for semantics processing and classification.

  • Jones, M. N., & Mewhort, D. J. (2007). Representing word meaning and order information in a composite holographic lexicon. Psychological Review, 114, 1.
  • Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse Processes, 25, 259–284.

5. Semantic Models: Some Applications

Relatively simple learning models trained on large corpuses of texts (e.g., digitized books) can become sensitive to the semantic meaning of words. These models project words into a geometric space, such that the distance between words represents their semantic similarity. The models offer both a theory of human semantics, and technology for semantics processing and classification.

  • Johns, B. T., Taler, V., Pisoni, D. B., Farlow, M. R., Hake, A. M., Kareken, D. A., … Johns, B. (2017). Cognitive Modeling as an Interface Between Brain and Behavior: Measuring the Semantic Decline in Mild Cognitive Impairment. Canadian Journal of Experimental Psychology= Revue Canadienne de Psychologie Experimentale. Retrieved from http://btjohns.com/pubs/JTPFHKUJ_CJEP_2017.pdf
  • Guest Speaker: Dr. Randall Jamieson. University of Manitoba.

6. Literature Review Assignment

7. Computational Classification Techniques

We do a short primer on neural networks and computational pattern classification techniques.

### Links

https://jalammar.github.io/visual-interactive-guide-basics-neural-networks/

8. Object Recognition systems, Decoding Brain states

Computer algorithms can now classify and identify many kinds of objects and patterns, which was a difficult problem only a number of years ago. We look at some examples.

-Readings TBD

The same pattern classification algorithms that can be used to classify faces or other objects, can also be used to classify and decode brain states. We look at a few compelling examples.

  • Horikawa, T., Tamaki, M., Miyawaki, Y., & Kamitani, Y. (2013). Neural decoding of visual imagery during sleep. Science, 340, 639–642.
  • Cowen, A. S., Chun, M. M., & Kuhl, B. A. (2014). Neural portraits of perception: reconstructing face images from evoked brain activity. Neuroimage, 94, 12–22.

9. Brain Training

Numerous websites and apps claim that users can enhance their cognitive skills by playing brain games. What does the data say?

  • Simons, D. J., Boot, W. R., Charness, N., Gathercole, S. E., Chabris, C. F., Hambrick, D. Z., & Stine-Morrow, E. A. L. (2016). Do “Brain-Training” Programs Work? Psychological Science in the Public Interest, 17, 103–186.

  • McCabe, J. A., Redick, T. S., & Engle, R. W. (2016). Brain-Training Pessimism, but Applied-Memory Optimism. Psychological Science in the Public Interest, 17, 187–191.

10. Appifying Education

How are cognitive principles and new tech being used to create improved educational resources and experiences?

  • Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving Students’ Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology. Psychological Science in the Public Interest, 14, 4–58.

  • Hirsh-Pasek, K., Zosh, J. M., Golinkoff, R. M., Gray, J. H., Robb, M. B., & Kaufman, J. (2015). Putting Education in “Educational” Apps: Lessons From the Science of Learning. Psychological Science in the Public Interest, 16, 3–34.

  • Roediger, H. L. (2013). Applying Cognitive Psychology to Education: Translational Educational Science. Psychological Science in the Public Interest, 14, 1–3.

  • Wartella, E. (2015). Educational Apps: What We Do and Do Not Know. Psychological Science in the Public Interest, 16, 1–2.

11. Detecting Liars

  • Loftus, E. F. (2010). Catching Liars. Psychological Science in the Public Interest, 11, 87–88.

  • Vrij, A., Granhag, P. A., & Porter, S. (2010). Pitfalls and Opportunities in Nonverbal and Verbal Lie Detection. Psychological Science in the Public Interest, 11, 89–121.

12. Augmented Reality & Smart Spaces

3-D headsets, google glasses, new phones, and more are capable of overlaying computer generated visuals on top of normal world as we see it. Is there any evidence this tech can augment cognition?

  • assigned reading TBD

The objects and spaces in our lives are increasingly infused with computers and internet connections. What are the opportunities and consequences for cognition?

  • assigned reading TBD

13. Cognition on drugs

What does current drug research tell us about enhancing aspects of cognition?

  • Mehlman, M. J. (2004). Cognition‐Enhancing Drugs. The milbank quarterly, 82(3), 483-506.
  • Greely, H., Sahakian, B., Harris, J., Kessler, R. C., Gazzaniga, M., Campbell, P., & Farah, M. J. (2008). Towards responsible use of cognitive-enhancing drugs by the healthy. Nature, 456(7223), 702-705.

14. Big Data

What is the intersection between cognitive research and the growing interest in big data?

-assigned reading TBD