HLP Lab Meeting Schedule

Spring Semester '13

Wednesdays at 1:30pm

Week	Date	Topic
1	23 Jan 13	Organizational Meeting
2	30 Jan 13	What's new?, "How to be a successful grad student", How to choose a research project, OmniFocus for organization
3	06 Feb 13	Beck et al (not noisy just wrong) and Pellicano & Burr (bayesian autism).(Ting)
4	13 Feb 13	Lagnado & Sloman (time as a guide) and perhaps one more on that topic (Ting)
5	20 Feb 13	TBA
6	27 Feb 13	TBA
7	06 Mar 13	CUNY Dry Runs
8	13 Mar 13	Spring Break (No Meeting)
9	20 Mar 13	People gone for CUNY (No Meeting)
10	27 Mar 13	Bozena modeling work
11	03 Apr 13	TBA
12	10 Apr 13	TBA
13	17 Apr 13	TBA
14	24 Apr 13	TBA
15	01 May 13	TBA

Possible readings and topics

(Bozena) Alon (2009) - How To Choose a Good Scientific Problem Alon_2009.pdf
(Alex) hierarchical Bayesian models (what is a chinese restaurant process? what is an indian buffet?). This could (should) stay at a conceptual level, and address questions like
- what kinds of problems do these approaches lend themselves to?
- for these problems, what kinds of theoretical or computational approaches were people taking before these hierarchical models existed?
- similarly, what are the alternatives/objections to these models nowadays?
- how are hierarchical bayesian models related to/different from multi-level regression?
(Alex) what do people really mean when they say "learning"? (maybe dick could be consulted about this?)
- what is the difference between supervised and unsupervised learning?
- what sub-types of learning exist within these categories?
- large parts of the field seem to have essentially forgotten about "classical" approaches to learning (associative learning, conditioning, etc.)
(Alex) Dry run for CUNY talk? maybe me and chigusa on the same day.
(Bozena) For later in the semester (perhaps mid/late March or so) I would be interested in presenting some modeling work I've been doing.
(Bozena) If we have a meeting on the general topic of how to get organized, I could talk about the software I'm using, OmniFocus, which implements David Allen's "Getting things done" philosophy. I could talk a bit about the general philosophy, and then show how the software works. It'd probably take about 15-20 minutes
(Dan) I think tutorials about stats and modeling would be most useful for me. I would also be interested in learning about study design constraints/rules with methodologies that I have yet to use (self paced reading) and definitely corpus analysis tutorials.
(Chigusa) I would be interested in reading some papers on assumptions about sampling in learning (like Xu and Tenenbaum and also the following paper) SAMPLING ASSUMPTIONS IN LANGUAGE LEARNING Anne Hsu and Thomas Griffiths (submitted)
(Ting) I've recently come across some nice papers that might be of interest to the lab in general:
- Beck, J. M., Ma, W. J., Pitkow, X., Latham, P. E., & Pouget, A. (2012). Not noisy, just wrong: the role of suboptimal inference in behavioral variability. Neuron, 74(1), 30–9. doi:10.1016/j.neuron.2012.03.016
- Lagnado, D. a, & Sloman, S. a. (2006). Time as a guide to cause. Journal of experimental psychology. Learning, memory, and cognition, 32(3), 451–60. doi:10.1037/0278-7393.32.3.451
- Pellicano, E., & Burr, D. (2012). When the world becomes “too real”: a Bayesian explanation of autistic perception. Trends in cognitive sciences, 16(10), 504–10. doi:10.1016/j.tics.2012.08.009
- Cohen, J. D., McClure, S. M., & Yu, A. J. (2007). Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 362(1481), 933–42. doi:10.1098/rstb.2007.2098