COURSE SYLLABUS

G89.2223
Perception and Attention
Fall 2013

Tuesdays & Thursdays
10 am - 11:15 am
Room 851 Meyer Hall

Last updated: Dec 17, 2013


9/3, 9/10, 9/12, 9/17

no class on 9/5 (Rosh Hashanah)

Color: trichromacy, color opponency, & chromatic adaptation (Heeger)

Principles

Readings: Wandell Chs 1, 3, 4, & 9.

Lecture slides:
Color matching & trichromacy lecture slides (5.9MB pdf)
Color opponency & adaptation lecture slides (4.6MB pdf)

Lecture notes:
Lecture notes on color (from undergrad perception course)
Lecture notes on the retina (from undergrad perception course)

Color matching tutorial (100KB, zip archive of matlab code)

9/19, 9/24, 9/26, 10/1 Detection and signal detection theory (Landy)

Principles

Readings: Wandell App 3; Signal Detection Theory handout; Hecht et al. (1942); Geisler (1989).

Supplementary readings:

  • Parker A. J. & Newsome W. T. (1998). Sense and the single neuron: Probing the physiology of perception, Annu. Rev. Neurosci., 227-277.
  • Cornsweet, T. N. (1970). Visual Perception. New York: Academic Press (chs. 2-4).
  • Duda, R. O., Hart, P. E. & Stork, D. G. (2001). Pattern Classification. New York: Wiley (chs. 2-3).
  • Green, D. M. & Swets, J. A. (1966/1974) Signal Detection Theory and Psychophysics. New York: Robert E. Krieger.
  • Macmillan, N. A. & Creelman, C. D. (1991). Detection Theory: A User's Guide. New York: Cambridge.
  • Wickens, T. D. (2002). Elementary Signal Detection Theory. New York: Oxford.
  • Coombs, C. H., Dawes, R. M. & Tversky, A. (1970). Mathematical Psychology, An Elementary Introduction. Englewood Cliffs, NJ: Prentice-Hall (ch. 6).

Additional readings about retinal responses near absolute threshold: Field, Sampath & Rieke (2005); Chichilnisky & Rieke (2005).

Signal detection tutorial (zip archive of matlab code)

Lecture slides (5.2MB pdf)

10/3, 10/8, 10/10, and 10/24

no class on 10/15
Linear systems theory: spatial vision (Landy)

Spatial Vision (Landy)

Principles

Readings: Wandell Chs 2, 3, 5, 6, 7, 8, & App 1; Signals, Linear Systems, & Convolution Handout.

An interactive guide to the Fourier transform
Tutorial on complex numbers

Supplementary readings:

  • Blakemore C & Sutton P (1969). Size adaptation: A new aftereffect. Science, 166, 245-247.
  • Campbell FW & Gubisch RW (1966). Optical quality of the human eye. Journal of Physiology, 186, 558-578.
  • Campbell FW & Robson JG (1968). Application of Fourier analysis to the visibility of gratings. Journal of Physiology, 197, 551-566.
  • Graham N (1980). Spatial-frequency channels in human vision: Detecting edges without edge detectors. In Harris, C. (Ed.), Visual Coding and Adaptability (pp. 215-252). Hillsdale, NJ: Erlbaum.
  • Graham N & Nachmias J (1971). Detection of grating patterns containing two spatial frequencies: A comparison of single-channel and multiple-channels models. Vision Research, 11, 251-259.
  • Watson AB & Robson JG (1981). Discrimination at threshold: Labelled detectors in human vision. Vision Research, 21, 1115-1122.
  • Wilson HR, McFarlane DK & Phillips GC (1983). Spatial frequency tuning by orientation selective units estimated by oblique masking. Vision Research, 23, 873-882.
  • Bracewell RN (2003). Fourier Analysis and Imaging. New York: Kluwer/Plenum.

Lecture slides (11.8MB pdf)

Matlab tutorials (1.6MB zipped archive, requires matlabPyrTools).

10/17, 10/22

Linear systems theory: auditory channels (Poeppel)

Readings:

  • Moore (1989). An Introduction to the Psychology of Hearing (3rd edition), London: Academic Press (ch 3).
  • Rosen & Howell (1991). Signals and Systems for Speech and Hearing, London: Academic Press (excerpt from ch12).
  • Shannon et al (1995). Speech recognition with primarily temporal cues, Science, 270:303-304.

Lecture slides (13MB ppt)

10/29, 11/5, 11/12

no class on 10/31 or 11/7
Attention (Carrasco)

Readings: Carrasco (2011); Carrasco (2006); Lu & Dosher (2004); Dosher & Lu (2000); Carrasco & Yeshurun (1998); Wolfe (1998); Palmer (1995); Reynolds, Pasternak, & Desimone (2000).

Lecture slides:
Lecture slides - part 1 (7.7MB powerpoint)
Lecture slides - part 2 (1.7MB powerpoint)
Lecture slides - part 3 (2.9MB powerpoint)
Lecture slides - part 4 (22.1MB powerpoint)

11/14, 11/19, 11/21, 11/26
Visual Motion Perception (Heeger)

Principles

Readings: Wandell Ch 10 & App 5; Adelson & Bergen (1985); Adelson & Movshon (1982); Weiss, Simoncelli, & Adelson (2002).

Supplementary readings:

Lecture slides:
Motion intro lecture slides (2.3MB pdf)
Functional specialization lecture slides (5.7MB pdf)
Computational theory lecture slides (9.1MB pdf)

Lecture notes:
Lecture notes on motion (from undergrad perception course)
Lecture notes on the visual cortex (from undergrad perception course)

Matlab code:
Motion tutorial (160KB zipped archive, requires matlabPyrTools)
MT model (matlab code available for download)

11/28 No class (Thanksgiving)
12/3, 12/5
Cue combination and Bayesian decision theory (Maloney)

Principles

Readings: Landy, Banks & Knill (2011), pp. 1-10; Ernst & Banks (2002); Maloney & Zhang (2010); Najemnik & Geisler (2005); Stocker & Simoncelli (2006).

Supplementary readings: Saunders & Knill (2005); Hudson, Maloney & Landy (2008); Zhang, Morvan & Maloney (2010).

Books for background reading on Bayesian estimation and decision theory (optional): Leanard & Hsu, Bayesian Methods: An analysis for statisticians and interdisciplinary researchers; Sivia, Data Analysis: A Bayesian Tutorial.

Lecture slides: cue combination (1.4MB pdf)
Lecture slides: Bayesian decision theory (600KB pdf)

Lecture notes:
Lecture notes on depth (from undergrad perception course)

12/10, 12/12
Recognition (Pelli)

Readings: Rosch et al. (1976); Pelli & Tillman (2008) with supplementary material; Treisman & Kanwisher (1998); Ranzato, Huang, Boureeau, & LeCun (2007).

Lecture slides (17.3MB pdf)

FACULTY

Marisa Carrasco, Rm. 971, 8-8328
marisa.carrasco@nyu.edu
David J. Heeger, Rm. 963, 8-7868
david.heeger@nyu.edu
Michael Landy, Rm. 961, 8-7857
landy@nyu.edu
Laurence Maloney, Rm. 877, 8-7851
laurence.maloney@nyu.edu
Denis Pelli, Rm. 279, 646-258-7524
denis.pelli@nyu.edu
David Poeppel, Rm 281, 2-7489
david.poeppel@nyu.edu

Readings

Available online by following the links provided above.

Assignments

Submit each assignment by email to the instructors listed. Please submit a pdf file (only pdf files will be accepted - not MS Word documents), along with matlab *.m files when relevant.

Assignment 1 (Heeger & Landy): Color and signal detection theory (due 10/11)

For this assignment, you will need the color matching tutorial (100KB, zip archive), the rod spectral sensitivity (1KB, matlab file), the cone spectral sensitivities (1KB, matlab file), and also the signal detection tutorial (89KB, zip archive).

Assignment 2 (Carrasco & Landy): Spatial vision,  attention, and linear systems theory (due 11/22)

It is hypothesized that one of the effects of covert attention is to change the bandwidth of spatial frequency and/or orientation channels.
(1) Given that arguably the function of attention is to improve discrimination at the attended location, would it be in the observer's best interest to broaden or to narrow spatial frequency and/or orientation bandwidths at the attended location? Justify your choice with reference to a model of a particular task that would predict improved performance with the hypothesized change in bandwidth.
(2) Design a psychophysical experiment to test this hypothesis:
     (a) Describe the experimental design including what kind of attention (spatial or feature, endogenous or exogenous) you are manipulating and how you are doing so, the particular stimuli, task, and procedure.
     (b) Explain how you will analyze the data.
     (c) Describe the potential outcomes and how they will either prove or disprove the hypothesis.
     (d) Include description of relevant control experiments that indicate you can measure a bandwidth and you have changed performance with your attentional manipulation.
Extra credit. Matlab simulations are welcome (you could generate a simulated data set assuming that the hypothesis is true, another simulated data set assuming that it is false, and walk through the analysis, results, and interpretation for each).

Assignment 3 (Heeger): Motion (due 12/6)

Write an essay, approximately 5 pages, with references and optionally with figures.

It is hypothesized that neurons in the visual system exhibit both progressively more selectivity and more invariance across the hierarchy of visual cortical areas. Use cortical areas V1 and MT to illustrate this. Explain what the neurons in each cortical area are selective for, what they are not selective for, and how selectivity is quantified. Explain in what way the neural responses are invariant; with respect to what stimulus manipulations are the responses invariant versus not invariant, and how that can be quantified. Putting these things together, describe how selectivity and invariance both increase from V1 to MT. Specifically, describe a computational model for the transformation from the retinal image to neural responses in V1 and then MT, and explain how that model achieves the changes in selectivity and invariance.

Assignment 4 (Maloney): Cue combination and Bayesian decision theory (due 12/20)

Please submit to Prof. Maloney.


david.heeger@nyu.edu