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ELL788: Computational Perception and Cognition

If you are doing the course, please join the Piazza forum (the access code has been announced in class).

The subject matter of this course is, in a sense, highly ambitious: we seek to model and computationally emulate the human mind. This is essentially a Cognitive Science course, with a pronounced computational bent. Cognitive Science is a relatively new discipline which sits at the interface of psychology, neuroscience, linguistics, philosophy, computer science, and electrical engineering. It seeks to understand the processes underlying human cognition by means of formal (especially computational) models. In this course we will also look at specific engineering applications of such models, in areas like Computer Vision and Robotics. This course can be seen as trying to merge two distinct, but complementary, objectives: one an engineering objective, the other a scientific one. As engineers, we seek to design intelligent systems that can emulate human performance on perception and cognition tasks. As scientists, we seek to turn this around and use such designed systems to better understand, or 'reverse engineer', the human mind itself.

Instructors: Hiranmay Ghosh and Sumeet Agarwal
3 credits (3-0-0)
I Semester 2015–16
M Th 17:00–18:20, IIA 204 (Bharti building)

Evaluation components

References

  1. Jay Friedenberg and Gordon Silverman. Cognitive Science: An Introduction to the Study of Mind. SAGE Publications, 2006.
  2. E. Bruce Goldstein. Sensation and Perception. Wadsworth, 8th Edition, 2010.
  3. José Luis Bermúdez. Cognitive Science: An Introduction to the Science of the Mind. Cambridge, 2010.

Planned schedule

Serial no. Lecture nos. Topics Instructor Slides
1 1–3 Introduction; Philosophical, Psychological, and Cognitive approaches to modeling the mind SA Introduction and Philosophical Perspectives
2 4 Neuroscientific foundations Tapan Gandhi
3 5–8 Perception (uni- and multi-modal): Vision, hearing, taste, touch, olfaction HG Understanding Mind; Visual Perception I; Visual Perception II; Auditory Perception; Cutaneous Perception
4 9–12 Cognitive models and Bayesian inferencing SA Bayesian models of cognition
5 13–14 Higher order perception like object and pattern recognition (biological and computational perspectives) SA Unsupervised learning; Higher-order perception
6 15–16 Visual perception of 3D space & scene; Perceptual processes for object recognition & memorization HG Perceiving visual space; Remembering visual space; Role of context in object detection
7 17 Student presentations on term paper proposals —
8 18–19 Computational models of attention HG Visual Attention; Audio and Multimodal Attention
9 20–22 Computational psycholinguistics SA; Samar Husain; Amitabha Mukerjee Language and Cognitive Science; Language and Cognition
10 23, 27 Applications: Audio quality assessment, compression & indexing HG Audio Engineering: Quality Assessment; Audio Engineering: Spatial Audio; Audio Engineering: Perceptual coding
11 24–26 Cognitive architectures; Cognitive robotics and embodied cognition SA How are cognitive systems organised?
12 28 Applications: Image quality assessment, compression, Haptic interfaces HG Memorability of images; Haptic/tactile displays

[Image credits: directactioneverywhere.com; mcgill.ca; edx.org; wikipedia.org; nu.ac.th; binghamton.edu.]

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