Readings for reverb inference project

**Reverb perception (Traer & McDermott, 2016): natural sound statistics & psychophysics that we're going to model

Reviews on perception as Bayesian inference**

Bayesian models of object perception (Kersten & Yuille, 2003): intro to Bayes and natural scene statistics
Pattern inference theory (Kersten & Schrater, 2001): one of the most clear & comprehensive expositions of this approach
The Role of Generative Knowledge in Object Perception (Battaglia et al., 2011): introduction to generative models in perception with reference to research example
Perception of Shading and Reflectance (Adelson & Pentland, 1996): not explicitly Bayesian, but gives a good intuition of thinking of perception as finding good explanations for sensory data

Modelling perception as Bayesian inference*

Bloj, Kersten, & Hurlbert, 1999: Perception of three-dimensional shape influences colour perception through mutual illumination
Weiss et al., 2002: Motion illusions as optimal percepts (in his thesis, he deals with the motion of multiple objects and elaborates the prior)
Freeman, 1994: The generic viewpoint assumption in a framework for visual perception (marginalizing out secondary variables)
Geisler et al. (2001): Edge co-occurrence in natural images predicts contour grouping performance -- this applies Bayesian modelling differently than the others (and us), but is good to know about

Bayesian probability and inference

Machine Learning: A Probabilistic Perspective (Murphy): Chapter 2 on probability basics, Chapter 3 has simple examples of applying Bayes
Techniques in Artificial Intelligence OCW: Lecture 14 for probability basics, Lecture 15 for Bayes nets (aka graphical models)
Chp. 14 in Artifical Intelligence: A Modern Approach (Russell & Norvig): Bayes nets and MCMC
Chp. 1 in MCMC in Practice (eds. Gilks, Richardson, & Spiegelhalter): technical intro to MCMC (not important right now)
Chp. 29 in Information Theory, Inference and Learning Algorithms (MacKay): inference algorithms for generative models (more technical, not at all necessary right now)
Probabilistic Models of Cognition workbook*

General perception papers

Perception across species: additional examples of ill-posed problems across species
Chapter 1 of 'Vision' by David Marr: roots of the MIT approach to perception and cognition. One would probably pin this project at the "computational level"

Auditory perception

In lab: Speech and Audio Signal Processing, by Gold, Morgan & Ellis (2011): basics of acoustics (pg141-189).
In lab: Signals, Sound and Sensation by Hartmann (1998)
General introduction to hearing (McDermott, 2013)
James' presentation on acoustics