Section 7.1

• How plausible do you find the basic Bayesian idea that belief comes in degrees?
• How does Bayes’s Rule work?

 

Section 7.2
• What is the connection between Bayesianism and Gestalt approaches to perception?
• How does the Bayesian account of binocular rivalry work? How plausible do you find it?

 

Section 7.3
• What is the problem posed by the St Petersburg game? Is that problem solved by distinguishing between monetary value and utility?
• Do you think that rational decision-makers should always act to maximize expected utility?
• What are the key differences in experimental set-up between the Platt and Glimcher experiments for detecting neurons sensitive to probability and those for detecting neurons sensitive to utility/value?
• What do you think we should conclude from the final set of experiments described in the text?

Bayes’ theorem (video from YouTube)

Calculating conditional probability (video from Khan academy)

Bayesian models of perception, cognition and learning (lecture video by Máté Lengyel from Central Europe University)

 

7.1 Bayesianism: A Primer

Formal representations of belief (entry from Stanford Encyclopedia of Philosophy)

Bayes’ theorem (entry from Stanford Encyclopedia of Philosophy)

Belief and degrees of belief (book chapter by Huber, 2009, in Degrees of Belief)

 

7.2 Perception as a Bayesian Problem

Hermann von Helmholtz (entry from Stanford Encyclopedia of Philosophy)

Gestalt principles (entry from Scholarpedia)

Binocular rivalry (entry from Scholarpedia)

Perception as an inference problem (book chapter by Olshausen, 2014, in The Cognitive Neurosciences)

Perception as unconscious inference (book chapter by Hatfield, 2002, in Perception and the Physical World: Psychological and Philosophical Issue in Perception)

Bayesian models of perceptual organization (book chapter by Feldman, 2015, In Handbook of perceptual organization)

Bayesian learning theory applied to human cognition (paper by Jacobs and Kruschke, 2011, in WIREs Cognitive Science)

Object perception as Bayesian inference (paper by Kersten, Mamassian, and Yuille, 2004, in Annual Review of Psychology )

A Primer on Binocular Rivalry, Including Current Controversies (paper by Blake, 2001, in Brain and Mind)

Binocular rivalry: a window into cortical competition and suppression (review paper by Zhang, Engel, and Kay, 2017, in Journal of the Indian Institute of Science)

Attention and conscious perception in the hypothesis testing brain (paper by Hohwy, 2012, in Frontiers in Psychology)

 

7.3 Neuroeconomics: Bayes in the Brain

Normative Theories of Rational Choice: Expected Utility (entry from Stanford Encyclopedia of Philosophy)

The St. Petersburg paradox (entry from Stanford Encyclopedia of Philosophy)

Human saccadic eye movements (entry from Scholarpedia)

Decision-Making: A Neuroeconomic Perspective (paper by Hardy-Vallé, 2007, in Philosophy Compass)

Saccadic and smooth pursuit eye movements in the monkey (article by Fuchs, 1967, in the Journal of Physiology)

Expectations and outcomes: decision-making in the primate brain (paper by McCoy and Platt, 2005, in Journal of Comparative Physiology A)

Neural correlates of decision variables in parietal cortex (paper by Platt and Glimcher, 1999, in Nature)

The neurobiology of visual-saccadic decision making (paper by Glimcher, 2003, in Annual Review of Neuroscience)

Neuroeconomics: The Consilience of Brain and Decision (paper by Glimcher and Rustichini, 2004, in Science)

Cortical substrates for exploratory decisions in humans (paper by Daw et al., 2006, in Nature)

Neural circuitry of information seeking (paper by Bromberg-Martin and Monosov, 2020, in Current Opinion in Behavioral Sciences)

Melioration: a theory of distributed choice (paper by Herrnstein and Prelec, 1991, in Journal of Economic Perspectives)

Utility maximization and melioration: internalities in individual choice (paper by Herrnstein et al., 1993, in Journal of Behavioral Decision Making)