Some Bayesian approaches to replication analysis and planning (talk video)

Some Bayesian approaches to replication analysis and planning. A talk presented at the Association for Psychological Science, Friday May 27, 2016. This video (below) is a pre-recording while sitting at my desk; the actual talk included some spontaneous additions about relations of the material to previous speakers' talks, and a few attempts at you-had-to-be-there humor.

If you have comments or questions about the talk, please post them with the video on YouTube, not here.

Here's a snapshot of the speakers:

Left to right: Joe Rodgers, John Kruschke, Larry Hedges, Pat Shrout (symposium organizer), and Scott Maxwell.

Bayesian inference in the (abnormal) mind

The (abnormal) mind can be modeled as a Bayesian inference engine, as summarized in the post, Bayesian reasoning implicated in some mental disorders. Excerpt:

“The brain is a guessing machine [i.e., Bayesian inference engine - JKK], trying at each moment of time to guess what is out there,” says computational neuroscientist Peggy Seriès. Guesses just slightly off — like mistaking a smile for a smirk — rarely cause harm. But guessing gone seriously awry may play a part in mental illnesses such as schizophrenia, autism and even anxiety disorders, Seriès and other neuroscientists suspect. They say that a mathematical expression known as Bayes’ theorem — which quantifies how prior expectations can be combined with current evidence — may provide novel insights into pernicious mental problems that have so far defied explanation.

For a tutorial about Bayesian models of perception and cognition, see Bayesian learning theory applied to human cognition.

Note that Bayesian modeling of data is a richly valuable approach regardless of whether any particular Bayesian model of mind is accurate. See this brief blog post for the distinction between (Bayesian) descriptive models of data, psychometric models, and models of mind.