basic idea is to ask students how you might evaluate these inference models on data (i.e. building on BDA chapter). Some notes:
take a noisy-or (or sum) causal reasoning task... is it a fep/blicket? (note analogy to the initial A,B,C coin flipping example in algorithms chapter)
have fake 'dataset' of responses (e.g. 'yes'/'no' blicket) as well as RTs at different settings of base rates/number of choices (e.g. assume experimenter had a way of manipulating these)
part A: ask students to write linking function from inference model to these data
part B: ask students to fill in some code of a BDA model over inference algorithm and rejection sampling & enumeration (or MCMC?), changing base rate and number of choices
part C: infer different parameters for different participants?
part D: open ended question about "do you think any of these algorithms are a good description of how you intuitively solve this problem"
basic idea is to ask students how you might evaluate these inference models on data (i.e. building on BDA chapter). Some notes: