Human estimation cue Integration 16.422 Humans as intuitive statisticians Good at estimating means, reasonably good at mid-range roportions · Not good on the tails Not so good at estimating variances and correlations Also not good at extrapolating non-linear trends Underestimate exponential growth Cue assimilation issues Missing Information overload Salience Underestimate cues that require calculation The need for heuristics
Human Estimation & Cue Integration 16.422 • Humans as intuitive statisticians – Good at estimating means, reasonably good at mid-range proportions • Not good on the tails – Not so good at estimating variances and correlations – Also not good at extrapolating non-linear trends • Underestimate exponential growth • Cue assimilation issues – Missing – Information overload – S a l i e n c e • Underestimate cues that requi re calculation – The need for heuristics
As-if heuristic 16.422 Cues are equally weighted and differential weights are not considered Regression to the mean Reliability of cues Letters of recommendation -content v tone Humans are poor intuitive or clinical predictors as compared to computers Multiple cues of different information value Cognitive parsimony Humans tend to reduce load on working memory Avoid processing of cues that require mental calculatⅰon
As-if Heuristic 16.422 • Cues are equally weighted and differential weights are not considered – Regression to the mean – Reliability of cues – Letters of recommendation – content v. tone • Humans are poor intuitive or clinical predictors as compared to computers – Multiple cues of different information value • Cognitive parsimony – Humans tend to reduce load on working memory. – Avoid processing of cues that require mental calculation