Two separate groups of medical residents each see a standardized patient (an actor trained to present symptoms to doctors). The patient reports five symptoms: headache, cough, ringing in the ears, dizziness, and love for cognitive science. These symptoms are typical of cogscitis, though some other disorders can also cause these symptoms. Group A of residents is told cogscitis is very rare in this patient group, while Group B is told it is very common. Both groups of residents say that the probability that the patient has cogscitis, given the observed symptoms, is 0.75. What might Kahneman say is going on here? The medical residents in both groups correctly used Bayesian inference to diagnose the patient. The medical residents in both groups correctly used the Q learning algorithm to diagnose the patient. The medical residents in Group B were responding to framing effects. The medical residents in Group A likely fell prey to base rate neglect, which often results from using the representativeness heuristic.
Added by Michael H.
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The scenario involves two groups of medical residents diagnosing a standardized patient with a set of symptoms that are indicative of a condition called cogscitis. However, one group is informed that cogscitis is rare, while the other group is told it is common in Show more…
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