Chater N, et al.
Current Directions in Psychological Science.
In Bayesian cognitive science, the mind is seen as a spectacular probabilistic-inference machine. But judgment and decision-making (JDM) researchers have spent half a century uncovering how dramatically and systematically people depart from rational norms. In this article, we outline recent research that opens up the possibility of an unexpected reconciliation. The key hypothesis is that the brain neither represents nor calculates with probabilities but approximates probabilistic calculations by drawing samples from memory or mental simulation. Sampling models diverge from perfect probabilistic calculations in ways that capture many classic JDM findings, which offers the hope of an integrated explanation of classic heuristics and biases, including availability, representativeness, and anchoring and adjustment.
Human probabilistic reasoning gets bad press. Decades of brilliant experiments, most notably by Daniel Kahneman and Amos Tversky (e.g., Kahneman, 2011; Kahneman, Slovic, & Tversky, 1982), have shown a plethora of ways in which people get into a terrible muddle when wondering how probable things are. Every psychologist has learned about anchoring, conservatism, the representativeness heuristic, and many other ways that people reveal their probabilistic incompetence. Creating probability theory in the first place was incredibly challenging, exercising great mathematical minds over several centuries (Hacking, 1990). Probabilistic reasoning is hard, and perhaps it should not be surprising that people often do it badly. This view is the starting point for the whole field of judgment and decision-making (JDM) and its cousin, behavioral economics.
Oddly, though, human probabilistic reasoning equally often gets good press. Indeed, many psychologists, neuroscientists, and artificial-intelligence researchers believe that probabilistic reasoning is, in fact, the secret of human intelligence.