Which would you prefer, an option offering a 70% chance of winning $1 or an option offering a 30% chance of winning $1? it may seem difficult to imagine circumstances under which people would systematically choose the lower-probability option. yet when that choice is repeatedly presented in a sequence of decisions, people do. suppose, for instance, that a 10-sided die will be rolled 10 times. the die has 7 green sides and 3 red sides. you are to predict, for each roll, whether a green or a red side will come up, and for each correct predic- tion you will be paid $1. many people, when faced with this binary prediction task, predict green for 7 rolls and predict red for the other 3 rolls (e.g., koehler & james, 2010). this phe- nomenon, called probability matching, is a robust finding that dates back to the probability-learning literature of the 1950s and 1960s. each time a probability matcher chooses to bet on red rather than on green, he or she is effectively choosing an inferior gamble offering a 30% chance of winning $1 over a superior gamble offering a 70% chance of winning $1. probability matching represents a long-standing puzzle in the field of decision making, because an alternative prediction strategy (referred to as maximizing ), in which the individual in our example would predict green on all 10 rolls, offers supe- rior expected returns. understanding why probability match- ing occurs is important because it is not readily accommodated even by prominent psychological models of decision making, such as prospect theory (kahneman & tversky, 1979), that share with the normative economic model the assumption that people will choose a gamble offering a higher probability of winning over an otherwise equivalent (and equivalently framed) gamble that offers a lower probability of winning. in a previous article, we argued that probability matching arises from an asymmetry in the availability of alternative pre- diction strategies: the probability-matching strategy comes to mind more readily than does the superior maximizing strategy (koehler & james, 2010). consistent with this claim, the results of our previous experiments showed that when both strategies were described to participants and thereby equated in availability, the maximizing strategy was used more fre- quently than when it had not been described. but why is the probability-matching strategy more avail- able in memory in the first place? we suggested that the gen- eration of expectations, a generally adaptive cognitive function (e.g., bar, 2007), may be the culprit (koehler and james, 2009). in the 10-sided-die problem, when thinking about the series of rolls as a whole, an expectation of 7 green outcomes and 3 red outcomes is readily evoked. the availability of this expectation, in turn, is hypothesized to make salient a