In two experiments we manipulated the prior probabilityof occurrence for two alternatives. After a first learningsession, in a second session the cue to bias the decisionwas reversed. Our investigation shows that subjects areable to learn the reverse bias only when the bias of the firstsession is in line with their expected outcome. When, dur-ing the first session, the actual outcome of the bias is notin line with the expected outcome, there is an inhibitionfor the reversal bias learning in the second session. Weinvestigate this phenomenon with computational modelsof choice showing that the inhibition of reversal is due toan increase in the rate at which subjects accumulate evi-dence for repeated, unexpected stimuli. We discuss a pos-sible theoretical explanation that links this phenomenonto similar results found in the literature on reversal learn-ing and to the effect of novelty on learning.

Frequently in psychology, simple tasks that are designed to tap a particular feature of cognition are used without considering the other mechanisms that might be at play. For example, the delayed-match-to-sample (DMTS) task is often used to examine short-term memory; however, a number of cognitive mechanisms interact to produce the observed behaviour, such as decision-making and attention processes. As these simple tasks form the basis of more complex psychological experiments and theories, it is critical to understand what strategies might be producing the recorded behaviour. The current paper uses the GEMS methodology, a system that generates models of cognition using genetic programming, and applies it to differing DMTS experimental conditions. We investigate the strategies that participants might be using, while looking at similarities and differences in strategy depending on task variations; in this case, changes to the interval between study and recall affected the strategies used by the generated models.

From an evolutionary perspective, it has been proposed that decision making should be sensitive to the overallmagnitude of the alternatives under consideration in order to resolve costly deadlocks and thus improve long-term rewardintake. We provide initial evidence that the overall magnitude of the alternatives affects decision making, by speeding updecision time in order to maximise a speed-value trade off. Implications for current computational models of decision making,in particular for the Drift Diffusion Model, are discussed.

Humans regularly invest time towards activities characterized by dramatic success or failure outcomes, where criti-cally, the outcome is uncertain ex-ante. How should people allocate time between such make-or-break activities and other safealternatives, where rewards are more predictable (e.g., linear) functions of time? We present a formal framework for studyingtime allocation between these two types of activities, and explore (optimal) behavior in both one-shot and dynamic versions ofthe problem. In the one-shot version, we illustrate the striking discontinuous relation between peoples skill and optimal timeallocation to the make-or-break task. In the dynamic version, we formulate both fully rational and boundedly rational strategies,both defined by a giving up threshold, which adaptively dictates when one should cease pursuit of the make- or-break goal.Comparing strategies across environments, we investigate the cost of sidestepping the computational burden of full rationality.