Numerous authors (e.g., Popper, 1959) argue that scientists should try to falsify rather than confirm theories. However, recent empirical work (Wason and Johnson-Laird, 1972) suggests the existence of a confirmation bias, at least on abstract problems. Using a more realistic, computer controlled environment modeled after a real research setting, subjects in this study first formulated hypotheses about the laws governing events occurring in the environment. They then chose between pairs of environments in which they could: (I) make observations which would probably confirm these hypotheses, or (2) test alternative hypotheses. Strong evidence for a confirmation bias involving failure to choose environments allowing tests of alternative hypotheses was found. However, when subjects did obtain explicit falsifying information, they used this information to reject incorrect hypotheses.

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Recent studies of the mathematical relationship between time and forgetting suggest that it is a power function rather than an exponential function, a finding that has important theoretical consequences. Through computational analysis and reanalyses of published data, we demonstrate that arithmetic averaging of exponential curves can produce an artifactual power curve, particularly when there are large and systematic differences among the slopes of the component curves. A series of simulations showed that the amount of power artifact is small when the slopes of the component curves are normally or rectangularly distributed and when the performance measure is noise free. However, the simulations also showed that the artifact can be quite large, depending on the shape of the noise distribution and restrictions in the performance range. We conclude that claims concerning the form of memory functions should consider whether the data are likely to contain artifact caused by averaging or by the presence of range-restricted noise.

It has long been known that subjects in certain inference tasks will seek evidence which can confirm their present hypotheses, even in situations where disconfirmatory evidence could be more informative. We sought to alter this tendency in a series of experiments which employed a rule discovery task, the 2-4-6 problem first described by Wason. The first experiment instructionally modified subjects confirmatory tendencies. While a disconfirmatory strategy was easily induced, it did not lead to greater efficiency in discovering the rule. The second experiment introduced subjects to the possibility of disconfirmation only after they had developed a strongly held hypothesis through the use of confirmatory evidence. This manipulation also failed to alter the efficiency of rule discovery. In the third experiment, subjects were taught to use multiple hypotheses at each step, in the manner of Platt's “Strong Inference”. This operation actually worsened performance. Finally, in the fourth experiment, the structure of the problem was altered slightly by asking subjects to seek two interrelated rules. A dramatic increase in performance resulted, perhaps because information which in previous tasks was seen as merely erroneous could now be related to an alternative rule. The four studies have broad implications for the psychological study of inference processes in general, and for the study of scientific inference in particular.

Advanced undergraduate science majors attempted for approximately 10h each to discover the laws governing a dynamic system. The system included 27 fixed objects, some of which influenced the direction of a moving particle. At a given time, any one screen of a nine-screen matrix could be observed on a plasma display screen. Confirmatory strategies were the rule, even though half the subjects had been carefully instructed in strong inference. Falsification was counterproductive for some subjects. It seems that a firm base of inductive generalizations, supported by confirmatory research, is a prerequisite to useful implementation of a falsification strategy.