The term "confirmation bias" was coined by English psychologist Peter Wason. In his initial experiment published in 1960 (which does not mention the term "confirmation bias"), he challenged participants to identify a rule applying to triples of numbers. At the outset, they were told that (2,4,6) fits the rule. Participants could generate their own triples and the experimenter told them whether or not each triple conformed to the rule.While the actual rule was simply "any ascending sequence", the participants had a great deal of difficulty in finding it, often announcing rules that were far more specific, such as "the middle number is the average of the first and last". The participants seemed to test only positive examples—triples that obeyed their hypothesized rule. For example, if they thought the rule was, "Each number is two greater than its predecessor," they would offer a triple that fit this rule, such as (11,13,15) rather than a triple that violates it, such as (11,12,19).Wason accepted falsificationism, according to which a scientific test of a hypothesis is a serious attempt to falsify it. He interpreted his results as showing a preference for confirmation over falsification, hence the term "confirmation bias". Wason also used confirmation bias to explain the results of his selection task experiment. In this task, participants are given partial information about a set of objects, and have to specify what further information they would need to tell whether or not a conditional rule ("If A, then B") applies. It has been found repeatedly that people perform badly on various forms of this test, in most cases ignoring information that could potentially refute the rule.However, a 1987 paper by Klayman and Ha argued that the Wason experiments had not actually demonstrated a bias towards confirmation, but instead a tendency to make tests consistent with the working hypothesis. They called this the "positive test strategy". This strategy is an example of a heuristic: a reasoning shortcut that is imperfect but easy to compute. Klayman and Ha used Bayesian probability and information theory as their standard of hypothesis-testing, rather than the falsificationism used by Wason. According to these ideas, each answer to a question yields a different amount of information, which depends on the person's prior beliefs. Thus a scientific test of a hypothesis is one that is expected to produce the most information. Since the information content depends on initial probabilities, a positive test can either be highly informative or uninformative. Klayman and Ha argued that when people think about realistic problems, they are looking for a specific answer with a small initial probability. In this case, positive tests are usually more informative than negative tests. However, in Wason's rule discovery task the answer—three numbers in ascending order—is very broad, so positive tests are unlikely to yield informative answers. Klayman and Ha supported their analysis by citing an experiment that used the labels "DAX" and "MED" in place of "fits the rule" and "doesn't fit the rule". This avoided implying that the aim was to find a low-probability rule. Participants had much more success with this version of the experiment.In light of this and other critiques, the focus of research moved away from confirmation versus falsification of an hypothesis, and to examine whether people test hypotheses in an informative way, or an uninformative but positive way. The search for "true" confirmation bias led psychologists to look at a wider range of effects in how people process information.

Question: What produces information in differing amounts?
Answer: each answer to a question