1970s

The 1970s were the decisive decade in which research on judgment under uncertainty turned everyday intuition into a systematic object of scientific investigation. During this period, the study of fast mental operations, sampling errors, question substitution, availability, representativeness, and Bayesian reasoning began to reveal that ordinary thinking could be predictably wrong even when it was not distorted by strong emotion. The decade also supplied the intellectual setting for the later distinction between System 1 and System 2 and for a broader debate over when professional intuition deserves trust.

Intellectual Background

In the 1970s, social scientists broadly shared two assumptions about human nature: people were generally rational and their thinking was normally sound, while fear, affection, hatred, and other emotions explained most departures from rationality. Research during the decade challenged both ideas. Systematic errors appeared in the thinking of normal people, and the source of those errors was traced to the design of cognition rather than to emotional corruption. This shift made irrationality a property of ordinary mental machinery rather than an exceptional failure of character.

The decade’s research also exposed the limits of the idea that people are intuitive statisticians. Sampling variation was a practical nuisance for researchers, because even a true population difference could disappear or reverse in a small sample. A study comparing the vocabularies of six-year-old girls and boys could fail to confirm a true hypothesis simply through sampling luck; only a sufficiently large sample reduces that risk. Yet people, including sophisticated researchers, showed a weak intuitive grasp of sampling effects and routinely behaved as though small samples were more representative and stable than they are.

Research Collaboration and Heuristics

The collaboration between Daniel Kahneman and Amos Tversky began in the early 1970s with a dispute over whether untrained people were good intuitive statisticians. In 1971–72, they spent their most productive year in Eugene, Oregon, as guests of the Oregon Research Institute. Their host, Paul Slovic, was Amos Tversky’s former classmate at Ann Arbor and a leading scholar of risk. Alongside jogging, barbecues, and taking children to basketball games, the collaborators ran dozens of experiments and wrote articles on judgment heuristics.

Their work described intuitive judgment as an economical process that often substitutes an easier question for a difficult one. The target question is the assessment a person intends to make; the heuristic question is the simpler question answered instead. A difficult investment decision, for example, may be replaced by the easier question of whether the person likes Ford cars. The substitution usually happens without awareness, allowing a rapid answer to feel like a response to the original problem.

The research program became especially influential through its analysis of three heuristics. Representativeness was used when people judged whether an object or event belonged to a class or process. Availability was used when people assessed the frequency of a category or the plausibility of a development by retrieving instances or scenarios from memory. Adjustment from an anchor shaped numerical predictions when a relevant starting value was available. These shortcuts were economical and often effective, but they also produced systematic and predictable errors.

The availability heuristic received sustained attention in Eugene. It was defined as judging frequency by the ease with which instances come to mind. A category whose examples were retrieved fluently would be judged large, even when retrieval ease reflected publicity rather than actual frequency. The same logic could make political adultery seem more common than adultery among physicians or lawyers: journalists report politicians’ transgressions more readily, and repeated coverage makes those cases easier to recall. Media attention therefore helps determine which public issues feel important, while neglected subjects fade from awareness.

The period also produced the letter-position experiment. People found it easier to generate words beginning with a particular letter than words having that letter in the third position, so they were expected to exaggerate the frequency of initial letters. The bias applied even to K, L, N, R, and V, letters that in fact occur more often in the third position. Ease of retrieval thus became a source of confidence and error at the same time.

From Intuitive Errors to Two Systems

The distinction between fast and slow thinking developed from psychological work conducted during and after the 1970s. Fast thinking included automatic perception and memory as well as two forms of intuition: expert recognition and heuristic judgment. Slow thinking referred to the effortful, deliberate activity recruited when an intuitive solution failed or when a problem demanded complex computation.

The later language of System 1 and System 2 gave this distinction a memorable form. System 1 operates quickly and automatically, with little effort or voluntary control. System 2 allocates attention to demanding mental activities and is associated with agency, choice, and concentration. The systems are not literal agents or separate brain structures; they are shorthand for automatic operations and for increased arousal, focused attention, and deliberate processing.

In ordinary waking life, System 1 continuously generates impressions, intuitions, intentions, and feelings, while System 2 usually remains in a comfortable low-effort mode. When System 2 endorses System 1’s suggestions, impressions become beliefs and impulses become voluntary actions. Difficult multiplication, surprise, or the detection of an impending error can mobilize greater attention. The invisible gorilla experiment illustrates the limits of this arrangement: people concentrating on a task may miss an obvious event and remain initially certain that they could not have missed it. The mind can be blind to the obvious and blind to its own blindness.

Expert Intuition and Its Limits

The 1970s also supplied the background for a later confrontation between the heuristics-and-biases approach and Naturalistic Decision Making. Gary Klein’s early studies of fireground commanders suggested that experienced professionals could make good decisions without comparing a long list of alternatives. Commanders drew on patterns accumulated through more than a decade of real and virtual experience, generated a plausible option, mentally simulated whether it would work, modified it when necessary, and moved to another option only if the first could not be made acceptable.

This recognition-primed decision model joined automatic pattern recognition with deliberate mental simulation. Its credibility depended on conditions that were not present in every domain: the environment had to be sufficiently regular to be predictable, and the professional had to have prolonged opportunity to learn its regularities. A therapist might acquire skill in anticipating a patient’s next remark without acquiring an equally reliable ability to forecast the patient’s condition a year later. A financial expert might be skilled in many parts of the trade without being able to pick stocks, and a Middle East expert might understand the region without predicting its future.

The adversarial collaboration between Daniel Kahneman and Gary Klein eventually yielded a principle that links the decade’s competing traditions. Subjective confidence is not a reliable guide to validity, because cognitive ease and a coherent story can suppress doubt and ignore missing information. Trust in intuition should instead depend on its provenance: the regularity of the environment and the quality and duration of the opportunity to learn its cues. The 1970s therefore opened two complementary lines of inquiry—how intuition systematically fails and how, under disciplined conditions, it can become genuine expertise.

Lasting Significance

In the fifth year of the Daniel Kahneman–Amos Tversky collaboration, the principal findings were presented in Science in the article “Judgment Under Uncertainty: Heuristics and Biases.” The article described simplifying shortcuts and explained roughly twenty biases as manifestations and demonstrations of heuristic judgment. Its reception exceeded expectations; by 2010, more than three hundred scholarly articles had referred to it, and its ideas had been applied to medical diagnosis, legal judgment, intelligence analysis, philosophy, finance, statistics, and military strategy.

The decade’s legacy was not a claim that intuition is useless. It was a method for distinguishing rapid recognition from unsupported confidence, and for asking which question the mind actually answered. Bayesian reasoning supplied a discipline for correcting intuitive impressions: base rates must remain relevant even when vivid evidence is present, and the diagnosticity of evidence is often exaggerated. The 1970s thus transformed a broad philosophical concern about rationality into an experimentally grounded account of how normal minds simplify uncertainty, why those simplifications generate recurring mistakes, and when deliberate correction is necessary.