格式塔心理学家假设人类基于启发式方法解决问题和感知对象。在20世纪初期，心理学家马克斯·韦特海默（Max Wertheimer）发现了人类将物体组合成图案的法则（例如，矩形形状的一组点）。今天最常研究的启发式算法是那些涉及决策的启发式算法。特沃斯基和卡尼曼1974年的着作“不确定性判断：启发式和偏见”引入了三个关键特征：代表性，锚定和调整以及可用性。在20世纪50年代，经济学家和政治学家赫伯特·西蒙发表了他的理性选择行为模型，该模型侧重于有限理性的概念：人们必须在有限的时间，心理资源和信息下做出决策的观点。 1974年，心理学家Amos Tversky和Daniel Kahneman指出了用于简化决策的特定心理过程。他们表明，人们在做出有关他们不确定的信息的决策时，依赖于一套有限的启发式方法 – 例如，在决定是否为现在的海外旅行或从今天开始的一周之后换钱。 Tversky和Kahneman也表明，尽管启发式算法很有用，但它们可能会导致思维错误，这些错误既可预测又不可预测。在20世纪90年代，关于启发式的研究，如Gerd Gigerenzer研究小组的工作所示，侧重于环境因素如何影响思维 – 特别是，思维所使用的策略受到环境的影响 – 而不是心灵的想法使用心理快捷方式来节省时间和精力。为了证明锚定和调整启发式，Tversky和Kahneman要求参与者估计非洲国家在联合国的百分比。他们发现，如果参与者作为问题的一部分得到初步估计（例如，实际百分比是高于还是低于65％？），他们的答案非常接近初始值，因此似乎是“锚定”的他们听到的第一个价值。为了解释具有启发性的代表性，特沃斯基和卡尼曼提供了一个名叫史蒂夫的个人的例子，他“非常害羞和退缩，总是乐于助人，但对人或现实几乎没有兴趣。一个温顺而又整洁的灵魂，他需要秩序和结构，以及对细节的热情。“史蒂夫在特定职业（例如图书管理员或医生）工作的概率是多少？研究人员得出结论，当被要求判断这种可能性时，个人会根据史蒂夫对给定职业的刻板印象的相似程度做出判断。代表性启发式允许人们基于对象与该类别的成员的相似程度来判断对象属于一般类别或类别的可能性。可用性启发式允许人们根据事件可以轻易想到的事件来评估事件发生的频率或发生的可能性。例如，有人可能会通过考虑他们认识的患有心脏病的人来估计有心脏病发作风险的中年人的百分比。 Tversky和Kahneman的研究结果促成了启发式和偏见研究计划的发展。锚定和调整启发式允许人们通过从初始值（“锚点”）开始并向上或向下调整该值来估计数字。但是，不同的初始值会导致不同的估计值，而这些估计值又会受到初始值的影响。
Gestalt psychologists postulated that humans solve problems and perceive objects based on heuristics. In the early 20th century, the psychologist Max Wertheimer identified laws by which humans group objects together into patterns (e.g. a cluster of dots in the shape of a rectangle). The heuristics most commonly studied today are those that deal with decision-making. Tversky and Kahneman’s 1974 work, Judgment under Uncertainty: Heuristics and Biases, introduced three key characteristics: representativeness, anchoring and adjustment, and availability. In the 1950s, economist and political scientist Herbert Simon published his A Behavioral Model of Rational Choice, which focused on the concept of on bounded rationality: the idea that people must make decisions with limited time, mental resources, and information. In 1974, psychologists Amos Tversky and Daniel Kahneman pinpointed specific mental processes used to simplify decision-making. They showed that humans rely on a limited set of heuristics when making decisions with information about which they are uncertain—for example, when deciding whether to exchange money for a trip overseas now or a week from today. Tversky and Kahneman also showed that, although heuristics are useful, they can lead to errors in thinking that are both predictable and unpredictable. In the 1990s, research on heuristics, as exemplified by the work of Gerd Gigerenzer’s research group, focused on how factors in the environment impact thinking–particularly, that the strategies the mind uses are influenced by the environment–rather than the idea that the mind uses mental shortcuts to save time and effort. To demonstrate the anchoring and adjustment heuristic, Tversky and Kahneman asked participants to estimate the percentage of African countries in the UN. They found that, if participants were given an initial estimate as part of the question (for example, is the real percentage higher or lower than 65%?), their answers were rather close to the initial value, thus seeming to be “anchored” to the first value they heard. To explain the representativeness heuristic, Tversky and Kahneman provided the example of an individual named Steve, who is “very shy and withdrawn, invariably helpful, but with little interest in people or reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail.” What is the probability that Steve works in a specific occupation (e.g. librarian or doctor)? The researchers concluded that, when asked to judge this probability, individuals would make their judgment based on how similar Steve seemed to the stereotype of the given occupation. The representativeness heuristic allows people to judge the likelihood that an object belongs in a general category or class based on how similar the object is to members of that category. The availability heuristic allows people to assess how often an event occurs or how likely it will occur, based on how easily that event can be brought to mind. For example, someone might estimate the percentage of middle-aged people at risk of a heart attack by thinking of the people they know who have had heart attacks. Tversky and Kahneman’s findings led to the development of the heuristics and biases research program. The anchoring and adjustment heuristic allows people to estimate a number by starting at an initial value (the “anchor”) and adjusting that value up or down. However, different initial values lead to different estimates, which are in turn influenced by the initial value.