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相关概念视频

The Availability Heuristic01:08

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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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Heuristics01:21

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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Problem-Solving01:29

Problem-Solving

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Effective problem-solving consists of two steps: 1. identifying the problem and 2. selecting the appropriate problem-solving strategy (i.e., a plan of action used to find a solution). Humans use four problem-solving strategies:
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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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相关实验视频

Updated: May 16, 2025

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal
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探索任务设计和启发式的交互通过一个无知识的选项生成任务的任务设计.

Yanlin Chen1, Yuen-Siang Ang1,2,3,4, Cristina Cusin4,5

  • 1Social and Cognitive Computing Department, Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.

Scandinavian journal of psychology
|April 1, 2025
PubMed
概括
此摘要是机器生成的。

选项生成研究表明,生成更多的选项并不总是提高质量. 观察到"少即多"效应,即流利性增加与独特性和多样性负相关,与"数量-品种-质量"预测相矛盾.

关键词:
创造力 创造力 创造力流动性的流性期权产生的可能是选择.

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科学领域:

  • 认知心理学 认知心理学
  • 决策科学 决策科学 决策科学

背景情况:

  • 选项生成研究传统上侧重于产生的选项的数量和质量.
  • 现有的研究使用不同的任务设计,复杂的比较和启发式应用.
  • 选项生成中的关键启发式包括"数量-品种-质量"",少-是-更多"和"首先选择" (TTF).

研究的目的:

  • 分析选项生成使用一种文化自由,教育独立的任务.
  • 将经验结果与"数量-品种-质量"",少-是-更多"和TTF启发式的预测进行比较.
  • 调查任务特征如何影响启发式适用性,并探索与现实生活决策的差异.

主要方法:

  • 从量化选项生成任务中获得的数据的二次分析,其中包括在触摸屏上找到路径.
  • 纳入健康人群 (44) 和患有重度抑郁症的患者 (54),以增强生态有效性.
  • 使用流性,独特性和多样性指标量化生成的选项.

主要成果:

  • 对于健康群体和MDD群体来说,提高流性与平均独特性,最大独特性和多样性负相关,支持"少即多"效应.
  • 这些发现与"数量-品种-质量"效应的预测相矛盾.
  • 规范路径独特性随路径指数的下降而下降,这与TTF启发式的预测相反.

结论:

  • 在这个特定的任务环境中",少是多"效应似乎比"数量-品种-质量"更适用.
  • TTF启发式的预测没有得到支持,这表明任务设计会影响启发式的坚持.
  • 任务特征显著影响哪些启发式的相关性,突出控制任务和现实世界的决策之间的分歧.