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Related Concept Videos

Cognitive Learning01:21

Cognitive Learning

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.
Tolman introduced the idea that behavior is influenced by...
Inductive Reasoning00:59

Inductive Reasoning

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
Reason and Intuition01:37

Reason and Intuition

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 brain can only use...
Reasoning01:30

Reasoning

Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
Deductive Reasoning01:16

Deductive Reasoning

Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction from inductive reasoning. It uses a general principle or law to predict specific results. From these general principles, a scientist can predict specific results that remain valid as long as the general principles are correct.For example, a researcher can make specific predictions from the hypothesis "butterflies are attracted...
Purposive Learning01:22

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a bonus...

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Related Experiment Video

Updated: Jun 6, 2026

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

Causal learning and inference as a rational process: the new synthesis.

Keith J Holyoak1, Patricia W Cheng

  • 1Department of Psychology, University of California, Los Angeles, Los Angeles, California 90095-1563, USA. holyoak@lifesci.ucla.edu

Annual Review of Psychology
|December 4, 2010
PubMed
Summary

Humans infer causal relationships using rational, Bayesian probabilistic inference, moving beyond simple associative learning. This causal framework accurately predicts judgments of causal strength and structure.

Related Experiment Videos

Last Updated: Jun 6, 2026

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

Area of Science:

  • Cognitive Science
  • Psychology
  • Artificial Intelligence

Background:

  • Human causal learning research has shifted towards integrating causal models with Bayesian inference.
  • Traditional associative learning models focus on observed events, omitting unobservable distal causal relations.

Purpose of the Study:

  • To describe the synthesis of causal models and Bayesian inference for human causal learning.
  • To review empirical evidence supporting the causal framework over associative alternatives.

Main Methods:

  • Utilizing Bayesian inference over causal strength and structure distributions.
  • Employing noisy-logical functions to model the integration of multiple causes.

Main Results:

  • Accurate prediction of human judgments regarding causal strength and structure in simple causal systems.
  • Dynamic learning models based on the causal framework explain acquisition patterns and align with neuroimaging data.

Conclusions:

  • The causal framework provides a robust explanation for human causal learning and inference.
  • This approach extends to various inductive tasks, including category-based and analogical reasoning.