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Related Concept Videos

Cause and Effect01:53

Cause and Effect

While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
The Representativeness Heuristic02:13

The Representativeness Heuristic

The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
Stereotype Threat and Self-fulfilling Prophecies02:09

Stereotype Threat and Self-fulfilling Prophecies

When we hold a stereotype about a person, we have expectations that he or she will fulfill that stereotype. A self-fulfilling prophecy is an expectation held by a person that alters his or her behavior in a way that tends to make it true. When we hold stereotypes about a person, we tend to treat the person according to our expectations. This treatment can influence the person to act according to our stereotypic expectations, thus confirming our stereotypic beliefs. Research by Rosenthal and...
Hindsight Biases01:12

Hindsight Biases

Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?
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 3, 2026

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Can semi-supervised learning explain incorrect beliefs about categories?

Charles W Kalish1, Timothy T Rogers, Jonathan Lang

  • 1Department of Educational Science, University of Wisconsin-Madison, 1025 West Johnson St., Madison, WI 53705, USA. cwkalish@wisc.edu

Cognition
|April 9, 2011
PubMed
Summary
This summary is machine-generated.

Unlabeled learning experiences significantly shape our understanding of category structures. This study shows how encountering items without category labels influences implicit and explicit category boundaries and beliefs about category members.

More Related Videos

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Related Experiment Videos

Last Updated: Jun 3, 2026

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Area of Science:

  • Cognitive Psychology
  • Perception and Cognition
  • Machine Learning Theory

Background:

  • Understanding how humans form and refine category structures is crucial for cognitive science.
  • Previous research often focuses on supervised learning, where category information is explicit.
  • The role of unlabeled experiences in shaping implicit and explicit category beliefs remains less understood.

Purpose of the Study:

  • To investigate the impact of unlabeled experiences on category structure beliefs.
  • To examine how unsupervised learning influences mental category boundaries and representativeness judgments.
  • To explore the consequences of altered category representations, such as the false-consensus effect.

Main Methods:

  • Conducted three experiments with 88 college-aged participants.
  • Utilized a one-dimensional categorization task with an initial supervised learning phase.
  • Assessed changes in implicit/explicit category boundaries, representativeness beliefs, and memory through extensive unsupervised categorization decisions.

Main Results:

  • Unsupervised experience significantly altered mental category boundaries and beliefs about category representatives.
  • Category boundaries shifted towards the middle of the stimulus range and the trough of bimodal distributions.
  • Beliefs about representative members shifted towards the modes of the unlabeled stimulus distribution.

Conclusions:

  • Unlabeled experiences play a critical role in shaping implicit and explicit category structure beliefs.
  • The range and frequency distribution of unlabeled stimuli directly influence category representations.
  • Altered representations can lead to a false-consensus effect, where disparate experiences result in agreement on category judgments.