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Related Concept Videos

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...
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...
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,...
Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
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The Nativist Approach01:21

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The nativist approach to infant cognitive development proposes that infants are born with inherent knowledge structures that allow them to interpret the world almost immediately. This perspective contrasts with earlier developmental theories, such as those proposed by Jean Piaget, which emphasized a more gradual acquisition of cognitive abilities through interaction with the environment. One key concept in this approach is object permanence — the understanding that objects continue to exist...

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Related Experiment Video

Updated: Jun 13, 2026

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization
05:35

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

Published on: April 19, 2017

Infants consider both the sample and the sampling process in inductive generalization.

Hyowon Gweon1, Joshua B Tenenbaum, Laura E Schulz

  • 1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Proceedings of the National Academy of Sciences of the United States of America
|May 4, 2010
PubMed
Summary
This summary is machine-generated.

Human infants generalize from limited data by assuming evidence is sampled selectively (strong sampling). They adjust inferences when sampling methods are explicitly shown, demonstrating sophisticated learning about data collection.

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Last Updated: Jun 13, 2026

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Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

Published on: April 28, 2016

Area of Science:

  • Cognitive Science
  • Developmental Psychology
  • Machine Learning Theory

Background:

  • Human learning involves inductive inference from limited evidence.
  • Generalization accuracy depends on understanding both property extensions and evidence sampling processes.
  • Distinguishing between weak (random) and strong (selective) sampling is crucial for flexible generalization.

Purpose of the Study:

  • To present a Bayesian model of how learners infer property extensions and sampling processes.
  • To investigate whether human infants (15 months) use strong sampling by default.
  • To examine if infants can adapt their inferences based on explicit cues about sampling methods.

Main Methods:

  • Developed a Bayesian computational model.
  • Conducted five behavioral experiments with 15-month-old infants.
  • Presented infants with positive examples and varied cues about sampling processes (none, weak, strong).

Main Results:

  • Infants defaulted to strong sampling inferences when sampling cues were absent.
  • Infants adjusted their generalization strategies when provided with explicit weak or strong sampling cues.
  • Evidence suggests infants' inferences are graded based on the strength of observed data.

Conclusions:

  • Infants possess sophisticated reasoning about data sampling processes.
  • This ability is foundational for generalization and learning from sparse data.
  • The findings support a Bayesian framework for understanding early inductive generalization.