Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Extrapolation: the sine qua non for abstraction in function learning

E L DeLosh1, J R Busemeyer, M A McDaniel

  • 1Department of Psychology, Colorado State University, Fort Collins 80523, USA. delosh@lamar.colostate.edu

Journal of Experimental Psychology. Learning, Memory, and Cognition
|July 1, 1997
PubMed
Summary

Participants demonstrated extrapolation in function learning, accurately capturing learned patterns beyond the training range. Individual differences were noted, and a hybrid model (EXAM) best explained extrapolation-association performance.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The Disjunction Effect in two-stage simulated gambles. An experimental study and comparison of a heuristic logistic, Markov and quantum-like model.

Cognitive psychology·2019
Same author

Hilbert space multidimensional modelling of continuous measurements.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2019
Same author

Comparing quantum versus Markov random walk models of judgements measured by rating scales.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2015
Same author

What is minimal about predictive inferences?

Psychonomic bulletin & review·2002
Same author

Many-to-one and one-to-many associative learning in a naturalistic task.

Journal of experimental psychology. Applied·2001
Same author

Use of situational judgment tests to predict job performance: a clarification of the literature.

The Journal of applied psychology·2001

Area of Science:

  • Cognitive psychology
  • Machine learning

Background:

  • Understanding how humans generalize learned relationships is crucial for cognitive modeling.
  • Investigating abstraction through function learning provides insights into rule induction and extrapolation.

Purpose of the Study:

  • To examine human extrapolation behavior in a function-learning task.
  • To evaluate computational models of learning and generalization.

Main Methods:

  • Participants learned stimulus-response mappings following linear, exponential, or quadratic functions.
  • Extrapolation and interpolation were tested with novel stimulus magnitudes.
  • Performance was evaluated against associative, rule-learning, and a hybrid (EXAM) model.

Main Results:

Related Experiment Videos

  • Participants successfully extrapolated beyond the learned range, mirroring function shapes with deviations.
  • Individual differences were prominent, especially in the quadratic function condition.
  • Training data density did not impact performance.

Conclusions:

  • Human extrapolation reflects a blend of associative learning and rule-based generalization.
  • The extrapolation-association model (EXAM) provides a strong account of observed behavior.
  • Further research should explore the mechanisms behind individual differences in abstraction.