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 Concept Videos

Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Signal and System01:26

Signal and System

A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional signals...
State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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...
Linear time-invariant Systems01:23

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...

You might also read

Related Articles

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

Sort by
Same author

Salt exposure disrupts memory retrieval in habituation and conditioned place preference in planaria (Dugesia japonica).

Journal of comparative psychology (Washington, D.C. : 1983)·2026
Same author

On the generality of behavioral theory and abstract phenomena.

Neurobiology of learning and memory·2026
Same author

Comparisons of extinction, counterconditioning, and novelty-facilitated extinction within ABA vs. ABC renewal designs.

Journal of experimental psychology. Animal learning and cognition·2026
Same author

The Ontogeny of the Generalization of Avoidance Behavior.

Developmental psychobiology·2026
Same author

When time matters: generalization gradients in delay and trace conditioning procedures.

Learning & behavior·2026
Same author

Trial Frequency Outweighs Trial Duration in Associative Learning: Generality and Boundary Conditions.

Quarterly journal of experimental psychology (2006)·2025

Related Experiment Video

Updated: Jun 11, 2026

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

A One-System Theory Which is Not Propositional.

James E Witnauer1, Gonzalo P Urcelay, Ralph R Miller

  • 1Institutional Address (Miller & Witnauer): Department of Psychology SUNY-Binghamton Binghamton, NY 13902-6000 USA; Institutional Address (Urcelay): Department of Experimental Psychology & Behavioural and Clinical Neuroscience Institute University of Cambridge Downing St. Cambridge, CB2 3EB United Kingdom.

The Behavioral and Brain Sciences
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

Human contingency learning involves both link-based and propositional reasoning, operating at different analytical levels. Current models often oversimplify by focusing on limited examples within these broad categories.

More Related Videos

Perspectives on Neuroscience
26:41

Perspectives on Neuroscience

Published on: July 31, 2007

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

Related Experiment Videos

Last Updated: Jun 11, 2026

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Perspectives on Neuroscience
26:41

Perspectives on Neuroscience

Published on: July 31, 2007

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

Area of Science:

  • Cognitive Science
  • Psychology
  • Artificial Intelligence

Background:

  • Human contingency learning is crucial for understanding decision-making and prediction.
  • Existing models of contingency learning often fall into link-based or propositional categories.
  • The relationship and integration between these model types require further clarification.

Purpose of the Study:

  • To propose that link-based and propositional approaches to human contingency learning represent distinct levels of analysis.
  • To argue that link-based architectures provide a foundational basis for propositional reasoning.
  • To critique the limited scope of existing comparative models in contingency learning research.

Main Methods:

  • Conceptual analysis of existing theoretical frameworks for human contingency learning.
  • Examination of the relationship between representational architectures (link-based) and reasoning processes (propositional).
  • Literature review and critique of comparative studies on contingency learning models.

Main Results:

  • Propositional reasoning in contingency learning is posited to depend on an underlying link-based architecture.
  • The distinction between link-based and propositional models highlights different levels of cognitive analysis.
  • Existing comparative models by Mitchell et al. are criticized for oversimplification and ignoring model diversity.

Conclusions:

  • The link-based approach offers a plausible foundation for propositional reasoning in human contingency learning.
  • A more nuanced understanding of contingency learning requires acknowledging the variety within model classes.
  • Future research should explore the integration and interplay between different levels of analysis in cognitive models.