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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:
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,...
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,
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...

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Related Experiment Video

Updated: Jul 4, 2026

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

Transitive reasoning as linear classification.

Vincent P Ferrera, Samuel Lippl, Kenneth Kay

    Biorxiv : the Preprint Server for Biology
    |July 3, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Transitive inference (TI) is explained by a simple linear classification model. This computational approach, using least-squares estimation (LSE), explains how we learn and make choices based on ordered item ranks.

    Related Experiment Videos

    Last Updated: Jul 4, 2026

    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
    • Computational Neuroscience
    • Behavioral Economics

    Background:

    • Transitive inference (TI) is crucial for reasoning about ordered relationships.
    • Current models often assume explicit linear representations for rank ordering.

    Purpose of the Study:

    • To investigate the computational mechanisms underlying transitive inference.
    • To explore how rank order representations are constructed and utilized for decision-making.

    Main Methods:

    • Applied least-squares estimation (LSE) to a standard serial learning task.
    • Formulated LSE as a linear classifier mapping task conditions to behavioral outcomes.

    Main Results:

    • The LSE model reproduced key TI features: generalization beyond training and a symbolic distance effect (SDE).
    • An internally ordered rank representation emerged from the classifier, explaining generalization and SDE.
    • A differencing operation was identified as the decision mechanism for pairwise choices.

    Conclusions:

    • Transitive inference can be reframed as a linear classification problem.
    • This challenges traditional assumptions about the cognitive mechanisms required for transitive reasoning.