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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...

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Related Experiment Video

Updated: Mar 31, 2026

Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm

Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm

Published on: May 15, 2019

Learning What to Want: Context-Sensitive Preference Learning.

Nisheeth Srivastava¹, Paul Schrater²

¹Dept of Psychology, UC San Diego, La Jolla, CA, United States of America.

|October 27, 2015

Summary

This summary is machine-generated.

This study introduces a new method to learn relative preferences directly from choice history, bypassing utility calculations. It explains rational decision-making and context effects, reconciling economic and psychological rationality definitions.

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Area of Science:

Decision Science
Cognitive Psychology
Behavioral Economics

Background:

Traditional models often rely on intermediate utility computations to infer preferences.
Understanding agent decision-making from observed choices is crucial for behavioral modeling.
Reconciling economic and psychological definitions of rationality remains a challenge.

Purpose of the Study:

To develop a novel method for learning relative preferences directly from choice histories.
To infer psychologically rational preferences using Bayesian inference from observable inputs.
To characterize conditions for using ordinal utilities and explain context effects in decision-making.

Main Methods:

Developed a method to learn relative preferences from choice histories without intermediate utility computation.
Employed Bayesian inference to model agent choices based on observable inputs.
Characterized conditions for ordinal utility representation and explained context effects.

Main Results:

Successfully inferred preferences that are psychologically rational from choice histories.
Identified conditions under which ordinal utilities are appropriate for modeling preferences.
Explained major categories of context effects by incorporating choice history influence.
Clarified the relationship between economic and psychological rationality.

Conclusions:

The proposed method offers a new way to understand and model preferences from observed choices.
It provides a unified framework for rational decision-making, incorporating psychological and economic perspectives.
The findings rationalize behaviors previously deemed irrational by behavioral economists.