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Incentive theory, or the "pull theory" of motivation, suggests that external rewards primarily drive behavior. Individuals are motivated to engage in activities when they anticipate a desirable outcome. This is why people often work hard for promotions or study intensively to achieve high grades. These incentives can be tangible, physical rewards such as money or promotions, or intangible, non-physical rewards like praise and social recognition.
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Related Experiment Video

Updated: Sep 6, 2025

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
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A reference-based theory of motivation and effort allocation.

Francesco Rigoli1, Giovanni Pezzulo2

  • 1Department of Psychology, City, University of London, Northampton Square, London, EC1V 0HB, UK. francesco.rigoli@city.ac.uk.

Psychonomic Bulletin & Review
|June 29, 2022
PubMed
Summary

This study introduces a new theory and computational model explaining motivation and effort. It shows that realistic performance standards, aligned with ability, best boost motivation and improve outcomes.

Keywords:
EffortMotivationReference-based modelSubjective value

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Area of Science:

  • Cognitive Psychology
  • Behavioral Economics
  • Computational Neuroscience

Background:

  • Motivation and effort are crucial for performance across various domains.
  • Traditional models linking motivation to reward struggle to explain phenomena like decreased motivation against weaker opponents.
  • Subjective value plays a significant role in motivation and effort allocation, highlighting limitations of purely objective reward-based theories.

Purpose of the Study:

  • To propose a novel theory and computational model for motivation and effort allocation based on reference-dependent evaluation.
  • To explain puzzling motivational dynamics observed in empirical data, such as reduced motivation in certain competitive scenarios.
  • To identify the conditions under which motivation and effort are optimally allocated.

Main Methods:

  • Development of a computational model based on reference-based evaluation of subjective value.
  • Simulations to test the model's explanatory power for motivational dynamics and associated feelings.
  • Theoretical analysis to derive normative principles for optimal effort allocation.

Main Results:

  • The model successfully explains previously puzzling motivational dynamics and subjective experiences.
  • Realistic performance standards, those matching an individual's actual ability, were identified as most beneficial for motivation and performance.
  • The model provides a framework for understanding how subjective value influences effort.

Conclusions:

  • Motivation and effort allocation are driven by reference-based evaluation of subjective value, influenced by performance standards and confidence.
  • Setting realistic performance standards is crucial for enhancing motivation and optimizing performance.
  • The proposed model offers a normative solution for optimal effort allocation, analogous to efficient coding principles in neural systems.