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Decision Making

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Related Experiment Video

Updated: May 21, 2025

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

Day-to-day fluctuations in motivation drive effort-based decision-making.

Samuel R C Hewitt^1,2, Agnes Norbury³, Quentin J M Huys³

¹Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Queen Square Institute of Neurology, University College London, London WC1B 5EH, United Kingdom.

Proceedings of the National Academy of Sciences of the United States of America

|March 17, 2025

Summary

Daily motivation levels significantly impact reward value and effort, especially in those with apathy. Understanding these fluctuations is key to comprehending motivated behavior and mental health conditions.

Keywords:

decision-making effort longitudinal motivation reward

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An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

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Published on: March 19, 2014

Area of Science:

Neuroscience
Psychology
Behavioral Economics

Background:

Studies on motivated behavior often overlook the dynamic nature of internal states.
Capturing real-time fluctuations in motivation and decision-making is crucial for a comprehensive understanding.

Purpose of the Study:

To investigate how natural variations in motivation (state motivation) affect the subjective value of rewards and the willingness to exert effort.
To examine the interplay between state motivation, trait motivation, and effort-based decision-making.

Main Methods:

A microlongitudinal design with 155 participants, collecting 3,344 state timepoints and analyzing 845 decision-making tasks.
Utilized smartphone-based momentary assessments to capture real-life fluctuations in motivation and decision-making.
Employed computational modeling to analyze the relationship between state motivation and future effort exertion.

Main Results:

Both current motivation levels (state) and long-term tendencies (trait) independently and interactively influence decision-making.
The link between motivation state and behavior was stronger in individuals with higher trait apathy.
State motivation prospectively increased reward sensitivity, predicting greater future effort.

Conclusions:

Daily fluctuations in motivation are tightly linked to cognitive processes and decision-making.
These dynamic changes are critical for understanding normal human behavior and the mechanisms underlying mental health disorders.
The findings highlight the importance of considering dynamic internal states in neurocomputational models of behavior.