Artificial intelligence and machine learning systems are increasingly replacing human
decision makers in commercial, healthcare, educational and government contexts. But …

Objective: The aim of this manuscript is to provide a review of contemporary research and
applications on dynamic decision making (DDM). Background: Since early DDM studies …

In two experiments, younger and older adults performed decision-making tasks in which
reward values available were either independent of or dependent on the previous sequence …

Losses were found to improve cognitive performance, and this has been commonly
explained by increased weighting of losses compared to gains (ie, loss aversion). We …

The Iowa gambling task (IGT) has been used in numerous studies, often to examine
decision-making performance in different clinical populations. Reinforcement learning (RL) …

Melioration—defined as choosing a lesser, local gain over a greater longer term gain—is a
behavioral tendency that people and pigeons share. As such, the empirical occurrence of …

We describe what can be gained from connecting cognition and consumer choice by
discussing two contexts ripe for interaction between the two fields. The first—context effects …

Models of human behavior in the Iowa Gambling Task (IGT) have played a pivotal role in
accounting for behavioral differences during decision-making. One critical difference …

Acute stress has been shown to influence reward sensitivity, feedback learning, and risk-
taking during decision-making, primarily through activation of the hypothalamic pituitary axis …

How do humans search for rewards? This question is commonly studied using multi-armed
bandit tasks, which require participants to trade off exploration and exploitation. Standard …