Decision making
How We Learn Value While Making Quick Decisions
Open access · cc by · source: Europe PMC
Decisions between high-value options are faster because people lower their threshold for making a choice, not because they process information faster.
How this study connects
Role on claims
Each row is a claim on a concept or method page where this paper supports, challenges, or qualifies the statement. Roles are hand-checked — not a model guess.
Faster is not always more confident or more accurate — here it tracked total value.
Value changes the speed of a choice independently of its accuracy. As participants learned option values they became both faster and more accurate, and choices between two high-reward options were made faster than between two low-reward options even though accuracy was unchanged.
Evidence for the claim as stated.
Inspect source passages
How We Learn Value While Making Quick Decisions
These passages occur verbatim in the stored paper text. They support the points listed below; they do not verify every statement in the explanation.
32 participants
A total of 32 participants (24 female, age: 18–36, M = 22.36, S D = 2.14) completed the experiment.
240 trials
Participants completed three experimental blocks of 80 trials, for a total of 240 trials.
Key findings
Participants became faster and more accurate as they learned the values of different options. When choosing between two highly rewarding options, participants made decisions faster than when choosing between two low-reward options, even though their accuracy remained the same. This speed increase occurred because participants lowered their decision threshold for attractive options, a process best explained by a model linking learned value directly to the decision boundary.
Methodology
Researchers tested 32 participants in a learning task where they chose between pairs of symbols with different hidden reward values. Over 240 trials, participants received immediate feedback on their choices, while the researchers measured their accuracy and reaction times. The study used these data to test a new mathematical model that combines reinforcement learning with an evidence-accumulation decision process.
Limitations
The study only tested binary choices, meaning the model cannot yet explain how people make decisions when presented with multiple options. It also relied on a relatively small sample of 27 analyzed participants, who were mostly young university students. Finally, the study cannot prove the underlying neural mechanisms behind these decisions, as it only collected behavioral data.
Related papers in this topic
Same topic cluster — not a recommendation engine.