Matthew M Walsh
Biographic Data
| ID | 3583902 |
|---|---|
| NAME | Matthew M Walsh |
| GIVEN NAMES | Matthew M |
| FAMILY NAME | Walsh |
| SIGNATURE | WALSH M M |
| AFFILIATIONS | Air Force Research Laboratory Wright‐Patterson Air Force Base OH USA |
| ORCID | 0000-0002-1457-3016 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Reciprocal relationships among influenza experiences, perceptions, and behavior
Verbalization of Decision Strategies in Multiple‐Cue Probabilistic Inference
In multiple‐cue probabilistic inference, people choose between alternatives based on several cues, each of which is differentially associated with an alternative's overall value. Various strategies have been proposed for probabilistic inference (e.g., weighted additive, tally, and take‐the‐best). These strategies differ in how many cue values they require to enact and in how they weight each cue. Do decision makers actually use any of these strat…
Navigating complex decision spaces
To behave adaptively, we must learn from the consequences of our actions. Doing so is difficult when the consequences of an action follow a delay. This introduces the problem of temporal credit assignment. When feedback follows a sequence of decisions, how should the individual assign credit to the intermediate actions that comprise the sequence? Research in reinforcement learning provides 2 general solutions to this problem: model-free reinforce…
No prominent works on this page.
Navigating complex decision spaces
To behave adaptively, we must learn from the consequences of our actions. Doing so is difficult when the consequences of an action follow a delay. This introduces the problem of temporal credit assignment. When feedback follows a sequence of decisions, how should the individual assign credit to the intermediate actions that comprise the sequence? Research in reinforcement learning provides 2 general solutions to this problem: model-free reinforce…
Verbalization of Decision Strategies in Multiple‐Cue Probabilistic Inference
In multiple‐cue probabilistic inference, people choose between alternatives based on several cues, each of which is differentially associated with an alternative's overall value. Various strategies have been proposed for probabilistic inference (e.g., weighted additive, tally, and take‐the‐best). These strategies differ in how many cue values they require to enact and in how they weight each cue. Do decision makers actually use any of these strat…
Reciprocal relationships among influenza experiences, perceptions, and behavior
Psychology (3 works) · Artificial Intelligence (2 works) · Cognitive psychology (2 works) · Computer Science (2 works) · Machine learning (2 works) · Perception (2 works) · Action selection (1 work) · Artificial Intelligence (1 work) · Attention Deficit Hyperactivity Disorder (1 work) · Child and Animal Learning Development (1 work)