Anna-Mari Rusanen
Biographic Data
| ID | 5816280 |
|---|---|
| NAME | Anna-Mari Rusanen |
| GIVEN NAMES | Anna-Mari |
| FAMILY NAME | Rusanen |
| SIGNATURE | RUSANEN A |
| AFFILIATIONS | University of Helsinki |
| ORCID | 0000-0002-4607-3956 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 4 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 1 |
Action control, forward models and expected rewards
The fundamental cognitive problem for active organisms is to decide what to do next in a changing environment. In this article, we analyze motor and action control in computational models that utilize reinforcement learning (RL) algorithms. In reinforcement learning, action control is governed by an action selection policy that maximizes the expected future reward in light of a predictive world model. In this paper we argue that RL provides a way…
From Fly Detectors to Action Control
According to radical enactivists, cognitive sciences should abandon the representational framework. Perceptuomotor cognition and action control are often provided as paradigmatic examples of nonrepresentational cognitive phenomena. In this article, we illustrate how motor and action control are studied in research that uses reinforcement learning algorithms. Crucially, this approach can be given a representational interpretation. Hence, reinforce…
On computational explanations
On computational explanations
Action control, forward models and expected rewards
The fundamental cognitive problem for active organisms is to decide what to do next in a changing environment. In this article, we analyze motor and action control in computational models that utilize reinforcement learning (RL) algorithms. In reinforcement learning, action control is governed by an action selection policy that maximizes the expected future reward in light of a predictive world model. In this paper we argue that RL provides a way…
From Fly Detectors to Action Control
According to radical enactivists, cognitive sciences should abandon the representational framework. Perceptuomotor cognition and action control are often provided as paradigmatic examples of nonrepresentational cognitive phenomena. In this article, we illustrate how motor and action control are studied in research that uses reinforcement learning algorithms. Crucially, this approach can be given a representational interpretation. Hence, reinforce…
Computer Science (3 works) · Action (physics) (2 works) · Artificial Intelligence (2 works) · Artificial Intelligence (2 works) · Cognition (2 works) · Cognitive science (2 works) · Control (management) (2 works) · Embodied and Extended Cognition (2 works) · Epistemology (2 works) · Metaphysics (2 works)