The brain is tasked with transmitting information quickly and reliably while limiting energy use. It must encode information without perfect knowledge of stimulus statistics, making efficient encoding impossible. We consider the possibility that the brain learns task-specific codes on-line, and that the efficiency of these codes can increase with exposure to task statistics. Use of task-indexed codes would parsimoniously explain, from information theoretic first principles, ubiquitous response time and accuracy improvements during task learning, including the Power Law of Learning. We also consider the implications of task-specific codes on automaticity and cognitive costs.
|Original language||English (US)|
|Title of host publication||Proceedings of ICCM 2018 - 16th International Conference on Cognitive Modeling|
|Editors||Ion Juvina, Joseph Houpt, Christopher Myers|
|Publisher||University of Wisconsin|
|Number of pages||6|
|State||Published - 2018|
|Event||16th International Conference on Cognitive Modeling, ICCM 2018 - Wisconsin, United States|
Duration: Jul 21 2018 → Jul 24 2018
|Name||Proceedings of ICCM 2018 - 16th International Conference on Cognitive Modeling|
|Conference||16th International Conference on Cognitive Modeling, ICCM 2018|
|Period||7/21/18 → 7/24/18|
Bibliographical notePublisher Copyright:
© 2020 The Authors. Published by Elsevier Ltd.
Copyright 2020 Elsevier B.V., All rights reserved.
- Decision making
- Information theory