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Rethinking intelligent behaviour through the lens of accurate prediction : Adaptive control in uncertain environments

Poth, Nina Laura ; Tjøstheim, Trond A. LU and Stephens, Andreas LU orcid (2025) In Philosophy and the Mind Sciences 6.
Abstract
While recent cognitive science research shows a renewed interest in understanding intelligence, there is still little consensus on what constitutes intelligent behaviour and how it should be assessed. Here we propose a refined approach to biological intelligence as accurate prediction, according to which intelligent behaviour should be understood as adaptive control driven by the minimisation of uncertainty in dynamic environments with limited information. Central to this view is the concept of accuracy, which we argue is key to determining the success of predictions. We identify tensions in applying this framework to contemporary artificial systems such as large-language models, which, despite their impressive capacities for abstract... (More)
While recent cognitive science research shows a renewed interest in understanding intelligence, there is still little consensus on what constitutes intelligent behaviour and how it should be assessed. Here we propose a refined approach to biological intelligence as accurate prediction, according to which intelligent behaviour should be understood as adaptive control driven by the minimisation of uncertainty in dynamic environments with limited information. Central to this view is the concept of accuracy, which we argue is key to determining the success of predictions. We identify tensions in applying this framework to contemporary artificial systems such as large-language models, which, despite their impressive capacities for abstract prediction, show deficits in terms of context-sensitive knowledge transfer. (Less)
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author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Accuracy, Adaptive control, Artificial intelligence, Biological intelligence, Embodied cognition, Intelligence, Intelligent behaviour, Predictive processing
in
Philosophy and the Mind Sciences
volume
6
pages
26 pages
ISSN
2699-0369
DOI
10.33735/phimisci.2025.11780
project
Cognitive Philosophy Research Group (CogPhi)
language
English
LU publication?
yes
id
b98f37e0-4485-43ac-99ea-4c033acbc94f
date added to LUP
2025-05-06 14:27:58
date last changed
2025-05-13 15:52:50
@article{b98f37e0-4485-43ac-99ea-4c033acbc94f,
  abstract     = {{While recent cognitive science research shows a renewed interest in understanding intelligence, there is still little consensus on what constitutes intelligent behaviour and how it should be assessed. Here we propose a refined approach to biological intelligence as accurate prediction, according to which intelligent behaviour should be understood as adaptive control driven by the minimisation of uncertainty in dynamic environments with limited information. Central to this view is the concept of accuracy, which we argue is key to determining the success of predictions. We identify tensions in applying this framework to contemporary artificial systems such as large-language models, which, despite their impressive capacities for abstract prediction, show deficits in terms of context-sensitive knowledge transfer.}},
  author       = {{Poth, Nina Laura and Tjøstheim, Trond A. and Stephens, Andreas}},
  issn         = {{2699-0369}},
  keywords     = {{Accuracy; Adaptive control; Artificial intelligence; Biological intelligence; Embodied cognition; Intelligence; Intelligent behaviour; Predictive processing}},
  language     = {{eng}},
  month        = {{05}},
  series       = {{Philosophy and the Mind Sciences}},
  title        = {{Rethinking intelligent behaviour through the lens of accurate prediction : Adaptive control in uncertain environments}},
  url          = {{https://lup.lub.lu.se/search/files/218618536/Poth_2025_-_Rethinking_intelligent_behaviour_through_thelens_of_accurate_prediction.pdf}},
  doi          = {{10.33735/phimisci.2025.11780}},
  volume       = {{6}},
  year         = {{2025}},
}