The timing of the cognitive cycle.
Tamas Madl1, Bernard J Baars, Stan Franklin
1Department of Philosophy (Cognitive Science), University of Vienna, Vienna, Austria. tamas.madl@gmail.com
Plos One
|May 5, 2011
Summary
Human cognition operates in cascading brain event cycles, involving perception, conscious broadcasting, and action selection. This model, the LIDA (Learning Intelligent Distribution Agent) cognitive cycle, takes 260-390 ms and aligns with neuroscientific data.
Area of Science:
- Cognitive Neuroscience
- Computational Neuroscience
- Artificial Intelligence
Background:
- Human cognition is theorized to involve recurring cycles of brain events.
- The LIDA (Learning Intelligent Distribution Agent) model proposes a fundamental cognitive cycle.
- Previous models lack detailed timing and neurobiological integration.
Purpose of the Study:
- To describe a timing model for the LIDA cognitive cycle.
- To align the cognitive cycle with empirical neuroscientific evidence.
- To demonstrate the model's application in simulating cognitive tasks.
Main Methods:
- Development of a timing model for the LIDA cognitive cycle based on empirical and simulation data.
- Analysis of neurophysiological evidence, including theta-gamma waves and brain region involvement.
- Implementation of LIDA-based software agents for reaction time and simultaneity tasks.
Main Results:
- A cognitive cycle duration of 260-390 ms is proposed, with perception (80-100 ms), conscious broadcast (200-280 ms), and action selection (60-110 ms post-broadcast).
- The model aligns with theta-gamma wave propagation in corticothalamic systems and involvement of frontal, striatal, and cerebellar regions.
- LIDA agents accurately simulated human performance in reaction time and simultaneity tasks.
Conclusions:
- The LIDA cognitive cycle provides a biologically and computationally plausible framework for understanding human cognition.
- The proposed timing model is consistent with key neuroscientific findings.
- The model's success in simulations highlights its potential for further research in cognitive architectures.
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