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Scaling laws in cognitive sciences.

Christopher T Kello¹, Gordon D A Brown, Ramon Ferrer-I-Cancho

¹Cognitive and Information Sciences University of California , Merced, 5200 North Lake Rd., Merced, CA 95343, USA. ckello@ucmerced.edu

Trends in Cognitive Sciences

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Summary

This summary is machine-generated.

Scaling laws, patterns repeated across analysis scales, are found in neural, behavioral, and linguistic activities. Their recurrence suggests unifying principles in cognitive science, indicating scaling invariance in cognitive mechanisms.

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Area of Science:

Cognitive Science
Neuroscience
Complexity Science

Background:

Scaling laws are prevalent across natural phenomena, including biological, neural, and behavioral systems.
These laws indicate processes or patterns recurring across different scales of analysis.
The ubiquity of scaling laws suggests underlying unifying principles in complex systems.

Purpose of the Study:

To explore the presence and implications of scaling laws in cognitive science.
To investigate the link between scaling laws and adaptive processes in biological and cognitive systems.
To understand how scaling invariance in cognitive mechanisms contributes to cognition.

Main Methods:

Analysis of existing literature on scaling laws in various scientific domains.
Theoretical exploration of the implications of scaling invariance in cognitive functions.
Examination of how scaling laws relate to complex systems near critical points.

Main Results:

Scaling laws are demonstrably present in cognitive phenomena such as perception, memory, and language.
Findings suggest cognitive mechanisms exhibit scaling invariance.
Multiplicative interactions among interdependent cognitive components are indicated by these scaling laws.

Conclusions:

The recurrence of scaling laws across diverse cognitive activities points to fundamental, unifying principles.
Scaling invariance in cognitive mechanisms is a key characteristic of cognitive function.
Cognitive systems may operate near critical points, facilitating adaptive processes and information processing across scales.