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Patterns before recognition: the historical ascendance of an extractive empiricism of forms

Interdisciplinary Studies

Patterns before recognition: the historical ascendance of an extractive empiricism of forms

B. Üstün

Discover the fascinating connections between cybernetics, Gestalt theory, and pattern recognition in this insightful article by Berkay Üstün. The research delves into how these frameworks shape our understanding of human perception and intelligence, while also revealing the tensions that arise from their interactions in a post-war American context. Uncover the implications for contemporary machine learning and beyond!

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~3 min • Beginner • English
Abstract
This article explores the complex convergence between cybernetics and Gestalt theory and its influence on the concept of pattern recognition. It finds a departure in the analogous ways each discipline extends their core frameworks toward social and anthropological objects. However, this shared ground is not without tensions. In the post-war American context, what is formalizable and realizable in mechanical structures has a certain explanatory authority—even if often misplaced—concerning perception and human intelligence. Cultural patterns feed into mechanical recognition of patterns, exemplifying "extractive empiricism" or the process of outsourcing experiential processes to mechanical systems. This mode of "proof" is also evident in cybernetic and cognitive psychological strategies toward Gestalt theory, leaving a significant legacy for contemporary machine learning approaches. By examining the early interactions between these rival paradigms, known for their quest for generalization, and disentangling their source status, this inquiry contributes to understanding the broad conceptual possibilities of pattern recognition beyond its narrow confines in engineering perspectives and machine learning discourse.
Publisher
Humanities and Social Sciences Communications
Published On
Jan 04, 2024
Authors
Berkay Üstün
Tags
cybernetics
Gestalt theory
pattern recognition
human perception
mechanical structures
machine learning
extractive empiricism
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