Patternseeking

Patternseeking is the tendency or process of searching for, identifying, and interpreting patterns, regularities, or structures in data, events, or experiences. The term is used across disciplines such as cognitive science, psychology, statistics, data science, and design. It encompasses conscious exploration of regularities as well as the spontaneous perception of order in randomness.

In cognitive terms, patternseeking is linked to the human propensity to impose structure on the environment. It overlaps with pattern recognition but is broader: patternseeking includes forming hypotheses about unknown relationships and testing them. Related concepts include apophenia (perceiving connections in random data) and pareidolia (seeing meaningful forms in ambiguous stimuli).

Applications and implications: In science and engineering, patternseeking drives hypothesis generation, model building, and data mining. In business and forecasting, it supports trend identification and anomaly detection. In art and design, identifying motifs and regularities informs style and creativity. However, patternseeking can lead to overinterpretation, spurious correlations, or confirmation bias if patterns are mistimed, accidental, or culturally biased.

Techniques to study and manage patternseeking include data visualization, exploratory data analysis, statistical testing, and validation on independent data. In machine learning, unsupervised and semi-supervised methods aim to discover latent patterns without predefined labels.