The disembodied predictor stance

Pattern recognition is typically described as the discipline investigating how to recognize patterns and regularities in data, with the description leaving tacit that these patterns and regularities are somehow exploited, applied, acted upon, or simply announced once recognized. The aforementioned omission is more than a linguistic one, and is reflected on the emphasis that technical, theoretical, and empirical work on pattern recognition places on the predictors it develops, analyzes, and deploys. Most research on pattern recognition adopts, effectively, a stance amounting to treating the predictors as being disembodied, taken to mean that they operate without affecting the environment about which they make predictions. This essay argues for the dismissal of this stance, and demonstrates that the adoption of an embodied predictor stance is philosophically and technically not only possible, but also desirable.