A Computational Complexity Perspective on Segmentation as a Cognitive Subcomputation. (1st December 2022) - Adolfi, Federico; Wareham, Todd; van Rooij, Iris
Abstract: Computational feasibility is a widespread concern that guides the framing and modeling of natural and artificial intelligence. The specification of cognitive system capacities is often shaped by unexamined intuitive assumptions about the search space and complexity of a subcomputation. However, a mistaken intuition might make such initial conceptualizations misleading for what empirical questions appear relevant later on. We undertake here computational‐level modeling and complexity analyses of segmentation — a widely hypothesized subcomputation that plays a requisite role in explanations of capacities across domains, such as speech recognition, music cognition, active sensing, event memory, action parsing, and statistical learning — as a case study to show how crucial it is to formally assess these assumptions. We mathematically prove two sets of results regarding computational hardness and search space size that may run counter to intuition, and position their implications with respect to existing views on the subcapacity. Abstract : Adolfi, Wareham l& van Rooij (2022) examine the computational properties of segmentation; a subcomputation conjectured to underlie cognitive capacities across domains such as speech recognition, music perception, event memory, and action parsing. How hard is segmentation and what complicates it? Using computational modeling and complexity analyses they prove that its properties may run counter to common (so far unexamined) intuitions in various subfields of the cognitive and brain sciences.