A Computational Complexity Perspective on Segmentation as a Cognitive Subcomputation

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.

Adaptive pacing in word segmentation and the Vowel-onset Paced Syllable Inference model

“…In this chapter, we have attempted to clarify the computational challenges posed by online speech inference, to propose how listeners solve these challenges, and to integrate these possible solutions in a mechanistic model of word segmentation, Vowel-onset Paced Syllable Inference. In the context of online speech processing, we have suggested that strict temporal constraints advantage computational efficiency, above and beyond tractability or computability [49]. Like others before us [47,48,52,70,73,84], we have highlighted the cost of search operations [172], the size of the search space, and the speed-accuracy tradeoff.…”

“…A particular challenge posed by word segmentation, and one absent from many theoretical accounts [33,48,49], is the necessity for online inference: linguistic signals arrive sequentially, vanishing after momentary articulation; listeners must make sense of the current input before it is overwritten by the next [50][51][52]. Results have suggested a central role for the syllabic timescale in both the pacing of speech and its intelligibility [1-6, 12, 15, 16, 53-56], with experiments on so-called repackaged speech suggesting an upper limit on the speech "information rate" of ∼9 syllables per second [15,57].…”

“…Existing theoretical accounts have largely addressed the issue of timing in speech segmentation by leveraging syllabic-timescale brain rhythms as a dynamic and self-organizing mechanism of speech segmentation [15,48,54,57,[62][63][64][65][66][67][68][69][70][71]. We pursue a different strategy: that of understanding the computational principles underlying word segmentation independently of their neurophysiological mechanisms (rhythmic or otherwise) [49,72]. Toward this end, we sketch how a recently proposed conceptual model of speech understanding-Syllable Inference (SI, Fig.…”

Reclaiming AI as a theoretical tool for cognitive science

“…Computational modelling of capacities can help us to make our assumptions precise and explicit, and to draw out their consequences, without the need to simulate the postulated computations (though simulations have their uses; more on that next). For instance, with formal computationallevel models and mathematical proof techniques at hand, one can critically assess claims of explanatory adequacy Egan, 2017;van Rooij & Baggio, 2021), claims of intractability (Adolfi, Wareham, & van Rooij, 2023), claims of tractability van Rooij, Evans, Muller, Gedge, & Wareham, 2008), claims of competing theories , claims of evolvability (Rich, Blokpoel, de Haan, & van Rooij, 2020;Woensdregt et al, 2021), and claims of approximability (Kwisthout & Van Rooij, 2013;Kwisthout, Wareham, & Van Rooij, 2011). 16 We acknowledge that computational modelling can also contribute to productive theory development without committing to computationalism (Guest & Martin, 2021;Morgan & Morrison, 1999).…”

“…This is no magic bullet, however; underdetermination of theory by data cannot be eliminated, and any ways of dealing with it will remain necessarily incomplete (cf. Adolfi, Wareham, & van Rooij, 2023;Devezer, 2023;Rich et al, 2021).…”