Current SPiN models emphasize the contribution of cognitive resources to speech understanding in challenging conditions. However, these approaches (1) overly focus on moderately degraded signals, (2) do not adequately distinguish the mechanisms underlying L1 and L2 performance, (3) tend to underestimate the contribution of individual differences in perceptual and linguistic abilities, and (4) do not fully specify how resource engagement (i.e., effort) changes across the signal-quality continuum.

To address these shortcomings, we propose an integrative framework that characterizes listening as a continuum governed by three limiting factors: Data, cognitive (or mental) resources, and linguistic abilities. Building on Norman and Bobrow’s (1975) distinction between data-limited and resource-limited processes, the Data-Resource-Language (DRL) framework extends this taxonomy to include a language-limited region. The DRL posits that the primary determinants of speech perception vary systematically with signal quality. When acoustic input is severely degraded (data-limited), comprehension is constrained by the capacity of our perceptual system, and engaging additional cognitive resources yields minimal benefit. As signal quality improves to a moderate level (resource-limited), cognitive resources such as working memory and attention control become crucial for integrating incomplete or ambiguous speech cues. This is the region conventionally targeted by “cognitive listening” research. Under near-optimal conditions (language-limited), performance asymptotes are determined primarily by individual differences in linguistic knowledge (e.g., vocabulary, syntactic fluency, and discourse comprehension) rather than by perceptual or cognitive factors.

The framework also formalizes the documented nonlinear relationship between resource engagement and intelligibility, often depicted as an inverted U-shaped function, with maximal cognitive engagement occurring at moderate signal quality. Evidence from task-evoked pupil responses (TEPR) supports this claim, showing that effort and motivation peak when additional cognitive investment can meaningfully enhance performance.

The DRL framework offers testable predictions across listener populations. For hearing-impaired listeners, reduced access to auditory data shifts all three processing regions rightward on the low-to-high signal-quality continuum, increasing cognitive demand even in favorable conditions and explaining the more permanent state of operating within an effortful, resource-limited region for that population. For non-native listeners, limited linguistic knowledge shifts the language-limited region leftward, expanding the range in which both linguistic and cognitive factors interact to constrain performance.

By integrating perceptual, cognitive, and linguistic determinants of speech perception, the DRL framework provides a unified account of how listener-specific abilities and signal characteristics jointly shape performance and resource engagement. DRL offers new pathways for theory development, data re-analysis, and clinical intervention in speech and hearing sciences.