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Parametric Cognitive Load Reveals Hidden Costs in the Neural Processing of Perfectly Intelligible Degraded Speech

2022· article· en· W4228999082 on OpenAlexafffund
Harrison Ritz, Conor J. Wild, Ingrid S. Johnsrude

Bibliographic record

VenueJournal of Neuroscience · 2022
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsSpeech perceptionIntelligibility (philosophy)Cognitive loadCognitionSpeech processingSpeech recognitionActive listeningCognitive resource theoryPerceptionComputer scienceSentencePsychologyCognitive psychologyCommunicationArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

Speech is often degraded by environmental noise or hearing impairment. People can compensate for degradation, but this requires cognitive effort. Previous research has identified frontotemporal networks involved in effortful perception, but materials in these works were also less intelligible, and so it is not clear whether activity reflected effort or intelligibility differences. We used functional magnetic resonance imaging to assess the degree to which spoken sentences were processed under distraction and whether this depended on speech quality even when intelligibility of degraded speech was matched to that of clear speech (close to 100%). On each trial, male and female human participants either attended to a sentence or to a concurrent multiple object tracking (MOT) task that imposed parametric cognitive load. Activity in bilateral anterior insula reflected task demands; during the MOT task, activity increased as cognitive load increased, and during speech listening, activity increased as speech became more degraded. In marked contrast, activity in bilateral anterior temporal cortex was speech selective and gated by attention when speech was degraded. In this region, performance of the MOT task with a trivial load blocked processing of degraded speech, whereas processing of clear speech was unaffected. As load increased, responses to clear speech in these areas declined, consistent with reduced capacity to process it. This result dissociates cognitive control from speech processing; substantially less cognitive control is required to process clear speech than is required to understand even very mildly degraded, 100% intelligible speech. Perceptual and control systems clearly interact dynamically during real-world speech comprehension. SIGNIFICANCE STATEMENT Speech is often perfectly intelligible even when degraded, for example, by background sound, phone transmission, or hearing loss. How does degradation alter cognitive demands? Here, we use fMRI to demonstrate a novel and critical role for cognitive control in the processing of mildly degraded but perfectly intelligible speech. We compare speech that is matched for intelligibility but differs in putative control demands, dissociating cognitive control from speech processing. We also impose a parametric cognitive load during perception, dissociating processes that depend on tasks from those that depend on available capacity. Our findings distinguish between frontal and temporal contributions to speech perception and reveal a hidden cost to processing mildly degraded speech, underscoring the importance of cognitive control for everyday speech comprehension.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.073
GPT teacher head0.337
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2022
Admission routes2
Has abstractyes

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