Effects of Sensorineural Hearing Loss on Cortical Event-Related Potential and Behavioral Measures of Speech-Sound Processing
Bibliographic record
Abstract
In Brief Objective To investigate systematically the effects of sensorineural hearing loss on cortical event-related potentials (ERPs) N1, MMN, N2 and P3 and their associated behavioral measures (d′ sensitivity and reaction time) to the speech sounds /ba/ and /da/ presented at 65 and 80 dB ppe SPL. Design Cortical ERPs were recorded to /ba/ and /da/ speech stimuli presented at 65 and 80 dB ppe SPL from 20 normal-hearing adults and 20 adults who are hearing impaired. The degree of sensorineural impairments at 1000 to 2000 Hz ranged from mild losses (defined as 25 to 49 dB HL) to severe/profound losses (75 to 120 dB HL). The speech stimuli were presented in an oddball paradigm and the cortical ERPs were recorded in both active and passive listening conditions for each stimulus intensity. Results Both ERP amplitudes and behavioral discrimination (d′) scores were lower for listeners with sensorineural hearing loss than for those with normal hearing. However, these differences in response strength were evident only for those listeners whose average hearing loss at 1000 to 2000 Hz exceeded 60 dB HL for the lower intensity stimuli and exceeded 75 dB HL for the higher intensity stimuli. In contrast, prolongations in the ERP and behavioral latencies, relative to responses from normal-hearing subjects, began with even mild (25 to 49 dB HL) threshold elevations. The amplitude and latency response changes that occurred with sensorineural hearing loss were significantly greater for the later ERP peaks (N2/P3) and behavioral discrimination measures (d′ and RT) in comparison with earlier (N1, MMN) responses. Conclusions The results indicate that latency measures are more sensitive indicators of the early effects of decreased audibility than are response strength (amplitude, d′ or percent correct) measures. Sensorineural hearing loss has a greater impact on higher level or “nonsensory” cortical processing in comparison with lower level or “sensory” cortical processing. Possible physiologic mechanisms within the cortex that may be responsible for these response changes are presented. Lastly, the possible clinical significance of these ERP and behavioral findings is discussed. There is considerable interest in the use of cortical event-related potentials (ERPs), especially MMN, for assessing populations where behavioral measures of speech detection and discrimination are difficult to obtain (e.g., infants, young children, and difficult-to-test patients). These potentials have shown promising, though preliminary, results in assessing benefit from sensory aids and monitoring of an individual's progress with auditory training. This study aimed to investigate systematically the effects of sensorineural hearing loss on cortical event-related potentials and their associated behavioral measures (d sensitivity and reaction-time) to the speech sounds /ba/ and /da/. Both ERP amplitudes and behavioral discrimination (d) scores were lower for listeners with sensorineural hearing loss than for those with normal hearing, but this effect was evident only for listeners with more pronounced hearing impairment. In contrast, prolongations in the ERP and behavioral latencies, relative to responses from normal-hearing subjects, began with even mild threshold elevations. The results indicate that latency measures are more sensitive indicators of the early effects of decreased audibility than are response strength (amplitude, d, or percent correct) measures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".