The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Recognizing Spoken Words: The Neighborhood Activation Model
Open accessAbstract
OBJECTIVE: A fundamental problem in the study of human spoken word recognition concerns the structural relations among the sound patterns of words in memory and the effects these relations have on spoken word recognition. In the present investigation, computational and experimental methods were employed to address a number of fundamental issues related to the representation and structural organization of spoken words in the mental lexicon and to lay the groundwork for a model of spoken word recognition. DESIGN: Using a computerized lexicon consisting of transcriptions of 20,000 words, similarity neighborhoods for each of the transcriptions were computed. Among the variables of interest in the computation of the similarity neighborhoods were: 1) the number of words occurring in a neighborhood, 2) the degree of phonetic similarity among the words, and 3) the frequencies of occurrence of the words in the language. The effects of these variables on auditory word recognition were examined in a series of behavioral experiments employing three experimental paradigms: perceptual identification of words in noise, auditory lexical decision, and auditory word naming. RESULTS: The results of each of these experiments demonstrated that the number and nature of words in a similarity neighborhood affect the speed and accuracy of word recognition. A neighborhood probability rule was developed that adequately predicted identification performance. This rule, based on Luce's (1959) choice rule, combines stimulus word intelligibility, neighborhood confusability, and frequency into a single expression. Based on this rule, a model of auditory word recognition, the neighborhood activation model, was proposed. This model describes the effects of similarity neighborhood structure on the process of discriminating among the acoustic-phonetic representations of words in memory. The results of these experiments have important implications for current conceptions of auditory word recognition in normal and hearing impaired populations of children and adults.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Lexical access and naming time
Forster, K. I., & Chambers, S. M. (1973) · Journal of Verbal Learning and Verbal Behavior · doi:10.1016/s0022-5371(73)80042-8 · Cited by 1,132
A computational analysis of uniqueness points in auditory word recognition
Luce, P. A. (1986) · Perception & Psychophysics · doi:10.3758/bf03212485 · Cited by 206
Self-citation
Cited by
The cortical organization of speech processing
Hickok, G., & Poeppel, D. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2113 · Cited by 5,495
Speech synthesis from neural decoding of spoken sentences
Anumanchipalli, G. K., Chartier, J., & Chang, E. F. (2019) · Nature · doi:10.1038/s41586-019-1119-1 · Cited by 883
Speech recognition in adverse conditions: A review.
Mattys, S. L., Davis, M. H., Bradlow, A. R., & Scott, S. K. (2012) · Language and Cognitive Processes · doi:10.1080/01690965.2012.705006 · Cited by 809
Peelle, J. E. (2018) · Ear & Hearing · doi:10.1097/aud.0000000000000494 · Cited by 672
Davis, M. H., Johnsrude, I. S., Hervais-Adelman, A., Taylor, K., & McGettigan, C. (2005) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.134.2.222 · Cited by 531
Metadata
- Authors
- Luce, P. A., & Pisoni, D. B.
- Venue
- Ear and Hearing
- Published
- 1998-02
- Volume
- 19
- Issue
- 1
- Pages
- 1-36
- ISSN
- 0196-0202 (ISSN-L)
- OpenAlex
- W2026992087
- Cited by
- 1,939
- References
- 2
- FWCI
- 52.35
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Recognizing Spoken Words: The Neighborhood Activation Model. https://app.cochat.ai/papers/10.1097/00003446-199802000-00001
- The paper
- Luce, P. A., & Pisoni, D. B. (1998). Recognizing Spoken Words: The Neighborhood Activation Model. Ear and Hearing. https://doi.org/10.1097/00003446-199802000-00001
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.