View in EDS PDF Full Text

Inside Semantic Feature Analysis: A Within-Trial Analysis of Feature Quantity and Quality.

Saved in:
Bibliographic Details
Title: Inside Semantic Feature Analysis: A Within-Trial Analysis of Feature Quantity and Quality.
Authors: Cavanaugh, Robert1 rcavanaugh1@mghihp.edu, Swiderski, Alexander2,3,4, Goldberg, Emily2,3, Hula, William D.2,3, Evans, William S.2, Dickey, Michael Walsh2,4
Source: American Journal of Speech-Language Pathology. Jul2026, Vol. 35 Issue 4, p1807-1817. 11p.
Subject Terms: *Chronic diseases, *Speech therapy, Anomia, Research funding, Rehabilitation of aphasic persons, Treatment effectiveness, Stroke rehabilitation, Semantics, Stroke, Confidence intervals, Data analysis software, Disease complications
Abstract: Purpose: Aphasia rehabilitation increasingly emphasizes the importance of understanding the mechanisms and ingredients underlying behavioral interventions. Semantic feature analysis (SFA) is a commonly used intervention for anomia in aphasia, but there is little evidence directly examining its active ingredients. Using within-trial responses during SFA, we sought to explore how generating semantic features might facilitate naming improvements. Method: A retrospective analysis evaluated data collected from a clinical trial focused on intensive SFA treatment for individuals with chronic aphasia. The study included 44 adults with chronic aphasia following left-hemisphere stroke. A pretrained semantic model was used to estimate the semantic relatedness between features and targets. Results: Participants were 2.6 (95% confidence interval [CI; 2.16, 3.12]) times more likely to correctly name target words after engaging in feature generation. Each additional feature generated was associated with a three percentage-point increase (95% confidence interval [CI; 0.2, 0.4]) in the probability of a correct response at the end of the trial; a 1 SD increase in semantic similarity (i.e., feature quality) was associated with a six percentage-point increase (95% CI [0.4, 0.10]). Model comparison favored semantic similarity over feature generation count in predicting final response accuracy. Conclusions: Findings provide converging evidence that semantic feature generation is an active ingredient in SFA treatment, emphasizing the importance of feature quantity and semantic quality, consistent with a spreading activation account of SFA's benefits. Further research is warranted to validate relatedness values from semantic model embeddings and to explore the relationship between within-trial feature generation and generalization to semantically related but untreated words. [ABSTRACT FROM AUTHOR]
Copyright of American Journal of Speech-Language Pathology is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: ehh
DbLabel: Education Research Complete
An: 195279705
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Inside Semantic Feature Analysis: A Within-Trial Analysis of Feature Quantity and Quality.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Cavanaugh%2C+Robert%22">Cavanaugh, Robert</searchLink><relatesTo>1</relatesTo><i> rcavanaugh1@mghihp.edu</i><br /><searchLink fieldCode="AR" term="%22Swiderski%2C+Alexander%22">Swiderski, Alexander</searchLink><relatesTo>2,3,4</relatesTo><br /><searchLink fieldCode="AR" term="%22Goldberg%2C+Emily%22">Goldberg, Emily</searchLink><relatesTo>2,3</relatesTo><br /><searchLink fieldCode="AR" term="%22Hula%2C+William+D%2E%22">Hula, William D.</searchLink><relatesTo>2,3</relatesTo><br /><searchLink fieldCode="AR" term="%22Evans%2C+William+S%2E%22">Evans, William S.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Dickey%2C+Michael+Walsh%22">Dickey, Michael Walsh</searchLink><relatesTo>2,4</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22American+Journal+of+Speech-Language+Pathology%22">American Journal of Speech-Language Pathology</searchLink>. Jul2026, Vol. 35 Issue 4, p1807-1817. 11p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Chronic+diseases%22">Chronic diseases</searchLink><br />*<searchLink fieldCode="DE" term="%22Speech+therapy%22">Speech therapy</searchLink><br /><searchLink fieldCode="DE" term="%22Anomia%22">Anomia</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Rehabilitation+of+aphasic+persons%22">Rehabilitation of aphasic persons</searchLink><br /><searchLink fieldCode="DE" term="%22Treatment+effectiveness%22">Treatment effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Stroke+rehabilitation%22">Stroke rehabilitation</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Stroke%22">Stroke</searchLink><br /><searchLink fieldCode="DE" term="%22Confidence+intervals%22">Confidence intervals</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+complications%22">Disease complications</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: Aphasia rehabilitation increasingly emphasizes the importance of understanding the mechanisms and ingredients underlying behavioral interventions. Semantic feature analysis (SFA) is a commonly used intervention for anomia in aphasia, but there is little evidence directly examining its active ingredients. Using within-trial responses during SFA, we sought to explore how generating semantic features might facilitate naming improvements. Method: A retrospective analysis evaluated data collected from a clinical trial focused on intensive SFA treatment for individuals with chronic aphasia. The study included 44 adults with chronic aphasia following left-hemisphere stroke. A pretrained semantic model was used to estimate the semantic relatedness between features and targets. Results: Participants were 2.6 (95% confidence interval [CI; 2.16, 3.12]) times more likely to correctly name target words after engaging in feature generation. Each additional feature generated was associated with a three percentage-point increase (95% confidence interval [CI; 0.2, 0.4]) in the probability of a correct response at the end of the trial; a 1 SD increase in semantic similarity (i.e., feature quality) was associated with a six percentage-point increase (95% CI [0.4, 0.10]). Model comparison favored semantic similarity over feature generation count in predicting final response accuracy. Conclusions: Findings provide converging evidence that semantic feature generation is an active ingredient in SFA treatment, emphasizing the importance of feature quantity and semantic quality, consistent with a spreading activation account of SFA's benefits. Further research is warranted to validate relatedness values from semantic model embeddings and to explore the relationship between within-trial feature generation and generalization to semantically related but untreated words. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of American Journal of Speech-Language Pathology is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=195279705
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1044/2026_AJSLP-25-00515
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 1807
    Subjects:
      – SubjectFull: Chronic diseases
        Type: general
      – SubjectFull: Speech therapy
        Type: general
      – SubjectFull: Anomia
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Rehabilitation of aphasic persons
        Type: general
      – SubjectFull: Treatment effectiveness
        Type: general
      – SubjectFull: Stroke rehabilitation
        Type: general
      – SubjectFull: Semantics
        Type: general
      – SubjectFull: Stroke
        Type: general
      – SubjectFull: Confidence intervals
        Type: general
      – SubjectFull: Data analysis software
        Type: general
      – SubjectFull: Disease complications
        Type: general
    Titles:
      – TitleFull: Inside Semantic Feature Analysis: A Within-Trial Analysis of Feature Quantity and Quality.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Cavanaugh, Robert
      – PersonEntity:
          Name:
            NameFull: Swiderski, Alexander
      – PersonEntity:
          Name:
            NameFull: Goldberg, Emily
      – PersonEntity:
          Name:
            NameFull: Hula, William D.
      – PersonEntity:
          Name:
            NameFull: Evans, William S.
      – PersonEntity:
          Name:
            NameFull: Dickey, Michael Walsh
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 10580360
          Numbering:
            – Type: volume
              Value: 35
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: American Journal of Speech-Language Pathology
              Type: main
ResultId 1