Effects of Semantic Feature Type, Diversity, and Quantity on Semantic Feature Analysis Treatment Outcomes in Aphasia

2020 ◽  
pp. 1-15
Author(s):  
William S. Evans ◽  
Rob Cavanaugh ◽  
Michelle L. Gravier ◽  
Alyssa M. Autenreith ◽  
Patrick J. Doyle ◽  
...  
Keyword(s):  
Treatment Outcomes ◽  
Treatment Response ◽  
Semantic Feature ◽  
Feature Analysis ◽  
Semantic Features ◽  
Feature Generation ◽  
Baseline Design ◽  
Improve Treatment ◽  
Future Work ◽  
Semantic Feature Analysis

Purpose Semantic feature analysis (SFA) is a naming treatment found to improve naming performance for both treated and semantically related untreated words in aphasia. A crucial treatment component is the requirement that patients generate semantic features of treated items. This article examined the role feature generation plays in treatment response to SFA in several ways: It attempted to replicate preliminary findings from Gravier et al. (2018), which found feature generation predicted treatment-related gains for both trained and untrained words. It examined whether feature diversity or the number of features generated in specific categories differentially affected SFA treatment outcomes. Method SFA was administered to 44 participants with chronic aphasia daily for 4 weeks. Treatment was administered to multiple lists sequentially in a multiple-baseline design. Participant-generated features were captured during treatment and coded in terms of feature category, total average number of features generated per trial, and total number of unique features generated per item. Item-level naming accuracy was analyzed using logistic mixed-effects regression models. Results Producing more participant-generated features was found to improve treatment response for trained but not untrained items in SFA, in contrast to Gravier et al. (2018). There was no effect of participant-generated feature diversity or any differential effect of feature category on SFA treatment outcomes. Conclusions Patient-generated features remain a key predictor of direct training effects and overall treatment response in SFA. Aphasia severity was also a significant predictor of treatment outcomes. Future work should focus on identifying potential nonresponders to therapy and explore treatment modifications to improve treatment outcomes for these individuals. Supplemental Material https://doi.org/10.23641/asha.12462596

Semantic Models and Translating

Target ◽  
1994 ◽  
Vol 6 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Paul Kussmaul
Keyword(s):  
Word Meaning ◽  
Semantic Feature ◽  
Feature Analysis ◽  
Semantic Features ◽  
Word Meanings ◽  
The Core ◽  
Semantic Models ◽  
Structural Semantics ◽  
Semantic Feature Analysis

Abstract This paper examines the relevance of three semantic models for translation. Structural semantics, more specifically semantic feature analysis, has given rise to the maxim that we should translate "bundles of semantic features". Prototype semantics suggests that word-meanings have cores and fuzzy edges which are influenced by culture. For translation this means that we do not necessarily translate bundles of features but have to decide whether to focus on the core or the fuzzy edges of the meaning of a particular word. Scenesand-frames semantics suggests that word meaning is influenced by context and the situation we are in. Word-meaning is thus not static but dynamic, and it is this dynamism which should govern our decisions as translators.

The relationship between verbal and gestural contributions in conversation

Gesture ◽  
2009 ◽  
Vol 9 (3) ◽  
pp. 312-336 ◽  
Author(s):  
Jennifer Gerwing ◽  
Meredith Allison
Keyword(s):  
Spatial Information ◽  
Semantic Feature ◽  
Feature Analysis ◽  
Semantic Features ◽  
Essential Information ◽  
Categorical Information ◽  
Complex Picture ◽  
Almost All ◽  
The Relationship ◽  
Semantic Feature Analysis

Gestures and their concurrent words are often said to be meaningfully related and co-expressive. Research has shown that gestures and words are each particularly suited to conveying different kinds of information. In this paper, we describe and compare three methods for investigating the relationship between gestures and words: (1) an analysis of deictic expressions referring to gestures, (2) an analysis of the redundancy between information presented in words vs. in gestures, and (3) an analysis of the semantic features represented in words and gestures. We also apply each of these three methods to one set of data, in which 22 pairs of participants used words and gestures to design the layout of an apartment. Each of the three analyses revealed a different picture of the complementary relationship between gesture and speech. According to the deictic analysis, participant speakers marked only a quarter of their gestures as providing essential information that was missing from the speech, but the redundancy analysis indicated that almost all gestures contributed information that was not in the words. The semantic feature analysis showed that participants conveyed spatial information in their gestures more often than in their words. A follow-up analysis showed that participants contributed categorical information (i.e., the name of each room) in their words. Of the three methods, the semantic feature analysis yielded the most complex picture of the data, and it served to generate additional analyses. We conclude that although analyses of deictic expressions and redundancy are useful for characterizing gesture use in differing conditions, the semantic feature method is best for exploring the complementary, semantic relationship between gesture and speech.

What Matters in Semantic Feature Analysis: Practice-Related Predictors of Treatment Response in Aphasia

2018 ◽  
Vol 27 (1S) ◽  
pp. 438-453 ◽  
Author(s):  
Michelle L. Gravier ◽  
Michael W. Dickey ◽  
William D. Hula ◽  
William S. Evans ◽  
Rebecca L. Owens ◽  
...  
Keyword(s):  
Treatment Response ◽  
Treatment Time ◽  
Semantic Feature ◽  
Feature Analysis ◽  
Treatment Trials ◽  
Total Treatment Time ◽  
Total Treatment ◽  
Number Of Treatment ◽  
Semantic Feature Analysis

Purpose This study investigated the predictive value of practice-related variables—number of treatment trials delivered, total treatment time, average number of trials per hour, and average number of participant-generated features per trial—in response to semantic feature analysis (SFA) treatment. Method SFA was administered to 17 participants with chronic aphasia daily for 4 weeks. Individualized treatment and semantically related probe lists were generated from items that participants were unable to name consistently during baseline testing. Treatment was administered to each list sequentially in a multiple-baseline design. Naming accuracy for treated and untreated items was obtained at study entry, exit, and 1-month follow-up. Results Item-level naming accuracy was analyzed using logistic mixed-effect regression models. The average number of features generated per trial positively predicted naming accuracy for both treated and untreated items, at exit and follow-up. In contrast, total treatment time and average trials per hour did not significantly predict treatment response. The predictive effect of number of treatment trials on naming accuracy trended toward significance at exit, although this relationship held for treated items only. Conclusions These results suggest that the number of patient-generated features may be more strongly associated with SFA-related naming outcomes, particularly generalization and maintenance, than other practice-related variables. Supplemental Materials https://doi.org/10.23641/asha.5734113

Phonomotor Versus Semantic Feature Analysis Treatment for Anomia in 58 Persons With Aphasia: A Randomized Controlled Trial

2019 ◽  
Vol 62 (12) ◽  
pp. 4464-4482 ◽  
Author(s):  
Diane L. Kendall ◽  
Megan Oelke Moldestad ◽  
Wesley Allen ◽  
Janaki Torrence ◽  
Stephen E. Nadeau
Keyword(s):  
Randomized Controlled Trial ◽  
Response Latency ◽  
Controlled Trial ◽  
Semantic Knowledge ◽  
Semantic Feature ◽  
Feature Analysis ◽  
Group Differences ◽  
Randomized Controlled ◽  
Confrontation Naming ◽  
Semantic Feature Analysis

Purpose The ultimate goal of anomia treatment should be to achieve gains in exemplars trained in the therapy session, as well as generalization to untrained exemplars and contexts. The purpose of this study was to test the efficacy of phonomotor treatment, a treatment focusing on enhancement of phonological sequence knowledge, against semantic feature analysis (SFA), a lexical-semantic therapy that focuses on enhancement of semantic knowledge and is well known and commonly used to treat anomia in aphasia. Method In a between-groups randomized controlled trial, 58 persons with aphasia characterized by anomia and phonological dysfunction were randomized to receive 56–60 hr of intensively delivered treatment over 6 weeks with testing pretreatment, posttreatment, and 3 months posttreatment termination. Results There was no significant between-groups difference on the primary outcome measure (untrained nouns phonologically and semantically unrelated to each treatment) at 3 months posttreatment. Significant within-group immediately posttreatment acquisition effects for confrontation naming and response latency were observed for both groups. Treatment-specific generalization effects for confrontation naming were observed for both groups immediately and 3 months posttreatment; a significant decrease in response latency was observed at both time points for the SFA group only. Finally, significant within-group differences on the Comprehensive Aphasia Test–Disability Questionnaire ( Swinburn, Porter, & Howard, 2004 ) were observed both immediately and 3 months posttreatment for the SFA group, and significant within-group differences on the Functional Outcome Questionnaire ( Glueckauf et al., 2003 ) were found for both treatment groups 3 months posttreatment. Discussion Our results are consistent with those of prior studies that have shown that SFA treatment and phonomotor treatment generalize to untrained words that share features (semantic or phonological sequence, respectively) with the training set. However, they show that there is no significant generalization to untrained words that do not share semantic features or phonological sequence features.

Take Time For Action: Reasoning about Linear Equations

2002 ◽  
Vol 8 (3) ◽  
pp. 146-148
Author(s):  
A.Susan Gay ◽  
Charlotte J. Keith
Keyword(s):  
Linear Equations ◽  
Semantic Feature ◽  
Feature Analysis ◽  
Mathematics Classrooms ◽  
Literacy Strategy ◽  
Semantic Feature Analysis

Have you heard of or used semantic feature analysis? It is a literacy strategy that can help students determine relationships among related vocabulary terms (Cooter and Flynt 1996). In mathematics classrooms, the use of semantic feature analysis is relatively new.

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