Semantic complexity in treatment of naming deficits in aphasia: evidence from well-defined categories

Abstract

Purpose: Our previous work on manipulating typicality of category exemplars during treatment of naming deficits has shown that training atypical examples generalizes to untrained typical examples but not vice versa. In contrast to natural categories that consist of fuzzy boundaries, well-defined categories (e.g., shapes) have rigid category boundaries. Whether these categories illustrate typicality effects similar to natural categories is under debate. The present study addressed this question in the context of treatment for naming deficits in aphasia.

Methods: Using a single-subject experiment design, 3 participants with aphasia received a semantic feature treatment to improve naming of either typical or atypical items of shapes, while generalization was tested to untrained items of the category.

Results: For 2 of the 3 participants, training naming of atypical examples of shapes resulted in improved naming of untrained typical examples. Training typical examples in 1 participant did not improve naming of atypical examples. All 3 participants, however, showed weak acquisition trends.

Conclusions: Results of the present study show equivocal support for manipulating typicality as a treatment variable within well-defined categories. Instead, these results indicate that acquisition and generalization effects within well-defined categories such as shapes are overshadowed by their inherent abstractness.

Figure 1

Naming accuracy in percentage points for atypical (trained) and typical items (untrained) for the category shapes during baseline and treatment phases for P1.

Figure 2

Naming accuracy in percentage points for typical (trained) and atypical (untrained) items for the category shapes for participant 2. Treatment is shifted to the untrained atypical examples during the second phase of treatment. Performance on the previously trained typical examples is assessed in the form of maintenance probes.

Figure 3

Naming accuracy in percentage points for atypical (trained) and typical (untrained) items for the category shapes during baseline and treatment phases for P3.