Word production deficits are a cardinal feature of aphasia, a communication disorder affecting 2.4 million
Americans (Simmons-Mackie & Cherney, 2018). Semantic Feature Analysis (SFA; Boyle & Coelho, 1995) is
one of the most used treatments for word production deficits in aphasia (Tierney-Hendricks et al., 2021). The
goal of SFA is to improve spoken word production by guiding persons with aphasia to produce semantic
features related to treatment target (traditionally, a concrete noun). The efficacy of SFA is well supported by
meta-analyses including over 50 participants (Efstratiadou et al., 2018; Quique et al., 2019). Feature
generation is a key active ingredient of SFA (Boyle, 2010; Evans et al., 2021, Cavanaugh, Swiderski et al.,
2022) and the treatment’s mechanism of action is hypothesized to be spreading activation between semantic
concepts (Boyle et al., 2022). However, the nature of the information encoded during spreading activation
remains unknown and characterization of the brain networks supporting this activity is incomplete.
One way to address this knowledge gap is to test whether competing semantic models (taxonomic,
distributional, or experiential) models better fit neural data elicited by semantic feature generation.
Taxonomic, distributional, and experiential models represent concepts by category membership, patterns of
co-occurrence, and sensory, motor, and affective experiences, respectively. Using representational similarity
analysis, Fernandino and colleagues (2022) found that experiential feature-vectors correlated more strongly
with neural data elicited by a concept-familiarity task than did taxonomic and distributional feature-vectors.
Distributional feature-vectors have also been used to match BOLD signal elicited by an SFA-analogous covert
semantic feature generation task to individual concept labels with over 80% accuracy (Anderson et al., 2016).
We propose to extend these findings to people with aphasia and age-matched healthy adults. Aim 1 will
identify the semantic model that most strongly correlates with BOLD signal elicited by semantic feature
generation. We hypothesize that experiential and distributional models will show the highest agreement with
the neural data for both participant groups. In Aim 2 we will decode the BOLD signal elicited by this task
associated with single object concepts; we hypothesize that decoder accuracy will exceed 80% for healthy adults
and be well above chance for participants with aphasia. In aim 3 we will identify relationships between decoder
accuracy and theoretically and clinically relevant psycholinguistic abilities along with critical patient
characteristics in persons with aphasia. We hypothesize that decoder accuracy will correlate more strongly with
semantic than phonological language tasks and negatively correlate with patient characteristics such as lesion
volume. This study will improve our understanding of the neurocognitive mechanisms underlying SFA and
support future developments, including the creation of in silico models enabling the study of normal and
impaired lexical semantic processing and its downstream effects on other language systems.