@phdthesis{LissonHernandez2022,
  author    = {Liss{\´o}n Hern{\´a}ndez, Paula J.},
  title     = {Computational models of sentence comprehension in aphasia},
  doi       = {10.25932/publishup-55548},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555487},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vi, 159},
  year      = {2022},
  abstract  = {It is well-known that individuals with aphasia (IWA) have difficulties understanding sentences that involve non-adjacent dependencies, such as object relative clauses or passives (Caplan, Baker, \& Dehaut, 1985; Caramazza \& Zurif, 1976). A large body of research supports the view that IWA's grammatical system is intact, and that comprehension difficulties in aphasia are caused by a processing deficit, such as a delay in lexical access and/or in syntactic structure building (e.g., Burkhardt, Pi{\~n}ango, \& Wong, 2003; Caplan, Michaud, \& Hufford, 2015; Caplan, Waters, DeDe, Michaud, \& Reddy, 2007; Ferrill, Love, Walenski, \& Shapiro, 2012; Hanne, Burchert, De Bleser, \& Vasishth, 2015; Love, Swinney, Walenski, \& Zurif, 2008). The main goal of this dissertation is to computationally investigate the processing sources of comprehension impairments in sentence processing in aphasia. In this work, prominent theories of processing deficits coming from the aphasia literature are implemented within two cognitive models of sentence processing -the activation-based model (Lewis \& Vasishth, 2005) and the direct-access model (McEl- ree, 2000)-. These models are two different expressions of the cue-based retrieval theory (Lewis, Vasishth, \& Van Dyke, 2006), which posits that sentence processing is the result of a series of iterative retrievals from memory. These two models have been widely used to account for sentence processing in unimpaired populations in multiple languages and linguistic constructions, sometimes interchangeably (Parker, Shvarts- man, \& Van Dyke, 2017). However, Nicenboim and Vasishth (2018) showed that when both models are implemented in the same framework and fitted to the same data, the models yield different results, because the models assume different data- generating processes. Specifically, the models hold different assumptions regarding the retrieval latencies. The second goal of this dissertation is to compare these two models of cue-based retrieval, using data from individuals with aphasia and control participants. We seek to answer the following question: Which retrieval mechanism is more likely to mediate sentence comprehension? We model 4 subsets of existing data: Relative clauses in English and German; and control structures and pronoun resolution in German. The online data come from either self-paced listening experiments, or visual-world eye-tracking experiments. The offline data come from a complementary sentence-picture matching task performed at the end of the trial in both types of experiments. The two competing models of retrieval are implemented in the Bayesian framework, following Nicenboim and Vasishth (2018). In addition, we present a modified version of the direct-acess model that - we argue - is more suitable for individuals with aphasia. This dissertation presents a systematic approach to implement and test verbally- stated theories of comprehension deficits in aphasia within cognitive models of sen- tence processing. The conclusions drawn from this work are that (a) the original direct-access model (as implemented here) cannot account for the full pattern of data from individuals with aphasia because it cannot account for slow misinterpretations; and (b) an activation-based model of retrieval can account for sentence comprehension deficits in individuals with aphasia by assuming a delay in syntactic structure building, and noise in the processing system. The overall pattern of results support an activation-based mechanism of memory retrieval, in which a combination of processing deficits, namely slow syntax and intermittent deficiencies, cause comprehension difficulties in individuals with aphasia.},
  language  = {en}
}