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Individuals with agrammatic Broca's aphasia experience difficulty when processing reversible non-canonical sentences. Different accounts have been proposed to explain this phenomenon. The Trace Deletion account (Grodzinsky, 1995, 2000, 2006) attributes this deficit to an impairment in syntactic representations, whereas others (e.g., Caplan, Waters, Dede, Michaud, & Reddy, 2007; Haarmann, Just, & Carpenter, 1997) propose that the underlying structural representations are unimpaired, but sentence comprehension is affected by processing deficits, such as slow lexical activation, reduction in memory resources, slowed processing and/or intermittent deficiency, among others. We test the claims of two processing accounts, slowed processing and intermittent deficiency, and two versions of the Trace Deletion Hypothesis (TDH), in a computational framework for sentence processing (Lewis & Vasishth, 2005) implemented in ACT-R (Anderson, Byrne, Douglass, Lebiere, & Qin, 2004). The assumption of slowed processing is operationalized as slow procedural memory, so that each processing action is performed slower than normal, and intermittent deficiency as extra noise in the procedural memory, so that the parsing steps are more noisy than normal. We operationalize the TDH as an absence of trace information in the parse tree. To test the predictions of the models implementing these theories, we use the data from a German sentence—picture matching study reported in Hanne, Sekerina, Vasishth, Burchert, and De Bleser (2011). The data consist of offline (sentence-picture matching accuracies and response times) and online (eye fixation proportions) measures. From among the models considered, the model assuming that both slowed processing and intermittent deficiency are present emerges as the best model of sentence processing difficulty in aphasia. The modeling of individual differences suggests that, if we assume that patients have both slowed processing and intermittent deficiency, they have them in differing degrees.
Background: In addition to the canonical subject-verb-object (SVO) word order, German also allows for non-canonical order (OVS), and the case-marking system supports thematic role interpretation. Previous eye-tracking studies (Kamide et al., 2003; Knoeferle, 2007) have shown that unambiguous case information in non-canonical sentences is processed incrementally. For individuals with agrammatic aphasia, comprehension of non-canonical sentences is at chance level (Burchert et al., 2003). The trace deletion hypothesis (Grodzinsky 1995, 2000) claims that this is due to structural impairments in syntactic representations, which force the individual with aphasia (IWA) to apply a guessing strategy. However, recent studies investigating online sentence processing in aphasia (Caplan et al., 2007; Dickey et al., 2007) found that divergences exist in IWAs' sentence-processing routines depending on whether they comprehended non-canonical sentences correctly or not, pointing rather to a processing deficit explanation. Aims: The aim of the current study was to investigate agrammatic IWAs' online and offline sentence comprehension simultaneously in order to reveal what online sentence-processing strategies they rely on and how these differ from controls' processing routines. We further asked whether IWAs' offline chance performance for non-canonical sentences does indeed result from guessing. Methods Procedures: We used the visual-world paradigm and measured eye movements (as an index of online sentence processing) of controls (N = 8) and individuals with aphasia (N = 7) during a sentence-picture matching task. Additional offline measures were accuracy and reaction times. Outcomes Results: While the offline accuracy results corresponded to the pattern predicted by the TDH, IWAs' eye movements revealed systematic differences depending on the response accuracy. Conclusions: These findings constitute evidence against attributing IWAs' chance performance for non-canonical structures to mere guessing. Instead, our results support processing deficit explanations and characterise the agrammatic parser as deterministic and inefficient: it is slowed down, affected by intermittent deficiencies in performing syntactic operations, and fails to compute reanalysis even when one is detected.
Judging the animacy of words
(2017)
The age at which members of a semantic category are learned (age of acquisition), the typicality they demonstrate within their corresponding category, and the semantic domain to which they belong (living, non-living) are known to influence the speed and accuracy of lexical/semantic processing. So far, only a few studies have looked at the origin of age of acquisition and its interdependence with typicality and semantic domain within the same experimental design. Twenty adult participants performed an animacy decision task in which nouns were classified according to their semantic domain as being living or non-living. Response times were influenced by the independent main effects of each parameter: typicality, age of acquisition, semantic domain, and frequency. However, there were no interactions. The results are discussed with respect to recent models concerning the origin of age of acquisition effects.
Judging the animacy of words
(2016)
The age at which members of a semantic category are learned (age of acquisition), the typicality they demonstrate within their corresponding category, and the semantic domain to which they belong (living, non-living) are known to influence the speed and accuracy of lexical/semantic processing. So far, only a few studies have looked at the origin of age of acquisition and its interdependence with typicality and semantic domain within the same experimental design. Twenty adult participants performed an animacy decision task in which nouns were classified according to their semantic domain as being living or non-living. Response times were influenced by the independent main effects of each parameter: typicality, age of acquisition, semantic domain, and frequency. However, there were no interactions. The results are discussed with respect to recent models concerning the origin of age of acquisition effects.