Rasch Model and Its Extensions for Analysis of Aphasic Deficits in Syntactic Comprehension

Abstract

Aphasia is the loss of the ability to produce and/or comprehend language, due to injury to brain areas responsible for these functions. Aphasic patients' performance on comprehension tests has traditionally been related both to the patient's individual ability and to the difficulty of the test questions. The natural choice for analysis of these test results is the Rasch model. It assumes that the probability of a patient responding correctly to a question is the inverse-logit function of the difference between the individual patient's ability and the difficulty of the test question. This study first modeled the way aphasic patients process different sentence types, as well as their ability to accomplish tasks using Rasch models. However, several scientifically important features of the data, such as the correlation of correct responses between two different comprehension tasks, and the association between response patterns in control sentences and response patterns in experimental sentences, were found to be inadequately captured by such models. Alternatively, we used a full Bayesian approach, exploring a mixture of generalized linear mixed models that clustered patients into similar response patterns and abilities. The mixture model was found to better describe the experimental results than any other model examined. The mixture model also expresses the hypothesis that aphasic patients can be classified into different ability and response profile groups, and that patients utilize different cognitive resources in different comprehension tasks. These results are scientifically important and could not have been discovered by using the simple Rasch model. This article has supplementary material online.

Dissociations and associations of performance in syntactic comprehension in aphasia and their implications for the nature of aphasic deficits

“…The between-task correlations and factor analyses replicate existing data (Caplan et al, 1996, 2006, 2007; Gutman et al, 2010, 2011). These results show that performance on all sentence types is significantly correlated in aphasia.…”

“…Evidence for this was summarized by Caplan (2012) and includes factor analyses of performance, which have routinely yielded first factors in unrotated analyses on which all sentence types in a study load significantly, which account for the great majority of variance in accuracy (up to 85%) (Caplan et al, 1996, 2007); Rasch models of aphasic performance (Gutman et al, 2010, 2011); and simulations of aphasic performance (Miyake et al, 1994, 1995). Coupled with the finding that more complex sentences are more often misunderstood than simpler sentences (Caplan et al, 1985, 1996, 2007), associations of performance have led to various “processing resource reduction” models of aphasic syntactic comprehension (Caplan, 2012: Miyake et al, 1994, 1995; Haarman and Kolk, 1991a, b; McNeil et al, 1991; see below for discussion).…”

A rational inference approach to group and individual-level sentence comprehension performance in aphasia

“…The fact that performance on actives and passives did not differ in the analysis comparing performance on active/passive and DO/PO structures reinforces the conclusion that performance was better on passives than on DOs and POs because it shows that the effect of active/passive versus DO/PO did not result because high accuracy for the active structures raised the mean accuracy for the active/passive structures. It is possible that resource-related accounts (Caplan et al, 2013; Gutman et al, 2011; Hula & McNeil, 2008; Murray, 2012) might be consistent with the current findings if the resource demands imposed by three-argument verbs are measurably greater than those imposed by passive structures, but this is speculative.…”

“…All existing models of aphasic sentence comprehension predict that PWA will perform more poorly with passives than actives, including structural-impairment models (e.g., the Trace Deletion Hypothesis, Grodzinsky, 2000; the Double Dependency Hypothesis; Mauner et al, 1993; Relativized Minimality models, Garraffa & Grillo, 2008; Varlakosta et al, 2014, the Intervener Hypothesis, Sheppard, et al, 2015; Sullivan et al, 2016; slowed-syntax models, Burkhardt et al, 2003; the Derived Order Problem Hypothesis, Bastiaanse & van Zonneveld, 2006), mapping theories (e.g. Schwartz et al, 1987; O’Grady & Lee, 2005), lexical-impairment models (e.g., the Delayed Lexical Access Model, Love et al, 2008; Ferrill et al, 2012; the Lexical Integration Deficit hypothesis, Meyer et al, 2012; see also the Lexical Bias Hypothesis; Gahl, 2002), and general processing deficit models (Caplan, et al, 2007; Gutman, et al, 2011; Hula & McNeil, 2008; Murray, 2012). As noted above, some current models also predict that PWA will perform more poorly on DO than PO structures.…”

Statistical Inference for Noisy Incomplete Binary Matrix

“…Specifically, we consider the inference for 1-bit matrix completion under a unidimensional nonlinear factor analysis model with the logit link. Such a nonlinear factor model is one of the most popular models for multivariate binary data, having received much attention from the theoretical perspective (Andersen, 1970;Haberman, 1977;Lindsay et al, 1991;Rice, 2004), as well as wide applications in various areas, including educational testing (van der Linden and Hambleton, 2013), word acquisition analysis (Kidwell et al, 2011), syntactic comprehension (Gutman et al, 2011), and analysis of health outcomes (Hagquist and Andrich, 2017). It is also referred to as the Rasch model (Rasch, 1960) in psychometrics literature.…”