Event Abstract

AutoRAT at your fingertips: Introducing the new Russian Aphasia Test on a tablet

  • 1 National Research University Higher School of Economics, Russia
  • 2 Moscow Research Institute of Psychiatry, Russia
  • 3 University of Groningen, Netherlands
  • 4 Center for Speech Pathology and Neurorehabilitation, Russia
  • 5 Lomonosov Moscow State University, Russia

Over 260 million people speak Russian worldwide, making it the 7th most common language by the number of speakers. Still there is a paucity of standardized linguistic tests for assessment of language disorders in Russian speakers, making it impossible to specify the type and severity of linguistic deficit in individuals in clinical practice and research studies. In response to these medical and experimental needs, a novel standardized aphasia test – the Russian Aphasia Test (RAT) – has been developed. The principal novelty of this test is that each subtest corresponds to a specific level of linguistic processing in one of the four language domains: auditory comprehension, oral production, reading, and writing. In selection of specific tasks for each level of processing we took into account the structure and materials of contemporary standardized aphasia tests in other languages (e.g., Comprehension Aphasia Test, Howard et al., 2010; Psycholinguistic Assessment of Language Processing in Aphasia, Kay et al., 1996), as well as modern (psycho)linguistics theories, and the structural and phonetic properties of the Russian language. Furthermore, to maximally standardize the presentation and scoring procedures the whole test was implemented on tablet: for each subtest the stimuli are presented automatically and the responses are recorded and scored (for comprehension tasks only) also automatically. The RAT is the first comprehensive language test to be fully automatized. Here we present the oral-modality automatized subtests of the RAT – for auditory comprehension and oral production. Both of them encompass the four target linguistic levels: phonological, lexical, syntactic and discourse. The comprehension subtests include: judgment of minimal pairs of nonwords; lexical decision; word to picture matching for objects and actions; sentence to picture matching for syntactic constructions of varying complexity; comprehension of orally presented stories indexed by response accuracy to a set of 16 yes-no questions on explicit and implicit content of the stories. The production subtests are: repetition of nonwords, real words and sentences; naming of objects and actions; sentence construction using syntactic priming paradigm; picture description. We will show how these tasks are presented on a tablet and how the scoring procedures are implemented. In addition, we will present data on preliminary standardization of the automatized test in a group of healthy age-matched controls (n=20) and individuals with different types of aphasia (n=20). We will discuss in detail the various performance patterns observed. This project is supported by Russian Scientific Foundation for Humanities, grant №14-04-00596.

References

Howard, D., Swinburn, K., & Porter, G. (2010). Putting the CAT out: what the comprehensive aphasia test has to offer, Aphasiology, 24, 56-74.
Kay, J., Lesser, R., & Coltheart, M. (2007). Psycholinguistic assessments of language processing in aphasia (PALPA): An introduction. Aphasiology, 10, 159-180.

Keywords: Aphasia, standardized language assessment, Computerized testing, Language comprehension, Language production, Psychometrics

Conference: 54th Annual Academy of Aphasia Meeting, Llandudno, United Kingdom, 16 Oct - 18 Oct, 2016.

Presentation Type: Poster Sessions

Topic: Academy of Aphasia

Citation: Ivanova M, Dragoy O, Akinina J, Soloukhina O, Iskra E, Khudyakova M and Akhutina T (2016). AutoRAT at your fingertips: Introducing the new Russian Aphasia Test on a tablet
. Front. Psychol. Conference Abstract: 54th Annual Academy of Aphasia Meeting. doi: 10.3389/conf.fpsyg.2016.68.00116

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Received: 30 Apr 2016; Published Online: 15 Aug 2016.

* Correspondence: Dr. Maria Ivanova, National Research University Higher School of Economics, Moscow, Russia, ivanova@berkeley.edu