Language Imaging Lab Aphasia Battery

Introduction

**Figure 1.** Schematic model of posterior langugage systems supporting repetition, comprehension, naming, propositional speech, and reading aloud.

The Language Imaging Laboratory has developed and tested an extensive suite of computerized tests for assessing language deficits in people with aphasia. The battery focuses on lexical-level phonologic, orthographic, and semantic tasks that assess the hypothetical network shown in Figure 1. This schematic model illustrates minimal architectural elements needed to account for behaviors such as naming objects, comprehending speech, and reading. Evidence for the processing subcomponents of the model comes from decades of neuropsychological and psycholinguistic research documenting behavioral dissociations and effects of sublexical and lexical variables on processing speed (e.g., Caplan, 1992; Levelt, 1989; Nadeau et al., 2000; Shallice, 1989). We typically supplement the battery with standardized measures from the Alberta Language Function Assessment Battery (a computerized battery developed by Chris Westbury at the University of Alberta (www.psych.ualberta.ca/~westburylab/downloads/alfab.download.html)), with various standardized measures of executive function and processing speed, and with the short form of the Boston Diagnostic Aphasia Test to enable traditional syndromic classification.

Background and Test Development

Our aim in developing this battery was to create a set of well-controlled instruments for defining specific psycholinguistic processing deficits for use in voxel-based behavior-lesion mapping studies. Most of the tests are designed to manipulate multiple stimulus factors of potential interest (e.g., word frequency, imageability, length), and to provide reasonable statistical power for detecting sensitivity to these factors, thus the tests tend to be somewhat lengthy. Many of the tests began as materials for fMRI experiments (e.g., Binder et al., 1999; Binder et al., 2003; Binder et al., 2005a; Binder et al., 2006; Binder et al., 2005b; Desai et al., 2006; Liebenthal et al., 2005; Mano et al., 2013; Pillay et al., 2018; Sabsevitz et al., 2003). Meeting the rigorous methodological standards of these experiments required compilation of published databases and development of tools for computing n-gram and n-phone frequencies; orthographic and phonologic neighborhood density and neighbor frequencies; word frequencies; spelling-sound consistency metrics; and other variables relevant to word recognition and production. These tools, which include automated routines for generating novel pseudowords based on English serial letter dependencies, were made freely available to the research community in 2005 as “MCWord” and are available at www.neuro.mcw.edu/mcword. Orthographic and phonologic statistics are based on the English CELEX database (Baayen et al., 1995). Other data used for stimulus selection and matching include imageability ratings averaged from multiple sources (Bird et al., 2001; Clark & Paivio, 2004; Coltheart, 2003; Cortese & Fugett, 2004; Schock et al., 2012); familiarity and age-of-acquisition ratings (Bird et al., 2001; Coltheart, 2003; Gilhooly & Logie, 1980); and published picture recognition norms (Snodgrass & Vanderwart, 1980; Snodgrass & Yuditsky, 1996). Many of the tests have been normed using healthy control samples (n = 24-30, depending on test) that were age-, sex-, and education-matched to our typical stroke patient sample. Tests results can thus be transformed to z-scores as needed, although in typical applications the focus is on dissociations in performance within an individual and how these dissociations vary within the stroke patient sample. In this approach, patients provide their own internal control data (Binder et al., 2016; Pillay et al., 2017a; Pillay et al., 2018; Pillay et al., 2014b).

Apparatus

We test patients in a quiet clinic area within a closed room. Auditory and visual stimuli are presented using a computer to maximize standardization and efficiency of presentation. Manual responses are recorded using a touch-sensitive computer screen. Accuracy and response time are scored automatically for tests with manual choice responses. Spoken responses are automatically digitized into the computer (16 bit, 44.1 kHz) and later transcribed phonetically from the digital recordings. The apparatus (Figure 2) consists of a laptop computer running custom software programmed in the "LiveCode" environment (livecode.com), a touch-sensitive LCD screen (MicroTouch model KTLC-151OT), a headphone set worn by the examiner, and a headphone set with attached microphone (Audio-Technica ATH-PDG1) worn by the participant. The headset microphone ensures a constant mouth-microphone orientation and distance, crucial for obtaining high-SNR recordings.

General Procedure

Tests begin with a very simple instruction screen (typically a single sentence), which is read aloud to the participant, followed by a short set of practice trials in which the participant receives corrective feedback and further explanation as needed. All trials are initiated by the participant touching a green square on the screen, and participants are free to take breaks between trials and between tests as needed.

Error Analysis

Errors on production tasks are categorized according to the following definitions:

Word Trials

Error Type	Definition	Example
Semantic	semantically-related word	lamp » desk
Mixed	semantically- and phonemically-related word	shirt » skirt
Formal	phonemically-related word	lamp » damp
Regularization	phonetic pronunciation of an exception word	soot » /sut/

Word and Nonword Trials

Error Type	Definition	Example
Formal Nonword	phonemically-related nonword	lamp » /bæmp/
Unrelated Word	unrelated word	bamp » mouse
Unrelated Nonword	unrelated nonword	lamp » /bos/
Omission	no complete response produced

Nonword Trials

Error Type	Definition	Example
Lexicalization	phonemically-related word	bamp » damp

The following list contains links to pages describing the LIL tests in more detail.

Phoneme Perception and Phonologic Short-Term Memory
Reading and Phonologic Access
Lexical Retrieval
- Visual Lexical Decision
- Picture Name Rhyme Matching
- Picture Naming
- Description Naming
- Written Picture Naming
Semantic Memory
- Auditory Word-Picture Matching
- Visual Word-Picture Matching
- Semantic Noun Matching
- Semantic Verb Matching
- Semantic Picture Matching
- Semantic Feature Matching
- Noun-Noun Decision

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