A Computational Account of Bilingual Aphasia Rehabilitation

Abstract

Current research on bilingual aphasia highlights the paucity in recommendations for optimal rehabilitation for bilingual aphasic patients (Roberts & Kiran, 2007; Edmonds & Kiran, 2006). In this paper, we have developed a computational model to simulate an English-Spanish bilingual language system in which language representations can vary by age of acquisition (AoA) and relative proficiency in the two languages to model individual participants. This model is subsequently lesioned by varying connection strengths between the semantic and phonological networks and retrained based on individual patient demographic information to evaluate whether or not the model's prediction of rehabilitation matched the actual treatment outcome. In most cases the model comes close to the target performance subsequent to language therapy in the language trained, indicating the validity of this model in simulating rehabilitation of naming impairment in bilingual aphasia. Additionally, the amount of cross-language transfer is limited both in the patient performance and in the model's predictions and is dependent on that specific patient's AoA, language exposure and language impairment. It also suggests how well alternative treatment scenarios would have fared, including some cases where the alternative would have done better. Overall, the study suggests how computational modeling could be used in the future to design customized treatment recipes that result in better recovery than is currently possible.

Figure 1

Schematic representation of the architecture of the bilingual DISLEX model adapted from Kroll & Stewart’s (1994) theoretical model.

Figure 2

Summary representation of cross-correlation functions between patient performance and model performance for trained language (in blue) and untrained language (in red). Three distinct groups of participants emerged, in the first group (A), model matched patient performance for both the trained and untrained language, in the second group (B), model matched patient performance for the trained language only, and in the third group (C), model matched patient performance for untrained language better than the trained language.

Figure 3

Representative samples of patient performance (white background) compared with model performance (black background). For the patients, the pre-treatment baselines are reported before the first vertical line, for the model the first data is computed as the average of the pre-treatment baselines for that patient. For each patient, the probes conducted during treatment are reported between the two vertical lines and the post-treatment probes (when administered) are reported after the second vertical line. (A) One representative patient where model output matches the trained language and the untrained language. (B) Two representative patients where model output matches the trained language. No changes are noted in the untrained language. (C) Sample of a patient and model performance where model matches performance for untrained language better than for the trained language. (D) Sample of a patient where model and patient performance do not change as a function of treatment.

Figure 3

Representative samples of patient performance (white background) compared with model performance (black background). For the patients, the pre-treatment baselines are reported before the first vertical line, for the model the first data is computed as the average of the pre-treatment baselines for that patient. For each patient, the probes conducted during treatment are reported between the two vertical lines and the post-treatment probes (when administered) are reported after the second vertical line. (A) One representative patient where model output matches the trained language and the untrained language. (B) Two representative patients where model output matches the trained language. No changes are noted in the untrained language. (C) Sample of a patient and model performance where model matches performance for untrained language better than for the trained language. (D) Sample of a patient where model and patient performance do not change as a function of treatment.

Figure 3

Representative samples of patient performance (white background) compared with model performance (black background). For the patients, the pre-treatment baselines are reported before the first vertical line, for the model the first data is computed as the average of the pre-treatment baselines for that patient. For each patient, the probes conducted during treatment are reported between the two vertical lines and the post-treatment probes (when administered) are reported after the second vertical line. (A) One representative patient where model output matches the trained language and the untrained language. (B) Two representative patients where model output matches the trained language. No changes are noted in the untrained language. (C) Sample of a patient and model performance where model matches performance for untrained language better than for the trained language. (D) Sample of a patient where model and patient performance do not change as a function of treatment.

Figure 3

Representative samples of patient performance (white background) compared with model performance (black background). For the patients, the pre-treatment baselines are reported before the first vertical line, for the model the first data is computed as the average of the pre-treatment baselines for that patient. For each patient, the probes conducted during treatment are reported between the two vertical lines and the post-treatment probes (when administered) are reported after the second vertical line. (A) One representative patient where model output matches the trained language and the untrained language. (B) Two representative patients where model output matches the trained language. No changes are noted in the untrained language. (C) Sample of a patient and model performance where model matches performance for untrained language better than for the trained language. (D) Sample of a patient where model and patient performance do not change as a function of treatment.

Figure 4

Model’s prediction of treatment in both languages. In this scenario, the patient was trained in Spanish and the model accurately predicts performance in both languages. When, however, the model is trained in English, there are greater improvements predicted in the trained language. See text for details.