Implicit-statistical learning in aphasia and its relation to lesion location

Quantifier processing and semantic flexibility in patients with aphasia

Processing of quantifiers such as "many" and "few" relies on number knowledge, linguistic abilities, and working memory. Negative quantifiers (e.g., "few," "less than half") induce higher processing costs than their positive counterparts. Furthermore, the meaning of some quantifiers is flexible and thus adaptable. Importantly, in neurotypical individuals, changing the meaning of one quantifier also leads to a generalized change in meaning for its polar opposite (e.g., the change of the meaning of "many" leads to the change of that of "few"). Here, we extended this research to patients with fluent and non-fluent aphasia after stroke. In two experiments, participants heard sentences of the type "Many/few of the circles are yellow/blue," each followed by a picture with different quantities of blue and yellow circles. The participants judged whether the sentence adequately described the picture. Each experiment consisted of three blocks: a baseline block to assess the participants' criteria for both quantifiers, a training block to shift the criteria for "many," and a test block, identical to the baseline to capture any changes in quantifier semantics. In Experiment 1, the change of the meaning of "many" was induced by using adaptation to small numbers (20-50%) of circles of the named color. In Experiment 2, explicit feedback was given in the training block after each response to rate proportions of 40% (or higher) as "many," whereas 40% is normally rather rated as "few." The objective was to determine whether people with fluent or non-fluent aphasia were able to process quantifiers appropriately and whether generalized semantic flexibility was present after brain damage. Sixteen out of 21 patients were able to perform the task. People with fluent aphasia showed the expected polarity effect in the reaction times and shifted their criteria for "many" with generalization to the untrained quantifier "few." This effect, however, was only obtained after explicit feedback (Experiment 2) but not by mere adaptation (Experiment 1). In contrast, people with non-fluent aphasia did not change the quantifier semantics in either experiment. This study contributes to gaining new insights into quantifier processing and semantic flexibility in people with aphasia and general underlying processing mechanisms.

Linear Mixed-Model Analysis Better Captures Subcomponents of Attention in a Small Sample Size of Persons With Aphasia

PURPOSE

Although there are several reports of attention deficits in aphasia, studies are typically limited to a single component within this complex domain. Furthermore, interpretation of results is affected by small sample size, intraindividual variability, task complexity, or nonparametric statistical models of performance comparison. The purpose of this study is to explore multiple subcomponents of attention in persons with aphasia (PWA) and compare findings and implications from various statistical methods-nonparametric, mixed analysis of variance (ANOVA), and linear mixed-effects model (LMEM)-when applied to a small sample size.

METHOD

Eleven PWA and nine age- and education-matched healthy controls (HCs) completed the computer-based Attention Network Test (ANT). ANT examines the effects of four types of warning cues (no, double, central, spatial) and two flanker conditions (congruent, incongruent) to provide an efficient way to assess the three subcomponents of attention (alerting, orienting, and executive control). Individual response time and accuracy data from each participant are considered for data analysis.

RESULTS

Nonparametric analyses showed no significant differences between the groups on the three subcomponents of attention. Both mixed ANOVA and LMEM showed statistical significance on alerting effect in HCs, orienting effect in PWA, and executive control effect in both PWA and HCs. However, LMEM analyses additionally highlighted significant differences between the groups (PWA vs. HCs) for executive control effect, which were not evident on either ANOVA or nonparametric tests.

CONCLUSIONS

By considering the random effect of participant ID, LMEM was able to show deficits in alerting and executive control ability in PWA when compared to HCs. LMEM accounts for the intraindividual variability based on individual response time performances instead of relying on measures of central tendencies.

Aging effects and feasibility of statistical learning tasks across modalities

Knowledge on statistical learning (SL) in healthy elderly is scarce. Theoretically, it is not clear whether aging affects modality-specific and/or domain-general learning mechanisms. Practically, there is a lack of research on simplified SL tasks, which would ease the burden of testing in clinical populations. Against this background, we conducted two experiments across three modalities (auditory, visual and visuomotor) in a total of 93 younger and older adults. In Experiment 1, SL was induced in all modalities. Aging effects appeared in the tasks relying on an explicit posttest to assess SL. We hypothesize that declines in domain-general processes that predominantly modulate explicit learning mechanisms underlie these aging effects. In Experiment 2, more feasible tasks were developed for which the level of SL was maintained in all modalities, except the auditory modality. These tasks are more likely to successfully measure SL in elderly (patient) populations in which task demands can be problematic.

The role of the hippocampus in statistical learning and language recovery in persons with post stroke aphasia

Although several studies have aimed for accurate predictions of language recovery in post stroke aphasia, individual language outcomes remain hard to predict. Large-scale prediction models are built using data from patients mainly in the chronic phase after stroke, although it is clinically more relevant to consider data from the acute phase. Previous research has mainly focused on deficits, i.e., behavioral deficits or specific brain damage, rather than compensatory mechanisms, i.e., intact cognitive skills or undamaged brain regions. One such unexplored brain region that might support language (re)learning in aphasia is the hippocampus, a region that has commonly been associated with an individual's learning potential, including statistical learning. This refers to a set of mechanisms upon which we rely heavily in daily life to learn a range of regularities across cognitive domains. Against this background, thirty-three patients with aphasia (22 males and 11 females, M = 69.76 years, SD = 10.57 years) were followed for 1 year in the acute (1-2 weeks), subacute (3-6 months) and chronic phase (9-12 months) post stroke. We evaluated the unique predictive value of early structural hippocampal measures for short-term and long-term language outcomes (measured by the ANELT). In addition, we investigated whether statistical learning abilities were intact in patients with aphasia using three different tasks: an auditory-linguistic and visual task based on the computation of transitional probabilities and a visuomotor serial reaction time task. Finally, we examined the association of individuals' statistical learning potential with acute measures of hippocampal gray and white matter. Using Bayesian statistics, we found moderate evidence for the contribution of left hippocampal gray matter in the acute phase to the prediction of long-term language outcomes, over and above information on the lesion and the initial language deficit (measured by the ScreeLing). Non-linguistic statistical learning in patients with aphasia, measured in the subacute phase, was intact at the group level compared to 23 healthy older controls (8 males and 15 females, M = 74.09 years, SD = 6.76 years). Visuomotor statistical learning correlated with acute hippocampal gray and white matter. These findings reveal that particularly left hippocampal gray matter in the acute phase is a potential marker of language recovery after stroke, possibly through its statistical learning ability.

Language learning in aphasia: A narrative review and critical analysis of the literature with implications for language therapy

People with aphasia (PWA) present with language deficits including word retrieval difficulties after brain damage. Language learning is an essential life-long human capacity that may support treatment-induced language recovery after brain insult. This prospect has motivated a growing interest in the study of language learning in PWA during the last few decades. Here, we critically review the current literature on language learning ability in aphasia. The existing studies in this area indicate that (i) language learning can remain functional in some PWA, (ii) inter-individual variability in learning performance is large in PWA, (iii) language processing, short-term memory and lesion site are associated with learning ability, (iv) preliminary evidence suggests a relationship between learning ability and treatment outcomes in this population. Based on the reviewed evidence, we propose a potential account for the interplay between language and memory/learning systems to explain spared/impaired language learning and its relationship to language therapy in PWA. Finally, we indicate potential avenues for future research that may promote more cross-talk between cognitive neuroscience and aphasia rehabilitation.