Non-linguistic learning and aphasia: evidence from a paired associate and feedback-based task

Abstract

Though aphasia is primarily characterized by impairments in the comprehension and/or expression of language, research has shown that patients with aphasia also show deficits in cognitive-linguistic domains such as attention, executive function, concept knowledge and memory. Research in aphasia suggests that cognitive impairments can impact the online construction of language, new verbal learning, and transactional success. In our research, we extend this hypothesis to suggest that general cognitive deficits influence progress with therapy. The aim of our study is to explore learning, a cognitive process that is integral to relearning language, yet underexplored in the field of aphasia rehabilitation. We examine non-linguistic category learning in patients with aphasia (n=19) and in healthy controls (n=12), comparing feedback and non-feedback based instruction. Participants complete two computer-based learning tasks that require them to categorize novel animals based on the percentage of features shared with one of two prototypes. As hypothesized, healthy controls showed successful category learning following both methods of instruction. In contrast, only 60% of our patient population demonstrated successful non-linguistic category learning. Patient performance was not predictable by standardized measures of cognitive ability. Results suggest that general learning is affected in aphasia and is a unique, important factor to consider in the field of aphasia rehabilitation.

Copyright © 2012 Elsevier Ltd. All rights reserved.

Figures

Figure 1

Sample animal stimuli contributed by Zeithamova et al. (2008). Animals are arranged according to the number of features with which they differ from each prototypical animal. The number of features by which an animal differs from each prototype is referred to as its distance from the prototype.

Figure 2

Structure of paired-associate (PA) and feedback-based (FB) instruction paradigms. Learning tasks both involved ten minute training phases followed by ten minute testing phases. During PA learning participants were provided with category labels with each stimulus presentation. During FB learning, participants had to guess each animal’s category affiliation, receiving feedback telling them whether they were correct or incorrect.

Figure 3

Analysis of category responses as a factor of feature dimension. Responses close to 50% represent equally salient feature dimensions. Prototypes for stimulus set 1 (upper plot) and stimulus set 2 (lower plot) shown.

Figure 4

Predicted percent “B” responses (%BResp) as a function of distance. Based on the hypothesized probabilistic relationship between %BResp and distance, successful category learning is thought to correspond to %BResp that increase linearly by a factor of 10 (slope of 10).

Figure 5

Mean %BResp as a function of distance and standard deviations for controls (left) and patients (right). Red lines represent predicted measures demonstrating successful learning of category structure.

Figure 6

Representative sample of individual patient results for nine participants, grouped by learner type. Dark lines reflect FB learning and gray lines represent PA learning. Red lines reflect model measures of successful learning.

Figure 7

Learning slopes for each patient participant. FB scores are presented in black, PA scores in gray. Slopes closest to positive 10 represent ideal learning of categories.

Figure 8

Pearson correlations between patient best slopes of learning and AQ, BNT and scores of memory, executive function, attention and visuospatial skills as measured by the CLQT. Visual inspection of the data demonstrated the presence of three clusters related to AQ scores (upper left plot).