Category learning in Alzheimer's disease and normal cognitive aging depends on initial experience of feature variability

Abstract

Semantic category learning is dependent upon several factors, including the nature of the learning task, as well as individual differences in the quality and heterogeneity of exemplars that an individual encounters during learning. We trained healthy older adults (n=39) and individuals with a diagnosis of Alzheimer's disease or Mild Cognitive Impairment (n=44) to recognize instances of a fictitious animal, a "crutter". Each stimulus item contained 10 visual features (e.g., color, tail shape) which took one of two values for each feature (e.g., yellow/red, curly/straight tails). Participants were presented with a series of items (learning phase) and were either told the items belonged to a semantic category (explicit condition) or were told to think about the appearance of the items (implicit condition). Half of participants saw learning items with higher similarity to an unseen prototype (high typicality learning set), and thus lower between-item variability in their constituent features; the other half learned from items with lower typicality (low typicality learning set) and higher between-item feature variability. After the learning phase, participants were presented with test items one at a time that varied in the number of typical features from 0 (antitype) to 10 (prototype). We examined between-subjects factors of learning set (lower or higher typicality), instruction type (explicit or implicit), and group (patients vs. elderly control). Learning in controls was aided by higher learning set typicality: while controls in both learning set groups demonstrated significant learning, those exposed to a high-typicality learning set appeared to develop a prototype that helped guide their category membership judgments. Overall, patients demonstrated more difficulty with category learning than elderly controls. Patients exposed to the higher-typicality learning set were sensitive to the typical features of the category and discriminated between the most and least typical test items, although less reliably than controls. In contrast, patients exposed to the low-typicality learning set showed no evidence of learning. Analysis of structural imaging data indicated a positive association between left hippocampal grey matter density in elderly controls but a negative association in the patient group, suggesting differential reliance on hippocampal-mediated learning. Contrary to hypotheses, learning did not differ between explicit and implicit conditions for either group. Results demonstrate that category learning is improved when learning materials are highly similar to the prototype.

Keywords: Alzheimer’s disease; Category learning; Episodic memory; Hippocampus; Medial temporal lobes; Neurodegenerative disease; Prototype extraction; Semantic memory.

Figure 1

Visual category exemplars: the crutter prototype and its antitype.

Figure 2

Test item endorsement rates as a function of item typicality. Lines indicate the mean endorsement rate per condition; shaded regions indicate the standard error of the mean for each condition.

Figure 3

Anatomical regions of interest (ROIs) from the OASIS-30 atlas, overlaid on the MNI-152 template brain. Mean grey matter density was calculated for each ROI and participant; these variables were used to predict individual learning scores in a multiple regression model, along with factors of group and training set. TOC (temporo-occipital cortex) includes fusiform and inferior temporal gyri; PFC (prefrontal cortex) includes middle and inferior frontal gyri. L=left, R=right.

Figure 4

Grey matter probability by group in anatomical regions of interest. Error bars represent the standard error of the mean. Hipp=hippocampus; TOC=temporo-occipital cortex; L=left; R=right. Two-sample, two-tailed t-test results, uncorrected for multiple comparisons: p<0.05*, p<0.01**, p<0.001***.

Figure 5

Associations between learning score and grey matter density in left hippocampus. Each data point represents 1 participant. X-axis: mean grey matter density in anatomically-defined left hippocampus ROI. Y-axis: category learning score. The best-fit linear regression line is shown for each group.

Figure 6

Learning history effects on test item categorization. Blue: items seen for the first time in the test phase; green: items previously encountered in the learning phase. Pat = patients; Cont=elderly controls; Low = low typicality learning set; High = high typicality learning set.