Deficits in category learning in older adults: Rule-based versus clustering accounts

Abstract

Memory research has long been one of the key areas of investigation for cognitive aging researchers but only in the last decade or so has categorization been used to understand age differences in cognition. Categorization tasks focus more heavily on the grouping and organization of items in memory, and often on the process of learning relationships through trial and error. Categorization studies allow researchers to more accurately characterize age differences in cognition: whether older adults show declines in the way in which they represent categories with simple rules or declines in representing categories by similarity to past examples. In the current study, young and older adults participated in a set of classic category learning problems, which allowed us to distinguish between three hypotheses: (a) rule-complexity: categories were represented exclusively with rules and older adults had differential difficulty when more complex rules were required, (b) rule-specific: categories could be represented either by rules or by similarity, and there were age deficits in using rules, and (c) clustering: similarity was mainly used and older adults constructed a less-detailed representation by lumping more items into fewer clusters. The ordinal levels of performance across different conditions argued against rule-complexity, as older adults showed greater deficits on less complex categories. The data also provided evidence against rule-specificity, as single-dimensional rules could not explain age declines. Instead, computational modeling of the data indicated that older adults utilized fewer conceptual clusters of items in memory than did young adults. (PsycINFO Database Record

Figure 1

Top: Stimuli could vary along three dimensions (size, color, and form). Bottom: Examples of category membership for the eight shapes organized into two groups (α and β) for the four categorization tasks (Types I to IV) used in the study.

Figure 2

Accuracy for young and older adults learning categorization Types I, II, III, and IV across six learning blocks (16 trials per block). Error bars are ±1 SE.

Figure 3

Magnitude of age deficits in learning for the different experimental conditions Types I to IV. Data are averaged across all six blocks. Error bars are ±1 SE.

Figure 4

Hamming distance for young and older adults across learning Blocks 1–6. The dashed line indicates the Hamming distance that would occur if participants were responding with 100% accuracy for each type, though this is necessary and not sufficient to produce perfect performance: matching this distance does not imply 100% accuracy. The hollow line indicates the distance corresponding to single-dimensional rule use (SD). Error bars are ±1 SE.

Figure 5

Proportion of different responses to all consecutive trials with maximally different stimuli (top panel), and to consecutive trials with maximally different stimuli where the previous response was correct (bottom panel). Error bars are ±1 SE. Black circles indicate predictions from the Rational Model of Categorization.

Figure 6

Model predictions for young and older adults learning categorization Types I, II, III, and IV across six learning blocks.

Figure 7

Visualization of how young and older adults clustered the items for Types I–IV. The plots underneath each type display the different ways the problem was clustered across participants. Within each plot, the three dimensions represent the three different physical dimensions, though the dimension identities have been ignored and cluster assignments renumbered to minimize the variety of different patterns. Gray and white circles indicate the feedback given to the items and the numbers within each circle label the cluster to which that item has been assigned. Text underneath each plot gives the number of young and older adults who used that set of clusters for that problem.