Longitudinal designs must deal with the confound between increasing age and increasing task experience (i.e., retest effects). Most existing methods for disentangling these factors rely on large sample sizes and are impractical for smaller scale projects. Here, we show that a measurement burst design combined with a model of retest effects can be used to study age-related change with modest sample sizes. A combined model of age-related change and retest-related effects was developed. In a simulation experiment, we show that with sample sizes as small as n = 8, the model can reliably detect age effects of the size reported in the longitudinal literature while avoiding false positives when there is no age effect. We applied the model to data from a measurement burst study in which eight subjects completed a burst of seven sessions of free recall every year for five years. Six additional subjects completed a burst only in years 1 and 5. They should, therefore, have smaller retest effects but equal age effects. The raw data suggested slight improvement in memory over five years. However, applying the model to the yearly-testing group revealed that a substantial positive retest effect was obscuring stability in memory performance. Supporting this finding, the control group showed a smaller retest effect but an equal age effect. Measurement burst designs combined with models of retest effects allow researchers to employ longitudinal designs in areas where previously only cross-sectional designs were feasible.
Huber and O'Reilly (2003) proposed that neural habituation aids perceptual processing, separating neural responses to currently viewed objects from recently viewed objects. However, synaptic depression has costs, producing repetition deficits. Prior work confirmed the transition from repetition benefits to deficits with increasing duration of a prime object, but the prediction of enhanced novelty detection was not tested. The current study examined this prediction with a same/different word priming task, using support vector machine (SVM) classification of EEG data, ERP analyses focused on the N400, and dynamic neural network simulations fit to behavioral data to provide a priori predictions of the ERP effects. Subjects made same/different judgements to a response word in relation to an immediately preceding brief target word; prime durations were short (50ms) or long (400ms), and long durations decreased P100/N170 responses to the target word, suggesting that this manipulation increased habituation. Following long duration primes, correct "different" judgments of primed response words increased, evidencing enhanced novelty detection. An SVM classifier predicted trial-by-trial behavior with 66.34% accuracy on held-out data, with greatest predictive power at a time pattern consistent with the N400. The habituation model was augmented with a maintained semantics layer (i.e., working memory) to generate behavior and N400 predictions. A second experiment used response-locked ERPs, confirming the model's assumption that residual activation in working memory is the basis of novelty decisions. These results support the theory that neural habituation enhances novelty detection, and the model assumption that the N400 reflects updating of semantic information in working memory.
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