With practice, humans tend to improve their performance on most tasks. But do such improvements then generalize to new tasks? Although early work documented primarily task-specific learning outcomes in the domain of perceptual learning [1-3], an emerging body of research has shown that significant learning generalization is possible under some training conditions [4-9]. Interestingly, however, research in this vein has focused nearly exclusively on just one possible manifestation of learning generalization, wherein training on one task produces an immediate boost to performance on the new task. For instance, it is this form of generalization that is most frequently referred to when discussing learning "transfer" [10, 11]. Essentially no work in this domain has focused on a second possible manifestation of generalization, wherein the knowledge or skills acquired via training, despite not being directly applicable to the new task, nonetheless allow the new task to be learned more efficiently [12-15]. Here, in both the visual category learning and visual perceptual learning domains, we demonstrate that sequentially training participants on tasks that share a common high-level task structure can produce faster learning of new tasks, even in cases where there is no immediate benefit to performance on the new tasks. We further show that methods commonly employed in the field may fail to detect or else conflate generalization that manifests as increased learning rate with generalization that manifests as immediate boosts to performance. These results thus lay the foundation for the various routes to learning generalization to be more thoroughly explored.

A sizeable body of work has demonstrated that participants have the capacity to show substantial increases in performance on perceptual tasks given appropriate practice. This has resulted in significant interest in the use of such perceptual learning techniques to positively impact performance in real-world domains where the extraction of perceptual information in the service of guiding decisions is at a premium. Radiological training is one clear example of such a domain. Here we examine a number of basic science questions related to the use of perceptual learning techniques in the context of a radiology-inspired task. On each trial of this task, participants were presented with a single axial slice from a CT image of the abdomen. They were then asked to indicate whether or not the image was consistent with appendicitis. We first demonstrate that, although the task differs in many ways from standard radiological practice, it nonetheless makes use of expert knowledge, as trained radiologists who underwent the task showed high (near ceiling) levels of performance. Then, in a series of four studies we show that (1) performance on this task does improve significantly over a reasonably short period of training (on the scale of a few hours); (2) the learning transfers to previously unseen images and to untrained image orientations; (3) purely correct/incorrect feedback produces weak learning compared to more informative feedback where the spatial position of the appendix is indicated in each image; and (4) there was little benefit seen from purposefully structuring the learning experience by starting with easier images and then moving on to more difficulty images (as compared to simply presenting all images in a random order). The implications for these various findings with respect to the use of perceptual learning techniques as part of radiological training are then discussed.

Human fluid intelligence emerges from the interactions of various cognitive processes. Although some classic models characterize intelligence as a unitary “general ability,” many distinct lines of research have suggested that it is possible to at least partially decompose intelligence into a set of subsidiary cognitive functions. Much of this work has focused on the relationship between intelligence and working memory, and more specifically between intelligence and the capacity-loading aspects of working memory. These theories focus on domain-general processing capacity limitations, rather than limitations specifically linked to working memory tasks. Performance on other capacity-constrained tasks, even those that have typically been given the label of “attention tasks,” may thus also be related to fluid intelligence. We tested a wide range of attention and working memory tasks in 7- to 9-year-old children and adults, and we used the results of these cognitive measures to predict intelligence scores. In a set of 13 measures we did not observe a single “positive manifold” that would indicate a general-ability understanding of intelligence. Instead, we found that a small number of measures were related to intelligence scores. More specifically, we found two tasks that are typically labeled as “attentional measures”, Multiple Object Tracking and Enumeration, and two tasks that are typically labeled as “working memory” measures, N-back and Spatial Span, were reliably related to intelligence. However, the links between attention and intelligence scores were fully mediated by working memory measures. In contrast, attention scores did not mediate the relations between working memory and intelligence. Furthermore, these patterns were indistinguishable across age groups, indicating a hierarchical cognitive basis of intelligence that is stable from childhood into adulthood.

The majority of theoretical models of learning consider learning to be a continuous function of experience. However, most perceptual learning studies use thresholds estimated by fitting psychometric functions to independent blocks, sometimes then fitting a parametric function to these block-wise estimated thresholds. Critically, such approaches tend to violate the basic principle that learning is continuous through time (e.g., by aggregating trials into large "blocks" for analysis that each assume stationarity, then fitting learning functions to these aggregated blocks). To address this discrepancy between base theory and analysis practice, here we instead propose fitting a parametric function to thresholds from each individual trial. In particular, we implemented a dynamic psychometric function whose parameters were allowed to change continuously with each trial, thus parameterizing nonstationarity. We fit the resulting continuous time parametric model to data from two different perceptual learning tasks. In nearly every case, the quality of the fits derived from the continuous time parametric model outperformed the fits derived from a nonparametric approach wherein separate psychometric functions were fit to blocks of trials. Because such a continuous trial-dependent model of perceptual learning also offers a number of additional advantages (e.g., the ability to extrapolate beyond the observed data; the ability to estimate performance on individual critical trials), we suggest that this technique would be a useful addition to each psychophysicist's analysis toolkit.

Given appropriate training, human observers typically demonstrate clear improvements in performance on perceptual tasks. However, the benefits of training frequently fail to generalize to other tasks, even those that appear similar to the trained task. A great deal of research has focused on the training task characteristics that influence the extent to which learning generalizes. However, less is known about what might predict the considerable individual variations in performance. As such, we conducted an individual differences study to identify basic cognitive abilities and/or dispositional traits that predict an individual's ability to learn and/or generalize learning in tasks of perceptual learning. We first showed that the rate of learning and the asymptotic level of performance that is achieved in two different perceptual learning tasks (motion direction and odd-ball texture detection) are correlated across individuals, as is the degree of immediate generalization that is observed and the rate at which a generalization task is learned. This indicates that there are indeed consistent individual differences in perceptual learning abilities. We then showed that several basic cognitive abilities and dispositional traits are associated with an individual's ability to learn (e.g., simple reaction time; sensitivity to punishment) and/or generalize learning (e.g., cognitive flexibility; openness to experience) in perceptual learning tasks. We suggest that the observed individual difference relationships may provide possible targets for future intervention studies meant to increase perceptual learning and generalization.