Paulo F. Carvalho

The benefits of interleaved and blocked study: Different tasks benefit from different schedules of study

Research on how information should be studied during inductive category learning has identified both interleaving of categories and blocking by category as beneficial for learning. Previous work suggests that this mixed evidence can be reconciled by taking into account within- and between-category similarity relations. In this article, we present a new moderating factor. Across two experiments, one group of participants studied categories actively (by studying the objects without correct category assignment and actively figuring out what the category was), either interleaved or blocked. Another group studied the same categories passively (objects and correct category assignment were simultaneously provided). Results from a subsequent generalization task show that whether interleaved or blocked study results in better learning depends on whether study is active or passive. One account of these results is that different presentation sequences and tasks promote different patterns of attention to stimulus components. Passive learning and blocking promote attending to commonalities within categories, while active learning and interleaving promote attending to differences between categories.

What you learn is more than what you see: what can sequencing effects tell us about inductive category learning?

Inductive category learning takes place across time. As such, it is not surprising that the sequence in which information is studied has an impact in what is learned and how efficient learning is. In this paper we review research on different learning sequences and how this impacts learning. We analyze different aspects of interleaved (frequent alternation between categories during study) and blocked study (infrequent alternation between categories during study) that might explain how and when one sequence of study results in improved learning. While these different sequences of study differ in the amount of temporal spacing and temporal juxtaposition between items of different categories, these aspects do not seem to account for the majority of the results available in the literature. However, differences in the type of category being studied and the duration of the retention interval between study and test may play an important role. We conclude that there is no single aspect that is able to account for all the evidence available. Understanding learning as a process of sequential comparisons in time and how different sequences fundamentally alter the statistics of this experience offers a promising framework for understanding sequencing effects in category learning. We use this framework to present novel predictions and hypotheses for future research on sequencing effects in inductive category learning.

Effects of interleaved and blocked study on delayed test of category learning generalization

Studying different concepts by frequently alternating between them (i.e., interleaving), improves discriminative contrast between different categories, while studying each concept in separate blocks emphasizes the similarities within each category. Interleaved study has been shown to improve learning of high similarity categories by increasing between-category comparison, while blocked study improves learning of low similarity categories by increasing within-category comparison. In addition, interleaved study presents greater temporal spacing between repetitions of each category compared to blocked study, which might present long-term memory benefits. In this study we asked if the benefits of temporal spacing would interact with the benefits of sequencing for making comparisons when testing was delayed, particularly for low similarity categories. Blocked study might be predicted to promote noticing similarities across members of the same category and result in short-term benefits. However, the increase in temporal delay between repetitions inherent to interleaved study might benefit both types of categories when tested after a longer retention interval. Participants studied categories either interleaved or blocked and were tested immediately and 24 h after study. We found an interaction between schedule of study and the type of category studied, which is consistent with the differential emphasis promoted by each sequential schedule. However, increasing the retention interval did not modulate this interaction or resulted in improved performance for interleaved study. Overall, this indicates that the benefit of interleaving is not primarily due to temporal spacing during study, but rather due to the cross-category comparisons that interleaving facilitates. We discuss the benefits of temporal spacing of repetitions in the context of sequential study and how it can be integrated with the attentional bias hypothesis proposed by Carvalho and Goldstone (2014a).

Organized simultaneous displays facilitate learning of complex natural science categories

Subjects learned to classify images of rocks into the categories igneous, metamorphic, and sedimentary. In accord with the real-world structure of these categories, the to-be-classified rocks in the experiments had a dispersed similarity structure. Our central hypothesis was that learning of these complex categories would be improved through observational study of organized, simultaneous displays of the multiple rock tokens. In support of this hypothesis, a technique that included the presentation of the simultaneous displays during phases of the learning process yielded improved acquisition (Experiment 1) and generalization (Experiment 2) compared to methods that relied solely on sequential forms of study and testing. The technique appears to provide a good starting point for application of cognitive-psychology principles of effective category learning to the science classroom.

It does belong together: cross-modal correspondences influence cross-modal integration during perceptual learning

Experiencing a stimulus in one sensory modality is often associated with an experience in another sensory modality. For instance, seeing a lemon might produce a sensation of sourness. This might indicate some kind of cross-modal correspondence between vision and gustation. The aim of the current study was to explore whether such cross-modal correspondences influence cross-modal integration during perceptual learning. To that end, we conducted two experiments. Using a speeded classification task, Experiment 1 established a cross-modal correspondence between visual lightness and the frequency of an auditory tone. Using a short-term priming procedure, Experiment 2 showed that manipulation of such cross-modal correspondences led to the creation of a crossmodal unit regardless of the nature of the correspondence (i.e., congruent, Experiment 2a or incongruent, Experiment 2b). However, a comparison of priming effects sizes suggested that cross-modal correspondences modulate cross-modal integration during learning, leading to new learned units that have different stability over time. We discuss the implications of our results for the relation between cross-modal correspondence and perceptual learning in the context of a Bayesian explanation of cross-modal correspondences.

An In Vivo Study of Self-Regulated Study Sequencing in Introductory Psychology Courses

Study sequence can have a profound influence on learning. In this study we investigated how students decide to sequence their study in a naturalistic context and whether their choices result in improved learning. In the study reported here, 2061 undergraduate students enrolled in an Introductory Psychology course completed an online homework tutorial on measures of central tendency, a topic relevant to an exam that counted towards their grades. One group of students was enabled to choose their own study sequence during the tutorial (Self-Regulated group), while the other group of students studied the same materials in sequences chosen by other students (Yoked group). Students who chose their sequence of study showed a clear tendency to block their study by concept, and this tendency was positively associated with subsequent exam performance. In the Yoked group, study sequence had no effect on exam performance. These results suggest that despite findings that blocked study is maladaptive when assigned by an experimenter, it may actually be adaptive when chosen by the learner in a naturalistic context.

Novel names extend for how long preschool children sample visual information

Known words can guide visual attention, affecting how information is sampled. How do novel words, those that do not provide any top-down information, affect preschoolers visual sampling in a conceptual task? We proposed that novel names can also change visual sampling by influencing how long children look. We investigated this possibility by analyzing how children sample visual information when they hear a sentence with a novel name versus without a novel name. Children completed a match-to-sample task while their moment-to-moment eye movements were recorded using eye-tracking technology. Our analyses were designed to provide specific information on the properties of visual sampling that novel names may change. Overall, we found that novel words prolonged the duration of each sampling event but did not affect sampling allocation (which objects children looked at) or sampling organization (how children transitioned from one object to the next). These results demonstrate that novel words change one important dynamic property of gaze: Novel words can entrain the cognitive system toward longer periods of sustained attention early in development.

Zebras and antelopes: category sparsity as the result of the relations between objects and within categories*

In their paper “Recognizing a zebra from its stripes and the stripes from ‘zebra’: the role of verbal labels in selecting category relevant information”, Perry and Lupyan (P&L) argue that sparse categories impose high selective attention demands, requiring one to choose what to attend to, compared to dense categories for which several dimensions can or must be used. Furthermore, P&L argue that labels are more useful for sparse categories because they “tune” perception towards the discriminative properties of objects. P&L show that categories for which there is substantial agreement by participants on what the common feature of that category is, have lower selective attention demands and benefit more from the inclusion of a label. In this commentary we focus on what constitutes a sparse category. We will attempt to make the case for the importance of considering both how many discriminative features as well as how many common but not discriminative features a category has in order to evaluate category sparsity. We propose that for a complete understanding of the selective attention processes and labelling effects on category learning one must take the categorisation space, rather than each isolated category, into account. A sparse category is a category for which there are only one or few discriminative features and various irrelevant ones. Increasing the variability in the features among items of the same category (the number of features irrelevant for categorisation) increases sparsity, whereas increasing the number of features varying between the categories (the number of features relevant for categorisation) reduces it (Kloos & Sloutsky, 2008). To identify the sparsity of a category, P&aL asked participants to list the common features to a set of 10 items of the same category, for example 10 pictures of zebras. When asked to list the features of a set of zebras, people are likely to list stripes. Stripes could be a good discriminative feature of zebras (not many animals have stripes), and also a highly characteristic property of that category (all zebras have stripes). However, this might not always be the case, depending on the properties of the contrasting categories and the variability among items of the same category. We believe this is an important consideration because P&L’s measure of sparsity takes into account each category in isolation (thus it is not clear how many discriminating features the categories possess), and does not take into account how many features vary among items of the same category. To illustrate the importance of considering the relation between categories for their sparsity, take the following example using different contrasting categories for the category zebra. One would agree that stripes are a good descriptor of zebras (Figure 1(a)), constituting a sufficient and necessary feature to describe this category – therefore zebra would be considered a sparse category. This would also be the case when one thinks of horses (Figure 1(b)) as a contrast group for the zebra category. However, when contrasting zebras with antelopes (Figure 1(c)) or okapis (Figure 1(d)) stripes are probably not the only good descriptor. In this case, zebra would constitute a denser category. Although people could still frequently list stripes as a commonality, they would also list a series of other features (potentially colour, horns and size), as well as one or more discriminating features. People might still agree that stripes is a good descriptor of zebra but it would no longer be the only or most frequently listed feature. Importantly, the category zebra did not change, it was the space of categories that changed,