Monitoring of learning at the category level when learning a natural concept: Will task experience improve its resolution?

Researchers have recently begun to investigate people's ability to monitor their learning of natural categories. For concept learning tasks, a learner seeks to accurately monitor learning at the category level — i.e., to accurately judge whether exemplars will be correctly classified into the appropriate category on an upcoming test. Our interest was in whether monitoring resolution at the category level would improve as participants gain task experience across multiple study-test blocks, as well as within each block. In four experiments, exemplar birds (e.g., American Goldfinch, Cassin's Finch) paired with each family name (e.g., Finch) were studied, and participants made a judgment of learning (JOL) for each exemplar. Of most interest, before and after studying the exemplars, participants made category learning judgments (CLJs), which involved predicting the likelihood of correctly classifying novel birds into each family. Tests included exemplars that had been studied or exemplars that had not been studied (novel). This procedure was repeated for either one or two additional blocks. The relative accuracy of CLJs did not improve across blocks even when explicit feedback was provided, whereas item-by-item JOL accuracy improved across blocks. Category level resolution did improve from pre-study to post-study on an initial block, but it did not consistently increase within later blocks. The stable accuracy of CLJs across blocks poses a theoretical and empirical challenge for identifying techniques to improve people's ability to judge their learning of natural categories.     

Recall-to-reject: The effect of category cues on false recognition

Four experiments examined the effect of category cueing on recall-to-reject, one of the central memory-editing mechanisms thought to prevent the occurrence of false memories. When category names were used as retrieval cues, the typically observed false recognition effect was eliminated for semantically associated distractors (Experiment 1a) and, moreover, a reduction in the absolute level of the false alarm rate was found for phonologically associated distractors (Experiment 2a). In addition to the old/new-recognition data, analyses using multinomial models support the interpretation that category cueing was successful in increasing the probability of recall-to-reject (Experiments 1b and 2b). The results are in line with dual-process theories of recognition memory and provide further evidence for recall-to-reject in single item recognition. They demonstrate its potential to reduce false recognition even when explicit instructions are not given. In addition, the results demonstrate that the paradigm can give rise to side effects that oppose recall-to-reject. A simultaneous familiarity increase can explain why many studies failed to find evidence for recall-to-reject in terms of false alarm rates.     

The role of exemplar typicality and encoding strategies in category retrieval-induced forgetting

Selective retrieval practice of category exemplars often impairs the recall of related items, a phenomenon known as retrieval-induced forgetting (RIF). In Experiment 1 the role of item typicality (high, low) and presentation format of category exemplars (random, grouped) were analysed, while in Experiment 2 two encoding strategies (inter and intracategory) to modulate RIF were tested. Exemplar typicality was the critical factor underlying RIF. Competition during retrieval practice rendered RIF in the typical exemplars, but RIF did not appear when the exemplars were low typicality. The greater impairment of strong exemplars is in line with the inhibitory account of RIF and the notion of interference dependence. Inhibition appeared with random and grouped presentations suggesting that presentation format of the exemplars is not a critical factor in modulating RIF in a category-cued recall task. Distinctive processing instructions using sentences that connected items from different categories (intercategory strategy) and integration instructions by using size to organise the exemplars within categories (intracategory strategy) easily avoided competition and the need of inhibition processes in recall.     

Memory bias for negative emotional words in recognition memory is driven by effects of category membership

Recognition memory studies often find that emotional items are more likely than neutral items to be labelled as studied. Previous work suggests this bias is driven by increased memory strength/familiarity for emotional items. We explored strength and bias interpretations of this effect with the conjecture that emotional stimuli might seem more familiar because they share features with studied items from the same category. Categorical effects were manipulated in a recognition task by presenting lists with a small, medium or large proportion of emotional words. The liberal memory bias for emotional words was only observed when a medium or large proportion of categorised words were presented in the lists. Similar, though weaker, effects were observed with categorised words that were not emotional (animal names). These results suggest that liberal memory bias for emotional items may be largely driven by effects of category membership.     

Constructing informative model priors using hierarchical methods

Despite their negative reputation, informative priors are very useful in inference. Priors that express psychologically meaningful intuitions damp out random fluctuations in the data due to sampling variability, without sacrificing flexibility. This article focuses on how an intuitively satisfying informative prior distribution can be constructed. In particular, it demonstrates how the hierarchical introduction of a parameterized generative account of the set of models under consideration naturally imposes a non-uniform prior distribution over the models, encoding existing intuitions about the models. The hierarchical approach for constructing informative model priors is made concrete using a worked example, the Varying Abstraction Model (VAM), a family of categorization models including and expanding the exemplar and prototype models. It is shown how psychological intuitions about the relative plausibilities of the models in the VAM can be formally captured in an informative prior distribution over these models, by specifying a theoretically informed process for generating the models in the VAM. The smoothing effect of the informative prior in estimation is demonstrated by considering ten previously published data sets from the category learning literature.     

Developmental differences in children's context-dependent word learning

In this study, 2.5-, 3-, and 4-year-olds (N = 108) participated in a novel noun generalization task in which background context was manipulated. During the learning phase of each trial, children were presented with exemplars in one or multiple background contexts. At the test, children were asked to generalize to a novel exemplar in either the same or a different context. The 2.5-year-olds’ performance was supported by matching contexts; otherwise, children in this age group demonstrated context dependent generalization. The 3-year-olds’ performance was also supported by matching contexts; however, children in this age group were aided by training in multiple contexts as well. Finally, the 4-year-olds demonstrated high performance in all conditions. The results are discussed in terms of the relationship between word learning and memory processes; both general memory development and memory developments specific to word learning (e.g., retention of linguistic labels) are likely to support word learning and generalization.     

Development of implicit and explicit category learning

We present two studies that examined developmental differences in the implicit and explicit acquisition of category knowledge. College-attending adults consistently outperformed school-age children on two separate information-integration paradigms due to children’s more frequent use of an explicit rule-based strategy. Accuracy rates were also higher for adults on a unidimensional rule-based task due to children’s more frequent use of the irrelevant dimension to guide their behavior. Results across these two studies suggest that the ability to learn categorization structures may be dependent on a child’s ability to inhibit output from the explicit system.     

Eyetracking and selective attention in category learning

An eyetracking version of the classic Shepard, Hovland, and Jenkins (1961) experiment was conducted. Forty years of research has assumed that category learning often involves learning to selectively attend to only those stimulus dimensions useful for classification. We confirmed that participants learned to allocate their attention optimally. We also found that learners tend to fixate all stimulus dimensions early in learning. This result obtained despite evidence that participants were also testing one-dimensional rules during this period. Finally, the restriction of eye movements to only relevant dimensions tended to occur only after errors were largely (or completely) eliminated. We interpret these findings as consistent with multiple-systems theories of learning which maximize information input in order to maximize the number of learning modules involved, and which focus solely on relevant information only after one module has solved the learning problem.     

Prototype-distortion category learning: a two-phase learning process across a distributed network

This paper reviews a body of work conducted in our laboratory that applies functional magnetic resonance imaging (fMRI) to better understand the biological response and change that occurs during prototype-distortion learning. We review results from two experiments (Little, Klein, Shobat, McClure, & Thulborn, 2004; Little & Thulborn, 2005) that provide support for increasing neuronal efficiency by way of a two-stage model that includes an initial period of recruitment of tissue across a distributed network that is followed by a period of increasing specialization with decreasing volume across the same network. Across the two studies, participants learned to classify patterns of random-dot distortions (Posner & Keele, 1968) into categories. At four points across this learning process subjects underwent examination by fMRI using a category-matching task. A large-scale network, altered across the protocol, was identified to include the frontal eye fields, both inferior and superior parietal lobules, and visual cortex. As behavioral performance increased, the volume of activation within these regions first increased and later in the protocol decreased. Based on our review of this work we propose that: (i) category learning is reflected as specialization of the same network initially implicated to complete the novel task, and (ii) this network encompasses regions not previously reported to be affected by prototype-distortion learning.     

Prior categorization can produce more efficient ordering

We hypothesized that prior categorization of an attribute might sometimes facilitate the ordering of stimuli, particularly when the number of stimuli is relatively large or some of them need to be remembered. In four experiments, participants were required to rank order either numbers or line lengths either using a selection sort or following prior categorization. The results indicated that prior categorization produced faster sorting when the number of stimuli to be ordered was relatively large and when the stimuli were not continuously visible.