Vancouver, Toronto, Montreal, Austin: Enhanced oddball memory through differentiation, not isolation

What makes a person, event, or object memorable? Enhanced memory for oddball items is long established, but the basis for these effects is not well understood. The present work clarifies the roles of isolation and differentiation in establishing new memories. According to the isolation account, items that are highly dissimilar to other items are better remembered. In contrast, recent category learning studies suggest that oddball items are better remembered because they must be differentiated from similar items. The present work pits the differentiation and isolation accounts against each other. The results suggest that differentiation, not isolation, leads to more accurate memory for deviant items. In contrast, gains for isolated items are attributable to reduced confusion with other items, as opposed to preferential storage.     

Tracking the emergence of memories: A category-learning paradigm to explore schema-driven recognition

Previous research has shown that prior knowledge structures or schemas affect recognition memory. However, since the acquisition of schemas occurs over prolonged periods of time, few paradigms allow the direct manipulation of schema acquisition to study their effect on memory performance. Recently, a number of parallelisms in recognition memory between studies involving schemas and studies involving category learning have been identified. The current paper capitalizes on these findings and offers a novel experimental paradigm that allows manipulation of category learning between individuals to study the effects of schema acquisition on recognition. First, participants learn to categorize computer-generated items whose category-inclusion criteria differ between participants. Next, participants study items that belong to either the learned category, the non-learned category, both, or neither. Finally, participants receive a recognition test that includes old and new items, either from the learned, the non-learned, or neither category. Using variations on this paradigm, four experiments were conducted. The results from the first three studies suggest that learning a category increases hit rates for old category-consistent items and false alarm rates for new category-consistent lures. Absent the category learning, no such effects are evident, even when participants are exposed to the same learning trials as those who learned the categories. The results from the fourth experiment suggest that, at least for false alarm rates, the effects of category learning are not solely attributable to frequency of occurrence of category-consistent items during learning. Implications for recognition memory as well as advantages of the proposed paradigm are discussed.     

Easy comes,easy goes? The link between learning and remembering and its exploitation in metacognition

The cue-utilization view in metacognition assumes that judgments of learning (JOLs) are based on inferences from mnemonic cues deriving from the online processing of items during learning. This view calls for a specification of the underlying heuristics, their validity in predicting memory performance, and the extent to which they are utilized. This study examines one such heuristic: easily learned, easily remembered (ELER). We first show that ease of learning, as indexed by self-paced study time and by the number of trials to acquisition, is indeed a valid cue for recall success. We then demonstrate that this correlation between learning and rememberingunderlies metacognitive predictions about the future recallability of different items. The results are discussed in terms of the idea that metacognitive judgments incorporate knowledge about the internal ecology of cognitive processes, much as the perception of the external world embodies knowledge about the ecological structure of the environment.     

Feature distribution and background knowledge in category learning

Three experiments examined the interactions of category structure and prior knowledge in category learning.Experiment 1 examined the distribution of atypical or 'crossover' features in category learning. In real categories, crossover features may be unevenly distributed found primarily in very unusual examples of a category (like whales or ostriches). In contrast, in many psychology experiments, each item has exactly one crossover feature. Even versus uneven distribution of crossover features did not affect category learning when the features were neutral. However, when the features were connected by prior knowledge, it was much harder for subjects to learn the structure with the uneven distribution of crossover features. Experiments 2 and 3 found similar results with a slightly less uneven condition. We conclude that learning is a function of the interaction of category structure and prior knowledge rather than either one alone. Furthermore, knowledge benefits learning even when the category contains contradictions of the knowledge, so long as the contradictions are not very salient.     

Two pathways to stimulus encoding in category learning?

Category learning theorists tacitly assume that stimuli are encoded by a single pathway. Motivated by theories of object recognition, we evaluated a dual-pathway account of stimulus encoding. The part-based pathway establishes mappings between sensory input and symbols that encode discrete stimulus features, whereas the image-based pathway applies holistic templates to sensory input. Our experiments used rule-plus-exception structures, in which one exception item in each category violates a salient regularity and must be distinguished from other items. In Experiment 1, we found discrete representations to be crucial for recognition of exceptions following brief training. Experiments 2 and 3 involved multisession training regimens designed to encourage either part- or image-based encoding. We found that both pathways are able to support exception encoding, but have unique characteristics. We speculate that one advantage of the part-based pathway is the ability to generalize across domains, whereas the image-based pathway provides faster and more effortless recognition.     

Prior knowledge and exemplar frequency

New concepts can be learned by statistical associations, as well as by relevant existing knowledge. We examined the interaction of these two processes by manipulating exemplar frequency and thematic knowledge and considering their interaction through computational modeling. Exemplar frequency affects category learning, with high-frequency items learned more quickly than low-frequency items, and prior knowledge usually speeds category learning. In two experiments in which both of these factors were manipulated, we found that the effects of frequency are greatly reduced when stimulus features are linked by thematic prior knowledge and that frequency effects on single stimulus features can actually be reversed by knowledge. We account for these results with the knowledge resonance model of category learning (Rehder & Murphy, 2003) and conclude that prior knowledge may change representations so that empirical effects, such as those caused by frequency manipulations, are modulated.     

Word frequency effects in immediate serial recall: Item familiarity and item co‐occurrence have the same effect

In immediate serial recall, high‐frequency words are better recalled than low‐frequency words. A prevalent interpretation of this effect suggests that, at the point of recall, degraded representations undergo a reconstruction process calling upon long‐term knowledge of the to‐be‐remembered items. Recently, Stuart and Hulme () following Deese (), suggested that high‐frequency items are better recalled due to their better long‐term associative links. Their results revealed that a familiarisation procedure involving repeated presentations of the to‐be‐remembered items in pairs abolished the usual frequency effect. In the experiment reported here, an alternative interpretation of this result is examined. Prior to the memory task, subjects received either no familiarisation, item familiarisation, or pair familiarisation. Both item and pair familiarisation improved the item recall of low‐frequency items to the same extent, suggesting that increased familiarity can account for the co‐occurrence effect.     

Retention performance as a function of the distinctiveness of memory traces and retention task

Summary In the acquisition phase of an incidental learning paradigm, words that are exemplars of categories were judged as to whether or not they belonged to a specified category. This was followed by a forced choice recognition task. The effects of three variables were studied: a) whether the acquisition task called for a positive or a negative response; b) the number of exemplar words per category; and c) whether distractor items in the recognition test came from the same category as the target words or from other categories. The results showed that recognition errors were influenced by all three variables. Unlike the variables a) and c) which showed significant main effects, the effect of number of exemplars per category was significant only for the same category distractor condition. In this condition more exemplars per category led to more recognition errors. The results are explained by assuming that retention performance is determined by two factors. The first factor is the distinctiveness of the memory traces. This is supposed to be a joint product of task-specific encoding and stimulus characteristics. The second factor is the distinctive similarity between the retrieval information and the stored memory traces. Although the results show some sublte effects that are not easy to explain, it is argued that this model offers a good account of the general findings.     

A further investigation of category learning by inference

Categories are learned in many ways besides by classification, for example, by making inferences about classified items. One hypothesis is that classifications lead to the learning of features that distinguish categories, whereas inferences promote the learning of the internal structure of categories, such as the typical features. Experiment 1 included single-feature and full-feature classification tests following either classification or inference learning. Consistent with predictions, inference learners did better on the single tests but worse on the full tests. Experiment 2 further showed that inference learners, unlike classification learners, were no better at classifying items that they had seen at study compared with equally typical items they had not seen at study. Experiment 3 showed that features queried about during inference learning were classified better than ones not queried about, although even the latter features showed some learning on single-feature tests. The discussion focuses on how different types of category learning lead to different category representations.     

The effect of category learning on sensitivity to within-category correlations

A salient property of many categories is that they are not just sets of independent features but consist of clusters of correlated features. Although there is much evidence that people are sensitive to between-categories correlations, the evidence about within-category correlations is mixed. Two experiments tested whether the disparities might be due to different learning and test tasks. Subjects learned about categories either by classifying items or by inferring missing features of items. Their knowledge of the correlations was measured with classification, prediction, typicality, and production tests. The inference learners, but not the classification learners, showed sensitivity to the correlations, although different tests were differentially sensitive. These results reconcile some earlier disparities and provide a more complete understanding of people's sensitivities to within-category correlations.     

Implicit and explicit memory without awareness

Summary Three experiments are reported which investigate the conscious status of subjects during an implicit-memory test. In all experiments the subjects either named each visually presented target item or generated each item from an anagram in a first phase of incidental learning. In a second phase, they were either given a visual word-stem completion task as an implicit-memory test or given a recognition task (Experiment 1), or a cued-recall task (Experiments 2 and 3) as explicit-memory tests. Finally, in a third phase the subjects were required to make decisions about the input status (i. e., they had to decide whether the item was present in the first phase) as well as about the output status of information (i. e., they had to decide whether the item had been completed, recognized or recalled in the second phase). A generation effect (i. e., generated items were remembered better than named items) was evident in the recognition and recall data, but only for items whose recognition or recall was accompanied by conscious recollection of their previous occurrence in the study list. Judgments about the input status were more precise, given that items had been consciously recognized or recalled rather than completed. The same pattern of findings was observed for judgments about the output status. The results are interpreted as evidence that subjects in implicit-memory tests are less aware of the fact that some of their productions are relevant to prior experiences. In addition, they are less aware of the fact that they are retrieving information from their memories. However, the same state of nonawareness may be present in explicit-memory tests, as was revealed by the performance of subjects on those items whose recognition or recall was not accompanied by conscious recollection.     

The acquisition of category structure in unsupervised learning

Four experiments examined the extent to which prior knowledge influences the acquisition of category structure in unsupervised learning conditions. Prior knowledge is general knowledge about a broad domain that explains why an object has the features it does. Category structure refers to the statistical regularities of features within and across categories. Subjects viewed items and then divided them up into the categories that seemed most natural. Each item had one feature that was related to prior knowledge and five features that were not. The results showed that even this small amount of prior knowledge helped subjects to discover the category structure. In addition, prior knowledge enhanced the learning of many of the category's features, and not just the features that were directly relevant to the knowledge. The results suggest that prior knowledge may help to integrate the features of a category, thereby improving the acquisition of category structure.     

A knowledge-resonance (KRES) model of category learning

This article introduces a connectionist model of category learning that takes into account the prior knowledge that people bring to new learning situations. In contrast to connectionist learning models that assume a feedforward network and learn by the delta rule or backpropagation, this model, the knowledge-resonance model, or KRES, employs a recurrent network with bidirectional symmetric connection whose weights are updated according to a contrastive Hebbian learning rule. We demonstrate that when prior knowledge is represented in the network, KRES accounts for a considerable range of empirical results regarding the effects of prior knowledge on category learning, including (1) the accelerated learning that occurs in the presence of knowledge, (2) the better learning in the presence of knowledge of category features that are not related to prior knowledge, (3) the reinterpretation of features with ambiguous interpretations in light of error-corrective feedback, and (4) the unlearning of prior knowledge when that knowledge is inappropriate in the context of a particular category.     

The advantages of mathematical modeling over traditional methods in the analysis of category clustering

A mathematical model for the analysis of category clustering is developed and testd. The model, which can be applied to categories of any size, is an extension of a two-item statistical model developed by Batchelder and Riefer (Psychological Review, 1980, 87, 375–397), and is equivalent to their model when categories consist of two items. The model is based on a current theory of clustering which postulates that the learning of a list of category items occurs on different hierarchical levels. Two category list-learning experiments are presented, and the data from these experiments are analyzed using the general statistical model. The first experiment reveals that the probabilities of storing and retrieving a cluster increase with category size, while the learning of items as singletons decreases. The effects of within-category spacing indicate that the storage of clusters decreases while cluster retrievability increases with an increase in input spacing. In the second experiment, the storage and retrieval of clusters are shown to be unaffected by whether the presentation of items is uncued or cued with the name of the category. However, the association of items decreases and the learning of items as singletons increases with uncued presentation. In the final sections, the general statistical model is compared to other methods for the measurement of category clustering. The model is shown to be superior to numerical indices of clustering, since these measures are not based on any theory of clustering, and because unitary measures cannot capture the multiprocess nature of categorized recall. The model is also argued to have certain advantages over other mathematical models that have been applied to category clustering, since these models cannot account for situations in which a portion of the items are clustered while others are learned singularly.     

On the Interaction of Prior Knowledge and Stimulus Structure in Category Learning

Contemporary theories of categorization propose that concepts are coherent in virtue of being embedded in a network of theories about the world. Those theories function to pick out some of the many possible features of a set of objects as most salient for purposes of classification, a process that is complex and still poorly understood (Murphy & Medin, 1985). Part of what makes this account incomplete is a lack of information as to (1) what makes a feature salient on a given occasion and (2) how feature salience interacts with category structure to determine the course of learning. We report on the results of three studies of category learning using complex schematic drawings to show that (1) the contrast set defined by one's initial encounters with category exemplars can be a source of individual differences in feature salience assignments; (2) such effects are short-lived in the face of clear evidence about actual feature diagnosticity; and (3) more robust prior hypotheses interact with category structure to either enhance learning or impede it. The enhancement occurs when the hypothesis emphasizes category-relevant features, even if the hypothesis is in fact incorrect. A hypothesis that assigns high salience to irrelevant features impedes learning. Learning does occur as feedback concerning category structure leads to enhanced salience for relevant features. Salience of irrelevant features remains high, however, suggesting that such learning as occurs involves augmentation and not total revision of the (incorrect) prior hypothesis.     

Chunking processes in the learning of event sequences: Electrophysiological indicators

The present study investigated whether effects of implicit learning (IL) are due to well-learned and explicitly represented parts of the stimulus material ("chunks"). To this purpose, event-related brain potentials (ERPs) were recorded during an oddball-version of a serial reaction time (RT) task: At unpredictable positions within a 16-item letter sequence, single deviant items replaced an item of the repeatedly presented standard sequence. After acquisition, the "process dissociation procedure" (Jacoby, 1991) was adopted to identify explicitly learned sequence parts for each participant. Acquisition of sequence knowledge was reflected in faster RTs for standard items than for deviant items and in enhanced N2b and P3b components for deviant items. While the ERP effects were obtained for explicitly represented sequence parts only, RT effects were independent of subsequent reproduction performance. These results indicated that (1) ERPs are a valid measure of explicit knowledge, (2) implicit and explicit knowledge coexist in serial RT tasks, and (3) chunking processes play a major role in the acquisition of explicit knowledge about event sequences.     

Children's category‐based inferences affect classification

Children learn many new categories and make inferences about these categories. Much work has examined how children make inferences on the basis of category knowledge. However, inferences may also affect what is learned about a category. Four experiments examine whether category‐based inferences during category learning influence category knowledge and thereby affect later classifications for 5‐ to 7‐year‐olds. The children learned to classify pictures of new types of creatures on the basis of a salient feature (colour) and then answered a question that required them to make an inference on the basis of other features. At test, children classified pictures that included only some features (without colour). Experiment 1 showed that the features relevant to the inference during learning led to better classification than did features irrelevant to the inference. Experiment 2 replicated this finding even when the relevant features were physically close to the irrelevant features. Experiments 3 and 4 found this effect even when the classification was learned prior to the inference task and even when no mention was made of the categories during inference learning. Taken together, these results show that making inferences during category learning can influence category knowledge and suggest a need to integrate the work on category learning and category‐based inferences.     

Effects of constrained choice on memory: The extension of the multiple-cue hypothesis to the self-choice effect

The self-choice effect is the phenomenon whereby self-chosen items are remembered better than experimenter-assigned items. This study examined whether the effect occurs when the choice is constrained by cuing, and whether the effect also occurs for unchosen items. In the experiment, 33 participants chose (choice condition) or were assigned (force condition) a target from three alternatives that were followed by a cue sentence as a criterion for the choice. Cue sentences corresponded to any of the three alternatives (free cuing) or to only one (constrained cuing). Participants then engaged in free recall of targets and subsequent recognition of all alternatives (chosen and unchosen items). Memory performance was enhanced by choice regardless of the constraints, but was also enhanced for unchosen items. These results indicate that "free choice" is not always critical for the self-choice effect, and that multiple cuing involving unchosen items is a plausible account for the retention advantage of choice procedures.     

Effects of item difficulty on the retrieval of solutions during cognitive skill acquisition: age differences

In cognitive skill learning, shifts to better strategies for obtaining solutions often occur while associations between problems and solutions are being strengthened. In two skill learning experiments, we examined the effects of item difficulty on the retrieval of solutions and the learning of problem-solution associations in younger and older adults. The results of both experiments demonstrated an 'easy effect' in both younger and older adults, such that the retrieval of solutions as well recognition memory for problems was best for the easier items. In addition, a 'hard effect' was found in younger adults, but not in older adults, whereby the retrieval of solutions as well as recognition memory for problems was better for harder items than for medium-difficulty items. The finding that increased computational demands at the item level delayed item memorization and the retrieval of solutions in older adults but not younger adults is consistent with a general-resources account of age-related differences in skill learning.     

Evidence for abstract, schematic knowledge of three spatial diagram representations

Spatial diagram representations such as hierarchies, matrices, and networks are important tools for thinking. Our data suggest that college students possess abstract schemas for these representations that include at least rudimentary information about their applicability conditions. In Experiment 1, subjects were better able to select the appropriate spatial diagram representation for a problem when cued to use general category information in memory about those representations than when cued to use specific example problems given during the experiment. The results of Experiment 2 showed that the superior performance in the general category condition was not based on a comparison of the test problems with examples in memory. The results of Experiment 3 showed that the superior performance was not due to learning that occurred during the experiment or to transfer appropriate processing. The General Discussion section considers the nature of students' representation schemas and the question of why college students have only rudimentary schemas for common and widely applicable diagrammatic representations.