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

The effects of procedural and conceptual knowledge on visual learning

Even though past research suggests that visual learning may benefit from conceptual knowledge, current interventions for medical image evaluation often focus on procedural knowledge, mainly by teaching classification algorithms. We compared the efficacy of pure procedural knowledge (three-point checklist for evaluating skin lesions) versus combined procedural plus conceptual knowledge (histological explanations for each of the three points). All students then trained their classification skills with a visual learning resource that included images of two types of pigmented skin lesions: benign nevi and malignant melanomas. Both treatments produced significant and long-lasting effects on diagnostic accuracy in transfer tasks. However, only students in the combined procedural plus conceptual knowledge condition significantly improved their diagnostic performance in classifying lesions they had seen before in the pre- and post-tests. Findings suggest that the provision of additional conceptual knowledge supported error correction mechanisms.

Reconciling shared versus context-specific information in a neural network model of latent causes

It has been proposed that, when processing a stream of events, humans divide their experiences in terms of inferred latent causes (LCs) to support context-dependent learning. However, when shared structure is present across contexts, it is still unclear how the "splitting" of LCs and learning of shared structure can be simultaneously achieved. Here, we present the Latent Cause Network (LCNet), a neural network model of LC inference. Through learning, it naturally stores structure that is shared across tasks in the network weights. Additionally, it represents context-specific structure using a context module, controlled by a Bayesian nonparametric inference algorithm, which assigns a unique context vector for each inferred LC. Across three simulations, we found that LCNet could (1) extract shared structure across LCs in a function learning task while avoiding catastrophic interference, (2) capture human data on curriculum effects in schema learning, and (3) infer the underlying event structure when processing naturalistic videos of daily events. Overall, these results demonstrate a computationally feasible approach to reconciling shared structure and context-specific structure in a model of LCs that is scalable from laboratory experiment settings to naturalistic settings.

Optimizing self-organized study orders: combining refutations and metacognitive prompts improves the use of interleaved practice

During category learning, students struggle to create an optimal study order: They often study one category at a time (i.e., blocked practice) instead of alternating between different categories (i.e., interleaved practice). Several interventions to improve self-study of categorical learning have been proposed, but these interventions have only been tested in learning tasks where students did not create the study order themselves. Instead, they decided which type of study order to follow. This pre-registered experiment examined whether an intervention that combines refutations and metacognitive prompts can enhance students' engagement in interleaved practice, specifically when they organize the learning materials themselves. Ninety-one undergraduate students were randomized into the intervention and control condition and learned visual categories. Prior to the intervention, students used more blocked practice. After the intervention, the use of interleaved practice significantly increased in both immediate and delayed-transfer tasks. More interleaved practice was associated with better classification performance. Our findings indicate that refutations and metacognitive prompts form a strong intervention that corrects students' erroneous beliefs and increases their engagement in interleaved practice.

No Simple Solutions to Complex Problems: Cognitive Science Principles Can Guide but Not Prescribe Educational Decisions

Cognitive science of learning points to solutions for making use of existing study and instruction time more effectively and efficiently. However, solutions are not and cannot be one-size-fits-all. This paper outlines the danger of overreliance on specific strategies as one-size-fits-all recommendations and highlights instead the cognitive learning processes that facilitate meaningful and long-lasting learning. Three of the most commonly recommended strategies from cognitive science provide a starting point; understanding the underlying processes allows us to tailor these recommendations to implement at the right time, in the right way, for the right content, and for the right students. Recommendations regard teacher training, the funding and incentivizing of educational interventions, guidelines for the development of educational technologies, and policies that focus on using existing instructional time more wisely.

Twelve tips for providing academic remediation to widening access learners in medical education

As medical schools expand access and diversity through widening access initiatives, there is an increasing need to provide academic remediation for learners during their first year in medical school. The previous educational experiences of widening access learners are often mismatched for continuing success in medical school. This article offers 12 tips for providing academic remediation to widening access learners and draws on insights from the learning sciences and research in psychosocial education to support academic development within a holistic framework.

Cognitive perspectives on maintaining physicians' medical expertise: IV. Best practices and open questions in using testing to enhance learning and retention

Although tests and assessments-such as those used to maintain a physician's Board certification-are often viewed merely as tools for decision-making about one's performance level, strong evidence now indicates that the experience of being tested is a powerful learning experience in its own right: The act of retrieving targeted information from memory strengthens the ability to use it again in the future, known as the testing effect. We review meta-analytic evidence for the learning benefits of testing, including in the domain of medicine, and discuss theoretical accounts of its mechanism(s). We also review key moderators-including the timing, frequency, order, and format of testing and the content of feedback-and what they indicate about how to most effectively use testing for learning. We also identify open questions for the optimal use of testing, such as the timing of feedback and the sequencing of complex knowledge domains. Lastly, we consider how to facilitate adoption of this powerful study strategy by physicians and other learners.

The Underappreciated Benefits of Interleaving for Category Learning

The present study examined the effects of study schedule (interleaving vs. blocking) and feature descriptions on category learning and metacognitive predictions of learning. Across three experiments, participants studied exemplars from different rock categories and later had to classify novel exemplars. Rule-based and information-based categorization was also manipulated by selecting rock sub-categories for which the optimal strategy was the one that aligned with the extraction of a simple rule, or the one that required integration of information that may be difficult to describe verbally. We observed consistent benefits of interleaving over blocking on rock classification, which generalized to both rule-based (Experiment 1) and information-integration learning (Experiments 1-3). However, providing feature descriptions enhanced classification accuracy only when the stated features were diagnostic of category membership, indicating that their benefits were limited to rule-based learning (Experiment 1) and did not generalize to information-integration learning (Experiments 1-3). Furthermore, our examination of participants' metacognitive predictions demonstrated that participants were not aware of the benefits of interleaving on category learning. Additionally, providing feature descriptions led to higher predictions of categorization even when no significant benefits on actual performance were exhibited.

Auditory category learning is robust across training regimes

Multiple lines of research have developed training approaches that foster category learning, with important translational implications for education. Increasing exemplar variability, blocking or interleaving by category-relevant dimension, and providing explicit instructions about diagnostic dimensions each have been shown to facilitate category learning and/or generalization. However, laboratory research often must distill the character of natural input regularities that define real-world categories. As a result, much of what we know about category learning has come from studies with simplifying assumptions. We challenge the implicit expectation that these studies reflect the process of category learning of real-world input by creating an auditory category learning paradigm that intentionally violates some common simplifying assumptions of category learning tasks. Across five experiments and nearly 300 adult participants, we used training regimes previously shown to facilitate category learning, but here drew from a more complex and multidimensional category space with tens of thousands of unique exemplars. Learning was equivalently robust across training regimes that changed exemplar variability, altered the blocking of category exemplars, or provided explicit instructions of the category-diagnostic dimension. Each drove essentially equivalent accuracy measures of learning generalization following 40 min of training. These findings suggest that auditory category learning across complex input is not as susceptible to training regime manipulation as previously thought.

Investigating interactions between types of order in categorization

This study simultaneously manipulates within-category (rule-based vs. similarity-based), between-category (blocked vs. interleaved), and across-blocks (constant vs. variable) orders to investigate how different types of presentation order interact with one another. With regard to within-category orders, stimuli were presented either in a "rule plus exceptions" fashion (in the rule-based order) or by maximizing the similarity between contiguous examples (in the similarity-based order). As for the between-category manipulation, categories were either blocked (in the blocked order) or alternated (in the interleaved order). Finally, the sequence of stimuli was either repeated (in the constant order) or varied (in the variable order) across blocks. This research offers a novel approach through both an individual and concurrent analysis of the studied factors, with the investigation of across-blocks manipulations being unprecedented. We found a significant interaction between within-category and across-blocks orders, as well as between between-category and across-blocks orders. In particular, the combination similarity-based + variable orders was the most detrimental, whereas the combination blocked + constant was the most beneficial. We also found a main effect of across-blocks manipulation, with faster learning in the constant order as compared to the variable one. With regard to the classification of novel stimuli, learners in the rule-based and interleaved orders showed generalization patterns that were more consistent with a specific rule-based strategy, as compared to learners in the similarity-based and blocked orders, respectively. This study shows that different types of order can interact in a subtle fashion and thus should not be considered in isolation.

Hippocampal and medial prefrontal cortices encode structural task representations following progressive and interleaved training schedules

Memory generalisations may be underpinned by either encoding- or retrieval-based generalisation mechanisms and different training schedules may bias some learners to favour one of these mechanisms over the other. We used a transitive inference task to investigate whether generalisation is influenced by progressive vs randomly interleaved training, and overnight consolidation. On consecutive days, participants learnt pairwise discriminations from two transitive hierarchies before being tested during fMRI. Inference performance was consistently better following progressive training, and for pairs further apart in the transitive hierarchy. BOLD pattern similarity correlated with hierarchical distances in the left hippocampus (HIP) and medial prefrontal cortex (MPFC) following both training schedules. These results are consistent with the use of structural representations that directly encode hierarchical relationships between task features. However, such effects were only observed in the MPFC for recently learnt relationships. Furthermore, the MPFC appeared to maintain structural representations in participants who performed at chance on the inference task. We conclude that humans preferentially employ encoding-based mechanisms to store map-like relational codes that can be used for memory generalisation. These codes are expressed in the HIP and MPFC following both progressive and interleaved training but are not sufficient for accurate inference.

Using Interleaving to Promote Inductive Learning in Educational Contexts

Abstract. Inductive learning, that is, abstracting conceptual knowledge, rules, or principles from exemplars, plays a major role in educational settings, from literacy acquisition to mathematics and science learning. Interleaving exemplars of different categories rather than presenting blocks might be a simple but powerful way to improve inductive learning by supporting discriminative contrast. Although a consistent advantage of interleaving has been demonstrated for visual materials, relatively few studies have examined educationally relevant materials, such as mathematical tasks, science problems, and verbal materials, and their results are mixed. We discuss how interleaving could be made fruitful for school learning of mathematics, science, and literacy acquisition. We conclude that interleaving should be tailored to the specific learning content and combined with supportive instructional measures that assist students in comparing exemplars for discriminating features. Finally, we sketch research gaps that revolve around the use of interleaved learning in the classroom.

Building lexical networks: Preschoolers extract different types of information in cross-situational learning

Children's everyday learning environment is semantically structured. For example, semantically related things (e.g., fork and spoon) usually co-occur in the same contexts. The current study examines the effects of semantically structured contexts on preschool-age children's (N = 65, 33 girls, age range: 52-68 months) use of statistical information to learn novel word-object mappings. Children were assigned into one of two conditions, in which objects from the same semantic category repeatedly co-occurred in the same trials (Same-category condition) or objects from different categories repeatedly co-occurred in the same trials (Different-categories condition). Children's word learning performance in the two conditions were comparable. However, their errors at test suggested that information extracted by children in the two conditions differed. Importantly, children in the Same-category condition extracted both statistical and semantic relationships from the stimuli.

A Computational Model of Context-Dependent Encodings During Category Learning

Although current exemplar models of category learning are flexible and can capture how different features are emphasized for different categories, they still lack the flexibility to adapt to local changes in category learning, such as the effect of different sequences of study. In this paper, we introduce a new model of category learning, the Sequential Attention Theory Model (SAT-M), in which the encoding of each presented item is influenced not only by its category assignment (global context) as in other exemplar models, but also by how its properties relate to the properties of temporally neighboring items (local context). By fitting SAT-M to data from experiments comparing category learning with different sequences of trials (interleaved vs. blocked), we demonstrate that SAT-M captures the effect of local context and predicts when interleaved or blocked training will result in better testing performance across three different studies. Comparatively, ALCOVE, SUSTAIN, and a version of SAT-M without locally adaptive encoding provided poor fits to the results. Moreover, we evaluated the direct prediction of the model that different sequences of training change what learners encode and determined that the best-fit encoding parameter values match learners' looking times during training.

Interleaved practice enhances memory and problem-solving ability in undergraduate physics

We investigated whether continuously alternating between topics during practice, or interleaved practice, improves memory and the ability to solve problems in undergraduate physics. Over 8 weeks, students in two lecture sections of a university-level introductory physics course completed thrice-weekly homework assignments, each containing problems that were interleaved (i.e., alternating topics) or conventionally arranged (i.e., one topic practiced at a time). On two surprise criterial tests containing novel and more challenging problems, students recalled more relevant information and more frequently produced correct solutions after having engaged in interleaved practice (with observed median improvements of 50% on test 1 and 125% on test 2). Despite benefiting more from interleaved practice, students tended to rate the technique as more difficult and incorrectly believed that they learned less from it. Thus, in a domain that entails considerable amounts of problem-solving, replacing conventionally arranged with interleaved homework can (despite perceptions to the contrary) foster longer lasting and more generalizable learning.

Chained study and the discovery of relational structure

Prior knowledge of relational structure allows people to quickly make sense of and respond to new experiences. When awareness of such structure is not necessary to support learning, however, it is unclear when and why individuals "spontaneously discover" an underlying relational schema. The present study examines the determinants of such discovery in discrimination-based transitive inference (TI), whereby people learn about a hierarchy of interrelated premises and are tested on their ability to draw inferences that bridge studied relations. Experiencing "chained" sequences of overlapping premises during training was predicted to facilitate the discovery of relational structure. Among individuals without prior knowledge of the hierarchy, chaining improved relational learning and was most likely to result in explicit awareness of the underlying relations between items. Observation of chained training sequences was also more effective than the self-generation of training sequences. These findings add to growing evidence that the temporal dynamics of training, including successive presentation of overlapping associations, are key to understanding spontaneous relational discovery during learning.

Visual category learning: Navigating the intersection of rules and similarity

Visual categorization is fundamental to expertise in a wide variety of disparate domains, such as radiology, art history, and quality control. The pervasive need to master visual categories has served as the impetus for a vast body of research dedicated to exploring how to enhance the learning process. The literature is clear on one point: no category learning technique is always superior to another. In the present review, we discuss how two factors moderate the efficacy of learning techniques. The first, category similarity, refers to the degree of featural overlap of exemplars. The second moderator, category type, concerns whether the features that define category membership can be mastered through learning processes that are implicit/non-verbal (information-integration categories) or explicit/verbal (rule-based categories). The literature on each moderator has been conducted almost entirely in isolation, such that their potential interaction remains underexplored. We address this gap in the literature by reviewing empirical and theoretical evidence that these two moderators jointly influence the efficacy of learning techniques.

Learning and generalization of within-category representations in a rule-based category structure

The task requirements during the course of category learning are critical for promoting within-category representations (e.g., correlational structure of the categories). Recent data suggest that for unidimensional rule-based structures, only inference training promotes the learning of within-category representations, and generalization across tasks is limited. It is unclear if this is a general feature of rule-based structures, or a limitation of unidimensional rule-based structures. The present work reports the results of three experiments further investigating this issue using an exclusive-or rule-based structure where successful performance depends upon attending to two stimulus dimensions. Participants were trained using classification or inference and were tested using inference. For both the classification and inference training conditions, within-category representations were learned and could be generalized at test (i.e., from classification to inference) and this result was dependent upon a congruence between local and global regions of the stimulus space. These data further support the idea that the task requirements during learning (i.e., a need to attend to multiple stimulus dimensions) are critical determinants of the category representations that are learned and the utility of these representations for supporting generalization in novel situations.

Exploring How Gender and Enjoyment Impact Learning in a Digital Learning Game

In digital learning games, do game mechanics that promote learning and those that promote enjoyment have different effects on students’ experience? Do males or females learn from or enjoy games more? We explored these questions in Decimal Point, a digital learning game that teaches decimal numbers and decimal operations to middle school students. In this work, we conducted a classroom study with two versions of the game, one that encourages students to play to learn and one that encourages students to play for enjoyment. We compared these two conditions to a control condition that is neutral regarding learning and enjoyment. Our results indicated that the enjoyment-focused group learned more efficiently than the control group, and that females had higher learning gains than males across all conditions, particularly on the near and middle transfer learning items. Post hoc analyses also revealed that the learning-focused group engaged in re-practicing the same mini-games, while the enjoyment-focused group demonstrated more exploration of different mini-games. These findings suggest that emphasizing learning or enjoyment can result in distinctive gameplay behaviors from students, and that our game can help bridge the typical gender gap in math education.

Easy-to-hard effects in perceptual learning depend upon the degree to which initial trials are "easy"

Starting perceptual training at easy levels before progressing to difficult levels generally produces better learning outcomes than constantly difficult training does. However, little is known about how "easy" these initial levels should be in order to yield easy-to-hard effects. We compared five levels of initial training block difficulty varying from very easy to hard in two auditory-discrimination learning tasks-a frequency modulation rate discrimination (Experiment 1) and a frequency range discrimination (Experiment 2). The degree of difficulty was based on individualized pretraining ~71% correct discrimination thresholds. Both experiments revealed a sweet spot for easy-to-hard effects. Conditions where initial blocks were either too easy or too difficult produced less benefit than did blocks of intermediate difficulty. Results challenge assumptions that sequencing effects in learning are related to attentional spotlighting of task-relevant dimensions. Rather, they support incremental learning models that account for easy-to-hard effects. Further, the results have implications for how perceptual training regimens should be designed to maximize the benefits of rehabilitative perceptual training.

Interleaved Learning in Elementary School Mathematics: Effects on the Flexible and Adaptive Use of Subtraction Strategies

Empirical findings show that students are often not capable of using number-based strategies and the standard written algorithm flexibly and adaptively to solve multi-digit subtraction problems. Previous studies have pointed out that students predominantly use the standard written algorithm after its introduction, regardless of task characteristics. Interleaved practice seems to be a promising approach to foster the flexible and adaptive use of strategies. In comparison to the usual blocked approach, in which strategies are introduced and practiced successively, they are presented intermixed in interleaved learning. Thus, the students have to choose an appropriate strategy on the basis of every task itself, and this leads to drawing comparisons between the different strategies. Previous research has shown inconsistent results regarding the effectivity of interleaving mathematical tasks. However, according to the attentional bias framework, interleaved practice seems to be a promising approach for teaching subtraction strategies to enhance the students’ flexibility and adaptivity. In this study, 236 German third graders were randomly assigned to either an interleaved or blocked condition. In the interleaved condition the comparison processes were supported by prompting the students to compare the strategies (between-comparison), while the students of the blocked approach were encouraged to reflect the adaptivity of a specific strategy for specific subtraction tasks (within-comparison). Both groups were taught to use different number-based strategies (i.e., shortcut strategies and decomposition strategies) and the standard written algorithm for solving three-digit subtraction problems spanning a teaching unit of 14 lessons. The results show that the students of the interleaved condition used the shortcut strategies more frequently than those of the blocked condition, while the students of the interleaved condition applied the decomposition strategies as well as the standard written algorithm less frequently. Furthermore, the students of the interleaved condition had a higher competence in the adaptive use of the shortcut strategies and the standard written algorithm. A subsequent cluster analysis revealed four groups differing in their degree of adaptivity. Being part of clusters with a comparatively high level of adaptivity was positively related to the prior arithmetical achievement and, even more so, to the interleaved teaching approach.

The easy-to-hard training advantage with real-world medical images

Many medical professions require practitioners to perform visual categorizations in domains such as radiology, dermatology, and neurology. However, acquiring visual expertise is tedious and time-consuming and the perceptual strategies mediating visual categorization skills are poorly understood. In this paper, the Ease algorithm was developed to predict an item’s categorization difficulty (Ease value) based on the item’s perceptual similarity to all within-category items versus between-category items in the dataset. In this study, Ease values were used to construct an easy-to-hard and hard-to-easy training schedule for teaching melanoma diagnosis. Whereas previous visual training studies suggest that an easy-to-hard schedule benefits learning outcomes, no studies to date have demonstrated the easy-to-hard advantage with complex, real-world images. In our study, 237 melanoma and benign images were collected for training and testing purposes. The diagnostic accuracy of images was verified by an expert dermatologist. Based on their Ease values, the items were grouped into easy, medium, and hard categories, each containing an equal number of melanoma and benign lesions. During training, participants categorized images of skin lesions as either benign or melanoma and were given corrective feedback after each trial. In the easy-to-hard training condition, participants learned to categorize all the easy items first, followed by the medium items, and finally the hard items. Participants in the hard-to-easy training condition learned items in the reverse order. Post-training results showed that training in both conditions transferred to the classification of new melanoma and benign images. Participants in the easy-to-hard condition showed modest advantages both in the acquisition and retention of the melanoma diagnosis skills, but neither scheduling condition exhibited a gross advantage. The Ease values of the items predicted categorization accuracy after, but not before training, suggesting that the Ease algorithm is a promising tool for optimizing medical training in visual categorization.

Model-based fMRI reveals dissimilarity processes underlying base rate neglect

Extensive evidence suggests that people use base rate information inconsistently in decision making. A classic example is the inverse base rate effect (IBRE), whereby participants classify ambiguous stimuli sharing features of both common and rare categories as members of the rare category. Computational models of the IBRE have posited that it arises either from associative similarity-based mechanisms or from dissimilarity-based processes that may depend on higher-level inference. Here we develop a hybrid model, which posits that similarity- and dissimilarity-based evidence both contribute to the IBRE, and test it using functional magnetic resonance imaging data collected from human subjects completing an IBRE task. Consistent with our model, multivoxel pattern analysis reveals that activation patterns on ambiguous test trials contain information consistent with dissimilarity-based processing. Further, trial-by-trial activation in left rostrolateral prefrontal cortex tracks model-based predictions for dissimilarity-based processing, consistent with theories positing a role for higher-level symbolic processing in the IBRE.

The impact of category structure and training methodology on learning and generalizing within-category representations

When interacting with categories, representations focused on within-category relationships are often learned, but the conditions promoting within-category representations and their generalizability are unclear. We report the results of three experiments investigating the impact of category structure and training methodology on the learning and generalization of within-category representations (i.e., correlational structure). Participants were trained on either rule-based or information-integration structures using classification (Is the stimulus a member of Category A or Category B?), concept (e.g., Is the stimulus a member of Category A, Yes or No?), or inference (infer the missing component of the stimulus from a given category) and then tested on either an inference task (Experiments 1 and 2) or a classification task (Experiment 3). For the information-integration structure, within-category representations were consistently learned, could be generalized to novel stimuli, and could be generalized to support inference at test. For the rule-based structure, extended inference training resulted in generalization to novel stimuli (Experiment 2) and inference training resulted in generalization to classification (Experiment 3). These data help to clarify the conditions under which within-category representations can be learned. Moreover, these results make an important contribution in highlighting the impact of category structure and training methodology on the generalization of categorical knowledge.

Quantity and Diversity: Simulating Early Word Learning Environments

The words in children's language learning environments are strongly predictive of cognitive development and school achievement. But how do we measure language environments and do so at the scale of the many words that children hear day in, day out? The quantity and quality of words in a child's input are typically measured in terms of total amount of talk and the lexical diversity in that talk. There are disagreements in the literature whether amount or diversity is the more critical measure of the input. Here we analyze the properties of a large corpus (6.5 million words) of speech to children and simulate learning environments that differ in amount of talk per unit time, lexical diversity, and the contexts of talk. The central conclusion is that what researchers need to theoretically understand, measure, and change is not the total amount of words, or the diversity of words, but the function that relates total words to the diversity of words, and how that function changes across different contexts of talk.

The effect of training methodology on knowledge representation in categorization

Category representations can be broadly classified as containing within-category information or between-category information. Although such representational differences can have a profound impact on decision-making, relatively little is known about the factors contributing to the development and generalizability of different types of category representations. These issues are addressed by investigating the impact of training methodology and category structures using a traditional empirical approach as well as the novel adaptation of computational modeling techniques from the machine learning literature. Experiment 1 focused on rule-based (RB) category structures thought to promote between-category representations. Participants learned two sets of two categories during training and were subsequently tested on a novel categorization problem using the training categories. Classification training resulted in a bias toward between-category representations whereas concept training resulted in a bias toward within-category representations. Experiment 2 focused on information-integration (II) category structures thought to promote within-category representations. With II structures, there was a bias toward within-category representations regardless of training methodology. Furthermore, in both experiments, computational modeling suggests that only within-category representations could support generalization during the test phase. These data suggest that within-category representations may be dominant and more robust for supporting the reconfiguration of current knowledge to support generalization.

Learning Object Names at Different Hierarchical Levels Using Cross-Situational Statistics

Objects in the world usually have names at different hierarchical levels (e.g., beagle, dog, animal). This research investigates adults' ability to use cross-situational statistics to simultaneously learn object labels at individual and category levels. The results revealed that adults were able to use co-occurrence information to learn hierarchical labels in contexts where the labels for individual objects and labels for categories were presented in completely separated blocks, in interleaved blocks, or mixed in the same trial. Temporal presentation schedules significantly affected the learning of individual object labels, but not the learning of category labels. Learners' subsequent generalization of category labels indicated sensitivity to the structure of statistical input.

Real-world visual statistics and infants' first-learned object names

We offer a new solution to the unsolved problem of how infants break into word learning based on the visual statistics of everyday infant-perspective scenes. Images from head camera video captured by 8 1/2 to 10 1/2 month-old infants at 147 at-home mealtime events were analysed for the objects in view. The images were found to be highly cluttered with many different objects in view. However, the frequency distribution of object categories was extremely right skewed such that a very small set of objects was pervasively present-a fact that may substantially reduce the problem of referential ambiguity. The statistical structure of objects in these infant egocentric scenes differs markedly from that in the training sets used in computational models and in experiments on statistical word-referent learning. Therefore, the results also indicate a need to re-examine current explanations of how infants break into word learning.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.

Developmental changes between childhood and adulthood in passive observational and interactive feedback-based categorization rule learning

As children start attending school they are more likely to face situations where they have to autonomously learn about novel object categories (e.g. by reading a picture book with descriptions of novel animals). Such autonomous observational category learning (OCL) gradually complements interactive feedback-based category learning (FBCL), where a child hypothesizes about the nature of a novel object, acts based on his prediction, and then receives feedback indicating the correctness of his prediction. Here we tested OCL and FBCL skills of elementary school children and adults. In both conditions, participants performed complex rule-based categorization tasks that required associating novel objects with novel category-labels. We expected children to perform better in FBCL tasks than in OCL tasks, whereas adults to be skilled in both tasks. As hypothesized, in early-phase learning children performed better in FBCL tasks than in OCL tasks. Unexpectedly, adults performed somewhat better in OCL tasks. Early-phase FBCL performance in the two age groups was matched, but the OCL performance of adults was higher than that of children. In late-phase learning there was only an age group main effect (adults > children). Moreover, performance in post-learning categorization tasks, that did not require label recollection, indicated that in FBCL tasks children were likely to directly learn the associations between an object and a category label, whereas in the OCL tasks they were likely to first learn which feature-dimensions were relevant. These findings shed light on developmental changes in cognitive control and learning mechanisms. Implications for educational settings are discussed.

Anchors aweigh: The impact of overlearning on entrenchment effects in statistical learning

Previous research has revealed that when learners encounter multiple artificial languages in succession only the first is learned, unless there are contextual cues correlating with the change in structure or if exposure to the second language is protracted. These experiments provided a fixed amount of exposure irrespective of when learning occurred. Here, the authors presented learners with 2 consecutive artificial languages testing learning after each minute of familiarization. In Experiment 1, learners received fixed input, and the authors replicated the primacy effect. In Experiment 2, learners advanced to the second language immediately following robust learning of the first language (thereby limiting additional exposure past the point of learning). Remarkably, learners tended to acquire and retain both languages, although contextual cues did not boost performance further. Notably, there was no correlation between performance on this task and a flanker task that measured inhibitory control. Overall, the findings suggest that anchoring effects in statistical learning may be because of overlearning. We speculate that learners may reduce their attention to the input once they achieve a low level of estimation uncertainty. (PsycINFO Database Record

Observational Word Learning: Beyond Propose-But-Verify and Associative Bean Counting

Learning new words is difficult. In any naming situation, there are multiple possible interpretations of a novel word. Recent approaches suggest that learners may solve this problem by tracking co-occurrence statistics between words and referents across multiple naming situations (e.g. Yu & Smith, 2007), overcoming the ambiguity in any one situation. Yet, there remains debate around the underlying mechanisms. We conducted two experiments in which learners acquired eight word-object mappings using cross-situational statistics while eye-movements were tracked. These addressed four unresolved questions regarding the learning mechanism. First, eye-movements during learning showed evidence that listeners maintain multiple hypotheses for a given word and bring them all to bear in the moment of naming. Second, trial-by-trial analyses of accuracy suggested that listeners accumulate continuous statistics about word/object mappings, over and above prior hypotheses they have about a word. Third, consistent, probabilistic context can impede learning, as false associations between words and highly co-occurring referents are formed. Finally, a number of factors not previously considered in prior analysis impact observational word learning: knowledge of the foils, spatial consistency of the target object, and the number of trials between presentations of the same word. This evidence suggests that observational word learning may derive from a combination of gradual statistical or associative learning mechanisms and more rapid real-time processes such as competition, mutual exclusivity and even inference or hypothesis testing.

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.

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.

Pigeons acquire multiple categories in parallel via associative learning: a parallel to human word learning?

Might there be parallels between category learning in animals and word learning in children? To examine this possibility, we devised a new associative learning technique for teaching pigeons to sort 128 photographs of objects into 16 human language categories. We found that pigeons learned all 16 categories in parallel, they perceived the perceptual coherence of the different object categories, and they generalized their categorization behavior to novel photographs from the training categories. More detailed analyses of the factors that predict trial-by-trial learning implicated a number of factors that may shape learning. First, we found considerable trial-by-trial dependency of pigeons' categorization responses, consistent with several recent studies that invoke this dependency to claim that humans acquire words via symbolic or inferential mechanisms; this finding suggests that such dependencies may also arise in associative systems. Second, our trial-by-trial analyses divulged seemingly irrelevant aspects of the categorization task, like the spatial location of the report responses, which influenced learning. Third, those trial-by-trial analyses also supported the possibility that learning may be determined both by strengthening correct stimulus-response associations and by weakening incorrect stimulus-response associations. The parallel between all these findings and important aspects of human word learning suggests that associative learning mechanisms may play a much stronger part in complex human behavior than is commonly believed.

How experimental trial context affects perceptual categorization

To understand object categorization, participants are tested in experiments often quite different from how people experience object categories in the real world. Learning and knowledge of categories is measured in discrete experimental trials, those trials may or may not provide feedback, trials appear one after another, after some fixed inter-trial interval, with hundreds of trials in a row, within experimental blocks with some structure dictated by the experimental design. In the real world, outside of certain educational and vocational contexts, opportunities to learn and use categories are intermixed over time with a whole multitude of intervening experiences. It is clear from any elementary understanding of human cognition that sequential effects matter, yet this understanding is often ignored, and categorization trials are often instead treated as independent events, immune to local trial context. In this perspective, we use some of our work to illustrate some of the consequences of the fact that categorization experiments have a particular trial structure. Experimental trial context can affect performance in category learning and categorization experiments in ways that can profoundly affect theoretical conclusions.

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).