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

Learning Theories Versus Practice: How Do Internal Medicine Residents Study for Licensing Examinations?

OBJECTIVE

 The ability to recall relevant medical knowledge within clinical contexts is a critical aspect of effective and efficient patient diagnosis and management. The ever-growing and changing body of medical literature requires learners to develop effective life-long learning techniques. Learners can more successfully build their fund of knowledge and ability to retrieve it by using evidence-based learning strategies. Our objective was to evaluate the study habits of internal medicine (IM) residents at an academic institution to understand if they apply key learning strategies for the American Board of Internal Medicine (ABIM) exam preparation. We also briefly review various learning strategies that can be applied to IM residency curricula.

METHODS

A web-based survey consisting of 16 multiple-response questions on study habits was filled out by the IM residents in 2019 at Tufts Medical Center.

RESULTS

Of the 75 residents invited to participate in the study, 69 responded (response rate = 92%). Of the responders, n=25 (36.2%) were post-graduate year (PGY)-1, n=20 (29.0%) were PGY-2, and n=24 (34.8%) were PGY-3 residents. More than half the residents (n=40, 58%) had Step 2 Clinical Knowledge (CK) scores > 250. Residents self-reported applying spaced learning (67%), interleaving (64%), retrieval (64%), and elaboration practices (46%) for exam preparation. There was a significant association between the Step 2 CK score and elaboration (p=0.017) technique but not with spaced learning, interleaving, or retrieval. The majority of residents felt not at all prepared (n=42, 60.9%) for the ABIM exam.

CONCLUSIONS

Despite two years of clinical training, 33% of the third-year residents felt inadequately prepared for the board certification exam. Incorporating evidence-based learning strategies into their daily curriculum may help them better prepare for the ABIM exam.

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.

Familiar Strategies Feel Fluent: The Role of Study Strategy Familiarity in the Misinterpreted-Effort Model of Self-Regulated Learning

Why do learners not choose ideal study strategies when learning? Past research suggests that learners frequently misinterpret the effort affiliated with efficient strategies as being indicative of poor learning. Expanding on past findings, we explored the integration of study habits into this model. We conducted two experiments where learners experienced two contrasting strategies—blocked and interleaved schedules—to learn to discriminate between images of bird families. After experiencing each strategy, learners rated each according to its perceived effort, learning, and familiarity. Next, learners were asked to choose which strategy they would use in the future. Mediation analyses revealed, for both experiments, that the more mentally effortful interleaving felt, the less learners felt they learned, and the less likely learners were to use it in future learning. Further, in this study, strategy familiarity predicted strategy choice, also mediated by learners’ perceived learning. Additionally, Study 2 verified that, in contrast to learners’ judgments, the less familiar interleaving schedule resulted in better learning. Consequently, learners are making ineffective learning judgments based on their perceptions of effort and familiarity and, therefore, do not make use of optimal study strategies in self-regulated learning decisions.

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.

The temporal structure of naming events differentially affects children's and adults' cross-situational word learning

To acquire novel words, learners often need to integrate information about word meanings across ambiguous learning events distributed in time. How does the temporal structure of those word learning events affect what learners encode? How do the effects of temporal structure differ in children and adults? In the current experiments, we asked how 4- to 7-year-old children's (N = 110) and adults' (N = 90) performance on a cross-situational word learning task is influenced by the temporal distribution of learning events. We tested participants in three training conditions, manipulating the number of trials that separated naming events for specific objects. In the Unstructured condition, the temporal distribution was varied; in the Massed condition, naming events occurred with few interleaved trials; and in the Interleaved condition, naming events occurred with many interleaved trials. Adults showed substantially larger benefits from the Massed condition than children, whereas children were equally successful at learning in the Massed and Interleaved conditions. These results provide evidence that adults differ from children in how they exploit temporal structure during cross-situational word learning.

Training set coherence and set size effects on concept generalization and recognition

Building conceptual knowledge that generalizes to novel situations is a key function of human memory. Category-learning paradigms have long been used to understand the mechanisms of knowledge generalization. In the present study, we tested the conditions that promote formation of new concepts. Participants underwent 1 of 6 training conditions that differed in the number of examples per category (set size) and their relative similarity to the category average (set coherence). Performance metrics included rates of category learning, ability to generalize categories to new items of varying similarity to prototypes, and recognition memory for individual examples. In categorization, high set coherence led to faster learning and better generalization, while set size had little effect. Recognition did not differ reliably among conditions. We also tested the nature of memory representations used for categorization and recognition decisions using quantitative prototype and exemplar models fit to behavioral responses. Prototype models posit abstract category representations based on the category's central tendency, whereas exemplar models posit that categories are represented by individual category members. Prototype strategy use during categorization increased with increasing set coherence, suggesting that coherent training sets facilitate extraction of commonalities within a category. We conclude that learning from a coherent set of examples is an efficient means of forming abstract knowledge that generalizes broadly. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Dissociation of the Perirhinal Cortex and Hippocampus During Discriminative Learning of Similar Objects

Discriminative learning is a paradigm that has been used in animal studies, in which memory of a stimulus is enhanced when it is presented with a similar stimulus rather than with a different one. Human studies have shown that through discriminative learning of similar objects, both item memory and contextual memories are enhanced. However, the underlying neural mechanisms for it are unclear. The hippocampus and perirhinal cortex (PRC) are two possible regions involved in discriminating similar stimuli and forming distinctive memory representations. In this study, 28 participants (15 males) were scanned using high-resolution fMRI when a picture (e.g., a dog) was paired with the same picture, with a similar picture of the same concept (e.g., another dog), or with a picture of a different concept (e.g., a cat). Then, after intervals of 20 min and 1 week, the participants were asked to perform an old/new recognition task, followed by a contextual judgment. The results showed that during encoding, there was stronger activation in the PRC for the "similar" than for the "same" and "different" conditions and it predicted subsequent item memory for the "similar" condition. The hippocampal activation decreased for the "same" versus the "different" condition and the DG/CA3 activation predicted subsequent contextual memory for the "similar" condition. These results suggested that the PRC and hippocampus are functionally dissociated in encoding simultaneously presented objects and predicting subsequent item and contextual memories after discriminative learning.SIGNIFICANCE STATEMENT How the brain separates similar input into nonoverlapping representations and forms distinct memory for them is a fundamental question for the neuroscience of memory. By discriminative learning of similar (vs different) objects, both item and contextual memories are enhanced. This study found functional dissociations between perirhinal cortex (PRC) and hippocampus in discriminating pairs of similar and different objects and in predicting subsequent memory of similar objects in their item and contextual aspects. The results provided clear evidence on the neural mechanisms of discriminative learning and highlighted the importance of the PRC and hippocampus in processing different types of object information when the objects were simultaneously presented.

Discriminative learning of similar objects enhances memory for the objects and contexts

How to improve our episodic memory is an important issue in the field of memory. In the present study, we used a discriminative learning paradigm that was similar to a paradigm used in animal studies. In Experiment 1, a picture (e.g., a dog) was either paired with an identical picture, with a similar picture of the same concept (e.g., another dog), or with a picture of a different concept (e.g., a cat). Then, after intervals of 10 min, 1 d, and 1 wk, participants were asked to perform a 2-alternative forced-choice (2AFC) task to discriminate between a repeated and a similar picture, followed by the contextual judgment. In Experiment 2, eye movements were measured when participants encoded the pairs of pictures. The results showed that by discriminative learning, there was better memory performance in the 2AFC task for the “same” and “similar” conditions than for the “different” condition. In addition, there was better contextual memory performance for the “similar” condition than for the other two conditions. With regard to the eye movements, the participants were more likely to fixate on the lure objects and made more saccades between the target and lure objects in the “similar” (versus “different”) condition. The number of saccades predicted how well the targets were remembered in both the 2AFC and contextual memory tasks. These results suggested that with discriminative learning of similar objects, detailed information could be better encoded by distinguishing the object from similar interferences, making the details and the contexts better remembered and retained over time.

Reward Learning over Weeks Versus Minutes Increases the Neural Representation of Value in the Human Brain

Over the past few decades, neuroscience research has illuminated the neural mechanisms supporting learning from reward feedback. Learning paradigms are increasingly being extended to study mood and psychiatric disorders as well as addiction. However, one potentially critical characteristic that this research ignores is the effect of time on learning: human feedback learning paradigms are usually conducted in a single rapidly paced session, whereas learning experiences in ecologically relevant circumstances and in animal research are almost always separated by longer periods of time. In our experiments, we examined reward learning in short condensed sessions distributed across weeks versus learning completed in a single "massed" session in male and female participants. As expected, we found that after equal amounts of training, accuracy was matched between the spaced and massed conditions. However, in a 3-week follow-up, we found that participants exhibited significantly greater memory for the value of spaced-trained stimuli. Supporting a role for short-term memory in massed learning, we found a significant positive correlation between initial learning and working memory capacity. Neurally, we found that patterns of activity in the medial temporal lobe and prefrontal cortex showed stronger discrimination of spaced- versus massed-trained reward values. Further, patterns in the striatum discriminated between spaced- and massed-trained stimuli overall. Our results indicate that single-session learning tasks engage partially distinct learning mechanisms from distributed training. Our studies begin to address a large gap in our knowledge of human learning from reinforcement, with potential implications for our understanding of mood disorders and addiction.SIGNIFICANCE STATEMENT Humans and animals learn to associate predictive value with stimuli and actions, and these values then guide future behavior. Such reinforcement-based learning often happens over long time periods, in contrast to most studies of reward-based learning in humans. In experiments that tested the effect of spacing on learning, we found that associations learned in a single massed session were correlated with short-term memory and significantly decayed over time, whereas associations learned in short massed sessions over weeks were well maintained. Additionally, patterns of activity in the medial temporal lobe and prefrontal cortex discriminated the values of stimuli learned over weeks but not minutes. These results highlight the importance of studying learning over time, with potential applications to drug addiction and psychiatry.

The benefit of self-testing and interleaving for synthesizing concepts across multiple physiology texts

A testing-based learning strategy is one that relies on the act of recalling (i.e., testing) information after exposure, and interleaving is a strategy in which the learning materials are presented in a serial order (e.g., texts 1, 2, 3, 1, 2, 3, 1, 2, 3) versus a blocked order (e.g., texts 1, 1, 1, 2, 2, 2, 3, 3, 3). Although both learning strategies have been thoroughly investigated, few studies have examined their additive effect with higher-order cognitive tasks such as the ability to identify themes across multiple texts, and none of those did so using physiology information. The purpose of the present study was to compare recall and thematic processing across five different physiology texts. Participants were randomly assigned to learn the texts using one of the following four learning strategies: 1) study-study-study (S-S-S) using a blocked order, 2) S-S-S using an interleaved order, 3) study-test-study (S-T-S) using a blocked order, and 4) S-T-S using an interleaved order. Over the course of the following week, the S-T-S groups had more stable recall of key text ideas compared with the S-S-S groups, and the S-T-S group had more stable recall of thematic information than the S-S-S group when interleaving was used as the presentation order.

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.

Revisiting Wittgenstein's puzzle: hierarchical encoding and comparison facilitate learning of probabilistic relational categories

Kittur et al. (2004, 2006) and Jung and Hummel (2011, 2014) showed that people have great difficulty learning relation-based categories with a probabilistic (i.e., family resemblance) structure, in which no single relation is shared by all members of a category. Yet acquisition of such categories is not strictly impossible: in all these studies, roughly half the participants eventually learned to criterion. What are these participants doing that the other half are not? We hypothesized that successful participants were those who divided the nominal categories into two or more sub-categories, each of which individually had a deterministic structure. We report three experiments testing this hypothesis: explicitly presenting participants with hierarchical (category and sub-category) structures facilitated the acquisition of otherwise probabilistic relational categories, but only when participants learned the subordinate-level (i.e., deterministic) categories prior to learning the nominal (i.e., probabilistic) categories and only when they were permitted to view multiple exemplars of the same category simultaneously. These findings suggest that one way to learn natural relational categories with a probabilistic structure [e.g., Wittgenstein’s (1953), category game, or even mother] is by learning deterministic subordinate-level concepts first and connecting them together under a common concept or label. They also add to the literature suggesting that comparison of multiple exemplars plays an instrumental role in relational learning.

Feature saliency and feedback information interactively impact visual category learning

Visual category learning (VCL) involves detecting which features are most relevant for categorization. VCL relies on attentional learning, which enables effectively redirecting attention to object’s features most relevant for categorization, while ‘filtering out’ irrelevant features. When features relevant for categorization are not salient, VCL relies also on perceptual learning, which enables becoming more sensitive to subtle yet important differences between objects. Little is known about how attentional learning and perceptual learning interact when VCL relies on both processes at the same time. Here we tested this interaction. Participants performed VCL tasks in which they learned to categorize novel stimuli by detecting the feature dimension relevant for categorization. Tasks varied both in feature saliency (low-saliency tasks that required perceptual learning vs. high-saliency tasks), and in feedback information (tasks with mid-information, moderately ambiguous feedback that increased attentional load, vs. tasks with high-information non-ambiguous feedback). We found that mid-information and high-information feedback were similarly effective for VCL in high-saliency tasks. This suggests that an increased attentional load, associated with the processing of moderately ambiguous feedback, has little effect on VCL when features are salient. In low-saliency tasks, VCL relied on slower perceptual learning; but when the feedback was highly informative participants were able to ultimately attain the same performance as during the high-saliency VCL tasks. However, VCL was significantly compromised in the low-saliency mid-information feedback task. We suggest that such low-saliency mid-information learning scenarios are characterized by a ‘cognitive loop paradox’ where two interdependent learning processes have to take place simultaneously.