The role of retrieval during study: Evidence of reminding from self-paced study time.

The reminding effect (Tullis, Benjamin, & Ross, Journal of Experimental Psychology: General, 143[4], 1526-1540, 2014) describes the increase in recall of a studied word when a related word is presented later in the study list. However, because the process of reminding is thought to occur during study, measures of test performance are indirect indicators of the process of reminding and are subject to influences that arise during testing. The present research seeks evidence of reminding during encoding. In two experiments, self-paced study times were used to index the online process of reminding. In Experiment 1, pairs of repeated words, related words, and unrelated words were included in a study list. Study times were shorter for words related to prior words in the list, but only when the lag between those two words was short. Relatedness affected study time by inspiring a reduction in the threshold for termination of study for related words under massed conditions. Experiment 2 replicated the reduction in study time for related words and further showed that the study time allotted to an associate of an earlier item predicted better memory for that earlier word on a cued-recall test. In this experiment, an advantage in memory was observed for related words, and self-paced study time of one word during encoding was predictive of later memory for a related word. These results suggest a link between the action of reminding at study, as indexed by changes in the distribution of study time, and later benefits to remembering, as revealed by the reminding effect.

Eyetracking reveals multiple-category use in induction.

Category information is used to predict properties of new category members. When categorization is uncertain, people often rely on only one, most likely category to make predictions. Yet studies of perception and action often conclude that people combine multiple sources of information near-optimally. We present a perception-action analog of category-based induction using eye movements as a measure of prediction. The categories were objects of different shapes that moved in various directions. Experiment 1 found that people integrated information across categories in predicting object motion. The results of Experiment 2 suggest that the integration of information found in Experiment 1 were not a result of explicit strategies. Experiment 3 tested the role of explicit categorization, finding that making a categorization judgment, even an uncertain one, stopped people from using multiple categories in our eye-movement task. Experiment 4 found that induction was indeed based on category-level predictions rather than associations between object properties and directions. (PsycINFO Database Record

Decision making under uncertain categorization.

Two experiments investigated how category information is used in decision making under uncertainty and whether the framing of category information influences how it is used. Subjects were presented with vignettes in which the categorization of a critical item was ambiguous and were asked to choose among a set of actions with the goal of attaining the desired outcome for the main character in the story. The normative decision making strategy was to base the decision on all possible categories; however, research on a related topic, category-based induction, has found that people often only consider a single category when making predictions when categorization is uncertain. These experiments found that subjects tend to consider multiple categories when making decisions, but do so both when it is and is not appropriate, suggesting that use of multiple categories is not driven by an understanding of whether categories are relevant to the decision. Similarly, although a framing manipulation increased the rate of multiple-category use, it did so in situations in which multiple-category use both was and was not appropriate.

The reminding effect: presentation of associates enhances memory for related words in a list.

One aspect of successful cognition is the efficient use of prior relevant knowledge in novel situations. Remindings-stimulus-guided retrievals of prior events-allow us to link prior knowledge to current problems by prompting us to retrieve relevant knowledge from events that are distant from the present. Theorizing in research on higher cognition makes much use of the concept of remindings, yet many basic mnemonic consequences of remindings are untested. Here we consider implications of reminding-based theories of the effects of repetition on memory (Benjamin & Tullis, 2010; Hintzman, 2011). Those theories suggest that the spacing of repeated presentations of material benefits memory when the later experience reminds the learner of the earlier one. When applied to memory for related, rather than repeated, material, these theories predict a reminding effect: a mnemonic boost caused by a nearby presentation of a related item. In 7 experiments, we assessed this prediction by having learners study lists of words that contained related word pairs. Recall performance for the first presentation in related pairs was higher than for equivalent items in unrelated pairs, while recognition performance for items in related pairs did not differ from those in unrelated pairs. Remindings benefit only the recollection of the retrieved episodes.

Remindings influence the interpretation of ambiguous stimuli.

Remindings-stimulus-guided retrievals of prior events-may help us interpret ambiguous events by linking the current situation to relevant prior experiences. Evidence suggests that remindings play an important role in interpreting complex ambiguous stimuli (Ross & Bradshaw Memory & Cognition, 22, 591-605, 1994); here, we evaluate whether remindings will influence word interpretation and memory in a new paradigm. Learners studied words on distinct visual backgrounds and generated a sentence for each word. Homographs were preceded by a biasing cue on the same background three items earlier, preceded by a biasing cue on a different background three items earlier, or followed by a biasing cue on the same background three items later. When biasing cues preceded the homographs on the same backgrounds as the homographs, the meanings of the homographs in learner-generated sentences were consistent with the biasing cues more often than in the other two conditions. These results show that remindings can influence word interpretation. In addition, later memory for the homographs and cues was greater when the meaning of the homograph in the sentence was consistent with the earlier biasing cue, suggesting that remindings enhanced mnemonic performance. Remindings play an important role in how we interpret ambiguous stimuli and enhance memory for the involved material.

Implicit and explicit processes in category-based induction: is induction best when we don't think?

In category-based induction (CBI), people use category information to predict unknown properties of exemplars. When an item's classification is uncertain, normative principles and Bayesian models suggest that predictions should integrate information across all possible categories. However, researchers previously have found that people often base their predictions on only a single category. In the present studies, we investigated the possible distinction between implicit and explicit processes in CBI. Predictions of an object's motion took the form of either a catching task (implicit) or a verbal answer (explicit). When subjects made predictions implicitly (Experiment 1), they used categories as Bayesian models predict. Explicit predictions (Experiment 2) showed clearly nonnormative use of categories. This distinction between implicit and explicit processes was replicated with a within-subjects design (Experiment 3). When subjects learned categories implicitly (categories were never mentioned) in Experiment 4, their explicit predictions did not reflect integration of information across categories but again showed a nonnormative pattern of category use. These results provide support for a distinction between implicit and explicit processes in CBI and furthermore suggest that the same category knowledge may result in normative or nonnormative responding, depending on the response mode.

The effect of explanation in simple binary decision tasks.

Many studies of explanation have focused on higher level tasks and on how explanations draw upon relevant prior knowledge, which then helps in understanding some event or observation. However, explanations may also affect performance in simple tasks even when they include no task-relevant information. In three experiments, we show that explanations adding no task-relevant information alter performance in a sequential binary decision task. Whereas people with no explanation for why two events occurred at different rates tended to predict each outcome in proportion to their probability of occurrence (to "probability match"), people with an explanation tended to predict the more likely event more often (to "overmatch," a better strategy). These results suggest a broader view of explanation, which includes a role in shaping simple tasks outside of higher level reasoning.

Classification versus inference learning contrasted with real-world categories.

Categories are learned and used in a variety of ways, but the research focus has been on classification learning. Recent work contrasting classification with inference learning of categories found important later differences in category performance. However, theoretical accounts differ on whether this is due to an inherent difference between the tasks or to the implementation decisions. The inherent-difference explanation argues that inference learners focus on the internal structure of the categories--what each category is like--while classification learners focus on diagnostic information to predict category membership. In two experiments, using real-world categories and controlling for earlier methodological differences, inference learners learned more about what each category was like than did classification learners, as evidenced by higher performance on a novel classification test. These results suggest that there is an inherent difference between learning new categories by classifying an item versus inferring a feature.

Category vs. Object Knowledge in Category-based Induction.

In one form of category-based induction, people make predictions about unknown properties of objects. There is a tension between predictions made based on the object's specific features (e.g., objects above a certain size tend not to fly) and those made by reference to category-level knowledge (e.g., birds fly). Seven experiments with artificial categories investigated these two sources of induction by looking at whether people used information about correlated features within categories, suggesting that they focused on feature-feature relations rather than summary categorical information. The results showed that people relied heavily on such correlations, even when there was no reason to think that the correlations exist in the population. The results suggested that people's use of this strategy is largely unreflective, rather than strategically chosen. These findings have important implications for models of category-based induction, which generally ignore feature-feature relations.

Uncertainty in category-based induction: when do people integrate across categories?

Two experiments investigated how people perform category-based induction for items that have uncertain categorization. Whereas normative considerations suggest that people should consider multiple relevant categories, much past research has argued that people focus on only the most likely category. A new method is introduced in which responses on individual trials can be classified as using single or multiple categories, an improvement on past methods that relied on null effects as evidence for single-category use. Experiment 1 found that people did use multiple categories when the most likely category gave an ambiguous induction but that few people did so when it gave an unambiguous induction. Experiment 2 suggested that the reluctance to use multiple categories arose from a cognitive shortcut, in which only one source of information is consulted. The experiments revealed significant individual differences, suggesting that use of multiple categories is one of a number of strategies that can be used rather than being the basis for most category-based induction.

Classifying partial exemplars: seeing less and learning more.

Categories underlie a variety of functions beyond just classification, including inference and explanation. To classify, people need to distinguish between categories, but other functions rely on within-category information (things true of a particular category, independent of others). Despite the need for both types of knowledge, recent work shows that classification does not lead to learning an important type of within-category information, prototypical nondiagnostic information. However, most classification studies are conducted under narrow conditions that do not cover many basic ways that people learn categories. In 2 experiments, the authors compared standard classification learning with a slightly different task where items appeared with occluded features (as many objects appear); they hypothesized that this change might lead to broader attention and learning of within-category, prototypical nondiagnostic information. The results support this prediction, offering evidence that classification can lead to learning within-category information. They discuss the possibility that other classification results may depend on specifics of the standard paradigm.

The role of category coherence in experience-based prediction.

Both real-world category knowledge and instance-based sample data are often available as sources of inductive inference. In three experiments using natural social categories, we test the influence of generalcategory knowledge on the use of category instances to make property inductions both to other category members and to others in the population. We find that a category's coherence--the extent to which its features are interrelated through prior knowledge (Murphy & Medin, 1985)--influences inductions positively to new category members and negatively to the population. This effect of coherence is strongest with small as compared with large samples of instances. The results are interpreted from both similarity and explanation-based perspectives.

Action information from classification learning.

Much of our learning comes from interacting with objects. Two experiments investigated whether or not arbitrary actions used during category learning with objects might be incorporated into object representations and influence later recognition judgments. In a virtual-reality chamber, participants used distinct arm movements to make different classification responses. During a recognition test phase, these same objects required arm movements that were consistent or inconsistent with the classification movement. In both experiments, consistent movements were facilitated relative to inconsistent movements, suggesting that arbitrary action information is incorporated into the representations.

Sorting out categories: incremental learning of category structure.

Two experiments examine how inferences might promote unsupervised and incremental category learning. Many categories have members related through overall similarity (e.g., a family resemblance structure) rather than by a defining feature. However, when people are asked to sort category members in a category construction task, they often do so by partitioning on a single feature. Starting from an earlier result showing that pairwise inferences increase family resemblance sorting (Lassaline & Murphy, 1996), we examine how these inferences lead to learning the family resemblance structure. Results show that the category structure is learned incrementally. The pairwise inferences influence participants' weightings of feature pairs that were specifically asked about, which in turn affects their sorting. The sorting then allows further learning of the categorical structure. Thus, the inferences do not directly lead learners to the family resemblance structure, but they do provide a foundation to build on as the participants make additional judgments.

Influence of multiple categories on the prediction of unknown properties.

Knowing an item's category helps us predict its unknown properties. Previous studies suggest that when asked to evaluate the probability of an unknown property, people tend to consider only an item's most likely category, ignoring alternative categories. In the present study, property prediction took the form of either a probability rating or a speeded binary-choice judgment. In keeping with past findings, the subjects ignored alternative categories in their probability ratings. However, their binary-choice judgments were influenced by alternative categories. This novel finding suggests that the way in which category knowledge is used in prediction depends critically on the form of the prediction.

The two faces of typicality in category-based induction.

Studies of category-based induction using different methods have found somewhat contradictory results for whether typical items are a stronger basis for induction. Typical category items are generally more similar to other category items than are atypical ones, and they are also more likely to be categorized into the category in question. We propose that the first aspect (representativeness) influences induction, but the second (uncertainty about the correct category) does not. Two experiments using artificial categories found support for this prediction. Two further experiments manipulated pictures of objects and also found that representativeness in the category influenced the strength of induction, but uncertainty of classification did not. Thus, the two aspects of typicality have different effects on category-based induction.

Inference and classification learning of abstract coherent categories.

Category learning research has primarily focused on how people learn to classify items using simple observable features. However, classification is only 1 way to learn categories. In addition, many concepts have an underlying coherence that explains the featural similarity among exemplars, such as abstract coherent concepts whose instances differ greatly on their observable features. In 3 experiments, the authors investigated how abstract coherent categories are acquired through 2 common means of category learning, classification and inference. Because inference promotes more focus on within-category information than does classification, they hypothesized that inference learning would lead to a better understanding of the underlying coherence of abstract coherent categories. All 3 experiments support this prediction.

Diagnosticity and prototypicality in category learning: a comparison of inference learning and classification learning.

Category knowledge allows for both the determination of category membership and an understanding of what the members of a category are like. Diagnostic information is used to determine category membership; prototypical information reflects the most likely features given category membership. Two experiments examined 2 means of category learning, classification and inference learning, in terms of sensitivity to diagnostic and prototypical information. Classification learners were highly sensitive to diagnostic features but not sensitive to nondiagnostic, but prototypical, features. Inference learners were less sensitive to the diagnostic features than were classification learners and were also sensitive to the nondiagnostic, prototypical, features. Discussion focuses on aspects of the 2 learning tasks that might lead to this differential sensitivity and the implications for learning real-world categories.

Learning categories by making predictions: an investigation of indirect category learning.

Categories are learned in many ways, but studies of category learning have generally focused on classification learning. This focus may limit the understanding of categorization processes. Two experiments were conducted in which participants learned categories of animals by predicting how much food each animal would eat. We refer to this as indirect category learning, because the task andthe feedback were not directly related to category membership, yet category learning was necessary for good performance in the task. In the first experiment, we compared the performance of participants who learned the categories indirectly with the performance of participants who first learned to classify the objects. In the second experiment, we replicated the basic findings and examined attention to different features during the learning task. In both experiments, participants who learned in the prediction-only condition displayed a broader distribution of attention than participants who learned in the classification-and-prediction condition did. Some participants in the prediction-only group learned the family resemblance structure of the categories, even when a perfect criterial attribute was present. In contrast, participants who first learned to classify the objects tended to learn the criterial attribute.

Category use and category learning.

Categorization models based on laboratory research focus on a narrower range of explanatory constructs than appears necessary for explaining the structure of natural categories. This mismatch is caused by the reliance on classification as the basis of laboratory studies. Category representations are formed in the process of interacting with category members. Thus, laboratory studies must explore a range of category uses. The authors review the effects of a variety of category uses on category learning. First, there is an extensive discussion contrasting classification with a predictive inference task that is formally equivalent to classification but leads to a very different pattern of learning. Then, research on the effects of problem solving, communication, and combining inference and classification is reviewed.