John Paul Minda

Better Mood and Better Performance Learning Rule-Described Categories Is Enhanced by Positive Mood

Theories of mood and its effect on cognitive processing suggest that positive mood may allow for increased cognitive flexibility. This increased flexibility is associated with the prefrontal cortex and the anterior cingulate cortex, both of which play crucial roles in hypothesis testing and rule selection. Thus, cognitive tasks that rely on behaviors such as hypothesis testing and rule selection may benefit from positive mood, whereas tasks that do not rely on such behaviors should not be affected by positive mood. We explored this idea within a category-learning framework. Positive, neutral, and negative moods were induced in our subjects, and they learned either a rule-described or a non-rule-described category set. Subjects in the positive-mood condition performed better than subjects in the neutral- or negative-mood conditions in classifying stimuli from rule-described categories. Positive mood also affected the strategy of subjects who classified stimuli from non-rule-described categories.

Learning Rule-Described and Non-Rule-Described Categories : A Comparison of Children and Adults

Three experiments investigated the ability of 3-, 5-, and 8-year-old children as well as adults to learn sets of perceptual categories. Adults and children performed comparably on categories that could be learned by either a single-dimensional rule or by associative learning mechanisms. However, children showed poorer performance relative to adults in learning categories defined by a disjunctive rule and categories that were nonlinearly separable. Increasing the task demands for adults resulted in child-like performance on the disjunctive categories. Decreasing the task demands for children resulted in more adult-like performance on the disjunctive categories. The authors interpret these results within a multiple-systems approach to category learning and suggest that children have not fully developed the same explicit category learning system as adults.

Expanding the basic science debate: the role of physics knowledge in interpreting clinical findings

Current research suggests a role for biomedical knowledge in learning and retaining concepts related to medical diagnosis. However, learning may be influenced by other, non-biomedical knowledge. We explored this idea using an experimental design and examined the effects of causal knowledge on the learning, retention, and interpretation of medical information. Participants studied a handout about several respiratory disorders and how to interpret respiratory exam findings. The control group received the information in standard “textbook” format and the experimental group was presented with the same information as well as a causal explanation about how sound travels through lungs in both the normal and disease states. Comprehension and memory of the information was evaluated with a multiple-choice exam. Several questions that were not related to the causal knowledge served as control items. Questions related to the interpretation of physical exam findings served as the critical test items. The experimental group outperformed the control group on the critical test items, and our study shows that a causal explanation can improve a student’s memory for interpreting clinical details. We suggest an expansion of which basic sciences are considered fundamental to medical education.

The Effects of Concurrent Verbal and Visual Tasks on Category Learning.

Current theories of category learning posit separate verbal and nonverbal learning systems. Past research suggests that the verbal system relies on verbal working memory and executive functioning and learns rule-defined categories; the nonverbal system does not rely on verbal working memory and learns non-rule-defined categories (E. M. Waldron & F. G. Ashby, 2001; D. Zeithamova & W. T. Maddox, 2006). However, relatively little research has explored the importance of visual working memory or visual processing for either system. The authors investigated the role of working memory (Experiment 1a and 1b), visual processing (Experiment 2), and executive functioning for each system, using a concurrent task methodology. It was found that visual tasks with high executive functioning demands and verbal tasks with high or low executive demands disrupted rule-defined learning, whereas any visual task, regardless of executive functioning demand, disrupted non-rule-defined learning. Taken together, these results confirm the importance of verbal working memory and executive functioning for the verbal system and provide new evidence for the importance of visual processing for the nonverbal system. These results help to clarify understanding of the nonverbal system and have implications for multiple systems theories of category learning (F. G. Ashby, L. A. Alfonso-Reese, A. U. Turken, & E. M. Waldron, 1998).

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 asirect category learning because the task and the 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.

Rule-based category learning in children: the role of age and executive functioning.

Rule-based category learning was examined in 4–11 year-olds and adults. Participants were asked to learn a set of novel perceptual categories in a classification learning task. Categorization performance improved with age, with younger children showing the strongest rule-based deficit relative to older children and adults. Model-based analyses provided insight regarding the type of strategy being used to solve the categorization task, demonstrating that the use of the task appropriate strategy increased with age. When children and adults who identified the correct categorization rule were compared, the performance deficit was no longer evident. Executive functions were also measured. While both working memory and inhibitory control were related to rule-based categorization and improved with age, working memory specifically was found to marginally mediate the age-related improvements in categorization. When analyses focused only on the sample of children, results showed that working memory ability and inhibitory control were associated with categorization performance and strategy use. The current findings track changes in categorization performance across childhood, demonstrating at which points performance begins to mature and resemble that of adults. Additionally, findings highlight the potential role that working memory and inhibitory control may play in rule-based category learning.

The Influence of Verbal and Nonverbal Processing on Category Learning

Abstract Categories are learned in a variety of ways and one important distinction concerns the effects of verbal and nonverbal processing on category learning. This chapter reviews the research from behavioral studies, computational modeling, and imaging studies that support this distinction. Although there is some consensus that subjects will often learn new categories by searching for verbal rules, there is less agreement in the literature about how categories are learned when a rule is not usable or when the subject has restricted access to verbal abilities. Accordingly, we outline a general theory of verbal and nonverbal category learning. We assume that verbal category learning relies on working memory and is primarily involved in rule-based categorization. Nonverbal category learning may rely on visual working memory and is primarily involved in similarity-based categorization. We present the results of several studies from our lab that test many of the predictions from this theory. Although we do not argue for two completely independent learning systems, we argue that the available evidence strongly supports the existence of these two approaches of learning categories.

Developing a unified list of physicians' reasoning tasks during clinical encounters.

Purpose The clinical reasoning literature focuses on how physicians reason while making decisions, rather than on what they reason about while performing their clinical tasks. In an attempt to provide a common language for discussing, teaching, and researching clinical reasoning, the authors undertook the task of developing a unified list of physicians’ reasoning tasks, or what they reason about, during clinical encounters. Method The authors compiled an initial list of 20 reasoning tasks based on the literature from four content areas—clinical reasoning, communications, medical errors, and clinical guidelines. In the summer and fall of 2010, they surveyed a purposive sample of 46 international experts in clinical reasoning and communications. From the results of the first survey, the authors refined their list of reasoning tasks, then resurveyed 22 of the original participants. From the results of the second survey, they further refined their list and validated the inclusion of the reasoning tasks. Results Twenty-four of 46 (52%) and 15 of 22 (65%) participants completed the first- and second-round surveys, respectively. Following the second-round survey, the authors’ list included 24 reasoning tasks, and a clinical example corresponding to each, that fell into four broad categories: framing the encounter (3), diagnosis (8), management (11), and self-reflection (2). Conclusions The development of this unified list represents a first step in offering a vocabulary for discussing, reflecting on, teaching, and studying physicians’ reasoning tasks during clinical encounters.

Ego depletion interferes with rule-defined category learning but not non-rule-defined category learning.

Considerable research on category learning has suggested that many cognitive and environmental factors can have a differential effect on the learning of rule-defined (RD) categories as opposed to the learning of non-rule-defined (NRD) categories. Prior research has also suggested that ego depletion can temporarily reduce the capacity for executive functioning and cognitive flexibility. The present study examined whether temporarily reducing participants’ executive functioning via a resource depletion manipulation would differentially impact RD and NRD category learning. Participants were either asked to write a story with no restrictions (the control condition), or without using two common letters (the ego depletion condition). Participants were then asked to learn either a set of RD categories or a set of NRD categories. Resource depleted participants performed more poorly than controls on the RD task, but did not differ from controls on the NRD task, suggesting that self regulatory resources are required for successful RD category learning. These results lend support to multiple systems theories and clarify the role of self-regulatory resources within this theory.

Continuous executive function disruption interferes with application of an information integration categorization strategy

Category learning is often characterized as being supported by two separate learning systems. A verbal system learns rule-defined (RD) categories that can be described using a verbal rule and relies on executive functions (EFs) to learn via hypothesis testing. A nonverbal system learns non-rule-defined (NRD) categories that cannot be described by a verbal rule and uses automatic, procedural learning. The verbal system is dominant in that adults tend to use it during initial learning but may switch to the nonverbal system when the verbal system is unsuccessful. The nonverbal system has traditionally been thought to operate independently of EFs, but recent studies suggest that EFs may play a role in the nonverbal system—specifically, to facilitate the transition away from the verbal system. Accordingly, continuously interfering with EFs during the categorization process, so that EFs are never fully available to facilitate the transition, may be more detrimental to the nonverbal system than is temporary EF interference. Participants learned an NRD or an RD category while EFs were untaxed, taxed temporarily, or taxed continuously. When EFs were continuously taxed during NRD categorization, participants were less likely to use a nonverbal categorization strategy than when EFs were temporarily taxed, suggesting that when EFs were unavailable, the transition to the nonverbal system was hindered. For the verbal system, temporary and continuous interference had similar effects on categorization performance and on strategy use, illustrating that EFs play an important but different role in each of the category-learning systems.