Identifying resource-rational heuristics for risky choice.

Perfectly rational decision making is almost always out of reach for people because their computational resources are limited. Instead, people may rely on computationally frugal heuristics that usually yield good outcomes. Although previous research has identified many such heuristics, discovering good heuristics and predicting when they will be used remains challenging. Here, we present a theoretical framework that allows us to use methods from machine learning to automatically derive the best heuristic to use in any given situation by considering how to make the best use of limited cognitive resources. To demonstrate the generalizability and accuracy of our method, we compare the heuristics it discovers against those used by people across a wide range of multi-attribute risky choice environments in a behavioral experiment that is an order of magnitude larger than any previous experiments of its type. Our method rediscovered known heuristics, identifying them as rational strategies for specific environments, and discovered novel heuristics that had been previously overlooked. Our results show that people adapt their decision strategies to the structure of the environment and generally make good use of their limited cognitive resources, although their strategy choices do not always fully exploit the structure of the environment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A computational process-tracing method for measuring people's planning strategies and how they change over time.

One of the most unique and impressive feats of the human mind is its ability to discover and continuously refine its own cognitive strategies. Elucidating the underlying learning and adaptation mechanisms is very difficult because changes in cognitive strategies are not directly observable. One important domain in which strategies and mechanisms are studied is planning. To enable researchers to uncover how people learn how to plan, we offer a tutorial introduction to a recently developed process-tracing paradigm along with a new computational method for measuring the nature and development of a person's planning strategies from the resulting process-tracing data. Our method allows researchers to reveal experience-driven changes in people's choice of individual planning operations, planning strategies, strategy types, and the relative contributions of different decision systems. We validate our method on simulated and empirical data. On simulated data, its inferences about the strategies and the relative influence of different decision systems are accurate. When evaluated on human data generated using our process-tracing paradigm, our computational method correctly detects the plasticity-enhancing effect of feedback and the effect of the structure of the environment on people's planning strategies. Together, these methods can be used to investigate the mechanisms of cognitive plasticity and to elucidate how people acquire complex cognitive skills such as planning and problem-solving. Importantly, our methods can also be used to measure individual differences in cognitive plasticity and examine how different types (pedagogical) interventions affect the acquisition of cognitive skills.

Rational use of cognitive resources in human planning.

Making good decisions requires thinking ahead, but the huge number of actions and outcomes one could consider makes exhaustive planning infeasible for computationally constrained agents, such as humans. How people are nevertheless able to solve novel problems when their actions have long-reaching consequences is thus a long-standing question in cognitive science. To address this question, we propose a model of resource-constrained planning that allows us to derive optimal planning strategies. We find that previously proposed heuristics such as best-first search are near optimal under some circumstances but not others. In a mouse-tracking paradigm, we show that people adapt their planning strategies accordingly, planning in a manner that is broadly consistent with the optimal model but not with any single heuristic model. We also find systematic deviations from the optimal model that might result from additional cognitive constraints that are yet to be uncovered.

Leveraging artificial intelligence to improve people's planning strategies.

SignificanceMany bad decisions and their devastating consequences could be avoided if people used optimal decision strategies. Here, we introduce a principled computational approach to improving human decision making. The basic idea is to give people feedback on how they reach their decisions. We develop a method that leverages artificial intelligence to generate this feedback in such a way that people quickly discover the best possible decision strategies. Our empirical findings suggest that a principled computational approach leads to improvements in decision-making competence that transfer to more difficult decisions in more complex environments. In the long run, this line of work might lead to apps that teach people clever strategies for decision making, reasoning, goal setting, planning, and goal achievement.

Cognitive prostheses for goal achievement.

Procrastination takes a considerable toll on people's lives, the economy and society at large. Procrastination is often a consequence of people's propensity to prioritize their immediate experiences over the long-term consequences of their actions. This suggests that aligning immediate rewards with long-term values could be a promising way to help people make more future-minded decisions and overcome procrastination. Here we develop an approach to decision support that leverages artificial intelligence and game elements to restructure challenging sequential decision problems in such a way that it becomes easier for people to take the right course of action. A series of four increasingly realistic experiments suggests that this approach can enable people to make better decisions faster, procrastinate less, complete their work on time and waste less time on unimportant tasks. These findings suggest that our method is a promising step towards developing cognitive prostheses that help people achieve their goals.

Evidence accumulation detected in BOLD signal using slow perceptual decision making.

BACKGROUND: We assessed whether evidence accumulation could be observed in the BOLD signal during perceptual decision making. This presents a challenge since the hemodynamic response is slow, while perceptual decisions are typically fast. NEW METHOD: Guided by theoretical predictions of the drift diffusion model, we slowed down decisions by penalizing participants for incorrect responses. Second, we distinguished BOLD activity related to stimulus detection (modeled using a boxcar) from activity related to integration (modeled using a ramp) by minimizing the collinearity of GLM regressors. This was achieved by dissecting a boxcar into its two most orthogonal components: an "up-ramp" and a "down-ramp." Third, we used a control condition in which stimuli and responses were similar to the experimental condition, but that did not engage evidence accumulation of the stimuli. RESULTS: The results revealed an absence of areas in parietal cortex that have been proposed to drive perceptual decision making but have recently come into question; and newly identified regions that are candidates for involvement in evidence accumulation. COMPARISON WITH EXISTING METHODS: Previous fMRI studies have either used fast perceptual decision making, which precludes the measurement of evidence accumulation, or slowed down responses by gradually revealing stimuli. The latter approach confounds perceptual detection with evidence accumulation because accumulation is constrained by perceptual input. CONCLUSIONS: We slowed down the decision making process itself while leaving perceptual information intact. This provided a more sensitive and selective observation of brain regions associated with the evidence accumulation processes underlying perceptual decision making than previous methods.

Osteopathic clinical training in three universities.

Osteopathic clinical medical education is similar to allopathic medical education in many ways but uniquely different in others. Some of the important differences include an emphasis on primary care, training in osteopathic manipulative medicine, and the reliance on community-based hospitals for clinical training sites.The authors explore the historical roots of osteopathic clinical training, describe the typical osteopathic clinical preparation, and discuss the challenges in delivering this clinical training. They describe the significantly different methods of delivering clinical training in three osteopathic medical schools: a traditional academic medical center, a school utilizing a statewide clinical consortium, and a school that employs a regional campus system.The authors used data from the annual survey of the 20 schools of osteopathic medicine operating in 2005-2006 to describe the typical clinical rotation experience of an osteopathic medical student and analyzed outcome data from the Comprehensive Osteopathic Medical Licensing Exam (COMLEX) Level 2-CE and COMLEX Level 2-PE for each of these three systems of clinical training. The overall success on these examinations by students from all three schools suggests that all three methods of clinical training can be successful.

A comprehensive review to establish priority learning objectives for medical students in the obstetrics and gynecology clerkship.

OBJECTIVE: This study was undertaken to describe the process used to identify, externally validate, and establish the priority learning objectives for medical students on the obstetrics and gynecology clerkship. STUDY DESIGN: We conducted a review of the APGO Medical Student Objectives in Obstetrics and Gynecology to establish which of these objectives should be given first priority. We used recommendations from external references to assess the validity of these selected objectives. We compared the distribution of objectives with levels of expected competency from Miller's pyramid. RESULTS: From a list of 267 unique learning objectives we identified 134 (50.2%) Priority 1 objectives students must master by the end of the clerkship. The recommendations from 17 external references were compared with this set of objectives, which demonstrated a significant correlation between the 2 (P < or = .001). Priority 1 objectives were associated with advanced levels of competency. CONCLUSION: External sources validated and helped prioritize the learning objectives.

Teaching critical appraisal: a pilot randomized controlled outcomes trial in undergraduate osteopathic medical education.

CONTEXT: Critical appraisal is an important skill for medical students. A proposed curriculum may be an effective teaching tool. OBJECTIVE: To determine whether the teaching of critical appraisal can be successfully introduced into an osteopathic clinical clerkship in obstetrics and gynecology. DESIGN: Osteopathic medical students (N=77) were assigned by lottery to one of eight rotation groups during their clinical clerkship in obstetrics and gynecology. Four of these rotation groups received instruction in critical appraisal (study group; received evidence-based medicine [EBM] curriculum; n=38); the other four rotation groups did not (control group; received non-EBM; n=39). The ability of the study EBM group to critically analyze the literature was compared with that of the control (non-EBM) group on the basis of results of a multiple-choice examination. SETTING: The University of Medicine and Dentistry of New Jersey-School of Osteopathic Medicine clinical clerkship in obstetrics and gynecology. RESULTS: The median scores for critical analysis were 41 for the control group and 64 for the study group. This difference was statistically significant (P<.001). CONCLUSION: The teaching of critical appraisal can be successfully introduced into a clerkship in obstetrics and gynecology.

To the point: medical education reviews--dealing with student difficulties in the clinical setting.

Learners with cognitive and behavioral difficulties are particularly challenging in the clinical setting. Cognitive difficulties in the clinical realm may relate to knowledge deficits and/or weak problem solving skills. Behavioral difficulties resulting from attitudinal or motivational problems may manifest themselves as specific unprofessional acts either committed or omitted in the course of caring for patients as well as in unprofessional behavior. A disproportionate amount of time and energy can be expended addressing the needs of such students. This paper reviews several types of difficulties encountered by educators and suggests strategies for preventing, assessing, and working effectively with challenging students in the clinical setting. Specific attention is directed to impaired students.

To the point: medical education reviews evaluation in context: assessing learners, teachers, and training programs.

Learners, teachers, and programs need to be evaluated. This article reviews the purpose and the current methods for evaluating all 3. Clinical impressions of the learner are yielding increasingly to direct observation and skill assessment. The Reporter, Interpreter, Manager, and Educator (RIME) method offers a unique way of assessing and providing formative feedback to the learner. Learning portfolios help document achievements and provide a collection for self-assessment and growth. Teachers benefit from feedback especially if followed up with consultation. Programs need both quantitative and qualitative data to document performance. National data gathered locally from exit surveys now exist that facilitate comparison of programs (eg, clerkships) within and across institutions. The emphasis on institutional accountability makes it critical to directly evaluate learners and their educational programs.

To the point: reviews in medical education teaching techniques.

Medical educators are faced with the difficult dilemma of decreased time available for teaching as a result of increased demands for clinical time. Yet few physicians have formal training in teaching methods. Educational research is reviewed to assist educators to select the best teaching technique to meet the learning objectives.

The process of translating women's health care competencies into educational objectives.

"Women's Health Care Competencies for Medical Students" lists the competencies undifferentiated medical students should be able to demonstrate before graduation. The Association of Professors of Gynecology and Obstetrics (APGO) was among the groups asked to convert specified competencies into educational objectives. Working individually, APGO's Undergraduate Medical Education Committee members used APGO's Medical Student Educational Objectives, Seventh Edition, to construct objectives appropriate to preassigned competencies. Referring to the work of Miller, members determined the level of professional competence medical students should reach for each objective. Members also selected evaluation tools appropriate for the skill and its competency level from the Toolbox of Assessment Methods created by the Accreditation Council for Graduate Medical Education and the American Board of Medical Specialties. The group collectively revised the work of its members using a standard format to generate the final product.

Nongynecologic healthcare screening in women.

The primary care physician is bombarded with recommendations regarding health screening and preventive services. Often, these recommendations and guidelines are in conflict. This article will not settle these debates; however, it will provide an overview of recommendations for screening in nongynecologic areas and a framework on how to evaluate conflicting recommendations to give reasonable guidance to patients.