Symbols enable people to organize and communicate about the world. However, the ways in which symbolic knowledge is learned and then represented in the mind are poorly understood. We present a formal analysis of symbolic learning-in particular, word learning-in terms of prediction and cue competition, and we consider two possible ways in which symbols might be learned: by learning to predict a label from the features of objects and events in the world, and by learning to predict features from a label. This analysis predicts significant differences in symbolic learning depending on the sequencing of objects and labels. We report a computational simulation and two human experiments that confirm these differences, revealing the existence of Feature-Label-Ordering effects in learning. Discrimination learning is facilitated when objects predict labels, but not when labels predict objects. Our results and analysis suggest that the semantic categories people use to understand and communicate about the world can only be learned if labels are predicted from objects. We discuss the implications of this for our understanding of the nature of language and symbolic thought, and in particular, for theories of reference.Keywords: Language; Learning; Representation; Concepts; Computational modeling; Prediction Symbolic thought and symbolic communication are defining human characteristics. Yet despite the benefits symbols bring in allowing us to organize, communicate about, manipulate, and master the world, our understanding of symbols and symbolic knowledge is poor. Centuries of pondering the nature of symbolic representation, in terms of concepts and categories and words and their meanings, has yielded more puzzles than answers (Murphy, 2002; Wittgenstein, 1953). Our impoverished understanding of symbolic learning, and especially how words and their meanings are learned, represented, and used, contrasts starkly with the progress made in other areas, where computational models of learning processes Correspondence should be sent to Michael Ramscar,

Obese people incur higher health care costs at a given point in time, but how rising obesity rates affect spending growth over time is unknown. We estimate obesity-attributable health care spending increases between 1987 and 2001. Increases in the proportion of and spending on obese people relative to people of normal weight account for 27 percent of the rise in inflation-adjusted per capita spending between 1987 and 2001; spending for diabetes, 38 percent; spending for hyperlipidemia, 22 percent; and spending for heart disease, 41 percent. Increases in obesity prevalence alone account for 12 percent of the growth in health spending.

We examine the impact of the rise in treated disease prevalence on the growth in Medicare beneficiaries' health care spending. Virtually all of this spending growth is associated with patients who are under medical management for five or more conditions. This is traced to both a rise in true disease prevalence and changes in clinical treatment thresholds. Using the metabolic syndrome as a case study, we find that the share of patients treated with medications has increased 11.5 percentage points in less than ten years. This raises important questions about the "fit" of how Medicare pays for services for complex medical management.

We calculate the level and growth in health care spending attributable to the fifteen most expensive medical conditions in 1987 and 2000. Growth in spending by medical condition is decomposed into changes attributable to rising cost per treated case, treated prevalence, and population growth. We find that a small number of conditions account for most of the growth in health care spending--the top five medical conditions accounted for 31 percent. For four of the conditions, a rise in treated prevalence, rather than rising treatment costs per case or population growth, accounted for most of the spending growth.

Antibiotic-resistant infections are a global health care concern. The Centers for Disease Control and Prevention estimates that 23,000 Americans with these infections die each year. Rising infection rates add to the costs of health care and compromise the quality of medical and surgical procedures provided. Little is known about the national health care costs attributable to treating the infections. Using data from the Medical Expenditure Panel Survey, we estimated the incremental health care costs of treating a resistant infection as well as the total national costs of treating such infections. To our knowledge, this is the first national estimate of the costs for treating the infections. We found that antibiotic resistance added $1,383 to the cost of treating a patient with a bacterial infection. Using our estimate of the number of such infections in 2014, this amounts to a national cost of $2.2 billion annually. The need for innovative new infection prevention programs, antibiotics, and vaccines to prevent and treat antibiotic-resistant infections is an international priority.