The congeries of theoretical views collectively referred to as "situated action" (SA) claim that humans and their interactions with the world cannot be understood using symbol-system models and methodology, but only by observing them within real-world contexts or building nonsymbolic models of them. SA claims also that rapid, real-time interaction with a dynamically changing environment is not amenable to symbolic interpretation of the sort espoused by the cognitive science of recent decades. Planning and representation, central to symbolic theories, are claimed to be irrelevant in everyday human activity.We will contest these claims, as well as their proponents' characterizations of the symbol-system viewpoint. We will show that a number of existing symbolic systems perform well in temporally demanding tasks embedded in complex environments, whereas the systems usually regarded as exemplifying SA are thoroughly symbolic (and representational), and, to the extent that they are limited in these respects, have doubtful prospects for extension to complex tasks. As our title suggests, we propose that the goals set forth by the proponents of SA can be attained only within the framework of symbolic systems. The main body of empirical evidence supporting our view resides in the numerous symbol systems constructed in the past 35 years that have successfully simulated broad areas of human cognition.During the past few years a point of view has emerged in artificial intelligence, often under the label of "situated action" (henceforth, SA), that denies that intelligent systems are correctly characterized as physical symbol systems, and especially denies that symbolic processing lies at the heart of --
The authors assume that individuals adapt rationally to a utility function given constraints imposed by their cognitive architecture and the local task environment. This assumption underlies a new approach to modeling and understanding cognition-cognitively bounded rational analysis-that sharpens the predictive acuity of general, integrated theories of cognition and action. Such theories provide the necessary computational means to explain the flexible nature of human behavior but in doing so introduce extreme degrees of freedom in accounting for data. The new approach narrows the space of predicted behaviors through analysis of the payoff achieved by alternative strategies, rather than through fitting strategies and theoretical parameters to data. It extends and complements established approaches, including computational cognitive architectures, rational analysis, optimal motor control, bounded rationality, and signal detection theory. The authors illustrate the approach with a reanalysis of an existing account of psychological refractory period (PRP) dual-task performance and the development and analysis of a new theory of ordered dual-task responses. These analyses yield several novel results, including a new understanding of the role of strategic variation in existing accounts of PRP and the first predictive, quantitative account showing how the details of ordered dual-task phenomena emerge from the rational control of a cognitive system subject to the combined constraints of internal variance, motor interference, and a response selection bottleneck.Keywords: rational adaptation, bounded optimality, cognitive architecture, theory comparison, response ordering, dual taskThe extraordinarily flexible and adaptive nature of human behavior presents both unique opportunities and unique challenges for developing a science of the mind and brain. On the one hand, treating the mind as an adaptive system opens up possibilities for deep explanations of behavior that are grounded primarily in the observable structure and contingencies of the task environment, along with an assumption of rationality or optimal adaptation. This insight is the point of departure for a range of approaches to understanding cognition and perception, including rational analysis and related Bayesian approaches (Anderson, 1990;Berthier, Rosenstein, & Barto, 2005;Bogacz, Brown, Moehlis, Holmes, & Cohen, 2006;Chater & Oaksford, 1999;Geisler, 2003;Tenenbaum, Griffiths, & Kemp, 2006), optimal motor control approaches (Maloney, Trommershäuser, & Landy, 2007;Meyer, Abrams, Kornblum, Wright, & Smith, 1988;Trommershäuser, Maloney, & Landy, 2003a, 2003bReichle & Laurent, 2006), as well as signal detection theory and ideal observer analysis (Green & Swets, 1966;Swets, Tanner, & Birdsall, 1961;Tanner & Swets, 1954). For example, in the arena of perception, ideal observer models demonstrate that human performance on some very simple discrimination tasks is limited only by external photon noise (Geisler, 2003). In the arena of memory, the decay over time of i...
SPRING 2019 17 T he purpose of this article is to draw attention to an aspect of intelligence that has not yet received significant attention from the AI community, but that plays a crucial role in a technology's (and a person's) effectiveness in the world, namely teaming intelligence. Over the past decade, there have been many successful attempts to apply technology to increasingly complex domains -domains once reserved almost exclusively for human effort. It is rare that an evening goes by without a news report of some event in the field of autonomous drones or self-driving cars or even the dangers of AI. Newly refined techniques and improved computing power have propelled AI into the forefront of both the media and human imagination once again. Today's
Background: Health records are the basis of clinical coding. In Portugal, relevant diagnoses and procedures are abstracted and categorised using an internationally accepted classification system and the resulting codes, together with the administrative data, are then grouped into diagnosis-related groups (DRGs). Hospital reimbursement is partially calculated from the DRGs. Moreover, the administrative database generated with these data is widely used in research and epidemiology, among other purposes. Objective: To explore the perceptions of medical coders (medical doctors) regarding possible problems with health records that may affect the quality of coded data. Method: A qualitative design using four focus groups sessions with 10 medical coders was undertaken between October and November 2017. The convenience sample was obtained from four public hospitals in Portugal. Questions related to problems with the coding process were developed from the literature and authors’ expertise. The focus groups sessions were taped, transcribed and analysed to elicit themes. Results: There are several problems, identified by the focus groups, in health records that influence the coded data: the lack of or unclear documented information; the variability in diagnosis description; “copy & paste”; and the lack of solutions to solve these problems. Conclusion and implications: The use of standards in health records, audits and physician awareness could increase the quality of health records, contributing to improvements in the quality of coded data, and in the fulfilment of its purposes (e.g. more accurate payments and more reliable research).
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