RESUMEN
Several authors assume that evaluative conditioning (EC) relies on high-level propositional thinking. In contrast, the dual-process perspective proposes two processing pathways, one associative and the other propositional, contributing to EC. Dual-process theorists argue that attitudinal ambiguity resulting from these two pathways' conflicting evaluations demonstrate the involvement of both automatic and controlled processes in EC. Previously, we suggested that amplitude variations of error-related negativity and error-positivity, two well-researched event-related potentials of performance monitoring, allow for the detection of attitudinal ambiguity at the neural level. The present study utilises self-reported evaluation, categorisation performance, and neural correlates of performance monitoring to explore associative-propositional ambiguity during social attitude formation. Our results show that compared to associative-propositional harmony, attitudinal ambiguity correlates with more neutral subjective evaluations, longer response times, increased error commission, and diminished error-related negativity amplitudes. While our findings align with dual-process models, we aim to offer a propositional interpretation. We discuss dual-process theories in the context of evolutionary psychology, suggesting that associative processes may only represent a small piece of the EC puzzle.
Asunto(s)
Cognición , Condicionamiento Psicológico , Humanos , Cognición/fisiología , Condicionamiento Psicológico/fisiología , Juicio/fisiología , Tiempo de Reacción/fisiología , Potenciales Evocados , EncéfaloRESUMEN
In the new stage for intelligent manufacturing of traditional Chinese medicine(TCM) from pilot demonstration to in-depth application and comprehensive promotion, how to raise the degree of intelligence for the process quality control system has become the bottleneck of the development of TCM production process control technology. This article has sorted out 226 TCM intelligent manufacturing projects that have been approved by the national and provincial governments since the implementation of the "Made in China 2025" plan and 145 related pharmaceutical enterprises. Then, the patents applied by these pharmaceutical enterprises were thoroughly retrieved, and 135 patents in terms of intelligent quality control technology in the production process were found. The technical details about intelligent quality control at both the unit levels such as cultivation, processing of crude herbs, preparation pretreatment, pharmaceutical preparations, and the production workshop level were reviewed from three aspects, i.e., intelligent quality sensing, intelligent process cognition, and intelligent process control. The results showed that intelligent quality control technologies have been preliminarily applied to the whole process of TCM production. The intelligence control of the extraction and concentration processes and the intelligent sensing of critical quality attributes are currently the focus of pharmaceutical enterprises. However, there is a lack of process cognitive patent technology for the TCM manufacturing process, which fails to meet the requirements of closed-loop integration of intelligent sensing and intelligent control technologies. It is suggested that in the future, with the help of artificial intelligence and machine learning methods, the process cognitive bottleneck of TCM production can be overcome, and the holistic quality formation mechanisms of TCM products can be elucidated. Moreover, key technologies for system integration and intelligent equipment are expected to be innovated and accelerated to enhance the quality uniformity and manufacturing reliability of TCM.
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Inteligencia Artificial , Medicina Tradicional China , Reproducibilidad de los Resultados , Control de Calidad , Inteligencia , Preparaciones FarmacéuticasRESUMEN
In the new stage for intelligent manufacturing of traditional Chinese medicine(TCM) from pilot demonstration to in-depth application and comprehensive promotion, how to raise the degree of intelligence for the process quality control system has become the bottleneck of the development of TCM production process control technology. This article has sorted out 226 TCM intelligent manufacturing projects that have been approved by the national and provincial governments since the implementation of the "Made in China 2025" plan and 145 related pharmaceutical enterprises. Then, the patents applied by these pharmaceutical enterprises were thoroughly retrieved, and 135 patents in terms of intelligent quality control technology in the production process were found. The technical details about intelligent quality control at both the unit levels such as cultivation, processing of crude herbs, preparation pretreatment, pharmaceutical preparations, and the production workshop level were reviewed from three aspects, i.e., intelligent quality sensing, intelligent process cognition, and intelligent process control. The results showed that intelligent quality control technologies have been preliminarily applied to the whole process of TCM production. The intelligence control of the extraction and concentration processes and the intelligent sensing of critical quality attributes are currently the focus of pharmaceutical enterprises. However, there is a lack of process cognitive patent technology for the TCM manufacturing process, which fails to meet the requirements of closed-loop integration of intelligent sensing and intelligent control technologies. It is suggested that in the future, with the help of artificial intelligence and machine learning methods, the process cognitive bottleneck of TCM production can be overcome, and the holistic quality formation mechanisms of TCM products can be elucidated. Moreover, key technologies for system integration and intelligent equipment are expected to be innovated and accelerated to enhance the quality uniformity and manufacturing reliability of TCM.
Asunto(s)
Inteligencia Artificial , Medicina Tradicional China , Reproducibilidad de los Resultados , Control de Calidad , Inteligencia , Preparaciones FarmacéuticasRESUMEN
Introduction: There are several widely used theories of health behavior change, which mostly utilize the social cognitive approach. These theories tend to posit that intention is a direct predictor of behavior, do not include automatic influences on behavior, and propose a one-size-fits-all theory for both initiators and maintainers. However, the intention-behavior gap is a well-observed phenomenon, researchers have highlighted that both automatic and reflective factors promote behavioral engagement, and predictors of behavior have been shown to differ between initiators and maintainers-three issues that necessitate theory advancement. To that end, the present research compares the utility of the Integrated Behavior Change Model (IBCM) - a social cognitive model that includes automatic factors involved in behavioral engagement and a moderator of the intention-behavior gap - to its theoretical predecessor, the Theory of Planned Behavior (TPB). Further, the relevance of the IBCM factors for predicting exercise behavior is compared in initiators versus maintainers. Method: Participants were 494 US undergraduates. Participants reported on variables from the IBCM (and TPB) at baseline and reported on their exercise behavior in two surveys at seven- and 14-days post-baseline. Results: Findings supported the first hypothesis that the IBCM would be more relevant for initiators in comparison with maintainers, using structural equation modeling. Specifically, only the paths between intrinsic motivation and affective attitude, affective attitude and intention, and intention and behavior were reliably found for maintainers. For initiators, the aforementioned paths were also reliably supported and the additional following paths were also supported: intrinsic motivation and perceived behavioral control, perceived behavioral control and intention, and intention and action planning. However, results did not support the second hypothesis that the IBCM would predict significantly more variance in behavior than its theoretical predecessor, the TPB. Specifically, the addition of action planning, implicit attitude, implicit motivation, and the interaction between intention and action planning only predicted an additional 0.3% (p < 0:05) of the variance in exercise behavior above and beyond intention. Conclusion: Results highlight the continued need for theoretical refinement in terms of delineating mechanisms of initiation and maintenance and the need for further development in terms of improving upon current predictions of behavior engagement and change.
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RATIONALE AND OBJECTIVES: One way to understand medical overuse at the clinician level is in terms of clinical decision-making processes that are normally adaptive but become maladaptive. In psychology, dual process models of cognition propose 2 decision-making processes. Reflective cognition is a conscious process of evaluating options based on some combination of utility, risk, capabilities, and/or social influences. Automatic cognition is a largely unconscious process occurring in response to environmental or emotive cues based on previously learned, ingrained heuristics. De-implementation strategies directed at clinicians may be conceptualized as corresponding to cognition: (1) a process of unlearning based on reflective cognition and (2) a process of substitution based on automatic cognition. RESULTS: We define unlearning as a process in which clinicians consciously change their knowledge, beliefs, and intentions about an ineffective practice and alter their behaviour accordingly. Unlearning has been described as "the questioning of established knowledge, habits, beliefs and assumptions as a prerequisite to identifying inappropriate or obsolete knowledge underpinning and/or embedded in existing practices and routines." We hypothesize that as an unintended consequence of unlearning strategies clinicians may experience "reactance," ie, feel their professional prerogative is being violated and, consequently, increase their commitment to the ineffective practice. We define substitution as replacing the ineffective practice with one or more alternatives. A substitute is a specific alternative action or decision that either precludes the ineffective practice or makes it less likely to occur. Both approaches may work independently, eg, a substitute could displace an ineffective practice without changing clinicians' knowledge, and unlearning could occur even if no alternative exists. For some clinical practice, unlearning and substitution strategies may be most effectively used together. CONCLUSIONS: By taking into account the dual process model of cognition, we may be able to design de-implementation strategies matched to clinicians' decision-making processes and avoid unintended consequence.