Logic feels so good-I like it! Evidence for intuitive detection of logicality in syllogistic reasoning

When people evaluate syllogisms, their judgments of validity are often biased by the believability of the conclusions of the problems. Thus, it has been suggested that syllogistic reasoning performance is based on an interplay between a conscious and effortful evaluation of logicality and an intuitive appreciation of the believability of the conclusions (e.g., Evans, Newstead, Allen, & Pollard, 1994). However, logic effects in syllogistic reasoning emerge even when participants are unlikely to carry out a full logical analysis of the problems (e.g., Shynkaruk & Thompson, 2006). There is also evidence that people can implicitly detect the conflict between their beliefs and the validity of the problems, even if they are unable to consciously produce a logical response (e.g., De Neys, Moyens, & Vansteenwegen, 2010). In 4 experiments we demonstrate that people intuitively detect the logicality of syllogisms, and this effect emerges independently of participants' conscious mindset and their cognitive capacity. This logic effect is also unrelated to the superficial structure of the problems. Additionally, we provide evidence that the logicality of the syllogisms is detected through slight changes in participants' affective states. In fact, subliminal affective priming had an effect on participants' subjective evaluations of the problems. Finally, when participants misattributed their emotional reactions to background music, this significantly reduced the logic effect.

Nature and judgment.

Mode of access: Internet.

Kritik der urtheilskraft.

Mode of access: Internet.

Experimentell-psychologische Untersuchungen über das Urteil : eine Einleitung in die logik /

Mode of access: Internet.

Rechte und Pflichten der Kritik; philosophische Laien-Predigten für das Volk der Denker.

Mode of access: Internet.

Die Urtheilsfunction. Eine psychologische und erkenntniskritische Untersuchung.

Mode of access: Internet.

Fuzzy logic for decision support in chronic care

Computerized clinical guidelines can provide significant benefits in terms of health outcomes and costs, however, their effective computer implementation presents significant problems. Vagueness and ambiguity inherent in natural language (textual) clinical guidelines makes them problematic for formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. In care plan on-line (CPOL), an intranet-based chronic disease care planning system for general practitioners (GPs) in use in South Australia, we formally treat fuzziness in interpretation of quantitative data, formulation of recommendations and unequal importance of clinical indicators. We use expert judgment on cases, as well as direct estimates by experts, to optimize aggregation operators and treat heterogeneous combinations of conjunction and disjunction that are present in the natural language decision rules formulated by specialist teams.

Quality of Judgment Assessment

The intersection of Artificial Intelligence and The Law stands for a multifaceted matter, and its effects set the advances on culture, organization, as well as the social matters, when the emergent information technologies are taken into consideration. From this point of view, the weight of formal and informal Conflict Resolution settings should be highlighted, and the use of defective data, information or knowledge must be emphasized. Indeed, it is hard to do it with traditional problem solving methodologies. Therefore, in this work the focus is on the development of decision support systems, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks. It is intended to evaluate the Quality-of-Judgments and the respective Degree-of-Confidence that one has on such happenings.

The STRATIFY tool and clinical judgment were poor predictors of falling in an acute hospital setting

Objective: To compare the effectiveness of the STRATIFY falls tool with nurses’ clinical judgments in predicting patient falls. Study Design and Setting: A prospective cohort study was conducted among the inpatients of an acute tertiary hospital. Participants were patients over 65 years of age admitted to any hospital unit. Sensitivity, specificity, and positive predictive value (PPV) and negative predictive values (NPV) of the instrument and nurses’ clinical judgments in predicting falls were calculated. Results: Seven hundred and eighty-eight patients were screened and followed up during the study period. The fall prevalence was 9.2%. Of the 335 patients classified as being ‘‘at risk’’ for falling using the STRATIFY tool, 59 (17.6%) did sustain a fall (sensitivity50.82, specificity50.61, PPV50.18, NPV50.97). Nurses judged that 501 patients were at risk of falling and, of these, 60 (12.0%) fell (sensitivity50.84, specificity50.38, PPV50.12, NPV50.96). The STRATIFY tool correctly identified significantly more patients as either fallers or nonfallers than the nurses (P50.027). Conclusion: Considering the poor specificity and high rates of false-positive results for both the STRATIFY tool and nurses’ clinical judgments, we conclude that neither of these approaches are useful for screening of falls in acute hospital settings.