323 research outputs found

    Linda Problem: Dampak Cognitive Reflection Task (CRT) pada Berpikir Mahasiswa

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    Latar Belakang: CRT merupakan instrumen penting untuk mengukur kecenderungan berpikir intuitif dan potensi individu dalam mengaktivasi proses menyadari. CRT dengan konten peluang perlu diterapkan karena luasnya penerapan konten tersebut dalam pembelajaran dan kehidupan sehari-hari.Metode: Penelitian ini merupakan penelitian kualitatis dengan jenis studi kasus tunggan (single case study). Instrumen penelitian ini adalah Peneliti, Linda problem, dan alat rekam audio-visual. Subjek penelitian ini adalah dua mahasiswa Pendidikan matematika. Dua subjek tersebut merupakan sumber data yang menghasilkan data berupa jawaban tertulis dan hasil rekaman audio visual saat memecahkan Linda problem. Teknik analisa data yang digunakan adalah teknik analisa data interaktif.Hasil: Hasil penelitian ini dikategorikan menjadi dua. Pertama adalah CRT berdampak pada aktivasi proses tanpa menyadari. Kedua adalah CRT berdampak pada aktivasi proses menyadari.Kesimpulan: CRT memberikan dampak pada berpikir subjek karena faktor kompleksitas masalah dan faktor budaya di Indonesi

    Experiencing simulated outcomes

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    Whereas much literature has documented difficulties in making probabilistic inferences, it has also emphasized the importance of task characteristics in determining judgmental accuracy. Noting that people exhibit remarkable efficiency in encoding frequency information sequentially, we construct tasks that exploit this ability by requiring people to experience the outcomes of sequentially simulated data. We report two experiments. The first involved seven well-known probabilistic inference tasks. Participants differed in statistical sophistication and answered with and without experience obtained through sequentially simulated outcomes in a design that permitted both between- and within-subject analyses. The second experiment involved interpreting the outcomes of a regression analysis when making inferences for investment decisions. In both experiments, even the statistically naĂŻve make accurate probabilistic inferences after experiencing sequentially simulated outcomes and many prefer this presentation format. We conclude by discussing theoretical and practical implications.probabilistic reasoning; natural frequencies; experiential sampling; simulation., leex

    Don’t blame the norms! On the challenges of ecological rationality

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    Enlightenment thinkers viewed logic and mathematical probability as the hallmarks of rationality. In psychological research on human (ir)rationality, human subjects are typically held accountable to this arcane ideal of Reason. If people fall short of these traditional standards, as indeed they often do, they are biased or irrational. Recent work in the program of ecological rationality, however, aims to rehabilitate human reason, and to upturn our traditional conception of rationality in the process. Put bluntly, these researchers are turning the tables on the traditionalist, showing that human reasoning often outperforms complex algorithms based on the traditional canons of rationality. If human reason still appears paltry from the vantage point of capital-R Rationality, then so much the worse for Rationality. Maybe the norms themselves are in need of revision. Perhaps human reasoning is better than rational. Though we welcome the naturalization of human reason, we argue that this backlash against the classical norms of rationality is uncalled for. Ecological rationality presents two apparent challenges to the traditional canons of rationality. In both cases, we contend, the norms emerge unscathed. In the first category, norms of rationality that appear violated by individual reasoners, re-emerge at the level of evolutionary adaptation. In the second category, the norms under challenge simply turn out to be not applicable to the case at hand. Moreover, we should keep in mind that, when they are assessing the efficiency of human reasoning, advocates of ecological rationality still use the traditional norms of rationality as a benchmark. We conclude that, even if we accept all the fascinating findings garnered by the advocates of ecological rationality (and there is ample reason to do so), we need not be taken in by the rhetoric against classical rationality, or the false opposition between logical and ecological rationality. When the dust has settled, the norms are still standing

    The benefits of prototypes: The case of medical concepts

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    In the present paper, we shall discuss the notion of prototype and show its benefits. First, we shall argue that the prototypes of common-sense concepts are necessary for making prompt and reliable categorisations and inferences. However, the features constituting the prototype of a particular concept are neither necessary nor sufficient conditions for determining category membership; in this sense, the prototype might lead to conclusions regarded as wrong from a theoretical perspective. That being said, the prototype remains essential to handling most ordinary situations and helps us to perform important cognitive tasks. To exemplify this point, we shall focus on disease concepts. Our analysis concludes that the prototypical conception of disease is needed to make important inferences from a practical and clinical point of view. Moreover, it can still be compatible with a classical definition of disease, given in terms of necessary and sufficient conditions. In the first section, we shall compare the notion of stereotype, as it has been introduced in philosophy of language by Hilary Putnam, with the notion of prototype, as it has been developed in the cognitive sciences. In the second section, we shall discuss the general role of prototypical information in cognition and stress its centrality. In the third section, we shall apply our previous discussion to the specific case of medical concepts, before briefly summarising our conclusions in section four

    On Cognitive Preferences and the Plausibility of Rule-based Models

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    It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that, all other things being equal, longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowd-sourcing study based on about 3.000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus on plausibility and relation to interpretability, comprehensibility, and justifiabilit

    Are emotions reliable guides for policy making? An evolutionary perspective

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    Technology has become all-important in modern society. For each application, it is crucial for society to have a good understanding of the risks and benefits involved. However, experts tend to assess the risks very differently than the public. One of the main reasons is that experts tend to rely on an objective analysis of the facts, whereas laypeople’s judgment is also based on other factors, including emotional responses. The question remains however whether that is a good thing. Some argue that emotions lead to biases and should be treated with great suspicion; others claim that the laypeople’s approach to risk is much richer and should also be taken into consideration. In this paper, I explore how we can answer that important question from an evolutionary perspective. First, I briefly outline the role of emotions in judgment and decision making. Next, I discuss two approaches that have defended the rationality of emotions: Roeser’s concept of emotions as trustworthy indicators of moral risks and Kahan’s cultural evaluator theory. Subsequently, I briefly discuss the evolution of emotions and their impact on risk assessment. I conclude from that account that emotions are not trustworthy guide for policy making

    A Description Logic Framework for Commonsense Conceptual Combination Integrating Typicality, Probabilities and Cognitive Heuristics

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    We propose a nonmonotonic Description Logic of typicality able to account for the phenomenon of concept combination of prototypical concepts. The proposed logic relies on the logic of typicality ALC TR, whose semantics is based on the notion of rational closure, as well as on the distributed semantics of probabilistic Description Logics, and is equipped with a cognitive heuristic used by humans for concept composition. We first extend the logic of typicality ALC TR by typicality inclusions whose intuitive meaning is that "there is probability p about the fact that typical Cs are Ds". As in the distributed semantics, we define different scenarios containing only some typicality inclusions, each one having a suitable probability. We then focus on those scenarios whose probabilities belong to a given and fixed range, and we exploit such scenarios in order to ascribe typical properties to a concept C obtained as the combination of two prototypical concepts. We also show that reasoning in the proposed Description Logic is EXPTIME-complete as for the underlying ALC.Comment: 39 pages, 3 figure

    Teaching critical thinking beyond philosophy

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