Journal articles: 'Predictive Reasoning'

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Stauffer, E. Shannon. "HIGH TECH VS PREDICTIVE REASONING." Orthopedics 18, no. 10 (October 1995): 967. http://dx.doi.org/10.3928/0147-7447-19951001-04.

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Oslington, Gabrielle, Joanne Mulligan, and Penny Van Bergen. "Third-graders’ predictive reasoning strategies." Educational Studies in Mathematics 104, no. 1 (May 2020): 5–24. http://dx.doi.org/10.1007/s10649-020-09949-0.

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Fernbach, Philip M., Adam Darlow, and Steven A. Sloman. "Asymmetries in predictive and diagnostic reasoning." Journal of Experimental Psychology: General 140, no. 2 (2011): 168–85. http://dx.doi.org/10.1037/a0022100.

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Rodrigo, María J., Manuel de Vega, and Javier Castaneda. "Updating mental models in predictive reasoning." European Journal of Cognitive Psychology 4, no. 2 (April 1992): 141–57. http://dx.doi.org/10.1080/09541449208406247.

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Lim, Tow Keang. "The predictive brain model in diagnostic reasoning." Asia Pacific Scholar 6, no. 2 (May 4, 2021): 1–8. http://dx.doi.org/10.29060/taps.2021-6-2/ra2370.

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Introduction: Clinical diagnosis is a pivotal and highly valued skill in medical practice. Most current interventions for teaching and improving diagnostic reasoning are based on the dual process model of cognition. Recent studies which have applied the popular dual process model to improve diagnostic performance by “Cognitive De-biasing” in clinicians have yielded disappointing results. Thus, it may be appropriate to also consider alternative models of cognitive processing in the teaching and practice of clinical reasoning. Methods: This is critical-narrative review of the predictive brain model. Results: The theory of predictive brains is a general, unified and integrated model of cognitive processing based on recent advances in the neurosciences. The predictive brain is characterised as an adaptive, generative, energy-frugal, context-sensitive action-orientated, probabilistic, predictive engine. It responds only to predictive errors and learns by iterative predictive error management, processing and hierarchical neural coding. Conclusion: The default cognitive mode of predictive processing may account for the failure of de-biasing since it is not thermodynamically frugal and thus, may not be sustainable in routine practice. Exploiting predictive brains by employing language to optimise metacognition may be a way forward

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Yuan, Ye, Zhong Kai Yang, and Qing Fu Li. "End Effect Processing for Empirical Mode Decomposition Using Fuzzy Inductive Reasoning." Applied Mechanics and Materials 55-57 (May 2011): 407–12. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.407.

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This paper focuses on the end effect problem of the empirical mode decomposition (EMD) algorithm, which results in a serious distortion in the EMD sifting process. A new method based on fuzzy inductive reasoning (FIR) is proposed to overcome the end effect. Fuzzy inductive reasoning method has simple inferring rules and strong predictive capability. The fuzzy inductive reasoning based method uses the sequence near the end as the input signal of fuzzy inductive reasoning model. This predictive value can be obtained after fuzzification, qualitative modeling ,qualitative simulation and debluring. The simulation results have shown that the fuzzy inductive reasoning based method has equivalent performance to the neural network based method.

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Wang, W. C. "Personalized Prediction Model for Hepatocellular Carcinoma With a Bayesian Clinical Reasoning Approach." Journal of Global Oncology 4, Supplement 2 (October 1, 2018): 210s. http://dx.doi.org/10.1200/jgo.18.84600.

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Background: Predictive models for the risk of hepatocellular carcinoma (HCC) are often appropriate for average-risk population but not tailored for a personalized prediction model for individual risk of hepatocellular carcinoma (HCC), namely personalized prediction model. Aim: The objective of this study is to build up an individually tailored predictive model for HCC by using a Bayesian clinical reasoning algorithm to stratify risk groups of the underlying population. Methods: Data were derived from a community-based screening cohort consisting of 98,552 subjects between 1999 and 2007. Information on HBV and HCV infection status, liver function test, AFT, family history of liver cancer, demographic characteristics, lifestyle variables and relevant biomarkers were collected. The occurrence of HCC was ascertained by the linkage of the nationwide cancer registry till the end of 2007. Bayesian clinical reasoning model was adopted by constructing the basic model taken as the prior model for average-risk subject. We then updated the basic model by sequentially incorporating other risk factors for HCC encrypted in the likelihood ratio to form posterior probability that was used for predicting individual risk of HCC. Results: By dint of Bayesian clinical reasoning model with a step-by-step update of the risk of HCC for the sequentially obtained information, a 57-year-old man was predicted to yield 0.69% of HCC risk with the prior model. After history-taking of having hepatitis B carrier (likelihood ratio [LR]: 3.65), family history (LR: 1.43), and no alcohol drinking (LR: 0.89), the posterior risk for HCC was enhanced up to 3.13%. After further biochemical examination, the updated risk of HCC for a man [the following biomarkers [ALT = 30 IU/L (LR: 0.78), AST = 56 IU/L (LR: 8.99), platelets = (203 × /μL) (unit cube of ten) (LR: 0.55)] was increase to 11.07%. Conclusion: We proposed a individually tailored prediction model for HCC by incorporating routine information with a sequential Bayesian clinical reasoning approach.

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Habeck, Christian, Qolamreza Razlighi, and Yaakov Stern. "Predictive utility of task-related functional connectivity vs. voxel activation." PLOS ONE 16, no. 4 (April 8, 2021): e0249947. http://dx.doi.org/10.1371/journal.pone.0249947.

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Functional connectivity, both in resting state and task performance, has steadily increased its share of neuroimaging research effort in the last 1.5 decades. In the current study, we investigated the predictive utility regarding behavioral performance and task information for 240 participants, aged 20–77, for both voxel activation and functional connectivity in 12 cognitive tasks, belonging to 4 cognitive reference domains (Episodic Memory, Fluid Reasoning, Perceptual Speed, and Vocabulary). We also added a model only comprising brain-structure information not specifically acquired during performance of a cognitive task. We used a simple brain-behavioral prediction technique based on Principal Component Analysis (PCA) and regression and studied the utility of both modalities in quasi out-of-sample predictions, using split-sample simulations (= 5-fold Monte Carlo cross validation) with 1,000 iterations for which a regression model predicting a cognitive outcome was estimated in a training sample, with a subsequent assessment of prediction success in a non-overlapping test sample. The sample assignments were identical for functional connectivity, voxel activation, and brain structure, enabling apples-to-apples comparisons of predictive utility. All 3 models that were investigated included the demographic covariates age, gender, and years of education. A minimal reference model using simple linear regression with just these 3 covariates was included for comparison as well and was evaluated with the same resampling scheme as described above. Results of the comparison between voxel activation and functional connectivity were mixed and showed some dependency on cognitive outcome; however, mean differences in predictive utility between voxel activation and functional connectivity were rather small in terms of within-modality variability or predictive success. More notably, only in the case of Fluid Reasoning did concurrent functional neuroimaging provided compelling about cognitive performance beyond structural brain imaging or the minimal reference model.

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Legaspi, Roberto, Raymund Sison, Ken-ichi Fukui, and Masayuki Numao. "Cluster-based predictive modeling to improve pedagogic reasoning." Computers in Human Behavior 24, no. 2 (March 2008): 153–72. http://dx.doi.org/10.1016/j.chb.2007.01.007.

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Williams, Patricia Couch, R. Steve McCallum, and Mellissa Testerman Reed. "Predictive Validity of the Cattell-Horn Gf-Gc Constructs to Achievement." Assessment 3, no. 1 (March 1996): 43–51. http://dx.doi.org/10.1177/107319119600300105.

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The predictive validity of cognitive constructs taken from Cattell-Horn's Gf-Gc Model was examined. Gf-Gc cognitive constructs were measured using the Woodcock-Johnson-Revised Tests of Cognitive Ability; they include processing speed, fluid reasoning, acculturation-knowledge, short-term memory, long-term retrieval, auditory processing, and visual processing. Scores from the Comprehensive Test of Basic Skills were used as the criterion measures for 104 elementary, middle, and high school students. Using multiple regression equations, various combinations of the Comprehension-Knowledge, Fluid Reasoning, and Processing Speed variables were consistently found to be the best predictors of achievement. Multiple Rs ranged from the .60s to .70s. Results provide evidence for the importance of cognitive constructs for predicting achievement and are potentially useful for understanding program planning and Aptitude x Treatment Interaction research.

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Aßfalg, André, and Karl Christoph Klauer. "Reasoners consider alternative causes in predictive and diagnostic reasoning." Journal of Experimental Psychology: Learning, Memory, and Cognition 45, no. 12 (December 2019): 2188–208. http://dx.doi.org/10.1037/xlm0000706.

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Kaufmann, Mareile, Simon Egbert, and Matthias Leese. "Predictive Policing and the Politics of Patterns." British Journal of Criminology 59, no. 3 (December 7, 2018): 674–92. http://dx.doi.org/10.1093/bjc/azy060.

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AbstractPatterns are the epistemological core of predictive policing. With the move towards digital prediction tools, the authority of the pattern is rearticulated and reinforced in police work. Based on empirical research about predictive policing software and practices, this article puts the authority of patterns into perspective. Introducing four ideal-typical styles of pattern identification, we illustrate that patterns are not based on a singular logic, but on varying rationalities that give form to and formalize different understandings about crime. Yet, patterns render such different modes of reasoning about crime, and the way in which they feed back into policing cultures, opaque. Ultimately, this invites a stronger reflection about the political nature of patterns.

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Hildebrandt, Marcel, Jorge Andres Quintero Serna, Yunpu Ma, Martin Ringsquandl, Mitchell Joblin, and Volker Tresp. "Reasoning on Knowledge Graphs with Debate Dynamics." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4123–31. http://dx.doi.org/10.1609/aaai.v34i04.6600.

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We propose a novel method for automatic reasoning on knowledge graphs based on debate dynamics. The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments – paths in the knowledge graph – with the goal to promote the fact being true (thesis) or the fact being false (antithesis), respectively. Based on these arguments, a binary classifier, called the judge, decides whether the fact is true or false. The two agents can be considered as sparse, adversarial feature generators that present interpretable evidence for either the thesis or the antithesis. In contrast to other black-box methods, the arguments allow users to get an understanding of the decision of the judge. Since the focus of this work is to create an explainable method that maintains a competitive predictive accuracy, we benchmark our method on the triple classification and link prediction task. Thereby, we find that our method outperforms several baselines on the benchmark datasets FB15k-237, WN18RR, and Hetionet. We also conduct a survey and find that the extracted arguments are informative for users.

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PARSONS, SIMON. "FURTHER RESULTS IN QUALITATIVE UNCERTAINTY." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 03, no. 02 (June 1995): 187–210. http://dx.doi.org/10.1142/s0218488595000141.

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This paper extends previous work on propagating qualitative uncertainty in networks in which a general approach to qualitative propagation was discussed. The work presented here includes results that make it possible to perform evidential and intercausal reasoning, in addition to the predictive reasoning already covered, in networks quantified with probability, possibility and Dempster-Shafer belief values. The use of these forms of reasoning, which include the phenomenon of “explaining away”, is illustrated with the use of a medical example.

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Morasse, Frédérick, Annie Bernier, Gabrielle Lalonde, Sébastien Hétu, and Miriam H. Beauchamp. "Sociomoral Reasoning Skills during Childhood: A Comprehensive and Predictive Approach." Brain Sciences 12, no. 9 (September 10, 2022): 1226. http://dx.doi.org/10.3390/brainsci12091226.

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Sociomoral reasoning (SMR) is an essential component of social functioning allowing children to establish judgments based on moral criteria. The progressive emergence and complexification of SMR during childhood is thought to be underpinned by a range of characteristics and abilities present in the preschool years. Past studies have mostly examined concurrent associations between individual factors and SMR. Using a more comprehensive and predictive approach to identify early predictors of school-age SMR would contribute to a more complete picture of SMR development. This study aimed to investigate the contribution of four domains of preschool predictors to SMR at school-age: demographic (age, sex, parental education), cognitive (executive and sociocognitive functions), behavioral (internalizing and externalizing behaviors), and familial (parent–child interactions, parental stress) factors. Parents of 122 children 3 to 5 years (M = 3.70, SD = 0.66 years, 51% girls) completed questionnaires and children were administered executive and sociocognitive tasks. Parent–child interactions were assessed using an observational approach. SMR was measured four years later using the SoMoral task. A four-step hierarchical regression analysis revealed that executive functions and internalizing problems were significant independent predictors of SMR. These findings provide a more comprehensive understanding of the early precursors of SMR during childhood.

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Fernbach, Philip M., Adam Darlow, and Steven A. Sloman. "Neglect of Alternative Causes in Predictive but Not Diagnostic Reasoning." Psychological Science 21, no. 3 (February 17, 2010): 329–36. http://dx.doi.org/10.1177/0956797610361430.

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Coles, Matthew, Djamel Azzi, and Barry Haynes. "A self‐healing mobile wireless sensor network using predictive reasoning." Sensor Review 28, no. 4 (September 12, 2008): 326–33. http://dx.doi.org/10.1108/02602280810902631.

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Anagnostopoulos, Christos, and Kostas Kolomvatsos. "Predictive intelligence to the edge through approximate collaborative context reasoning." Applied Intelligence 48, no. 4 (August 7, 2017): 966–91. http://dx.doi.org/10.1007/s10489-017-1032-y.

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Bell, J. "Natural Events." Journal of Artificial Intelligence Research 30 (November 6, 2007): 361–412. http://dx.doi.org/10.1613/jair.2383.

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This paper develops an inductive theory of predictive common sense reasoning. The theory provides the basis for an integrated solution to the three traditional problems of reasoning about change; the frame, qualification, and ramification problems. The theory is also capable of representing non-deterministic events, and it provides a means for stating defeasible preferences over the outcomes of conflicting simultaneous events.

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Lucifora, Chiara, Gabriella Martino, Anna Curcuruto, Mohammad Ali Salehinejad, and Carmelo Mario Vicario. "How Self-Control Predicts Moral Decision Making: An Exploratory Study on Healthy Participants." International Journal of Environmental Research and Public Health 18, no. 7 (April 6, 2021): 3840. http://dx.doi.org/10.3390/ijerph18073840.

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Research on moral reasoning calls into question self-control, which encompasses impulsivity, compulsivity, and inhibitory control. However, a thorough investigation exploring how these three dimensions can affect moral reasoning in response to different scenarios is unavailable. We addressed this topic by testing the predictive role of these three dimensions of self-control on appraisals for ethical violations related with different types of scenarios. Overall, our results suggest that all three dimensions of self-control are involved in moral reasoning, depending on the type of appraisal and provided moral scenarios.

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Minn, Sein, Jill-Jênn Vie, Koh Takeuchi, Hisashi Kashima, and Feida Zhu. "Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12810–18. http://dx.doi.org/10.1609/aaai.v36i11.21560.

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Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting their performance to adjust the curriculum accordingly. Deep Learning based models like Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) have shown significant predictive performance compared with traditional models like Bayesian Knowledge Tracing (BKT) and Performance Factors Analysis (PFA). However, it is difficult to extract psychologically meaningful explanations from the tens of thousands of parameters in neural networks, that would relate to cognitive theory. There are several ways to achieve high accuracy in student performance prediction but diagnostic and prognostic reasonings are more critical in learning science. In this work, we present Interpretable Knowledge Tracing (IKT), a simple model that relies on three meaningful features: individual skill mastery, ability profile (learning transfer across skills) and problem difficulty by using data mining techniques. IKT’s prediction of future student performance is made using a Tree Augmented Naive Bayes Classifier (TAN), therefore its predictions are easier to explain than deep learning based student models. IKT also shows better student performance prediction than deep learning based student models without requiring a huge amount of parameters. We conduct ablation studies on each feature to examine their contribution to student performance prediction. Thus, IKT has great potential for providing adaptive and personalized instructions with causal reasoning in real-world educational systems.

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Dvoinin, A. M., and E. S. Trotskaya. "Cognitive Predictors of Academic Success: How Do the General Patterns Work in the Early Stages of Education?" Психологическая наука и образование 27, no. 2 (2022): 42–52. http://dx.doi.org/10.17759/pse.2022270204.

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The article provides an overview of modern works devoted to the study of cognitive predictors of academic success. The general patterns of forecasting are revealed: the most powerful and universal predictor of academic success at different stages of school education is psychometric intelligence; creativity is less significant and rather unstable. It is argued that these patterns are poorly traced at the level of preschool education. Particular cognitive functions are significant for predicting the future educational achievements of preschoolers: information processing speed, visual perception (in combination with motor functions), short-term memory, and attention. Spatial abilities have a certain prognostic potential, though reasoning in preschoolers is not a strong predictor of academic success; executive functions have the greatest predictive power. It is noted that the general patterns in predicting the academic success of students can be traced in elementary school: the predictive potentials of psychometric intelligence are revealed, the power of individual cognitive abilities (in particular, spatial abilities) increases, the contribution of executive functions to the prediction decreases. The general tendency for non-cognitive factors (educational motivation, some personality traits) to increase with age also begins to appear in elementary school.

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Schult, Johannes, Franziska T. Fischer, and Benedikt Hell. "Tests of Scholastic Aptitude Cover Reasoning Facets Sufficiently." European Journal of Psychological Assessment 32, no. 3 (July 2016): 215–19. http://dx.doi.org/10.1027/1015-5759/a000247.

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Abstract. This study explores how reasoning facets relate to tests of scholastic aptitude and to academic performance. Intelligence test scores and academic aptitude test scores from freshman students in science (n = 284) and economics (n = 359) as well as subsequent grades from their first year in college were used to analyze structural equation models. The direct influence of reasoning facets on academic performance is fully mediated by academic aptitude test scores. Numeric abilities dominate the aptitude tests’ predictive power. Verbal reasoning explains a significant amount of aptitude test score variance in science but not in economics. The mediation analysis suggests that verbal, numeric, and figural reasoning are covered sufficiently by the aptitude tests.

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Rochberg, Francesca. "Reasoning, Representing, and Modeling in Babylonian Astronomy." Journal of Ancient Near Eastern History 5, no. 1-2 (October 25, 2018): 131–47. http://dx.doi.org/10.1515/janeh-2018-0009.

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AbstractThis paper considers Babylonian astronomical predictive schemes as a source for the study of reasoning and representing via modeling. Two principal questions are addressed: first, whether Babylonian astronomical modeling can be usefully included in the conversation about scientific reasoning with models, and second, how and what the representational value of the practice of astronomical modeling was in ancient Babylonia. It is found that the Babylonian astronomical schemes demonstrate the adaptability and various capabilities of the process of modeling as a powerful tool of representation for scientific knowledge and theorizing.

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White, Stephen J. "INTENTION AND PREDICTION IN MEANS-END REASONING." American Philosophical Quarterly 55, no. 3 (July 1, 2018): 251–66. http://dx.doi.org/10.2307/45128619.

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Abstract How, if at all, does one’s intention to realize an end bear on the justification for taking the means to that end? Theories that allow that intending an end directly provides a reason to take the means are subject to a well-known "bootstrapping" objection. On the other hand, "anti-psychologistic" accounts—which seek to derive instrumental reasons directly from the reasons that support adopting the end itself—have unacceptable implications where an agent faces multiple rationally permissible options. An alternative, predictive, role for intention in means-end reasoning is considered and rejected. A new proposal is then developed, according to which instrumental reasons are not merely reasons to perform an act necessary for a given end, but to perform the act for the sake of that end.

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Hadiati, Soka, Dwi Fajar Saputri, Wahyudi Wahyudi, and Adi Pramuda. "Model-Based Reasoning in the Deductive Laboratory Work with Arduino and Sensor Module." Berkala Ilmiah Pendidikan Fisika 9, no. 3 (November 1, 2021): 266. http://dx.doi.org/10.20527/bipf.v9i3.8735.

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Laboratory work that has been applied so far is more dominant in deductive laboratory work, but this type of laboratory work is not optimal in stimulating students' reasoning ability. Up to now, model-based reasoning studies on laboratory work have been substantial (especially in measurement and troubleshooting activities) and have not seen overall laboratory activity, including the use of alternative instrumentation such as Arduino and its sensor module. Designing the best laboratory work requires the role of model-based reasoning studies using Arduino devices. A mixed-method research design was used in this study with a theoretical study to obtain a predictive pattern for deductive laboratory work model which was matched with the observations of 50 undergraduate physics education students in laboratory work. Revision activities were shown by students in laboratory work depending on the capabilities of reasoning. The results of this study indicate that revision activities in deductive laboratory work using Arduino can stimulate students' reasoning. Arduino can lead students to explore their thinking and reasoning to solve problems in laboratory work.

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Flexer, Michael J. "If p0, then 1: The impossibility of thinking out cases." History of the Human Sciences 33, no. 3-4 (September 3, 2020): 175–97. http://dx.doi.org/10.1177/0952695120944032.

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Forrester’s proposed seventh style of reasoning – thinking in cases – functions as an analogous, dyadic relationship that, whilst indebted philosophically to the logical reasoning and semiotics of Charles Peirce, is prone to creating feedback loops between induction and deduction, precluding novel abductive hypotheses from advancing medical knowledge. Reasoning with a Peircean triadic model opens up the contexts and methods of meaning-making and reasoning through medical cases, and the potent influence of their genre conventions, to intellectual critical scrutiny. Vitally, it offers a third mode – abduction – that this article argues needs to be reintroduced into Forrester’s model of reasoning with cases. This article demonstrates this by applying a Peircean triadic model of reasoning to Forrester’s own model, tracing a shared genealogy but one in which the abductive element was lost. The article goes on to illustrate the explanatory and predictive potential of Peircean abductive reasoning and the necessary re-theorising of the case this entails. This argument is supported through an analysis of early case reports of what would become HIV/Aids, drawn from the Case Records of Massachusetts General Hospital series in the New England Journal of Medicine.

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Bredemeier, Brenda Jo Light. "Children's Moral Reasoning and Their Assertive, Aggressive, and Submissive Tendencies in Sport and Daily Life." Journal of Sport and Exercise Psychology 16, no. 1 (March 1994): 1–14. http://dx.doi.org/10.1123/jsep.16.1.1.

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The purpose of this study was to investigate the relationship between children's moral reasoning and their assertive, aggressive, and submissive action tendencies in sport and daily life contexts. The 106 fourth- through seventh-grade children were asked to reason about hypothetical sport and daily life moral dilemmas and to respond to two behaviorally validated, self-report instruments designed to assess action tendencies in sport and daily life conflict situations. Multiple regression analyses indicated that moral reasoning scores were predictive of action tendencies, with reasoning positively related to assertion and negatively related to aggression. Results were interpreted in light of a congruence between Haan's (1978) descriptions of moral levels and the moral implications of the action tendencies under consideration. Gender and school-level differences in action tendencies were also noted; no gender or school-level differences in moral reasoning were found.

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Palengka, Inelsi, Dwi Juniati, and Abadi Abadi. "Mathematical reasoning of prospective mathematics teachers in solving problems based on working memory capacity differences." Eurasia Journal of Mathematics, Science and Technology Education 18, no. 12 (November 25, 2022): em2193. http://dx.doi.org/10.29333/ejmste/12670.

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This study aims to investigate algorithmic reasoning and creative reasoning of prospective mathematics teachers in solving problems. This research is a qualitative research. The research subjects are prospective mathematics teachers with high working memory capacity (HWMC) and low working memory capacity (LWMC). Based on the results on algorithmic reasoning, it is known that for subjects with HWMC using algorithms to solve the given problems. While subjects with LWMC using sum and multiplication rules to solve the given problems. On creative mathematical reasoning it is known that for subjects with HWMC using a novel strategy, providing predictive and verification reasons for the selection of strategies, and providing convincing reasons accordance with the intrinsic mathematical properties. While subjects with low memory capacity had difficulty providing a verification reason and the reason that the way of solving was in accordance with the intrinsic mathematical properties.

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Baniecki, Hubert, and Przemyslaw Biecek. "Responsible Prediction Making of COVID-19 Mortality (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15755–56. http://dx.doi.org/10.1609/aaai.v35i18.17874.

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For high-stakes prediction making, the Responsible Artificial Intelligence (RAI) is more important than ever. It builds upon Explainable Artificial Intelligence (XAI) to advance the efforts in providing fairness, model explainability, and accountability to the AI systems. During the literature review of COVID-19 related prognosis and diagnosis, we found out that most of the predictive models are not faithful to the RAI principles, which can lead to biassed results and wrong reasoning. To solve this problem, we show how novel XAI techniques boost transparency, reproducibility and quality of models.

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PARSONS, SIMON. "SOME ELEMENTS OF THE THEORY OF QUALITATIVE POSSIBILISTIC NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 02, no. 01 (March 1994): 67–90. http://dx.doi.org/10.1142/s0218488594000079.

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This paper presents some results concerning the qualitative behaviour of possibilistic networks. The behaviour of singly connected networks is analysed, providing the foundations for qualitative reasoning about changes in possibility values in both predictive and evidential directions. The problems inherent in handling multiply connected networks are also discussed, and a possible solution is proposed. The behaviour of qualitative possibilistic networks is compared to qualitative probabilistic networks, and an example of the kind of reasoning that is permitted by the use of these networks is provided.

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Assegie, Tsehay Admassu, Thulasi Karpagam, Radha Mothukuri, Ravulapalli Lakshmi Tulasi, and Minychil Fentahun Engidaye. "Extraction of human understandable insight from machine learning model for diabetes prediction." Bulletin of Electrical Engineering and Informatics 11, no. 2 (April 1, 2022): 1126–33. http://dx.doi.org/10.11591/eei.v11i2.3391.

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Explaining the reason for model’s output as diabetes positive or negative is crucial for diabetes diagnosis. Because, reasoning the predictive outcome of model helps to understand why the model predicted an instance into diabetes positive or negative class. In recent years, highest predictive accuracy and promising result is achieved with simple linear model to complex deep neural network. However, the use of complex model such as ensemble and deep learning have trade-off between accuracy and interpretability. In response to the problem of interpretability, different approaches have been proposed to explain the predictive outcome of complex model. However, the relationship between the proposed approaches and the preferred approach for diabetes prediction is not clear. To address this problem, the authors aimed to implement and compare existing model interpretation approaches, local interpretable model agnostic explanation (LIME), shapely additive explanation (SHAP) and permutation feature importance by employing extreme boosting (XGBoost). Experiment is conducted on diabetes dataset with the aim of investigating the most influencing feature on model output. Overall, experimental result evidently appears to reveal that blood glucose has the highest impact on model prediction outcome.

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Jeon, Sohyun, JaeYoon Chang, and HyeJoo Lee. "A study on the predictive validity of conditional reasoning test for aggression." Korean Journal of Industrial and Organizational Psychology 30, no. 4 (November 30, 2017): 541–62. http://dx.doi.org/10.24230/kjiop.v30i4.541-562.

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The purpose of this study was to examine the channeling model to investigate the interaction effect of implicit measure (Conditional Reasoning Test for Aggression: CRT-A) and self-reported measure of aggression to predict helping behavior. Expatriates who were dispatched to China or India (N=101) participated in this study. To reduce the common method bias, their host country co-workers (N=320) evaluated the expatriates’ helping behavior as a behavioral criterion. The results showed that there was a significance difference in helping behavior among the expatriates who had high CRT-A scores. Specifically, latent aggressive individuals who consciously perceived themselves as being non-aggressive but implicitly rationalized their aggressive behavior were less likely to show helping behavior. The academic implications of the current study, along with suggestions for the future research, were discussed.

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Guo, Steve Zhongshi, Angus Weng Hin Cheong, and Chris Fei Shen. "Depth of Reasoning and Information Processing: A Predictive Model of SARS Behavior." Asian Journal of Communication 15, no. 3 (November 2005): 274–88. http://dx.doi.org/10.1080/01292980500261589.

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Nawaz, Falak, Naeem Khalid Janjua, and Omar Khadeer Hussain. "PERCEPTUS: Predictive complex event processing and reasoning for IoT-enabled supply chain." Knowledge-Based Systems 180 (September 2019): 133–46. http://dx.doi.org/10.1016/j.knosys.2019.05.024.

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Kennedy, Robert S., Dennis R. Baltzley, Janet J. Turnage, and Marshall B. Jones. "Factor Analysis and Predictive Validity of Microcomputer-Based Tests." Perceptual and Motor Skills 69, no. 3_suppl (December 1989): 1059–74. http://dx.doi.org/10.2466/pms.1989.69.3f.1059.

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11 tests were selected from two microcomputer-based performance test batteries because previously these tests exhibited rapid stability (< 10 min. of practice) and high retest reliability efficiencies ( r> 0.707 for each 3 min. of testing). The battery was administered three times to each of 108 college students (48 men and 60 women) and a factor analysis was performed. Two of the three identified factors appear to be related to information processing (“encoding” and “throughput/decoding”), and the third named an “output/speed” factor. The spatial, memory, and verbal tests loaded on the “encoding” factor and included Grammatical Reasoning, Pattern Comparison, Continuous Recall, and Matrix Rotation. The “throughput/decoding” tests included perceptual/numerical tests like Math Processing, Code Substitution, and Pattern Comparison. The output speed factor was identified by Tapping and Reaction Time tests. The Wonderlic Personnel Test was group administered before the first and after the last administration of the performance tests. The multiple Rs in the total sample between combined Wonderlic as a criterion and less than 5 min. of microcomputer testing on Grammatical Reasoning and Math Processing as predictors ranged between 0.41 and 0.52 on the three test administrations. Based on these results, the authors recommend a core battery which, if time permits, would consist of two tests from each factor. Such a battery is now known to permit stable, reliable, and efficient assessment.

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Kennedy, Robert S., Dennis R. Baltzley, Janet J. Turnage, and Marshall B. Jones. "Factor Analysis and Predictive Validity of Microcomputer-Based Tests ,." Perceptual and Motor Skills 69, no. 3-2 (December 1989): 1059–74. http://dx.doi.org/10.1177/00315125890693-201.

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11 tests were selected from two microcomputer-based performance test batteries because previously these tests exhibited rapid stability (<10 min. of practice) and high retest reliability efficiencies (r>0.707 for each 3 min. of testing). The battery was administered three times to each of 108 college students (48 men and 60 women) and a factor analysis was performed. Two of the three identified factors appear to be related to information processing (“encoding” and “throughput/ decoding”), and the third named an “output/speed” factor. The spatial, memory, and verbal tests loaded on the “encoding” factor and included Grammatical Reasoning, Pattern Comparison, Continuous Recall, and Matrix Rotation. The “throughput/ decoding” tests included perceptual/numerical tests like Math Processing, Code Substitution, and Pattern Comparison. The output speed factor was identified by Tapping and Reaction Time tests. The Wonderlic Personnel Test was group administered before the first and after the last administration of the performance tests. The multiple Rs in the total sample between combined Wonderlic as a criterion and less than 5 min. of microcomputer testing on Grammatical Reasoning and Math Processing as predictors ranged between 0.41 and 0.52 on the three test administrations. Based on these results, the authors recommend a core battery which, if time permits, would consist of two tests from each factor. Such a battery is now known to permit stable, reliable, and efficient assessment.

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Berkowitz, Michal, and Elsbeth Stern. "Which Cognitive Abilities Make the Difference? Predicting Academic Achievements in Advanced STEM Studies." Journal of Intelligence 6, no. 4 (October 26, 2018): 48. http://dx.doi.org/10.3390/jintelligence6040048.

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Previous research has shown that psychometrically assessed cognitive abilities are predictive of achievements in science, technology, engineering and mathematics (STEM) even in highly selected samples. Spatial ability, in particular, has been found to be crucial for success in STEM, though its role relative to other abilities has been shown mostly when assessed years before entering higher STEM education. Furthermore, the role of spatial ability for mathematics in higher STEM education has been markedly understudied, although math is central across STEM domains. We investigated whether ability differences among students who entered higher STEM education were predictive of achievements during the first undergraduate year. We assessed 317 undergraduate students in Switzerland (150 from mechanical engineering and 167 from math-physics) on multiple measures of spatial, verbal and numerical abilities. In a structural equation model, we estimated the effects of latent ability factors on students’ achievements on a range of first year courses. Although ability-test scores were mostly at the upper scale range, differential effects on achievements were found: spatial ability accounted for achievements in an engineering design course beyond numerical, verbal and general reasoning abilities, but not for math and physics achievements. Math and physics achievements were best predicted by numerical, verbal and general reasoning abilities. Broadly, the results provide evidence for the predictive power of individual differences in cognitive abilities even within highly competent groups. More specifically, the results suggest that spatial ability’s role in advanced STEM learning, at least in math-intensive subjects, is less critical than numerical and verbal reasoning abilities.

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Varma, Keisha, Martin Van Boekel, and Sashank Varma. "Middle School Students’ Approaches to Reasoning about Disconfirming Evidence." Journal of Educational and Developmental Psychology 8, no. 1 (January 20, 2018): 28. http://dx.doi.org/10.5539/jedp.v8n1p28.

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This study investigated differences in how middle school children reason about disconfirming evidence. Scientists evaluate hypotheses against evidence, rejecting those that are disconfirmed. Although this instant rationality propels empirical science, it works less for theoretical science, where it is often necessary to delay rationality – to tolerate disconfirming evidence in the short run. We used behavioral measures to identify two groups of middle-school children: strict reasoners who prefer instant rationality and quickly dismiss disconfirmed hypotheses, and permissive reasoners who prefer delayed rationality and retain disconfirmed hypotheses for further evaluation. We measured their scientific reasoning performance as well as their cognitive ability and motivational orientation. What distinguished the groups was not overall differences in these variables, but their predictive relation. For strict reasoners, better scientific reasoning was associated with faster processing, whereas for permissive reasoners, better scientific reasoning was associated with more deliberate thinking – slower processing and broader consideration of both disconfirmed and alternate hypotheses. These findings expand our understanding of “normative” scientific reasoning.

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Harris, Douglas H. "Prediction of Inspection Performance with a Dynamic, Computer-Based, Multi-Aptitude Test." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 41, no. 1 (October 1997): 574–78. http://dx.doi.org/10.1177/1071181397041001127.

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A dynamic, computer-based, multi-aptitude test was designed to predict success on inspection jobs that require the combined aptitudes of general cognitive ability, abstract reasoning, and spatial visualization. This approach to the prediction of job performance is in contrast to most existing methods for predicting performance on technical jobs, in which aptitudes are measured one at a time by static tests. The test was computer administered and scored, requiring 36 minutes of testing time. Validation studies showed that the test was highly reliable, correlated with other measures of these key aptitudes, and was predictive of the job performance of nondestructive testing operators. These results indicated that the test can be used with confidence for the early identification of individuals who will benefit most from training and experience on certain inspection jobs, and who will be most likely to meet operator qualification standards for these jobs.

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Spenser, Karin A., Ray Bull, Lucy Betts, and Belinda Winder. "Executive functioning as a predictive measure of offending behaviour." Journal of Criminal Psychology 9, no. 1 (January 24, 2019): 10–22. http://dx.doi.org/10.1108/jcp-07-2018-0032.

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PurposeProsociality is considered important in the study of offenders and associated cognitive skills: theory of mind, empathic understanding and moral reasoning, are said to enable self-control and reduce the risk of offending behaviours. Previous research has made associations between these skills and executive functioning; however, research into a link between them, in an offending population, is limited. The paper aims to discuss this issue.Design/methodology/approachTo further understand the practicalities of this, the present study considered the predictive abilities of the constructs believed to underpin executive functioning: working memory, cognitive flexibility and inhibitory control, in relation to theory of mind, empathic understanding and moral reasoning. In total, 200 male and female offenders completed measures in all six constructs.FindingsUsing path analysis working memory was demonstrated to be predictive of theory of mind and empathic understanding, cognitive flexibility was found to be predictive of theory of mind, and inhibitory control was found to be predictive of theory of mind, empathic understanding and moral reasoning.Research limitations/implicationsThe study focussed on offenders serving a custodial sentence of six months or less and did not differentiate between crime categories or take into consideration the socio-environmental backgrounds or ethnicity. Therefore, considering these things could further establish the generalisability of the current findings. It is noted that the more focussed the intervention is to the specific needs of an offender, the greater the impact will be. Therefore, pre-screening tests for the constructs discussed may be able to more accurately assess an offenders’ suitability for a programme, or indeed tailor it to meet the specific needs of that person.Practical implicationsThese findings may enable practitioners to more accurately assess offenders’ suitability for interventions aimed at reducing offending behaviours by improving levels of prosociality and develop more focussed programmes to meet the specific needs of individual offenders to reduce re-offending.Social implicationsAs recommended in the study, a more tailored approach to offender rehabilitation may be a potential aid to reducing levels of recidivism.Originality/valueThe present study adds to the literature as it is the first to consider whether the constructs of executive functioning can predict levels of theory of mind, empathic understanding and moral reasoning and so provide a more accurate method in assessing the cognitive abilities of offenders prior to participation in rehabilitative interventions.

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Cho, Eunsoo, Lynn S. Fuchs, Pamela M. Seethaler, Douglas Fuchs, and Donald L. Compton. "Dynamic Assessment for Identifying Spanish-Speaking English Learners’ Risk for Mathematics Disabilities: Does Language of Administration Matter?" Journal of Learning Disabilities 53, no. 5 (January 23, 2020): 380–98. http://dx.doi.org/10.1177/0022219419898887.

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We examined dynamic assessment’s (DA’s) added value over traditional assessments for identifying Spanish-speaking English learners’ (ELs) risk for developing mathematics disabilities, as a function of the language of test administration (English vs. Spanish), type of math outcome, and EL’s language dominance. At the start of first grade, ELs ( N = 368) were randomly assigned to English-DA or Spanish-DA conditions, were assessed on static mathematics measures and domain-general (language, reasoning) measures in English, and completed DA in their assigned language condition. At year’s end, they were assessed on calculation and word-problem solving outcomes in English. Results from multigroup path models indicated that Spanish-DA mitigates the impact of ELs’ language dominance on DA performance. Moreover, ELs’ language dominance moderated DA’s predictive validity differentially depending on DA language and type of outcome. Spanish-DA showed higher predictive validity in Spanish-dominant ELs than English-dominant ELs when predicting calculations but not word-problem solving. English-DA was predictive for both outcomes, regardless of ELs’ language dominance.

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Cao, Hongfei, Yu Li, Carla M. Allen, Michael A. Phinney, and Chi-Ren Shyu. "Visual Reasoning Indexing and Retrieval Using In-Memory Computing." International Journal of Semantic Computing 10, no. 03 (September 2016): 299–322. http://dx.doi.org/10.1142/s1793351x16400110.

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Research has shown that visual information of multimedia is critical in highly-skilled applications, such as biomedicine and life sciences, and a certain visual reasoning process is essential for meaningful search in a timely manner. Relevant image characteristics are learned and verified with accumulated experiences during the reasoning processes. However, such processes are highly dynamic and elusive to computationally quantify and therefore challenging to analyze, let alone to make the knowledge sharable across users. In this paper we study real-time human visual reasoning processes with the aid of gaze tracking devices. Temporal and spatial representations are proposed for gaze modeling, and a visual reasoning retrieval system utilizing in-memory computing under Big Data ecosystems is designed for real-time search of similar reasoning models. Simulated data derived from human subject experiments show that the system has a reasonably high accuracy and provides predictive estimations for hardware requirements versus data sizes for exhaustive searches. Comparison between various visual action classifiers show challenges in modeling visual actions. The proposed system provides a theoretical framework and computing platform for advancement in visual semantic computing, as well as potential applications in medicine, social science, and arts.

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Ajjanagadde, Venkat, and Lokendra Shastri. "Rules and Variables in Neural Nets." Neural Computation 3, no. 1 (February 1991): 121–34. http://dx.doi.org/10.1162/neco.1991.3.1.121.

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A fundamental problem that must be addressed by connectionism is that of creating and representing dynamic structures (Feldman 1982; von der Malsburg 1985). In the context of reasoning with systematic and abstract knowledge, this problem takes the form of the variable binding problem. We describe a biologically plausible solution to this problem and outline how a knowledge representation and reasoning system can use this solution to perform a class of predictive inferences with extreme efficiency. The proposed system solves the variable binding problem by propagating rhythmic patterns of activity wherein dynamic bindings are represented as the synchronous firing of appropriate nodes.

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Sheng, Yang, Jiahan Zhang, Chunhao Wang, Fang-Fang Yin, Q. Jackie Wu, and Yaorong Ge. "Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study." Technology in Cancer Research & Treatment 18 (January 1, 2019): 153303381987478. http://dx.doi.org/10.1177/1533033819874788.

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Knowledge models in radiotherapy capture the relation between patient anatomy and dosimetry to provide treatment planning guidance. When treatment schemes evolve, existing models struggle to predict accurately. We propose a case-based reasoning framework designed to handle novel anatomies that are of same type but vary beyond original training samples. A total of 105 pelvic intensity-modulated radiotherapy cases were analyzed. Eighty cases were prostate cases while the other 25 were prostate-plus-lymph-node cases. We simulated 4 scenarios: Scarce scenario, Semiscarce scenario, Semiample scenario, and Ample scenario. For the Scarce scenario, a multiple stepwise regression model was trained using 85 cases (80 prostate, 5 prostate-plus-lymph-node). The proposed workflow started with evaluating the feature novelty of new cases against 5 training prostate-plus-lymph-node cases using leverage statistic. The case database was composed of a 5-case dose atlas. Case-based dose prediction was compared against the regression model prediction using sum of squared residual. Mean sum of squared residual of case-based and regression predictions for the bladder of 13 identified outliers were 0.174 ± 0.166 and 0.459 ± 0.508, respectively ( P = .0326). For the rectum, the respective mean sum of squared residuals were 0.103 ± 0.120 and 0.150 ± 0.171 for case-based and regression prediction ( P = .1972). By retaining novel cases, under the Ample scenario, significant statistical improvement was observed over the Scarce scenario ( P = .0398) for the bladder model. We expect that the incorporation of case-based reasoning that judiciously applies appropriate predictive models could improve overall prediction accuracy and robustness in clinical practice.

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Öztürk, Mesut, Ümit Demir, and Yaşar Akkan. "Investigation of Proportional Reasoning Problem Solving Processes of Seventh Grade Students: A Mixed Method Research." International Journal on Social and Education Sciences 3, no. 1 (January 4, 2021): 48–67. http://dx.doi.org/10.46328/ijonses.66.

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This study was carried out to examine proportional reasoning problem solving processes of seventh grade students. This study was conducted with the explanatory sequential mixed method design. In this respect, firstly, quantitative data from 56 students were collected and analyzed. Then, qualitative data of the study was collected from six students selected according to their success and analyzed. The quantitative data of the study was consisted of a proportional reasoning skill test and analyzed using predictive statistics. The qualitative data of the study were collected through activity cards and the think-aloud protocol, and content analysis was applied to the collected qualitative data. The findings of the study showed that students were most successful at qualitative comparison. On the other hand, the least success was observed for quantitative comparison, and female students were found to be more successful in proportional reasoning problems.

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Cafolla, Ralph. "Piagetian Formal Operations and other Cognitive Correlates of Achievement in Computer Programming." Journal of Educational Technology Systems 16, no. 1 (September 1987): 45–55. http://dx.doi.org/10.2190/gw1p-7bbk-dvwr-w3tu.

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The purpose of this study was to determine if success in writing computer programs is related to level of cognitive development, verbal ability, and mathematics reasoning. Subjects were drawn from students enrolled in beginning computer programming classes. Each subject was given the School and College Ability Test (SCAT II) test to assess mathematics and verbal levels and the Inventory of Piaget's Developmental Tasks (IPDT) to assess level of cognitive development. The instructor's final examination was used as a measure of computer programming ability. This study supports prior research showing mathematics reasoning and verbal ability were each significant predictors of success in computer programming. Level of cognitive development, as defined by Piaget, also predicts success in computer programming ability. Regression analysis showed that level of cognitive development, when combined with verbal reasoning, is linearly predictive of programming ability.

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Avena, Jennifer S., Betsy B. McIntosh, Oscar N. Whitney, Ashton Wiens, and Jennifer K. Knight. "Successful Problem Solving in Genetics Varies Based on Question Content." CBE—Life Sciences Education 20, no. 4 (December 2021): ar51. http://dx.doi.org/10.1187/cbe.21-01-0016.

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Student and expert problem solving in genetics was characterized by analyzing written step-by-step processes in four different content areas. A few processes that were associated with correct answers differed depending on content area, but reasoning was consistently predictive of successful problem solving across all content areas.

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How, Meng-Leong, Yong Jiet Chan, and Sin-Mei Cheah. "Predictive Insights for Improving the Resilience of Global Food Security Using Artificial Intelligence." Sustainability 12, no. 15 (August 4, 2020): 6272. http://dx.doi.org/10.3390/su12156272.

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Unabated pressures on food systems affect food security on a global scale. A human-centric artificial intelligence-based probabilistic approach is used in this paper to perform a unified analysis of data from the Global Food Security Index (GFSI). The significance of this intuitive probabilistic reasoning approach for predictive forecasting lies in its simplicity and user-friendliness to people who may not be trained in classical computer science or in software programming. In this approach, predictive modeling using a counterfactual probabilistic reasoning analysis of the GFSI dataset can be utilized to reveal the interplay and tensions between the variables that underlie food affordability, food availability, food quality and safety, and the resilience of natural resources. Exemplars are provided in this paper to illustrate how computational simulations can be used to produce forecasts of good and bad conditions in food security using multi-variant optimizations. The forecast of these future scenarios is useful for informing policy makers and stakeholders across domain verticals, so they can make decisions that are favorable to global food security.

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Hou, Meibaohong. "The Impact of Parents on Adolescent Moral Development." Journal of Education, Humanities and Social Sciences 8 (February 7, 2023): 1177–82. http://dx.doi.org/10.54097/ehss.v8i.4447.

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Development of moral reasoning and the family factors affecting adolescent moral development had been a research focus for a long time. Levels of moral reasoning increase with age, and is correlated to social interactions, and some extent is determined by the ability of internalizing moral values of the surroundings and moral authorities. A good family cohesion (close bond within the family), flexible family adaptability and effective communication styles towards moral issues is undoubtedly correlated to higher levels of adolescent moral reasoning as they open the individuals susceptible to well-accepted social norms and social rules which then shape the moral reasoning. The mother's and father’s roles in child development are not quite the same; the father’s use of transactive dialogue and comparing, and the mother’s being sensitive to the child’s moral views are more predictive of better development. However, the gender difference in parents as a factor was less researched in the past, which needs to be explored in future studies. This paper provides a general review and existing discussions on family influence on adolescence moral development.

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Journal articles on the topic 'Predictive Reasoning'

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