The Experts below are selected from a list of 7812 Experts worldwide ranked by ideXlab platform
Oshin Vartanian - One of the best experts on this subject based on the ideXlab platform.
-
the prospects of working memory training for improving Deductive Reasoning
Frontiers in Human Neuroscience, 2015Co-Authors: Erin L Beatty, Oshin VartanianAbstract:Cognitive (brain) training has been a major focus of study in recent years. In applied settings, the excitement regarding this research programme emanates from its prospects for far transfer—defined as observing performance benefits in outcome measures that are contextually, structurally or superficially dissimilar to the trained task (Perkins and Salomon, 1994). By and large, researchers have focused on training working memory (WM). This is not surprising, given the ubiquity of WM requirements for thinking (Baddeley, 2003). Currently, much evidence suggests that adaptive training on WM tasks can increase WM skills. In contrast, consistent evidence regarding far transfer is lacking (see Melby-Lervag and Hulme, 2013), although there is evidence to suggest that when the training modality is visuospatial, the likelihood of transfer and the long-term stability of its benefits are enhanced (Melby-Lervag and Hulme, 2013; Stephenson and Halpern, 2013). Theoretically, there is reason to suspect that interventions that increase WM skills and/or capacity could improve Deductive Reasoning. This prediction stems from the observation that individual differences in WM capacity predict Deductive Reasoning performance on conflict problems where the believability of conclusions conflicts with logical validity (e.g., Newstead et al., 2004). Conflict problems require WM resources because their correct solution depends on the suppression of the heuristic system (System I) in favor of responding in accordance with the analytic system (System II). Evidence for this interpretation was provided by De Neys (2006), who presented participants with conflict and non-conflict syllogisms while also burdening their executive resources with a secondary task. Specifically, the between-subjects manipulation of WM load consisted of presenting a 3 × 3 matrix prior to each syllogism, wherein the matrix was filled with a complex four-dot pattern (high load) or with three dots on a horizontal line (low load)1. After making a validity judgment, participants reproduced the matrix pattern. This experimental design required them to maintain the matrix pattern in WM while Reasoning. Whereas the high load condition impaired performance on conflict problems, there was no effect of load on non-conflict problems. This demonstrates that overcoming belief-logic conflict is limited by WM capacity. WM training could also lead to improvement in Deductive Reasoning via its effect on fluid intelligence—typically measured using matrix Reasoning tasks. Specifically, much evidence suggests that general cognitive ability and Deductive Reasoning are positively correlated (Stanovich and West, 2000). In addition, a recent meta-analysis demonstrated that training specifically on the n-back family of WM tasks leads to a small but positive effect on fluid intelligence (Au et al., 2014). Therefore, theoretically, increases in fluid intelligence could mediate the link between n-back training and Deductive Reasoning, offering an indirect route for improving the latter (Figure (Figure11). Figure 1 Two possible routes for improving Deductive Reasoning by working memory training. The solid arrow depicts a direct effect. The dashed arrows depict an indirect effect. Recently, Aries et al. (2014) investigated the combined effect of Reasoning strategy and WM training on school performance. The participants for Experiment 1 were enrolled in lower-level Higher Secondary Education history classes. During the 6-week intervention period, participants in the control condition were taught using a “conservative” method that involved the introduction of new subjects in new paragraphs, and the answering of Reasoning questions from the textbook. In contrast, for participants in the experimental condition the same material was embedded within two WM training tasks: n-back and the Odd One Out. This approach ensured that training was contextualized within the subject matter of the history class. For example, on each trial of the Odd One Out four historical words or pictures were presented successively on the screen, three of which were related (e.g., were drawn from agrarian civilizations) whereas the fourth was not (i.e., was a depiction of hunter-gatherer civilization). The participant had to maintain all four stimuli in WM to select the odd one out. In the n-back task, nouns (e.g., farming) and pictures (e.g., hieroglyphics) drawn from the content of the history class were used as stimuli. In addition, the experimenters trained Reasoning strategies using a modification of the IMPROVE method (see Mevarech and Kramarski, 2003). This intervention is designed to teach the structure of Reasoning, and works by testing understanding of the problems, highlighting similarities between problems, applying strategies for solving problems, and prompting reflection on the Reasoning process. Compared to the control condition, students in the experimental condition exhibited significant gains in performance on Reasoning questions in official school tests that necessitate inference making—a difference that remained significant 16 weeks after the termination of training. Subsequently, participants in Experiment 2 who were enrolled in higher-level Higher Secondary Education history classes received either WM or Reasoning strategy training. On its own, Reasoning strategy but not WM training improved school test performance. The results of Aries et al. (2014) suggest that for students of relatively lower ability, the combination of WM and Reasoning strategy training can be a successful recipe for improving Reasoning. This is likely because whereas the former enhances WM skills, the latter facilitates the acquisition of the cognitive tools for logic. For students of higher ability there might be less room for improving WM (i.e., a ceiling effect), such that learning the structure of Reasoning becomes a relatively more important factor for improving performance. Although the results of the two experiments are not directly comparable because of differences in the composition of the samples and intervention strategies, they do suggest that differences in baseline ability must be taken into account while assessing transfer effects (see Jaeggi et al., 2014). In conclusion, it appears useful to pursue the possibility that WM training could benefit Deductive Reasoning directly by increasing WM skills, or indirectly by increasing fluid intelligence. Critically, Aries et al.'s successful intervention consisted of embedding WM training with domain-relevant material. It has yet to be demonstrated whether a domain-general intervention to train WM will exhibit a similar transfer profile in the context of Deductive Reasoning. In addition, the extent to which successful transfer to Deductive Reasoning will require supplementing WM training with strategy training remains an open question.
Yukari Shirota - One of the best experts on this subject based on the ideXlab platform.
-
Visualization of Deductive Reasoning for Joint Distribution Probability in Simple Topic Model
2017Co-Authors: Yukari Shirota, Takako Hashimoto, Basabi ChakrabortyAbstract:IwarBayesian inference is widely used in various application field such as data engineering. When we derive the posterior, we have to combine many theorems or rules such as the Bayes’ theorem. The derivation of the posterior expression is quite difficult, even if we use the probabilistic graphical model. So we propose a Deductive Reasoning based approach for that. The concrete Deductive diagram for a simple topic model is presented in the paper. The Deductive Reasoning diagram clarifies which theorems and how they are used in the deduction. In addition, the three conditional independence pattern rules which are used frequently in the posterior derivation are explained visually.
-
Deductive Reasoning for Joint Distribution Probability in Simple Topic Model
2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), 2016Co-Authors: Yukari Shirota, Takako Hashimoto, Basabi ChakrabortyAbstract:Bayesian inference is widely used in various application field such as data engineering. When we derive the posterior, we have to combine many theorems or rules such as the Bayes' theorem. The derivation of the posterior expression is quite difficult, even if we use the probabilistic graphical model. So we propose a Deductive Reasoning based approach for that. The concrete Deductive diagram for a simple topic model is presented in the paper. The Deductive Reasoning diagram clarifies which theorems and how they are used in the deduction. In addition, the three conditional independence pattern rules which are used frequently in the posterior derivation are explained visually.
-
IIAI-AAI - Deductive Reasoning for Joint Distribution Probability in Simple Topic Model
2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), 2016Co-Authors: Yukari Shirota, Takako Hashimoto, Basabi ChakrabortyAbstract:Bayesian inference is widely used in various application field such as data engineering. When we derive the posterior, we have to combine many theorems or rules such as the Bayes' theorem. The derivation of the posterior expression is quite difficult, even if we use the probabilistic graphical model. So we propose a Deductive Reasoning based approach for that. The concrete Deductive diagram for a simple topic model is presented in the paper. The Deductive Reasoning diagram clarifies which theorems and how they are used in the deduction. In addition, the three conditional independence pattern rules which are used frequently in the posterior derivation are explained visually.
-
knowledge visualization of the Deductive Reasoning for word problems in mathematical economics
Databases in Networked Information Systems, 2013Co-Authors: Yukari Shirota, Takako Hashimoto, Pamela StanworthAbstract:In solving word problems in mathematical economics, such as national income determination problems and various financial problems, two different knowledge bases are required: a database of math formulas and a database of economics theories. For this we have developed the knowledge bases, with which we offer our students an effective education support system for economics word math problems. Solving a word math problem is nothing more or less than conducting a process of Deductive Reasoning to find the unknown of the problem. To construct the Deductive Reasoning process is to collect missing pieces of information from the knowledge bases, to bridge between the given data and the unknown of the problem. To promote students’ use of the formula and theory knowledge bases in our educational support system, we have visualized the Reasoning processes as a solution plan graph and collected these charts to make a content center for teaching materials. The paper shows that a solution plan graph can play a role of a good user interface for accessing the knowledge bases. We illustrate a solution plan graph and its annotation technology for constructing the solution plan graph.
-
DNIS - Knowledge Visualization of the Deductive Reasoning for Word Problems in Mathematical Economics
Databases in Networked Information Systems, 2013Co-Authors: Yukari Shirota, Takako Hashimoto, Pamela StanworthAbstract:In solving word problems in mathematical economics, such as national income determination problems and various financial problems, two different knowledge bases are required: a database of math formulas and a database of economics theories. For this we have developed the knowledge bases, with which we offer our students an effective education support system for economics word math problems. Solving a word math problem is nothing more or less than conducting a process of Deductive Reasoning to find the unknown of the problem. To construct the Deductive Reasoning process is to collect missing pieces of information from the knowledge bases, to bridge between the given data and the unknown of the problem. To promote students’ use of the formula and theory knowledge bases in our educational support system, we have visualized the Reasoning processes as a solution plan graph and collected these charts to make a content center for teaching materials. The paper shows that a solution plan graph can play a role of a good user interface for accessing the knowledge bases. We illustrate a solution plan graph and its annotation technology for constructing the solution plan graph.
Nicola Canessa - One of the best experts on this subject based on the ideXlab platform.
-
the effect of social content on Deductive Reasoning an fmri study
Human Brain Mapping, 2005Co-Authors: Nicola Canessa, Alessandra Gorini, Stefano F Cappa, Massimo Piattellipalmarini, Massimo Danna, Ferruccio Fazio, Daniela PeraniAbstract:Psychological studies of Deductive Reasoning have shown that subjects' performance is affected significantly by the content of the presented stimuli. Specifically, subjects find it easier to reason about contexts and situations with a social content. In the present study, the effect of content on brain activation was investigated with functional magnetic resonance imaging (fMRI) while subjects were solving two versions of the Wason selection task, which previous behavioral studies have shown to elicit a significant content effect. One version described an arbitrary relation between two actions (Descriptive: "If someone does …, then he does …"), whereas the other described an exchange of goods between two persons (Social-Exchange: "If you give me …, then I give you …"). Random-effect statistical analyses showed that compared to baseline, both tasks activated frontal medial cortex and left dorsolateral frontal and parietal regions, confirming the major role of the left hemisphere in Deductive Reasoning. In addition, although the two Reasoning conditions were identical in logical form, the social-exchange task was also associated with right frontal and parietal activations, mirroring the left-sided activations common to both Reasoning tasks. These results suggest that the recruitment of the right hemisphere is dependent on the content of the stimuli presented. Hum Brain Mapp 26:30 - 43, 2005. © 2005 Wiley-Liss, Inc.
Joy Hirsch - One of the best experts on this subject based on the ideXlab platform.
-
the dynamics of Deductive Reasoning an fmri investigation
Neuropsychologia, 2009Co-Authors: Diana Rodriguezmoreno, Joy HirschAbstract:Although the basis for Deductive Reasoning has been a traditional focus of philosophical discussion, the neural correlates and mechanisms that underlie Deductive Reasoning have only recently become the focus of scientific investigation. In syllogistic Deductive Reasoning information presented in two related sequential premises leads to a subsequent conclusion. While previous imaging studies have identified frontal, parietal, temporal, and occipital complexes that are activated during these Reasoning events, there are substantive differences among the findings with respect to the specific regions engaged in Reasoning and the contribution of language areas. Further, little is known about the various stages of information processing during Reasoning. Using event-related fMRI and an auditory and visual conjunction technique, we identified a long-range supramodal network active during Reasoning processes including areas in the left frontal and parietal regions as well as the bilateral caudate nucleus. Time courses of activation for each of these regions suggest that Reasoning processes emerge during the presentation of the second premise, and remain active until the validation of the conclusion. Thus, areas within the frontal and parietal regions are differentially engaged at different time points in the Reasoning process consistent with coordinated intra-network interactions.
Takako Hashimoto - One of the best experts on this subject based on the ideXlab platform.
-
Visualization of Deductive Reasoning for Joint Distribution Probability in Simple Topic Model
2017Co-Authors: Yukari Shirota, Takako Hashimoto, Basabi ChakrabortyAbstract:IwarBayesian inference is widely used in various application field such as data engineering. When we derive the posterior, we have to combine many theorems or rules such as the Bayes’ theorem. The derivation of the posterior expression is quite difficult, even if we use the probabilistic graphical model. So we propose a Deductive Reasoning based approach for that. The concrete Deductive diagram for a simple topic model is presented in the paper. The Deductive Reasoning diagram clarifies which theorems and how they are used in the deduction. In addition, the three conditional independence pattern rules which are used frequently in the posterior derivation are explained visually.
-
Deductive Reasoning for Joint Distribution Probability in Simple Topic Model
2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), 2016Co-Authors: Yukari Shirota, Takako Hashimoto, Basabi ChakrabortyAbstract:Bayesian inference is widely used in various application field such as data engineering. When we derive the posterior, we have to combine many theorems or rules such as the Bayes' theorem. The derivation of the posterior expression is quite difficult, even if we use the probabilistic graphical model. So we propose a Deductive Reasoning based approach for that. The concrete Deductive diagram for a simple topic model is presented in the paper. The Deductive Reasoning diagram clarifies which theorems and how they are used in the deduction. In addition, the three conditional independence pattern rules which are used frequently in the posterior derivation are explained visually.
-
IIAI-AAI - Deductive Reasoning for Joint Distribution Probability in Simple Topic Model
2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), 2016Co-Authors: Yukari Shirota, Takako Hashimoto, Basabi ChakrabortyAbstract:Bayesian inference is widely used in various application field such as data engineering. When we derive the posterior, we have to combine many theorems or rules such as the Bayes' theorem. The derivation of the posterior expression is quite difficult, even if we use the probabilistic graphical model. So we propose a Deductive Reasoning based approach for that. The concrete Deductive diagram for a simple topic model is presented in the paper. The Deductive Reasoning diagram clarifies which theorems and how they are used in the deduction. In addition, the three conditional independence pattern rules which are used frequently in the posterior derivation are explained visually.
-
knowledge visualization of the Deductive Reasoning for word problems in mathematical economics
Databases in Networked Information Systems, 2013Co-Authors: Yukari Shirota, Takako Hashimoto, Pamela StanworthAbstract:In solving word problems in mathematical economics, such as national income determination problems and various financial problems, two different knowledge bases are required: a database of math formulas and a database of economics theories. For this we have developed the knowledge bases, with which we offer our students an effective education support system for economics word math problems. Solving a word math problem is nothing more or less than conducting a process of Deductive Reasoning to find the unknown of the problem. To construct the Deductive Reasoning process is to collect missing pieces of information from the knowledge bases, to bridge between the given data and the unknown of the problem. To promote students’ use of the formula and theory knowledge bases in our educational support system, we have visualized the Reasoning processes as a solution plan graph and collected these charts to make a content center for teaching materials. The paper shows that a solution plan graph can play a role of a good user interface for accessing the knowledge bases. We illustrate a solution plan graph and its annotation technology for constructing the solution plan graph.
-
DNIS - Knowledge Visualization of the Deductive Reasoning for Word Problems in Mathematical Economics
Databases in Networked Information Systems, 2013Co-Authors: Yukari Shirota, Takako Hashimoto, Pamela StanworthAbstract:In solving word problems in mathematical economics, such as national income determination problems and various financial problems, two different knowledge bases are required: a database of math formulas and a database of economics theories. For this we have developed the knowledge bases, with which we offer our students an effective education support system for economics word math problems. Solving a word math problem is nothing more or less than conducting a process of Deductive Reasoning to find the unknown of the problem. To construct the Deductive Reasoning process is to collect missing pieces of information from the knowledge bases, to bridge between the given data and the unknown of the problem. To promote students’ use of the formula and theory knowledge bases in our educational support system, we have visualized the Reasoning processes as a solution plan graph and collected these charts to make a content center for teaching materials. The paper shows that a solution plan graph can play a role of a good user interface for accessing the knowledge bases. We illustrate a solution plan graph and its annotation technology for constructing the solution plan graph.
