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Gary D Phye - One of the best experts on this subject based on the ideXlab platform.
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Inductive Reasoning a training approach
Review of Educational Research, 2008Co-Authors: Karl Josef Klauer, Gary D PhyeAbstract:Researchers have examined Inductive Reasoning to identify different cognitive processes when participants deal with Inductive problems. This article presents a prescriptive theory of Inductive Reasoning that identifies cognitive processing using a procedural strategy for making comparisons. It is hypothesized that training in the use of the procedural Inductive Reasoning strategy will improve cognitive functioning in terms of (a) increased fluid intelligence performance and (b) better academic learning of classroom subject matter. The review and meta-analysis summarizes the results of 74 training experiments with nearly 3,600 children. Both hypotheses are confirmed. Further, two moderating effects were observed: Training effects on intelligence test performance increased over time, and positive problem-solving transfer to academic learning is greater than transfer to intelligence test performance. The results cannot be explained by placebo or test-coaching effects. It is concluded that the proposed strate...
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inducing Inductive Reasoning does it transfer to fluid intelligence
Contemporary Educational Psychology, 2002Co-Authors: Karl Josef Klauer, Klaus Willmes, Gary D PhyeAbstract:Based on a prescriptive theory of Inductive Reasoning, a training program to foster Inductive Reasoning has been developed. Children from 12 first-grade classes, mean age about 7 years, N = 279, participated in a training experiment. The children of 6 classes were trained to apply a strategy to reason Inductively while the children of the remaining classes continued their regular classroom activities. It was expected that trained children would outperform the untrained children with respect to Raven's Coloured Progressive Matrices but not with respect to a vocabulary test, thus indicating convergent and discriminant or domain-specific training effects. Results confirmed this expectation. Moreover, it was expected that training would improve performance on the Inductive subtests of Cattell's Culture Fair Test 1, but not influence subtests that did not involve Inductive Reasoning. Considerable transfer to both kinds of subtests was found on the immediate transfer task. However, with a delayed posttest 6 months later, the expected differential training effect could be observed. Finally, a LISREL model analysis confirmed the hypothesis that training children to reason Inductively improved fluid but not crystallized intelligence.
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Chapter 14 – Inductive Reasoning and Problem Solving: The Early Grades
Handbook of Academic Learning, 1997Co-Authors: Gary D PhyeAbstract:Publisher Summary This chapter considers where Reasoning “fits” within the context of higher-order thinking skills with references to reviews of curriculum programs that have been developed to promote thinking, Reasoning, and problem-solving skills in the United States and Europe. It discusses cognitive intervention and academic achievement, where teaching may involve individual students, small groups of students, or entire classrooms. It also introduces a cognitive training program for children from ages 6 to 10 that teaches Inductive Reasoning and problem-solving skills. Attitude is defined as “a state of mind or feeling.” Most of a teacher's day is spent providing students with domain-specific information. Formal Reasoning is typically thought of as having two distinct forms. One is Inductive Reasoning; the other is deductive. Cognitive training program, a highly practical training program helps children in the primary or elementary grades (ages 6 to 10) achieve greater competence in problem solving and Inductive Reasoning. Generalization (GE) is the process whereby the common attribute that can be used to group a number of objects. Identification of the common attribute between objects provides the basis for forming a common group, class, category, or concept.
Peipeng Liang - One of the best experts on this subject based on the ideXlab platform.
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activity in the fronto parietal network indicates numerical Inductive Reasoning beyond calculation an fmri study combined with a cognitive model
Scientific Reports, 2016Co-Authors: Peipeng Liang, Niels Taatgen, Jelmer P Borst, Kuncheng LiAbstract:Numerical Inductive Reasoning refers to the process of identifying and extrapolating the rule involved in numeric materials. It is associated with calculation, and shares the common activation of the fronto-parietal regions with calculation, which suggests that numerical Inductive Reasoning may correspond to a general calculation process. However, compared with calculation, rule identification is critical and unique to Reasoning. Previous studies have established the central role of the fronto-parietal network for relational integration during rule identification in numerical Inductive Reasoning. The current question of interest is whether numerical Inductive Reasoning exclusively corresponds to calculation or operates beyond calculation, and whether it is possible to distinguish between them based on the activity pattern in the fronto-parietal network. To directly address this issue, three types of problems were created: numerical Inductive Reasoning, calculation, and perceptual judgment. Our results showed that the fronto-parietal network was more active in numerical Inductive Reasoning which requires more exchanges between intermediate representations and long-term declarative knowledge during rule identification. These results survived even after controlling for the covariates of response time and error rate. A computational cognitive model was developed using the cognitive architecture ACT-R to account for the behavioral results and brain activity in the fronto-parietal network.
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Age-related increase in brain activity during task-related and -negative networks and numerical Inductive Reasoning.
International Journal of Clinical and Experimental Pathology, 2014Co-Authors: Peipeng Liang, Zhigang Qi, Kuncheng LiAbstract:Objective: Recent neuroimaging studies have shown that elderly adults exhibit increased and decreased activation on various cognitive tasks, yet little is known about age-related changes in Inductive Reasoning. Methods: To investigate the neural basis for the aging effect on Inductive Reasoning, 15 young and 15 elderly subjects performed numerical Inductive Reasoning while in a magnetic resonance (MR) scanner. Results: Functional magnetic resonance imaging (fMRI) analysis revealed that numerical Inductive Reasoning, relative to rest, yielded multiple frontal, temporal, parietal, and some subcortical area activations for both age groups. In addition, the younger participants showed significant regions of task-induced deactivation, while no deactivation occurred in the elderly adults. Direct group comparisons showed that elderly adults exhibited greater activity in regions of task-related activation and areas showing task-induced deactivation (TID) in the younger group. Conclusions: Our findings suggest an age-related deficiency in neural function and resource allocation during Inductive Reasoning.
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common and dissociable neural correlates associated with component processes of Inductive Reasoning
NeuroImage, 2011Co-Authors: Peipeng Liang, Ning Zhong, Yanhui Yang, Jie Lu, Kuncheng LiAbstract:Abstract The ability to draw numerical Inductive Reasoning requires two key cognitive processes, identification and extrapolation. This study aimed to identify the neural correlates of both component processes of numerical Inductive Reasoning using event-related fMRI. Three kinds of tasks: rule induction (RI), rule induction and application (RIA), and perceptual judgment (Jud) were solved by twenty right-handed adults. Our results found that the left superior parietal lobule (SPL) extending into the precuneus and left dorsolateral prefrontal cortex (DLPFC) were commonly recruited in the two components. It was also observed that the fronto-parietal network was more specific to identification, whereas the striatal–thalamic network was more specific to extrapolation. The findings suggest that numerical Inductive Reasoning is mediated by the coordination of multiple brain areas including the prefrontal, parietal, and subcortical regions, of which some are more specific to demands on only one of these two component processes, whereas others are sensitive to both.
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Brain Informatics - Brain activation and deactivation in human Inductive Reasoning: an fMRI study
Brain Informatics, 2010Co-Authors: Peipeng Liang, Ning Zhong, Shengfu Lu, Yanhui Yang, Kuncheng LiAbstract:In order to study the cognitive neural mechanism of human Inductive Reasoning, both the positive and negative activation should be combined. However, most studies only focus on the positive activation and the negative activation of Inductive Reasoning has not been reported. The present study will examine the two aspects simultaneously. Two experimental tasks were designed according to the magnitude of shared attributes: sharing two common attributes (2T) and sharing one common attribute (1T), and rest acted as control task. 2T and 1T tasks are both Inductive Reasoning tasks. 2T task contains the component of perceptual features. integration, while 1T does not. Fourteen college students participated in this study. It was showed that, as compared to rest condition, induction activated a distributed regions including prefrontal cortex (BA 6, 9, 11, 46, 47), caudate, putamen, thalamus, etc., and these regions were related to task difficulty. This may reflect the important role the prefrontal-striatal-thalamus loop in Inductive Reasoning. The fMRI result also showed the significant negative activation of the right superior temporal gyrus (BA 22), the left angular gyrus (BA 39), bilateral middle frontal gyrus (BA 8, 9, 10), posterior cingulated cortex (BA 31) in Inductive Reasoning as compared to rest condition. These results were consistent with previous studies of default mode network. Future work were required to examine if there exist induction specific positive activation network and negative activation network, and what the relationship between the two networks.
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Recruitment of the pre-motor area in human Inductive Reasoning: An fMRI study
Cognitive Systems Research, 2010Co-Authors: Shengfu Lu, Peipeng Liang, Yanhui Yang, Kuncheng LiAbstract:Recent studies indicated that the pre-motor area may be recruited in human higher level cognitive functions, including Inductive Reasoning. In the present study, a typical task of Inductive Reasoning, function-finding, was explored using functional MRI. fMRI results showed the significant activation of bilateral pre-motor cortex (BA 6), and its left lateralization. Taking together the previous studies and the present experimental design, we concluded that the left pre-motor cortex in the present study may associate with implicit relation synthesis, while the right pre-motor cortex may reflect spatial information processing involved in arithmetic rules.
Evan Heit - One of the best experts on this subject based on the ideXlab platform.
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Inductive Reasoning: Experimental, Developmental, and Computational Approaches - Inductive Reasoning : experimental, developmental, and computational approaches
2020Co-Authors: Aidan Feeney, Evan HeitAbstract:Preface Aidan Feeney and Evan Heit 1. What is induction and why study it? Evan Heit 2. The development of Inductive Reasoning Brett K. Hayes 3. Interpreting asymmetries of projection in children's Inductive Reasoning Douglas Medin and Sandra Waxman 4. Property generalization as causal Reasoning Bob Rehder 5. Availability in category-based induction Patrick Shafto, John Coley and Anna Vitkin 6. From similarity to chance Sergey Blok, Daniel Osherson and Douglas Medin 7. Theory-based Bayesian models of Inductive Reasoning Joshua Tenenbaum, Charles Spence and Patrick Shafto 8. Use of single or multiple categories in category-based induction Gregory Murphy and Brian Ross 9. Abductive inference: From philosophical analysis to neutral mechanisms Paul Thagard 10. Mathematical induction and induction in mathematics Lance Rips and Jennifer Asmuth 11. Induction, deduction, and argument strength in human Reasoning and argumentation Mike Oaksford and Ulrike Hahn 12. Individual differences, dual processes, and induction Aidan Feeney 13. Taxonomising induction Steve Sloman.
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Inductive Reasoning 2.0.
Wiley Interdisciplinary Reviews: Cognitive Science, 2017Co-Authors: Brett K. Hayes, Evan HeitAbstract:Inductive Reasoning entails using existing knowledge to make predictions about novel cases. The first part of this review summarizes key Inductive phenomena and critically evaluates theories of induction. We highlight recent theoretical advances, with a special emphasis on the structured statistical approach, the importance of sampling assumptions in Bayesian models, and connectionist modeling. A number of new research directions in this field are identified including comparisons of Inductive and deductive Reasoning, the identification of common core processes in induction and memory tasks and induction involving category uncertainty. The implications of induction research for areas as diverse as complex decision-making and fear generalization are discussed. This article is categorized under: Psychology > Reasoning and Decision Making Psychology > Learning.
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Inductive Reasoning.
Wiley interdisciplinary reviews. Cognitive science, 2010Co-Authors: Brett K. Hayes, Evan Heit, Haruka SwendsenAbstract:Inductive Reasoning entails using existing knowledge or observations to make predictions about novel cases. We review recent findings in research on category-based induction as well as theoretical models of these results, including similarity-based models, connectionist networks, an account based on relevance theory, Bayesian models, and other mathematical models. A number of touchstone empirical phenomena that involve taxonomic similarity are described. We also examine phenomena involving more complex background knowledge about premises and conclusions of Inductive arguments and the properties referenced. Earlier models are shown to give a good account of similarity-based phenomena but not knowledge-based phenomena. Recent models that aim to account for both similarity-based and knowledge-based phenomena are reviewed and evaluated. Among the most important new directions in induction research are a focus on induction with uncertain premise categories, the modeling of the relationship between Inductive and deductive Reasoning, and examination of the neural substrates of induction. A common theme in both the well-established and emerging lines of induction research is the need to develop well-articulated and empirically testable formal models of induction. Copyright © 2010 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website.
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Inductive Reasoning experimental developmental and computational approaches
2007Co-Authors: Aidan Feeney, Evan HeitAbstract:Preface Aidan Feeney and Evan Heit 1. What is induction and why study it? Evan Heit 2. The development of Inductive Reasoning Brett K. Hayes 3. Interpreting asymmetries of projection in children's Inductive Reasoning Douglas Medin and Sandra Waxman 4. Property generalization as causal Reasoning Bob Rehder 5. Availability in category-based induction Patrick Shafto, John Coley and Anna Vitkin 6. From similarity to chance Sergey Blok, Daniel Osherson and Douglas Medin 7. Theory-based Bayesian models of Inductive Reasoning Joshua Tenenbaum, Charles Spence and Patrick Shafto 8. Use of single or multiple categories in category-based induction Gregory Murphy and Brian Ross 9. Abductive inference: From philosophical analysis to neutral mechanisms Paul Thagard 10. Mathematical induction and induction in mathematics Lance Rips and Jennifer Asmuth 11. Induction, deduction, and argument strength in human Reasoning and argumentation Mike Oaksford and Ulrike Hahn 12. Individual differences, dual processes, and induction Aidan Feeney 13. Taxonomising induction Steve Sloman.
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Properties of Inductive Reasoning
Psychonomic Bulletin & Review, 2000Co-Authors: Evan HeitAbstract:This paper reviews the main psychological phenomena of Inductive Reasoning, covering 25 years of experimental and model-based research, in particular addressing four questions. First, what makes a case or event generalizable to other cases? Second, what makes a set of cases generalizable? Third, what makes a property or predicate projectable? Fourth, how do psychological models of induction address these results? The key results in Inductive Reasoning are outlined, and several recent models, including a new Bayesian account, are evaluated with respect to these results. In addition, future directions for experimental and model-based work are proposed.
Frank C Keil - One of the best experts on this subject based on the ideXlab platform.
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CogSci - Argument scope in Inductive Reasoning: Evidence for an abductive account of induction
Cognitive Science, 2020Co-Authors: Samuel G B Johnson, Thomas Merchant, Frank C KeilAbstract:Our ability to induce the general from the specific is a hallmark of human cognition. Inductive Reasoning tasks ask participants to determine how strongly a set of premises (e.g., Collies have sesamoid bones) imply a conclusion (Dogs have sesamoid bones). Here, we present evidence for an abductive theory of Inductive Reasoning, according to which Inductive strength is determined by treating the conclusion as an explanation of the premises, and evaluating the quality of that explanation. Two Inductive Reasoning studies found two signatures of explanatory Reasoning, previously observed in other studies: (1) an evidential asymmetry between positive and negative evidence, with observations casting doubt on a hypothesis given more weight than observations in support; and (2) a latent scope effect, with ignorance about potential evidence counting against a hypothesis. These results suggest that Inductive Reasoning relies on the same hypothesis evaluation mechanisms as explanatory Reasoning.
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argument scope in Inductive Reasoning evidence for an abductive account of induction
Cognitive Science, 2015Co-Authors: Samuel G B Johnson, Thomas Merchant, Frank C KeilAbstract:Our ability to induce the general from the specific is a hallmark of human cognition. Inductive Reasoning tasks ask participants to determine how strongly a set of premises (e.g., Collies have sesamoid bones) imply a conclusion (Dogs have sesamoid bones). Here, we present evidence for an abductive theory of Inductive Reasoning, according to which Inductive strength is determined by treating the conclusion as an explanation of the premises, and evaluating the quality of that explanation. Two Inductive Reasoning studies found two signatures of explanatory Reasoning, previously observed in other studies: (1) an evidential asymmetry between positive and negative evidence, with observations casting doubt on a hypothesis given more weight than observations in support; and (2) a latent scope effect, with ignorance about potential evidence counting against a hypothesis. These results suggest that Inductive Reasoning relies on the same hypothesis evaluation mechanisms as explanatory Reasoning.
Kuncheng Li - One of the best experts on this subject based on the ideXlab platform.
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activity in the fronto parietal network indicates numerical Inductive Reasoning beyond calculation an fmri study combined with a cognitive model
Scientific Reports, 2016Co-Authors: Peipeng Liang, Niels Taatgen, Jelmer P Borst, Kuncheng LiAbstract:Numerical Inductive Reasoning refers to the process of identifying and extrapolating the rule involved in numeric materials. It is associated with calculation, and shares the common activation of the fronto-parietal regions with calculation, which suggests that numerical Inductive Reasoning may correspond to a general calculation process. However, compared with calculation, rule identification is critical and unique to Reasoning. Previous studies have established the central role of the fronto-parietal network for relational integration during rule identification in numerical Inductive Reasoning. The current question of interest is whether numerical Inductive Reasoning exclusively corresponds to calculation or operates beyond calculation, and whether it is possible to distinguish between them based on the activity pattern in the fronto-parietal network. To directly address this issue, three types of problems were created: numerical Inductive Reasoning, calculation, and perceptual judgment. Our results showed that the fronto-parietal network was more active in numerical Inductive Reasoning which requires more exchanges between intermediate representations and long-term declarative knowledge during rule identification. These results survived even after controlling for the covariates of response time and error rate. A computational cognitive model was developed using the cognitive architecture ACT-R to account for the behavioral results and brain activity in the fronto-parietal network.
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Age-related increase in brain activity during task-related and -negative networks and numerical Inductive Reasoning.
International Journal of Clinical and Experimental Pathology, 2014Co-Authors: Peipeng Liang, Zhigang Qi, Kuncheng LiAbstract:Objective: Recent neuroimaging studies have shown that elderly adults exhibit increased and decreased activation on various cognitive tasks, yet little is known about age-related changes in Inductive Reasoning. Methods: To investigate the neural basis for the aging effect on Inductive Reasoning, 15 young and 15 elderly subjects performed numerical Inductive Reasoning while in a magnetic resonance (MR) scanner. Results: Functional magnetic resonance imaging (fMRI) analysis revealed that numerical Inductive Reasoning, relative to rest, yielded multiple frontal, temporal, parietal, and some subcortical area activations for both age groups. In addition, the younger participants showed significant regions of task-induced deactivation, while no deactivation occurred in the elderly adults. Direct group comparisons showed that elderly adults exhibited greater activity in regions of task-related activation and areas showing task-induced deactivation (TID) in the younger group. Conclusions: Our findings suggest an age-related deficiency in neural function and resource allocation during Inductive Reasoning.
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common and dissociable neural correlates associated with component processes of Inductive Reasoning
NeuroImage, 2011Co-Authors: Peipeng Liang, Ning Zhong, Yanhui Yang, Jie Lu, Kuncheng LiAbstract:Abstract The ability to draw numerical Inductive Reasoning requires two key cognitive processes, identification and extrapolation. This study aimed to identify the neural correlates of both component processes of numerical Inductive Reasoning using event-related fMRI. Three kinds of tasks: rule induction (RI), rule induction and application (RIA), and perceptual judgment (Jud) were solved by twenty right-handed adults. Our results found that the left superior parietal lobule (SPL) extending into the precuneus and left dorsolateral prefrontal cortex (DLPFC) were commonly recruited in the two components. It was also observed that the fronto-parietal network was more specific to identification, whereas the striatal–thalamic network was more specific to extrapolation. The findings suggest that numerical Inductive Reasoning is mediated by the coordination of multiple brain areas including the prefrontal, parietal, and subcortical regions, of which some are more specific to demands on only one of these two component processes, whereas others are sensitive to both.
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Brain Informatics - Brain activation and deactivation in human Inductive Reasoning: an fMRI study
Brain Informatics, 2010Co-Authors: Peipeng Liang, Ning Zhong, Shengfu Lu, Yanhui Yang, Kuncheng LiAbstract:In order to study the cognitive neural mechanism of human Inductive Reasoning, both the positive and negative activation should be combined. However, most studies only focus on the positive activation and the negative activation of Inductive Reasoning has not been reported. The present study will examine the two aspects simultaneously. Two experimental tasks were designed according to the magnitude of shared attributes: sharing two common attributes (2T) and sharing one common attribute (1T), and rest acted as control task. 2T and 1T tasks are both Inductive Reasoning tasks. 2T task contains the component of perceptual features. integration, while 1T does not. Fourteen college students participated in this study. It was showed that, as compared to rest condition, induction activated a distributed regions including prefrontal cortex (BA 6, 9, 11, 46, 47), caudate, putamen, thalamus, etc., and these regions were related to task difficulty. This may reflect the important role the prefrontal-striatal-thalamus loop in Inductive Reasoning. The fMRI result also showed the significant negative activation of the right superior temporal gyrus (BA 22), the left angular gyrus (BA 39), bilateral middle frontal gyrus (BA 8, 9, 10), posterior cingulated cortex (BA 31) in Inductive Reasoning as compared to rest condition. These results were consistent with previous studies of default mode network. Future work were required to examine if there exist induction specific positive activation network and negative activation network, and what the relationship between the two networks.
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Recruitment of the pre-motor area in human Inductive Reasoning: An fMRI study
Cognitive Systems Research, 2010Co-Authors: Shengfu Lu, Peipeng Liang, Yanhui Yang, Kuncheng LiAbstract:Recent studies indicated that the pre-motor area may be recruited in human higher level cognitive functions, including Inductive Reasoning. In the present study, a typical task of Inductive Reasoning, function-finding, was explored using functional MRI. fMRI results showed the significant activation of bilateral pre-motor cortex (BA 6), and its left lateralization. Taking together the previous studies and the present experimental design, we concluded that the left pre-motor cortex in the present study may associate with implicit relation synthesis, while the right pre-motor cortex may reflect spatial information processing involved in arithmetic rules.