• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.747-752
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• 2020

This study examines cognitive impairment, which is one of the results from social exclusion and leads to logical reasoning disorders. This study also investigate how cognitive errors called abductive inference error occur due to cognitive impairment. Present study was performed with 81 college students. Participants were randomly assigned to the group who has experienced social exclusion or to the group who has not experience the social exclusion. We analyzed how the degree of error of abductive inference differs according to the social exclusion experience. The group who has experienced social exclusion showed a higher level of abductive inference error than the group who has not experience. The abductive condition inference value of the group who has experienced social exclusion was higher in the group with the deduction condition inference value of 90% than in the group with the deduction condition inference value of 10%, and the difference was also significant. This study extended the concepts of cognitive impairments, escape theory, cognitive narrowing which are used to explain addiction behavior to human cognitive bias. Also this study confirmed that social exclusion experience increased cognitive impairment and abductive inference error. Future research directions and implications were discussed and suggested.

• Proceedings of the CALSEC Conference
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• 2004.02a
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• pp.134-149
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• 2004

This paper is aimed at proposing a fuzzy inference mechanism to enhancing the quality of cognitive map-based inference. Its main virtue lies in the two mechanisms： (1) a mechanism for avoiding a synchronization problem which is often observed during inference process with traditional cognitive map, and (2) a mechanism for fuzzifying decision maker's subjective judgment. Our proposed fuzzy inference mechanism (FIM) is basically based on the cognitive map stratification algorithm which can stratify a cognitive map into number of strata and then overcome the synchronization problem successfully. Besides, the proposed FIM depends on fuzzy membership function which is administered by decision maker. With an illustrative B2B negotiation problem, we applied the proposed FIM, deducing theoretical and practical implications. Implementation was conducted by Matlab language.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.289-304
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• 2010

This paper proposes a novel scheme for cooperative spectrum sensing on distributed cognitive radio networks. A fuzzy logic rule - based inference system is proposed to estimate the presence possibility of the licensed user's signal based on the observed energy at each cognitive radio terminal. The estimated results are aggregated to make the final sensing decision at the fusion center. Simulation results show that significant improvement of the spectrum sensing accuracy is achieved by our schemes.

• The Mathematical Education
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• v.34 no.1
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• pp.17-63
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• 1995

The exploration of Mathematics-learningmodel on the basis of Cognitive development The purpose of this paper is to sequenctialize Mathematics-learning contents, and to explore teaching-learning model for mathematics, with on the basis of the theory of cognitive development and the period of condservation formation for children. The Specific topics are as follows: (1) Systemizing those theories of cognitive development which are related to Mathematics - learning for children. (2) Organizing a sequence of Mathematics - learning, on the basis of experimental research for the period of conservation formation for children. (3) Comparing the effects of 4 types of teaching - learning model, on the basis of inference activity and operational learning principle. $\circled1$ Induction-operation(IO) $\circled2$ Induction-explanation(IE) $\circled3$ Deduction-operation(DO) $\circled4$ Deduction-explanation(DE) The results of the subjects are as follows: (1) Cognitive development theory and Mathe-matics education. $\circled1$ Congnitive development can be achieved by constant space and Mathematics know-ledge is obtained by the interaction of experience and reason. $\circled2$ The stages of congnitive development for children form a hierarchical system, its function has a continuity and acts orderly. Therefore we need to apply cognitive development for children to teach mathematics systematically and orderly. (2) Sequence of mathematical concepts. $\circled1$ The learning effect of mathematical concepts occurs when this coincides with the period of conservation formation for children. $\circled2$ Mathematics Curriculum of Elementary Schools in Korea matches with the experimental research about the period of Piaget's conservation formation. (3) Exploration of a teaching-learning model for mathematics. $\circled1$ Mathematics learning is to be centered on learning by experience such as observation, operation, experiment and actual measurement. $\circled2$ Mathematical learning has better results in from inductional inference rather than deductional inference, and from operational inference rather than explanatory inference.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.2
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• pp.104-109
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• 2016

In this paper, a localization method for multiple robots based on Bayesian inference is proposed when multiple robots adopting multi-RAT (Radio Access Technology) communications exist in cognitive radio networks. Multiple robots are separately defined by primary and secondary users as in conventional mobile communications system. In addition, the heterogeneous spectrum environment is considered in this paper. To improve the performance of localization for multiple robots, a realistic multiple primary user distribution is explained by using the probabilistic graphical model, and then we introduce the Gibbs sampler strategy based on Bayesian inference. In addition, the secondary user selection minimizing the value of GDOP (Geometric Dilution of Precision) is also proposed in order to overcome the limitations of localization accuracy with Gibbs sampling. Via the simulation results, we can show that the proposed localization method based on GDOP enhances the accuracy of localization for multiple robots. Furthermore, it can also be verified from the simulation results that localization performance is significantly improved with increasing number of observation samples when the GDOP is considered.

• Korean Journal of Child Studies
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• v.25 no.5
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• pp.179-191
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• 2004

An emotion inference task was used to investigate children's cognitive cuing by age, cue type, and situation-emotion fit. Subjects were 41 of 3-, and 5-year-old children from two different day-care centers in Seoul and Kyonggi province. Each child was individually interviewed with pictorial tasks. 5-year-old children demonstrated more cuing, thinking, and application responses than 3-year-old children. Particularly, they showed more situation and thinking responses in situation-emotion match than in situation-emotion mismatch. 3-year-old children showed more past-oriented responses than 5-year-old children. In the interpersonal cue story, there were more situation, past-oriented and thinking responses than in the interobjective ones. The interpersonal cue story produced more situation responses in situation-emotion match than in situation-emotion mismatch.

• Korean Journal of Cognitive Science
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• v.10 no.4
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• pp.71-83
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• 1999

Conventional symbolic inference systems lack flexibility because they do not well reflect flexible semantic structure of knowledge and use symbolic logic for their basic inference mechanism. For solving this problem. we have recently proposed the 'Connectionist Semantic Network(CSN)' as a model for flexible knowledge representation and inference based on neural networks. The CSN is capable of carrying out both approximate reasoning and commonsense reasoning based on similarity and association. However. we have difficulties in representing general and structured high-level knowledge and variable binding using the connectionist framework of the CSN. In this paper. we propose a hybrid system called SymCSN(Symbolic CSN) that combines a symbolic module for representing general and structured high-level knowledge and a connectionist module for representing and learning low-level semantic structure Simulation results show that the SymCSN is a plausible model for human-like flexible knowledge representation and inference.

• Korean Journal of Cognitive Science
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• v.13 no.1
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• pp.13-21
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• 2002

Fuzzy logic ignores some information in the reasoning process. Neural network is powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule-inference network. And the traditional propagation rule is modified. Experiments are performed to compare search costs by sequential searching and searching by priority. The experimental results show that the searching by priority is more efficient than the sequential searching as the size of the fuzzy inference network becomes larder and an the number of searching increases.

• Asia Marketing Journal
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• v.14 no.2
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• pp.23-37
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• 2012

Individuals often make their judgments on the basis of the ease or difficulty with which information comes to mind (for reviews, see Greifeneder, Bless, and Pham 2010; Schwarz 1998, 2004). Recent research, however, has documented that variables known to determine the degree of cognitive resources invested in information processing such as personal relevance (Grayson and Schwarz 1999; Rothman and Schwarz 1998), accuracy motivation (Aarts and Dijksterhuis 1999), and processing capacity (Menon and Raghubir 2003) can affect the extent to which individuals draw on metacognitive difficulty in making their judgments. The primary aim of this research is thus to investigate whether individuals with substantial cognitive resources or those with lack of cognitive resources are more likely to draw on metacognitive difficulty when making their product evaluations. The findings from two laboratory experiments indicate that individuals who perceive a greater level of fit between their self-regulatory orientation and temporal construal (Experiment 1), and between their self-construal and the type of product benefit appeal (Experiment 2) are more likely than those who perceive the lack of such fit to evaluate a target product less positively after thinking of many rather than a few positive reasons. The findings provide supporting evidence for the two-stage backward inference process involved with the effect of metacognitive difficulty on consumer judgments in that consumer judgments based on metacognitive difficulty may require greater cognitive resources than those based on the content of information generated. Also, the current research documents further empirical evidence for the relationship between self-regulatory orientation-construal level fit and cognitive resources such that perceived regulatory-construal level fit can increase consumer willingness to invest cognitive resources into their judgment tasks. Last, the findings can help marketers differentiate purchase situations where asking consumers to think of many positive benefits from purchase situations where asking consumers to think of a few key benefits is relatively more beneficial.

• Korean Journal of Cognitive Science
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• v.26 no.2
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• pp.209-239
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• 2015

Previous studies on the effect of incidental semantic priming on judgment, have focused mainly on mutually exclusive hypotheses. However, the present study explored whether incidental semantic activation affects diagnostic inference depending on the type of the hypothesis: mutually exclusive hypotheses vs compatible hypotheses. In Experiment 1, in case of mutually exclusive hypotheses, the final hypothesis was selected according to the incidental semantic priming, but there was no difference in the number of generated hypothesis in comparison with the control. However, for compatible hypotheses (i.e., both hypotheses can be true), the semantic priming affected the number of generated hypotheses, but not the selection of the final hypothesis. The same pattern of results was observed even when the cognitive load was increased. In Experiment 2, we found a boundary condition of incidental semantic activation on diagnostic inference. When cues related to each of the hypotheses were presented simultaneously, the incidental semantic effect disappeared. These results suggest that people consider all possible cues when making diagnostic inference in daily life. In light of these findings, further research on hypothesis generation/evaluation should take the type of hypothesis into account.