• Management & Information Systems Review
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• v.33 no.1
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• pp.99-118
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• 2014

This study is a product launch strategy Preannouncing companies associated with temporal distance( in the near future/ distant future) and prior knowledge level(high knowledge/ low knowledge), the memory - based judgments(Global product judgment/ Discrete product judgment) and the purchase intention appears, the difference between the empirical verification of what was discriminatory. The study, first, Preannouncing main effect of temporal distance on judgments remember the difference between the purchase intention and consistent global product judgment is more discrete product judgment were higher awareness, purchase intention is higher. Second, Preannouncing high level of product knowledge in global product judgment showed that compared to discrete product judgment. In addition, low levels of knowledge than a discrete product judgment that global product judgment and purchase intention shown that a high level of consumer knowledge through systematic information processing and the road leads to higher purchase attitude. Third, Preannouncing according to the temporal distance and level of knowledge about the interaction effect results in the near future in terms of the high level of knowledge consumers global product judgment was higher than the discrete product judgment. On the other hand, a low level of knowledge of conditions in the distant future, consumers are more discrete product judgment recognized global product judgment showed that high.

• Annual Conference on Human and Language Technology
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• 1999.10e
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• pp.335-342
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• 1999

The experiments reported here investigated whether seeing a speaker's face (visible speech) affects the perception and memory of Korean speech sounds. In order to exclude the possibility of top-down, knowledge-based influences on perception and memory, the experiments tested people with no knowledge of Korean. The first experiment examined whether visible speech (Auditory and Visual - AV) assists English native speakers (with no knowledge of Korean) in the detection of a syllable within a Korean speech phrase. It was found that a syllable was more likely to be detected within a phrase when the participants could see the speaker's face. The second experiment investigated whether English native speakers' judgments about the duration of a Korean phrase would be affected by visible speech. It was found that in the AV condition participant's estimates of phrase duration were highly correlated with the actual durations whereas those in the AO condition were not. The results are discussed with respect to the benefits of communication with multimodal information and future applications.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.169-178
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• 2010

Agent based simulation is an approach for the analysis of a system's long term behavior where the entities in the system behave independently by their own judgement and memory, but influence each other to cope with given environment. In this paper we developed an agent based simulation model for the analysis of behavioral mechanism of team formation. In the process of team formation members' mutual preference is an important factor although each member can join up with one's own will. Also a team performance can vary by the member's own experience. We implemented the developed model using Netlogo 4.1, and verified the model by simulation. From the simulation results we found that the model successfully performed necessary functions using behavioral rules, judgments, and evolutionary processes by memory. As a further study we will be able to apply the model for analyzing various ecological behavior of team formation.

• Korean Journal of Cognitive Science
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• v.20 no.3
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• pp.357-381
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• 2009

Although it is known that there are personality characteristic variances in the sensitivity to environmental feedback, the trait individual difference has scarcely been explored in the context of recognition memory decision. The present study investigated this issue by examining the relationship between the feedback-based adaptive flexibility of recognition criterion positioning and personality differences in general sensitivity to non-laboratory outcomes. Experiment 1 demonstrated that veridical feedback itself had little effect on the recognition decision criterion whereas Experiment 2 demonstrated that biased feedback manipulations selectively restricted to high confidence errors, induced shifts even in the overall Old/New category criterion. Critically, individual differences in stable personality characteristic linked to reward seeking(Behavioral Activation System-BAS) and anxiety avoidance (Behavioral Inhibition System-BIS) has been shown to predict the sensitivity of subjects to this form of feedback-induced criterion learning. This data further support the idea that incremental reinforcement-based learning mechanism not often considered important during explicit recognition decisions may play a key role in criterion setting.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.