• Korean Journal of Child Studies
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• v.34 no.6
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• pp.1-11
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• 2013

The purpose of this study was to investigate the process of counterfactual reasoning which children undergo, based on mental model theory and dual process theory. The subjects were 120 four-year-olds and 120 five-year-olds from Ulsan. Counterfactual reasoning task conditions were created, including task type and content, which were type 1-specific, type 1-general, type 2-specific, type 2-general. There were two stories used for each task condition. Children's counterfactual reasoning score range was 0 to 8. Data were analyzed using SPSS by mean, standard deviation, one sample t-test, repeated measures of Anova. The results of this study were as follows. First, children's counterfactual reasoning was above chance level regardless of the task condition. Second, children's counterfactual reasoning was lowest when type 1-specific or type 2-specific tasks were given, slightly higher when type1-general tasks were given, and the highest when type 2-general tasks were given. There was no significant difference between 4-year-old and 5-year-old children's counterfactual reasoning.

• Human Ecology Research
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• v.58 no.2
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• pp.131-148
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• 2020

This study examined the dual mediating effects of preschoolers' executive function and counterfactual thinking in the relationship between maternal reflective functioning and preschoolers' higher-order thinking. Participants included 293 preschoolers and their mothers in Daegu city and Gyeongsang province. The mothers completed questionnaires regarding their reflective functioning, preschoolers' executive function, and creative thinking. Teachers completed questionnaires regarding preschoolers' critical thinking and caring thinking. Preschoolers' counterfactual thinking was measured by interviews. Data were analyzed using descriptive statistics and Pearson correlation analysis with SPSS 25.0. Path analysis and bootstrapping were used with AMOS 25.0 program. Also, specific indirect effect were analyzed using AMOS user-defined estimand function. The primary results of this study are as follows. Maternal reflective functioning had an indirect effect on preschoolers' critical thinking and caring thinking through preschoolers' executive function and counterfactual thinking; however, maternal reflective functioning had no indirect effect on preschoolers' creative thinking through preschoolers' executive function and counterfactual thinking. This study revealed that maternal reflective functioning and preschoolers' executive function and counterfactual thinking need to be considered simultaneously to explain the level of preschoolers' higher-order thinking. The results highlight the dual mediating effects of preschoolers' executive function and counterfactual thinking in the relationship between maternal reflective functioning and preschoolers' higher-order thinking.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.589-603
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• 2023

Deep generative models target to infer the underlying true data distribution, and it leads to a huge success in generating fake-but-realistic data. Regarding such a perspective, the data attributes can be a crucial factor in the data generation process since non-existent counterfactual samples can be generated by altering certain factors. For example, we can generate new portrait images by flipping the gender attribute or altering the hair color attributes. This paper proposes counterfactual disentangled variational autoencoder generative adversarial networks (CDVAE-GAN), specialized for data attribute level counterfactual data generation. The structure of the proposed CDVAE-GAN consists of variational autoencoders and generative adversarial networks. Specifically, we adopt a Gaussian variational autoencoder to extract low-dimensional disentangled data features and auxiliary Bernoulli latent variables to model the data attributes separately. Also, we utilize a generative adversarial network to generate data with high fidelity. By enjoying the benefits of the variational autoencoder with the additional Bernoulli latent variables and the generative adversarial network, the proposed CDVAE-GAN can control the data attributes, and it enables producing counterfactual data. Our experimental result on the CelebA dataset qualitatively shows that the generated samples from CDVAE-GAN are realistic. Also, the quantitative results support that the proposed model can produce data that can deceive other machine learning classifiers with the altered data attributes.

• (The) Korean Journal of Educational Psychology
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• v.32 no.3
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• pp.355-377
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• 2018

This study aimed to examine the effects of upward counterfactual thinking in academic majors, academic major satisfaction, and job-seeking anxiety on university students' happiness along with the grade levels(the lower and upper grades). The participants were 581 university students in Seoul, Korea. Upward counterfactual thinking in academic majors (related to schoolwork and job-seeking) had negative effects on academic major satisfaction. Upward counterfactual thinking in academic majors related to job-seeking had positive effects on job-seeking anxiety. Academic major satisfaction decreased job-seeking anxiety, and promoted happiness. Job-seeking anxiety had negative effects on happiness. In the path model between variables, the differences according to the grade were found in the influence of upward counterfactual thinking in academic majors related to schoolwork on major satisfaction and in the influence of job-seeking anxiety on happiness. These results are expected to help select interventions that would foster happiness among university students.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.307-332
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• 2022

One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding performance for bankruptcy prediction models using artificial intelligence techniques. However, since most machine learning algorithms are "black-box," AI has been identified as a prominent research topic for providing users with an explanation. Although there are many different approaches for explanations, this study focuses on explaining a bankruptcy prediction model using a counterfactual example. Users can obtain desired output from the model by using a counterfactual-based explanation, which provides an alternative case. This study introduces a counterfactual generation technique based on a genetic algorithm (GA) that leverages both domain knowledge (i.e., causal feasibility) and feature importance from a black-box model along with other critical counterfactual variables, including proximity, distribution, and sparsity. The proposed method was evaluated quantitatively and qualitatively to measure the quality and the validity.

• East Asian Economic Review
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• v.28 no.1
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• pp.69-94
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• 2024

The Covid-19 pandemic has caused unprecedented disruptions across industries worldwide. This paper aims to analyze the economic impact of the pandemic on the sales performance of basic commercial areas in Seoul, Korea. Using a regression analysis with credit card transaction data, the study underscores the critical nature of determining the reference point for comparison. Firstly, in comparison to the revenue in the same quarter before the onset of the pandemic, a significant decrease in revenue was observed across most categories during the pandemic periods. Secondly, when compared to the counterfactual revenue in the same period, extrapolated by an exponential smoothing forecasting, the overall revenue decrease during the periods was less pronounced, except in a few categories. Interestingly, certain categories appeared to witness marginal increases in sales after the pandemic. The paper discusses some policy implications of these findings.

• Language and Information
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• v.15 no.2
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• pp.47-65
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• 2011

A Korean adverb '거의(geoui)' modifies predicates to generate complex predicates which have meanings of 'nearly' complete or typical properties of the modified predicates in quantities, degrees, and frequencies. The modified predicates 'complete' or 'typical' properties are referred counterfactually as standards for the generated predicates' meanings of deficiencies. These counterfactual standards can be formalized by a counterfactual conditional operator of the intensional semantics in Cresswell(1990). The deficiencies in the quantities, degrees, or frequencies of the properties can be expressed formally introducing a world-independent measure of comparison. The measure can be manufactured out of relations between intensional things at indices and their equivalence classes. The world-independent measure of comparison has a semantic structure under-specified in quantity, degree, and frequency, and seems very well-suited in describing lexical meaning of '거의(geoui)'. The lexical-semantic analysis of '거의(geoui)' shows explicitly the plausibility of the indispensable existence of the comparing measure which works across real and counterfactual worlds in natural language meaning. On the other hand, we examined Kim, young-hee(1985)'s proposal of a transition of quantificational meaning for Korean degree adverbs, where he tried to explain the quantificational meaning of Korean degree adverbs in general including '거의(geoui)' with several syntactic and semantic constraints of 'contextual deletion'. But it is shown that the quantificational meanings of the degree adverbs which Kim(1985) discussed are also explained better by their under-specified meanings in quantities, frequencies and degrees with the world-independent measure of comparison applied to their paradigmatic lexical constraint rather than Kim(1985)'s transition of meaning.

• Korean Journal of Forensic Psychology
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• v.11 no.1
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• pp.63-87
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• 2020

Judgment and decision-making studies have shown that people are easily influenced and biased by information irrelevant to the object of judgment. There is a great deal of research that indicates that bias exists in the legal judgment scene. One of them is a bias induced by defendants' character evidence. This study examined whether cognitive activities such as discussion, counterfactual thinking, and peer assessment could reduce the bias induced by the character evidece. In Experiment 1, 121 college students were asked to give the percentage they believed the defendant to be guilty. There was no cognitive activity for the control group. There were three different cognitive activities for the experimental group: discussion, counterfactual thinking and discussion, and counterfactual thinking and peer assessment. Results showed reduction in bias for all the experimental groups, and there was no difference between them. In Experiment 2, there were 125 participants from general population for the same procedure as in Experiment 1. Results showed reduction in bias only for the counterfactual thinking and discussion group. In general discussion, we speculated the implication of the results and the reason for the difference between the two experiments.

• Korean Journal of Logic
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• v.18 no.1
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• pp.133-153
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• 2015

In his paper "Structural Equations Approach to Token Causation: The Active Route Account Revisited" Professor Sungsu Kim defends the active route account. The active route account is the theory of causation which overcomes counterexamples to the counterfactual theories of causation, while maintaining the counterfactual theorist's essential intuition that an effect depends counterfactually on a cause. Unfortunately, there are counterexamples to the active route account itself. Professor Sungsu Kim attempts to defend the active route account by rebutting those counterexamples. In this paper, I argue that his defense of the active route account is not successful.

• Korean Journal of Logic
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• v.18 no.2
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• pp.265-289
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• 2015

The idea that an effect counterfactually depends on its cause is simple and intuitive. However, this simple idea runs into various difficulties. The active route account, in order to avoid the difficulties, analyzes causation in terms of counterfactual dependence under certain control. In her recent article, Seahwa Kim criticizes Sungsu Kim's earlier attempt to defend the active route account from its counterexamples. Her criticisms are convincing, and defenders of the active route account or counterfactual analysis of causation in general need another defense. In response, a two-step defense is proposed. First, the scope of the active route account is restricted to 'proximate' causal relation. Second, a control over factors that are in proximate causal relation is offered to figure out 'distant' causal relation. The result is that with proper control, an effect indeed counterfactually depends on its cause.