Effects of Structural Factors on TV Commercial Processing

Maki Sakamoto,Koichi Yamamoto,Tetsuya Hayashida 서울대학교 인지과학연구소 2008 Journal of Cognitive Science Vol.9 No.1

Metrics such as understanding of the ad or purchase intent are widely used as a benchmark of effectiveness in TV commercial testing. However, it is quite rare that we are able to clearly understand why the benchmarks scores are high or low. This study investigates how structural variables relating to salience asymmetries in the visual presentation of TV commercials affect the processing of TV commercials. Asymmetries of cognitive salience suggest that brand information, which tends to be non-human, inanimate, and abstract, has difficulty in gaining TV commercial viewers` attention. In this study we seek for effective ways to lead viewers to process brand information within TV commercial. The commercials examined for this purpose are those for products in two cosmetic product categories, namely lipstick and foundation. Based on the hierarchy of cognitive salience we present the hypothetical structural factors which appeal to the reference point ability and effectively lead consumers to process brand information. We tested these hypotheses by psychological experiments. Each commercial was tested among 51 to 54 participants. A total sample of 211 usable questionnaires was obtained. Results suggest that when celebrities are effectively used as a salient reference point accompanied by structural factors that successively shift the attention to brand information, understanding of product benefits is enhanced. An example of such a structural factor that showed significant contribution to understanding of product benefits (p <. 01) was the structural factor The last half of the commercial does not have consecutive cuts showing full views of celebrities.

Holistic Processing Affects Surface Texture Perception: Approach from Japanese Sound Symbolic Words

( Jinhwan Kwon ),( Tatsuki Kagitani ),( Maki Sakamoto ) 서울대학교 인지과학연구소 2017 Journal of Cognitive Science Vol.18 No.3

The human visual system is able to perceive not only the macrostructure (form and shape) of a surface, but also its microstructure (texture). Some evidence suggests that microstructural characteristics are processed independently of macrostructural features. However, the human visual system can interpret a variety of information about the physical world, enabling the recognition and semantic categorization of complex visual scenes at a glance. This remarkable perceptual ability relies heavily on holistic processing, which is achieved by estimating the global statistical summary of an image. On the other hand, texture is an important source of information for distinguishing between artificial and naturally occurring surfaces in images. In addition, it is reported that Japanese sound symbolic words are useful to express fine differences in texture and synesthetic characteristics. However, there is no evidence comparing the characteristics of surface texture perception between whole- and part-based images using sound symbolic words. The objective of the present study was to examine whether sound symbolic words for describing the surface texture perception differs between whole-based images related to the holistic processing and part-based images. In Experiment 1, we examined the effect of wholebased images in surface texture perception using sound symbolic words. In Experiment 2, we examined the effect of part-based images in surface texture perception using sound symbolic words. The results revealed that the sensory and symbolic descriptors differed in texture perceptions between whole-based and part-based image processing. These findings suggest that sound symbolic words can describe differences in surface texture between whole-based and part-based images at a fine resolution.

Color Recommendation for Text Based on Colors Associated with Words

Liba, Saki,Nakamura, Tetsuaki,Sakamoto, Maki Korea Society of Industrial Information Systems 2012 한국산업정보학회논문지 Vol.17 No.1

In this paper, we propose a new method to select colors representing the meaning of text contents based on the cognitive relation between words and colors, Our method is designed on the previous study revealing the existence of crucial words to estimate the colors associated with the meaning of text contents, Using the associative probability of each color with a given word and the strength of color association of the word, we estimate the probability of colors associated with a given text. The goal of this study is to propose a system to recommend the cognitively plausible colors for the meaning of the input text. To build a versatile and efficient database used by our system, two psychological experiments were conducted by using news site articles. In experiment 1, we collected 498 words which were chosen by the participants as having the strong association with color. Subsequently, we investigated which color was associated with each word in experiment 2. In addition to those data, we employed the estimated values of the strength of color association and the colors associated with the words included in a very large corpus of newspapers (approximately 130,000 words) based on the similarity between the words obtained by Latent Semantic Analysis (LSA). Therefore our method allows us to select colors for a large variety of words or sentences. Finally, we verified that our system cognitively succeeded in proposing the colors associated with the meaning of the input text, comparing the correct colors answered by participants with the estimated colors by our method. Our system is expected to be of use in various types of situations such as the data visualization, the information retrieval, the art or web pages design, and so on.

Color Recommendation for Text Based on Colors Associated with Words

Saki liba,Tetsuaki Nakamura,Maki Sakamoto 한국산업정보학회 2012 한국산업정보학회논문지 Vol.17 No.1

In this paper, we propose a new method to select colors representing the meaning of text contents based on the cognitive relation between words and colors. Our method is designed on the previous study revealing the existence of crucial words to estimate the colors associated with the meaning of text contents. Using the associative probability of each color with a given word and the strength of color association of the word, we estimate the probability of colors associated with a given text. The goal of this study is to propose a system to recommend the cognitively plausible colors for the meaning of the input text. To build a versatile and efficient database used by our system, two psychological experiments were conducted by using news site articles. In experiment 1, we collected 498 words which were chosen by the participants as having the strong association with color. Subsequently, we investigated which color was associated with each word in experiment 2. In addition to those data, we employed the estimated values of the strength of color association and the colors associated with the words included in a very large corpus of newspapers (approximately 130,000 words) based on the similarity between the words obtained by Latent Semantic Analysis (LSA). Therefore our method allows us to select colors for a large variety of words or sentences. Finally, we verified that our system cognitively succeeded in proposing the colors associated with the meaning of the input tort comparing the correct colors answered by participants with the estimated colors by our method. Our system is expected to be of use in various types of situations such as the data visualization, the information retrieval, the art or web pages design, and so on.