Automaticity and Control in the Effect of Arousal on Persuasion

232 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2005.The study failed to detect the mediating role of accessibility and attribution. This failure is likely to be due to issues related to the measurement sequence. However, the study did show differences in the moderating role of awareness under the different processing strategy conditions. The results show that under substantive processing, awareness reverses the effect of arousal on judgment. These results are consistent with both the predictions based on the spreading activation mechanism and with past research. Under heuristic processing arousal affects judgment only under high-awareness condition, but had insignificant effect under low awareness condition. These results contradict both the hypothesis and earlier research on misattribution. These distinct moderating roles of awareness under different processing conditions can be attributed to differences in the mechanisms underlying the effect of arousal on judgment.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Emotion and motion: Toward emotion recognition based on standing and walking.

Emotion recognition is key to interpersonal communication and to human-machine interaction. Body expression may contribute to emotion recognition, but most past studies focused on a few motions, limiting accurate recognition. Moreover, emotions in most previous research were acted out, resulting in non-natural motion, which is unapplicable in reality. We present an approach for emotion recognition based on body motion in naturalistic settings, examining authentic emotions, natural movement, and a broad collection of motion parameters. A lab experiment using 24 participants manipulated participants' emotions using pretested movies into five conditions: happiness, relaxation, fear, sadness, and emotionally-neutral. Emotion was manipulated within subjects, with fillers in between and a counterbalanced order. A motion capture system measured posture and motion during standing and walking; a force plate measured center of pressure location. Traditional statistics revealed nonsignificant effects of emotions on most motion parameters; only 7 of 229 parameters demonstrate significant effects. Most significant effects are in parameters representing postural control during standing, which is consistent with past studies. Yet, the few significant effects suggest that it is impossible to recognize emotions based on a single motion parameter. We therefore developed machine learning models to classify emotions using a collection of parameters, and examined six models: k-nearest neighbors, decision tree, logistic regression, and the support vector machine with radial base function and linear and polynomial functions. The decision tree using 25 parameters provided the highest average accuracy (45.8%), more than twice the random guess for five conditions, which advances past studies demonstrating comparable accuracies, due to our naturalistic setting. This research suggests that machine learning models are valuable for emotion recognition in reality and lays the foundation for further progress in emotion recognition models, informing the development of recognition devices (e.g., depth camera), to be used in home-setting human-machine interactions

Market Shares Follow the Zipf Distribution

The Zipf distribution is known to describe various natural phenomena, including city populations in the United States, frequency of English words in literature, immune system response in human beings, and certain aspects of Internet traffic. Using data from 70 markets, we show that the market shares by rank order follow the Zipf distribution. Our work makes a fundamental contribution in understanding the distribution of market shares, as we make no assumption about the order of entry and our results are valid for arbitrary number of competitors in market. We compare the predictions of our model with those from the analytical models in marketing literature. The comparison reveals that market share predicted by the Zipf model fits well with the predictions from the analytical models.

Fish Out of Water: understanding decision making and copying strategies as second language consumers through a situational literacy perspective

Purpose This paper aims to study English as second language (ESL) consumers in the USA. The authors seek to focus on consumers who are literate in their native country, yet akin to fish out of water due to language difficulties and unfamiliarity with the marketplace. Design/methodology/approach Using qualitative interviews of 31 informants and shopping observations of a small subset, the authors examined cognitive predilections, decision making, emotional trade‐offs, and coping strategies of ESL consumers. Findings The findings relate to cognitive predilections, decision making and emotional trade‐offs, and coping strategies of ESL consumers. Originality/value The authors analyze ESL consumers from a situational literacy perspective, viewing the situations faced by ESL consumers in terms of functional literacy skills. The findings provide a variety of new insights, and have important theoretical and practical implications for theory and practice