Doctor of Philosophy / Department of Psychology / Gary Brase / Consumers’ choice behaviors are influenced by multifaceted decision factors. Information processing theory (Bettman, 1979) predicts that consumers’ decision processes are constructed at the time of a decision, and consumers’ meta-goals model (Bettman, Luce, & Payne, 1998) posits that their choice behaviors are generally guided by the minimization of cognitive effort and negative emotion, and the maximization of accuracy and justification. Insightful as this model may be at describing how consumers’ choices are shaped, it does not specify the interactions among the goals, assess the benefits of cognitive effort, or fairly evaluates the importance of emotion and justification in consumer decision making. Thus, the current studies collectively propose an alternative meta-goals choice model. Particularly, the current studies speculate on the importance of regret in consumer decision making. Consistent with previous research, Study 1 shows that participants trade-off between cognitive effort and accuracy goals depending on the monetary value of the products at hand, suggesting that the cognitive effort-accuracy trade-off may be moderated by price. Study 2 investigated the impact of cognitive effort on the experience of regret, showing the effectiveness of cognitive effort in attenuating the experienced regret. This relationship is explored with respect to the role of cognitive effort as a means of the justification factor in Study 3. Results showed that unjustified (wasted) cognitive effort did not make a substantial difference in post-purchase regret, but under spent cognitive effort generated more post-purchase regret. Study 4 examined both anticipated and experienced regret in relation to cognitive effort and justification; results showed that an exertion of cognitive effort is helpful in reducing the experience of regret but the justification of the choice also affects this relationship especially when the choice during the decision search is incongruent with the final decision. These results from current studies suggest interrelations between consumers’ four meta-goals. Based on these findings, an alternative meta-goals model is proposed which includes the benefits of cognitive effort exertion on consumer decision making, shedding light on how and when consumers choose to exert effort in an attempt to alleviate the potential future experience of regret.

The benefits of observing a model when acquiring a new motor skill are well known, however, there is little research on the influence of viewing angle of the model. The purpose of the present experiment was to assess whether a looking-glass (face on) or subjective (facing away) viewing angle would result in different acquisition and retention levels when learning a complex Zumba dance sequence. Greater cognitive effort was expected during the looking-glass condition, consequently resulting in slower acquisition but greater physical performance scores and error recognition/identification. Thirty females were evenly divided into the looking-glass or subjective group and began with the pre-test phase to assess degrees of motivation, self-efficacy, and physical performance. Participants were then lead through six acquisition dances, within which they performed the to-be-learned sequence 18 times. An assessment of cognitive effort followed, then post-test performances and error recognition/identification scores were obtained to conclude the study. While both the looking-glass and subjective conditions demonstrated equal rates of acquisition (p>.05), the looking-glass group performed significantly fewer errors during the post-test (p<.05) and were significantly better at identifying errors when a video of the dance sequence was shown from the same viewing angle as the acquisition phase (p<.05). No differences were reported between the two conditions with respect to cognitive effort (p>.05). Based on the results of this study, the looking-glass viewing angle appears to result in better learning of a dance sequence, but cannot be explained by cognitive effort.

The benefits of observing a model when acquiring a new motor skill are well known, however, there is little research on the influence of viewing angle of the model. The purpose of the present experiment was to assess whether a looking-glass (face on) or subjective (facing away) viewing angle would result in different acquisition and retention levels when learning a complex Zumba dance sequence. Greater cognitive effort was expected during the looking-glass condition, consequently resulting in slower acquisition but greater physical performance scores and error recognition/identification. Thirty females were evenly divided into the looking-glass or subjective group and began with the pre-test phase to assess degrees of motivation, self-efficacy, and physical performance. Participants were then lead through six acquisition dances, within which they performed the to-be-learned sequence 18 times. An assessment of cognitive effort followed, then post-test performances and error recognition/identification scores were obtained to conclude the study. While both the looking-glass and subjective conditions demonstrated equal rates of acquisition (p>.05), the looking-glass group performed significantly fewer errors during the post-test (p<.05) and were significantly better at identifying errors when a video of the dance sequence was shown from the same viewing angle as the acquisition phase (p<.05). No differences were reported between the two conditions with respect to cognitive effort (p>.05). Based on the results of this study, the looking-glass viewing angle appears to result in better learning of a dance sequence, but cannot be explained by cognitive effort.

The benefits of observing a model when acquiring a new motor skill are well known, however, there is little research on the influence of viewing angle of the model. The purpose of the present experiment was to assess whether a looking-glass (face on) or subjective (facing away) viewing angle would result in different acquisition and retention levels when learning a complex Zumba dance sequence. Greater cognitive effort was expected during the looking-glass condition, consequently resulting in slower acquisition but greater physical performance scores and error recognition/identification. Thirty females were evenly divided into the looking-glass or subjective group and began with the pre-test phase to assess degrees of motivation, self-efficacy, and physical performance. Participants were then lead through six acquisition dances, within which they performed the to-be-learned sequence 18 times. An assessment of cognitive effort followed, then post-test performances and error recognition/identification scores were obtained to conclude the study. While both the looking-glass and subjective conditions demonstrated equal rates of acquisition (p>.05), the looking-glass group performed significantly fewer errors during the post-test (p<.05) and were significantly better at identifying errors when a video of the dance sequence was shown from the same viewing angle as the acquisition phase (p<.05). No differences were reported between the two conditions with respect to cognitive effort (p>.05). Based on the results of this study, the looking-glass viewing angle appears to result in better learning of a dance sequence, but cannot be explained by cognitive effort.

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The concept of attention is complex and multifaceted and can be approached from many perspectives. One such perspective is of attention as a limited pool of resources. Kahneman’s (1973) model of limited capacity provides a basis for understanding constraints on attention, including the costs of divided attention. In the same vein as Kahneman’s model, the Framework for Understanding Effortful Listening (FUEL; Pichora-Fuller et al., 2016) applies the concept of limited attentional capacity to the demands of listening in a variety of contexts. The current work examines novel combinations of the methods commonly used in the field of Cognitive Hearing Science to address questions about the nature of attention allocation when listening in noise and under the constraints of divided attention. I first combined listening in noise with a secondary continuous working memory task and measured pupillary response as an index of cognitive work and listening effort. Here, I found that listening task demands affect performance on the working memory task. The shared demands of listening and working memory were not, however, evident in the pupil dilation patterns. As a result, I followed these findings by employing a different divided attention method. With the use of a temporally discrete secondary task that either closely overlapped with the listening task or did not closely overlap, I found the same carryover effects of listening demands on secondary task. Most importantly, I found that these demands interacted and were clearly present in the pupil dilation patterns, demonstrating the importance of the timing of the task demands. Together, the studies in this thesis provide evidence that these two secondary tasks access the same attentional resources as those accessed in the primary listening task and that this overlapping demand for resources can be seen in the pupillary response. / Thesis / Doctor of Philosophy (PhD) / Listening to speech in a noisy environment is a cognitively difficult and effortful task. Attending to more than one task at a time is similarly demanding and effortful. These two kinds of tasks are assumed to use the same limited pool of cognitive resources that we have available to us. This thesis combines listening in noise with divided attention tasks to demonstrate this overlap in demands for cognitive resources using novel combinations of these kinds of tasks. Additionally, this thesis uses the pupillary response—a well-studied index of cognitive effort—to further examine the nature of these overlapping task demands. These studies found that the demands of these tasks do, in fact, overlap, and contribute evidence to the current literature supporting the underlying assumption that these two tasks, and the pupillary response as a measure of effort, are accessing the same pool of limited resources.

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nÃo hà / Este trabalho apresenta uma pesquisa de cunho quantitativo que analisa se e de que maneira o esforÃo cognitivo e a fluÃncia de escrita nos processos cognitivos envolvidos na produÃÃo escrita em inglÃs (L2/LE) sÃo afetados pela tarefa de produÃÃo (anÃncio, aviso e convite) e pelo meio (celular, tablet e computador). Partindo de uma lacuna nos estudos sobre tecnologia e escrita em lÃngua estrangeira, e tendo como aporte teÃrico os modelos cognitivos de escrita (HAYES, FLOWER, 1980; HAYES, 1996; BEREITER, SCARDAMALIA, 1987, 1992) e estudos sobre esforÃo cognitivo (KELLOGG, 1987; PIOLAT et al, 2001; OLIVE et al, 2009) e fluÃncia (KELLOGG, 1996; STAPLETON, 2010; LATIF, 2013), nosso estudo verificou o esforÃo cognitivo, medido pelo tempo despendido no processo de planejamento e no processo de traduÃÃo na produÃÃo textual em funÃÃo da tarefa realizada e em funÃÃo do meio utilizado. TambÃm nos propomos a observar a fluÃncia, estimada pelo nÃmero de palavras digitadas em um minuto no processo de traduÃÃo, na produÃÃo de gÃneros textuais distintos em celulares, tablets e computadores. Para isso, tivemos como participantes da pesquisa 34 professores brasileiros de inglÃs (LE), graduados (ou graduandos) em Letras com habilitaÃÃo em inglÃs. A seleÃÃo dos participantes se deu por amostragem de conveniÃncia. Cada participante teve que realizar trÃs tarefas de escrita, uma em cada um dos meios estudados na pesquisa. Durante a produÃÃo, a tela dos dispositivos foi gravada usando os programas Camtasia Studio e Mobizen, alÃm de uma cÃmera externa. Em seguida, foram tabulados os tempos gastos no processo de planejamento e no processo de traduÃÃo na realizaÃÃo de cada tarefa em cada um dos meios, bem como a quantidade de palavras produzidas em cada tarefa e em cada um dos meios. Realizados testes estatÃsticos para comparaÃÃo das diferenÃas no esforÃo cognitivo e na fluÃncia de escrita em funÃÃo das tarefas e em funÃÃo dos meios, os resultados mostraram que o esforÃo cognitivo no processo de planejamento pode ser influenciado tanto pelo meio quanto pela tarefa de produÃÃo. Jà o processo de traduÃÃo sofre maior influÃncia da tarefa do que do meio. Os resultados indicaram que a tarefa de produÃÃo à o principal fator influenciador na quantidade de palavras, e que a fluÃncia de escrita nÃo à influenciada pelo meio, mas pode ser influenciada pela tarefa proposta. Esta pesquisa se mostra relevante por lanÃar luz sobre como os processos cognitivos do escritor podem ser afetados pela tarefa e pelo meio, e pode auxiliar professores de lÃngua estrangeira no processo decisÃrio de como inserir a tecnologia nas aulas de escrita. / This quantitative research analyzes if and how cognitive effort and writing fluency in the cognitive processes of text writing in English (ESL/EFL) are affected by the writing task (advert, notice, and invitation) and by the writing medium (cellphone, tablet, and computer). Considering a gap in the studies about technology and writing, the theoretical foundations of this research include the cognitive models of writing research (HAYES, FLOWER, 1980; HAYES, 1996; BEREITER, SCARDAMALIA, 1987, 1992), research on cognitive effort (KELLOGG, 1987; PIOLAT et al, 2001; OLIVE et al, 2009) and writing fluency (KELLOGG, 1996; STAPLETON, 2010; LATIF, 2013). Our research analysed cognitive effort, measured by the time spent in the planning process and in the translation process of written production, in relation to the task and in relation to the medium. We also observed the writing fluency, estimated by the number of words typed in one minute in the translation process, in relation to the task and in relation to the medium. 34 Brazilian teachers of EFL volunteered to take part in our research. They were selected through convenience sampling, under the conditions of being familiar with the type of text to be produced in the research, with the medium of composition used in the research, and the condition of being a licensed (or almost) teacher, so as to guarantee the language proficiency. We pilot tested our method with 4 participants, and the results helped us refine our data collection method. Each participant was asked to write three texts (advert, notice, and invitation), each text in one of the media studied. During the production, the screen of each device was recorded using Camtasia Studio and Mobizen, and a phone camera. After all data were collected, the time spent in each task and each medium as well as the number of words typed in each task and each medium were sorted, compared and statistically tested for significance. Results show that cognitive effort in the planning process may be affected either by the writing task or by the writing medium. In the translation process, cognitive effort is more affected by the the task than by the the medium. Results also show that the demands of the task are the highest influencing factor to the increase of the number of words produced. The fluency is not affected by the writing medium but might be affected by the writing task. The relevance of this research resides in its shedding light onto the manner the cognitive processes of writing may be affected by the task proposed and by the writing medium. It might also help EFL teachers in the deciding process as to how to insert technology in writing classes.

In our daily lives, we encounter a multitude of emotional challenges in various contexts, necessitating the use of diverse emotion regulation strategies for adaptive responses (Aldao et al., 2015; Bonanno and Burton, 2013). These strategies require cognitive control, making them cognitively demanding processes (Gross, 2015b; Ochsner et al., 2012). Yet, it remains unknown whether different strategies impose different levels of cognitive effort. This thesis aims to address this gap in the literature by conducting a comparative analysis of the cognitive effort required for specific emotion regulation strategies. Our research takes a comprehensive approach to evaluating effort, utilizing physiological and subjective measures. In the field of emotion regulation choice, the role of effort remains less explored. We seek to deepen the understanding by not only investigating the self-reported reasons behind participants’ choice behaviour, but also by employing rigorous statistical analyses to identify the factors that influence these choices. Prior studies on the determinants of emotion regulation choice have faced certain limitations. While some determinants favouring specific strategies have been identified, there is a lack of information regarding the strategies that are not chosen. Additionally, it is unclear to what extent individuals prefer their chosen strategy over other available options. These issues can be addressed by assigning subjective values to all choice options, as these values are central to the decisionmaking process. This thesis has developed and validated a novel paradigm that measures the individual subjective values associated with emotion regulation strategies. We not only examined whether these values can effectively predict actual choice behaviour, but also delved into the variables that predict these subjective values, offering a holistic perspective on the complex interplay between cognitive effort and emotion regulation choice. To enhance comprehension on the impact of cognitive effort on emotion regulation, Study 1 and Study 2 aimed to compare the necessary effort between the two emotion regulation strategies distancing and expressive suppression. To our knowledge, this was the first work striving to comprehensively examine effort during emotion regulation by assessing it through various means. Two studies were carried out to examine the impact of cognitive effort on emotion regulation choice. Specifically, it was explored whether decision-making behaviour is more influenced by the cognitive effort required to implement the strategy or the effectiveness of the strategy. To answer these research questions, cognitive effort and effectiveness of distancing and suppression were quantified through subjective (ratings) and physiological (pupillometry, heart rate) measures. To explore choice behaviour, participants were instructed to finally employ either of the strategies again. Our findings highlight that the strategies did not differ substantially in terms of physiological effort, yet they did exhibit substantial disparities in the subjectively perceived effort. These differences between strategies also manifested in their choice behaviour: Two thirds of the participants opted for suppression once again, despite its lower effectiveness. Participants cited lower effort as the reason for their choice. By measuring effort through various means, these two studies provide a comprehensive understanding of the cognitive effort involved in emotion regulation. Furthermore, the results on the determinants of emotion regulation choice not only enrich research, but also provide starting points for practical applications. Paradigms commonly employed in emotion regulation choice research - as in Study 1 and Study 2 - suffer from a drawback as they entail a loss of information, providing only binary data concerning whether or not a strategy was chosen. However, it is unclear to what extent the favoured strategy was preferred compared to the alternative options. In Study 3, a registered report, the objective was to develop a novel paradigm to determine the subjective values attributed to each emotion regulation strategy at hand. The cognitive effort discounting paradigm by Westbrook et al. (2013) provided the foundation, but was developed and extended further. Participants completed an emotion regulation paradigm whereby they were prompted to employ three strategies - distraction, distancing, and expressive suppression. Next, we conducted our novel discounting paradigm to determine the subjective value of each strategy. In our study, we were not only able to show that subjective values predict subsequent choice behaviour, but that, in addition to self-assessed utility, subjective effort in particular was predictive of subjective values. Our innovative paradigm has extensive potential applications, even beyond the field of emotion regulation. By utilising this paradigm, it becomes feasible to ascertain subjective values of demand levels that lack any objective effort hierarchy. In summary, the present thesis makes an important contribution to deepen the understanding of the role of cognitive effort in emotion regulation and emotion regulation choice. Cognitive effort and regulatory success were comprehensively examined by assessing trait measures (questionnaires), subjective ratings and peripheral physiological measures (pupillometry, electromyography, electrocardiogram). Our main finding, which could be replicated across three pre-registered studies, shows that the use of strategies is associated with different levels of subjective effort for individuals. This leads to the majority of individuals choosing the strategy that is associated with less effort for them, rather than the strategy that is more effective. Finally, the thesis outlines a novel paradigm that enables the calculation of individual subjective values attributed to emotion regulation strategies. This facilitates a more comprehensive evaluation of the determinants of emotion regulation choice, as well as the strength of their preference for such strategies. Thus, it adds to the expanding literature on the field of emotion regulation flexibility by presenting starting points for assessing individuals’ flexibility and adaptability.:Contents Summary Preface 1. Theoretical Background 1.1. Fundamentals of emotions and emotion regulation 1.1.1. Emotions 1.1.2. Emotion regulation 1.2. The cognitive control of emotions 1.2.1. Cognitive control and cognitive effort 1.2.2. Cognitive effort and emotion regulation 1.3. Peripheral physiological consequences of intentional emotion regulation 1.4. Flexible emotion regulation 1.4.1. The extended process model of emotion regulation 1.5. Emotion regulation choice 1.5.1. A conceptual framework of emotion regulation choice 1.5.2. Determinants of emotion regulation choice 2. Scope of the thesis and study overview 3. Effort beats effectiveness in emotion regulation choice: Differences between suppression and distancing in subjective and physiological measures (Study 1 and 2) 3.1. Theoretical Background 3.1.1. Effort and emotion regulation 3.1.2. The present study 3.2. Study 1 3.2.1. Method 3.2.2. Results 3.3. Study 2 3.3.1. Method 3.3.2. Results 3.4. Exploratory analyses 3.4.1. Statistics 3.4.2. Results 3.5. Discussion 3.5.1. Effort plays a crucial role in the selection of an ER strategy 3.5.2. Individual differences in personality traits do not explain individual differences in arousal and effort 3.5.3. Subjective arousal and effort are not reflected in physiological arousal and effort 3.6. Limitations 3.7. Conclusion 4. Estimating individual subjective values of emotion regulation strategies 4.1. Introduction 4.2. Method 4.2.1. Ethics information 4.2.2. Pilot data 4.2.3. Design 4.2.4. Sampling plan 4.2.5. Analysis plan 4.3. Results 4.3.1. Participants and descriptive statistics 4.3.2. Confirmatory analyses 4.3.3. Exploratory analyses 4.4. Discussion 4.4.1. Ecological validity of subjective values of emotion regulation strategies 4.4.2. Trait character of subjective values 4.4.3. Limitations 4.4.4. Conclusion 5. General Discussion 5.1. Summary of results 5.2. The role of cognitive effort in emotion regulation 5.2.1. Effects of effort in the selection stage 5.2.2. Effects of effort in the implementation stage 5.2.3. Post-regulation effects of effort 5.3. Regulatory goals in emotion regulation 5.3.1. The interaction of regulatory goals and cognitive effort 5.4. Individual subjective values of choice options 5.4.1. Factors influencing subjective values 5.4.2. Subjective values: States or traits? 5.5. Methodological considerations 5.5.1. Experimental design 5.5.2. Assessment of physiological effort and arousal 5.6. Future directions 5.7. Conclusion References Appendix A. Supplementary Information Study 1 and Study 2 Appendix B. Supplementary Information Study 3 List of Figures List of Tables List of Abbreviations Erklärung