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Contemporary cognitive theories argue that socially anxious individuals adopt self-protective behavioural strategies under conditions of perceived social threat in order to prevent or diminish the likelihood of negative social outcomes. When performed in an anxiety-provoking but otherwise innocuous situation, however, safety behaviours are posited to facilitate biased processing of threat-relevant information, disrupt behavioural performance, elicit negative social responses, and ultimately prevent disconfirmation of fear-relevant beliefs. This dissertation project was designed to evaluate the effects of safety behaviour reduction strategies on a number of the core processes relevant to the persistence of pathological social fear. Two studies were conducted to address the following issues: Do safety behaviour reduction strategies influence socially anxious individuals’ (1) self- and social-judgments, (2) social performance, (3) the interpersonal reactions of oneself and others, and (4) appraisals of future social events. In study 1, in vivo safety behaviours were manipulated in a sample of 50 socially anxious students during a laboratory social interaction. Participants were randomly assigned to either a safety behaviour reduction (SB, n = 25) or exposure alone (control, n = 25) condition, and subsequently took part in two conversations with a trained experimental assistant. Results revealed that participants in the SB group displayed more accurate self-judgments of anxiety-related behaviour, improved social performance, and evoked more positive partner reactions. Study 2 was designed to replicate and extend the findings of study 1 in a sample of 80 patients seeking treatment for social anxiety disorder (SAD). Following a baseline conversation, participants were randomly assigned to the graduated exposure (GE, n = 40) or safety behaviour reduction condition (SB, n = 40). Consistent with the findings of study 1, the SB group displayed more accurate self-judgments about visible displays of anxiety, more effective social behaviour, and were better liked by their interaction partner relative to GE participants. Moreover, relative to controls, participants in the SB group made less negative judgments about the likelihood of previously identified feared outcomes pertaining to future social events. Implications of the present findings for elucidating the role of safety behaviours in the maintenance of SAD, and its treatment outcome will be considered.

Artificial intelligence (AI) has made astonishing breakthroughs in recent years and achieved comparable or even better performance compared to humans on many real-world tasks and applications. However, it is still far from reaching human-level intelligence in many ways. Specifically, although AI may take inspiration from neuroscience and cognitive psychology, it is dramatically different from humans in both what it learns and how it learns. Given that current AI cannot learn as effectively and efficiently as humans do, a natural solution is analyzing human learning processes and projecting them into AI design. This dissertation presents three studies that examined cognitive theories and established frameworks to integrate crucial human cognitive learning elements into AI algorithms to build human learning–augmented AI in the context of text analytics. The first study examined compositionality—how information is decomposed into small pieces, which are then recomposed to generate larger pieces of information. Compositionality is considered as a fundamental cognitive process, and also one of the best explanations for humans' quick learning abilities. Thus, integrating compositionality, which AI has not yet mastered, could potentially improve its learning performance. By focusing on text analytics, we first examined three levels of compositionality that can be captured in language. We then adopted design science paradigms to integrate these three types of compositionality into a deep learning model to build a unified learning framework. Lastly, we extensively evaluated the design on a series of text analytics tasks and confirmed its superiority in improving AI's learning effectiveness and efficiency. The second study focused on transfer learning, a core process in human learning. People can efficiently and effectively use knowledge learned previously to solve new problems. Although transfer learning has been extensively studied in AI research and is often a standard procedure in building machine learning models, existing techniques are not able to transfer knowledge as effectively and efficiently as humans. To solve this problem, we first drew on the theory of transfer learning to analyze the human transfer learning process and identify the key elements that elude AI. Then, following the design science paradigm, a novel transfer learning framework was proposed to explicitly capture these cognitive elements. Finally, we assessed the design artifact's capability to improve transfer learning performance and validated that our proposed framework outperforms state-of-the-art approaches on a broad set of text analytics tasks. The two studies above researched knowledge composition and knowledge transfer, while the third study directly addressed knowledge itself by focusing on knowledge structure, retrieval, and utilization processes. We identified that despite the great progress achieved by current knowledge-aware AI algorithms, they are not dealing with complex knowledge in a way that is consistent with how humans manage knowledge. Grounded in schema theory, we proposed a new design framework to enable AI-based text analytics algorithms to retrieve and utilize knowledge in a more human-like way. We confirmed that our framework outperformed current knowledge-based algorithms by large margins with strong robustness. In addition, we evaluated more intricately the efficacy of each of the key design elements. / Doctor of Philosophy / This dissertation presents three studies that examined cognitive theories and established frameworks to integrate crucial human cognitive learning elements into artificial intelligence (AI) algorithm designs to build human learning–augmented AI in the context of text analytics. The first study examined compositionality—how information is decomposed into small pieces, which are then recomposed to generate larger pieces of information. Design science research methodology has been adopted to propose a novel deep learning–based framework that can incorporate three levels of compositionality in language with significantly improved learning performance on a series of text analytics tasks. The second study went beyond that basic element and focused on transfer learning—how humans can efficiently and effectively use knowledge learned previously to solve new problems. Our novel transfer learning framework, which is grounded in the theory of transfer learning, has been validated on a broad set of text analytics tasks with improved learning effectiveness and efficiency. Finally, the third study directly addressed knowledge itself by focusing on knowledge structure, retrieval, and utilization processes. We drew on schema theory and proposed a new design framework to enable AI-based text analytics algorithms to retrieve and utilize knowledge in a more human-like way. Lastly, we confirmed our design's superiority in dealing with knowledge on several common text analytics tasks compared to existing knowledge-based algorithms.

The most representative models of cognitive development following the new outcomes of psychological theories are presented. Then a brief analysis of the models in terms of six factors related to different areas in psychology and social sciences (importance of each stage, processes, knowledge, individual differences, context and limits in the cognitive development) is developed. Finally, an integration of the model developed by Sincoff and Sternberg (1989) is presented. / Se presentan los modelos más representativos del desarrollo cognitivo según los avances en las teorías psicológicas. Luego se realiza un breve análisis de los modelos en función a seis factores relacionados con diferentes áreas de la psicología y las ciencias sociales (importancia de cada etapa, procesos, conocimiento, diferencias individuales, contexto y limitaciones en el desarrollo cognitivo). Finalmente se presenta una propuesta de integración de los modelos actuales del desarrollo cognitivo desarrollado por Sincoff y Sternberg (1989)

Emotions have a remarkable capacity to mobilize an individual and shape a person’s behavior in order to ultimately lead to a higher wellbeing. The importance of emotions is further emphasized by pathological cases of people who suffer from an inability to normally regulate their emotional life, such as people who suffer from major depression disorder (MDD), eating disorders, or borderline personality disorder. Given the central role emotions play in our lives, it is very easy to understand the great interest cognitive neuroscientists have in this research field. Emotions have been approached in the last decades from different angles and as such, distinct theories arose. The goal of this study is to give a comprehensive overview of the emotion theories that exist, with a focus on three of the fastest developing cognitive theories of emotions: Frijda’s action-readiness, Russell’s core affect and the communicative theory. Additionally, the neural correlates of emotions will be discussed, focusing on the role of amygdala in the negative emotion of fear. Neuroimaging studies that reveal a correlation between the amygdala and emotions, fear in particular, will be described. Given that the ability of self-regulation is crucial for the achievements of our aims and goals, fMRI studies designed to investigate neural the underpinnings of emotion regulation will be presented. The process of cognitive reappraisal will be used to point towards the brain regions that act as down-regulators for the activity of amygdala while processing negatively valenced stimuli.

abstract: The typical engineering curriculum has become less effective in training construction professionals because of the evolving construction industry needs. The latest National Science Foundation and the National Academies report indicate that industry-valued skills are changing. The Associated General Contractors of America recently stated that contractors expect growth in all sectors; however, companies are worried about the supply of skilled professionals. Workforce development has been of a growing interest in the construction industry, and this study approaches it by conducting an exploratory analysis applied to students that have completed a mandatory internship as part of their construction program at Arizona State University, in the School of Sustainable Engineering and the Built Environment. Data is collected from surveys, including grades by a direct evaluator from the company reflecting each student’s performance based on recent Student Learning Objectives. Preliminary correlations are computed between scores received on the 15 metrics in the survey and the final industry suggested grade. Based on the factors identified as highest predictors: ingenuity and creativity, punctuality and attendance, and initiative; a prognostic model of student performance in the construction industry is generated. With regard to graduate employability, student performance in the industry and human predispositions are also tested in order to evaluate their contribution to the generated model. The study finally identifies threats to validity and opportunities presented in a dynamic learning environment presented by internships. Results indicate that measuring student performance during internships in the construction industry creates challenges for the evaluator from the host company. Scoring definitions are introduced to standardize the evaluators’ grading based on observations of student behavior. 12 questions covering more Student Learning Objectives identified by the industry are added to the survey, potentially improving the reliability of the predictive model. / Dissertation/Thesis / Doctoral Dissertation Construction Management 2019

This thesis explores the ideological role of metaphor in British governmental discourses on ¿social exclusion¿. A hybrid methodology, combining approaches from Corpus Linguistics, Critical Discourse Analysis and cognitive theories of metaphor, is used to address how social exclusion and other metaphors are deployed to create an ideologically vested representation of society. The data consists of linguistic metaphors identified from a 400,000+ word machine-readable corpus of British governmental texts on social exclusion covering a ten year period (1997- 2007). From these surface level features of text, underlying systematic and conceptual metaphors are then inferred. The analysis reveals how the interrelation between social exclusion and a range of other metaphors creates a dichotomous representation of society in which social problems are discursively placed outside society, glossing inequalities within the included mainstream and placing the blame for exclusion on the cultural deficiencies of the excluded. The solution to the problem of exclusion is implicit within the logic of its conceptual structure and involves moving the excluded across the ¿boundary¿ to join the ¿insiders¿. The welfare state has a key role to play in this and is underpinned by a range of metaphors which anticipate movement on the part of the excluded away from a position of dependence on the state. This expectation of movement is itself metaphorically structured by the notion of a social contract in which the socially excluded have a responsibility to try and include themselves in society in return for the right of (temporary) state support. Key systematic metaphors are explained by reference to a discourse-historical view of ideological change in processes of political party transformation. / BISA and CSV

This thesis explores the ideological role of metaphor in British governmental discourses on 'social exclusion'. A hybrid methodology, combining approaches from Corpus Linguistics, Critical Discourse Analysis and cognitive theories of metaphor, is used to address how social exclusion and other metaphors are deployed to create an ideologically vested representation of society. The data consists of linguistic metaphors identified from a 400,000+ word machine-readable corpus of British governmental texts on social exclusion covering a ten year period (1997- 2007). From these surface level features of text, underlying systematic and conceptual metaphors are then inferred. The analysis reveals how the interrelation between social exclusion and a range of other metaphors creates a dichotomous representation of society in which social problems are discursively placed outside society, glossing inequalities within the included mainstream and placing the blame for exclusion on the cultural deficiencies of the excluded. The solution to the problem of exclusion is implicit within the logic of its conceptual structure and involves moving the excluded across the 'boundary' to join the 'insiders'. The welfare state has a key role to play in this and is underpinned by a range of metaphors which anticipate movement on the part of the excluded away from a position of dependence on the state. This expectation of movement is itself metaphorically structured by the notion of a social contract in which the socially excluded have a responsibility to try and include themselves in society in return for the right of (temporary) state support. Key systematic metaphors are explained by reference to a discourse-historical view of ideological change in processes of political party transformation.

The Common Core State Standards for Mathematics (CCSSM) require students with learning disabilities in mathematics to use a range of cognitive, skills, and foundational numerical competencies to learn and understand complex standards. Students with learning disabilities in mathematics experience deficits in cognitive processes skills and foundational numerical competencies which have emerged as underlying barriers associated with mastering CCSSM. Examining the impact of high-stakes assessments on readiness for college and careers and student achievement may provide evidence that deficits in cognitive processing skills and numerical competencies can impact achievement levels. Using the cognitive theoretical frameworks of Bandura and Gagné, along with the concepts of cognitive learning, instructional interventions, and inclusion, the relationship between students' scores in the algebraic foundations (AF) intervention inclusion method and the regular algebra (RA) nonintervention inclusion method, as measured on the end of the year assessments were examined in this study. An ANCOVA design was used to test the statistical significance of the relationship between the two intervention methods and the use of cognitive and numerical competencies for the two groups and to analyze the disparity in achievement scores between the AF intervention inclusion method and RA nonintervention inclusion method. The results revealed a statistically significant relationship between cognitive processing skills and foundational numerical competencies as measured on the final exam for both methods. The intended audience include academic communities using evidence-based inventions to improve college and career readiness results, leading to positive social change.

Arguably the most popular current theories of dreaming are the functional theories, including the emotional adaptation or problem-solving theory. These theories revolve around the idea that dreams may serve an independent adaptive function, helping us to adjust to, cope with, or resolve emotionally difficult life circumstances, problems and concerns. Contrary to these theories, other researchers have argued that dreams may have no function of their own, but are an epiphenomenon of REM sleep. The cognitive theories of dreaming suggest that dream content is continuous with waking concerns and preoccupations, and that dreaming about waking concerns is not adaptive but reflective, in a similar way that waking thought or daydreaming is reflective, of what is uppermost in the mind of the dreamer. A relatively small body of research (e.g., Barrett, 1993; Cartwright, 1991; Kramer, 1993) relating to individuals who have experienced major stressful life events, is often cited as support for the theory that dreams serve the specific function of helping us to adjust or adapt to current events. Until recently, this body of work has gone largely unexamined and unreplicated, though some have questioned the findings and their implications for the function of dreaming. The research presented in this thesis examined whether dream content reflects a process of adjustment in people who had recently experienced a marital separation, by investigating the relationship between their dream content in relation to measures of adjustment over time. In Study 1, 97 recently separated participants and 93 married controls were tested on personality and coping factors, asked to answer questions about their dream content, and then monitored over 12 months for change in their adjustment. In Study 2, a subset of 42 separated participants kept dream logs for a period of four weeks. Their dream reports were subjected to a qualitative analysis of thematic content, including threat and threat mastery, and analyses were conducted to explore the relationship between threat content, mastery and adjustment. In Study 3, a subset of eight Study 2 participants participated in a case study analysis which investigated contextual information about their individual situations in relation to their dream content and adjustment, in order to explore, in a more detailed way, the relationship between dream themes, adjustment, and waking concerns. Study 4 was designed to compare the findings of the previous studies with a separate sample, using three different methodologies for the collection of dream content data. This study was carried out to replicate the previous studies with the addition of a laboratory-based data collection technique. In Study 4, 18 separated participants spent one night in the sleep laboratory, monitored with a Nightcap, which allowed dream data to be collected from them via questionnaires, dream logs, and REM awakenings. Across all of the studies, and regardless of the method used to measure dream recall and content, there was a significant concurrent relationship between better adjustment and fewer dreams relating to participants’ marital situations. Those with the most distress were the same ones who were dreaming excessively about their separation. These findings suggest that dreams are continuous with waking preoccupation, and do not function to aid adjustment. As such, they did not support the functional adaptation theories of dreaming. The findings were more consistent with the cognitive theories of dreaming, including the theory that dreams have meaning, but no independent function of their own. A significant relationship was, however, found between ego strength, coping style and adjustment, highlighting the greater influence of internal personal resources in adjusting to difficult life circumstances. While these findings do not discount the suggestion that individuals derive significant personal meaning from their dreams, nor the possibility that dreams may reflect something of the function of REM sleep, they do suggest that “adaptationist” assumptions of functional theories of dreaming may be unfounded.