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1. Networked experiments and modeling for producing collective identity in a group of human subjects using an iterative abduction framework

   https://doi.org/10.1007/s13278-019-0620-8  

   Cedeno‑Mieles, Vanessa; Hu, Zhihao; Ren, Yihui; Deng, Xinwei; Adiga, Abhigin; Barrett, Christopher; Contractor, Noshir; Ekanayake, Saliya; Epstein, Joshua M.; Goode, Brian J.; et al (January 2020, Social network analysis and mining)

   null (Ed.)

   Group or collective identity is an individual’s cognitive, moral, and emotional connection with a broader community, category, practice, or institution. There are many different contexts in which collective identity operates, and a host of application domains where collective identity is important. Collective identity is studied across myriad academic disciplines. Consequently, there is interest in understanding the collective identity formation process. In laboratory and other settings, collective identity is fostered through priming a group of human subjects. However, there have been no works in developing agent-based models for simulating collective identity formation processes. Our focus is understanding a game that is designed to produce collective identity within a group. To study this process, we build an online game platform; perform and analyze controlled laboratory experiments involving teams; build, exercise, and evaluate network-based agent-based models; and form and evaluate hypotheses about collective identity. We conduct these steps in multiple abductive iterations of experiments and modeling to improve our understanding of collective identity as this looping process unfolds. Our work serves as an exemplar of using abductive looping in the social sciences. Findings on collective identity include the observation that increased team performance in the game, resulting in increased monetary earnings for all players, did not produce a measured increase in collective identity among them. 

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2. GIFTS: Making Research Experiences Meaningful through Critical Self-Reflection

   DeCrescenzo, Peter; Jangha, S. (June 2023, American Society of Engineering Education)

   In this Great Ideas for Teaching Students (GIFTS) paper, we offer learning outcomes that we are beginning to recognize from our eight-week research experience for undergraduates (REU). There are four characteristics that have been found to be essential to success in Science, Technology, Engineering, and Mathematics (STEM) fields: a strong sense of STEM identity, scientific self-efficacy, a sense of belonging, and a psychological sense of community. This is especially true for first-year and transfer students pursuing STEM undergraduate degrees. A variety of studies have been published that go into detail about why these characteristics in particular have such a significant effect on student performance and retention. This paper will present Critical Self-Reflection as a practical way to integrate development of these characteristics into student research experiences to foster experiential learning that goes beyond increasing technical skills. STEM students are not often trained to critically self-reflect on their experiences in classroom and research settings. An inability for undergraduates to reflect intentionally on their experiences creates greater risk for attrition from STEM disciplines. Curated reflective experiences in collaborative learning settings can offer professional development opportunities to enhance students’ social and technical communication skills. There are four phases within the scaffolded Critical Self-Reflection framework: Learning to Reflect, Reflection for Action, Reflection in Action, and Reflection on Action. When applying the evidence-based practice, STEM undergraduate researchers describe their perceptions via three activities: creating a legacy statement, participating in facilitated dialogue sessions, and writing curated reflection journal entries within an REU. Through critical self-reflection exercises, we are beginning to find growth of first-year and transfer STEM undergraduates in the following areas: understanding of their role in the lab; confidence in their researcher identity; expression of agency; observation and communication skills; and intentionality for action. Participating in this self-reflection allows students to make meaning of their experience enabling them to hone the aforementioned characteristics that creates a pathway from their undergraduate experience to undergraduate degree completion, graduate degree attainment, and to the STEM workforce. 

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3. The law in computation: What machine learning, artificial intelligence, and big data mean for law and society scholarship

   https://doi.org/10.1111/lapo.12164  

   DoCarmo, Tania; Rea, Stephen; Conaway, Evan; Emery, John; Raval, Noopur (April 2021, Law & Policy)

   Abstract Computational systems, including machine learning, artificial intelligence, and big data analytics, are not only inescapable parts of social life but are also reshaping the contours of law and legal practice. We propose turning more law and social science (LSS) attention to new technological developments through the study of “law in computation,” that is, computational systems' integration with regulatory and administrative procedures, the sociotechnical infrastructures that support them, and their impact on how individuals and populations are interpellated through the law. We present a range of cases in three areas of inquiry ‐ algorithmic governance, jurisdiction and agency ‐ on issues such as immigration enforcement, data sovereignty, algorithmic warfare, biometric identity regimes, and gig economies, for which examining law in computation illuminates how new technological systems' integration with legal processes pushes the distinction between “law on the books” and “law in action” into new domains.We then propose future directions and methods for research. As computational systems become ever more sophisticated, understanding the law in computation is critical not only for LSS scholarship, but also for everyday civics. 

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4. Temporal Dynamics of Brain Activity Predicting Sense of Agency over Muscle Movements

   https://doi.org/10.1523/JNEUROSCI.1116-23.2023  

   Veillette, John P.; Lopes, Pedro; Nusbaum, Howard C. (September 2023, The Journal of Neuroscience)

   Our muscles are the primary means through which we affect the external world, and the sense of agency (SoA) over the action through those muscles is fundamental to our self-awareness. However, SoA research to date has focused almost exclusively on agency over action outcomes rather than over the musculature itself, as it was believed that SoA over the musculature could not be manipulated directly. Drawing on methods from human–computer interaction and adaptive experimentation, we use human-in-the-loop Bayesian optimization to tune the timing of electrical muscle stimulation so as to robustly elicit a SoA over electrically actuated muscle movements in male and female human subjects. We use time-resolved decoding of subjects' EEG to estimate the time course of neural activity which predicts reported agency on a trial-by-trial basis. Like paradigms which assess SoA over action consequences, we found that the late (post-conscious) neural activity predicts SoA. Unlike typical paradigms, however, we also find patterns of early (sensorimotor) activity with distinct temporal dynamics predicts agency over muscle movements, suggesting that the “neural correlates of agency” may depend on the level of abstraction (i.e., direct sensorimotor feedback versus downstream consequences) most relevant to a given agency judgment. Moreover, fractal analysis of the EEG suggests that SoA-contingent dynamics of neural activity may modulate the sensitivity of the motor system to external input. SIGNIFICANCE STATEMENTThe sense of agency, the feeling of “I did that,” when directing one's own musculature is a core feature of human experience. We show that we can robustly manipulate the sense of agency over electrically actuated muscle movements, and we investigate the time course of neural activity that predicts the sense of agency over these actuated movements. We find evidence of two distinct neural processes: a transient sequence of patterns that begins in the early sensorineural response to muscle stimulation and a later, sustained signature of agency. These results shed light on the neural mechanisms by which we experience our movements as volitional. 

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5. Examining Black Diaspora Participation in Engineering using Narrative Inquiry

   Shittu, E.; Dashiell-Shoffner, J.; Kim, H.N. (January 2021, ASEE CoNECD Conference 2021.)

   null (Ed.)

   This paper examines the use of quantitative research agendas on systems modeling to study anticipatory cognition and cultural competency. This combination results in an integrative science approach to explore the intersectionality of metacognition, academic self-efficacy, stereotype threat, scholarly reasoning and identity among minority black diaspora graduate students. Extant literature focuses on social support models, but the novelty of the approach in this paper examines metacognition in action within a culturally-aware context. Data were collected as semi-structured narrative inquiry to capture metacognition during learning using narrative identity construction as a tool. There was a total of five students in the study including three females and two male participants in their first year of the graduate studies. However, the analysis focused on three of the participants who provided data consistently for eleven months -- two males and one female. The participants provided data including responses to Likert scale questions, and weekly video narratives in response to three sets of questions each week in an n-of-1 big data approach. This approach has the empirical benefit of allowing more inclusive and personalized analyses to draw conclusions. By observing the requirements of an approved IRB protocol, the analysis based on the transcripts of the video recordings, and the examination of change within each individual over time was confidential and conducted with de-identified data. Video recordings are coded and analyzed using HyperRESEARCH TM version 3.7.5. The result calibrates students’ comprehension, integration, and application of impactful, data-driven research skills. The metacognitive development portion examines the influence and dynamics of anticipatory cognition, stereotype threat, identity, and academic self-efficacy as the students’ progress through the process of quantitative skills mastery. This paper reports on the highlights of the distilled data on: (i) anticipatory cognition -- construct to describe use of prospective memory to simulate future associations and expected outcomes; (ii) academic self-efficacy -- captures the perceived level of confidence in the participants to engage successfully in specific cognitive acts associated with academic mastery; (iii) stereotype threat -- captures the anxiety associated with the salience of status as a member of a group that is stereotyped as underperforming in a specific area; (iv) identity or categorization of the self as a scholar and engineer. Other themes emerging include perseverance or determination, isolation, extant knowledge, future anticipation, and problem solution focus. 

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