More Like this

1. Trust/distrust in Multi-disciplinary Collaboration: Some Feminist Reflections

   https://doi.org/10.28968/cftt.v6i2.32956  

   Leighton, Mary; Roberts, Elizabeth F (November 2020, Catalyst: Feminism, Theory, Technoscience)

   null (Ed.)

   In this paper we theorize trust as emerging in different material/infrastructural and epistemic realities as part of our multidisciplinary collaboration about water, called Neighborhood Environments as Socio-Techno-Bio Systems: Water Quality, Public Trust, and Health in Mexico City (NESTSMX)”. This collaboration, led by feminist anthropologists, brings together anthropology, environmental-public health, and environmental engineering researchers to analyze how neighborhoods as “socio-techno-bio systems” shape how people trust or distrust water. Our project follows the infrastructures and social structures that move water in and out of neighborhoods, households, and bodies making them trust it more or less. At the same time our multi-disciplinary research team inhabits different epistemic research environments that creates tensions about how we make knowledge and what counts as data. Trust and distrust then shapes how we constitute both our object of inquiry and how we know it. 

   more » « less
2. Understanding the practices, perceptions, and (dis)trust of generative AI among instructors: A mixed-methods study in the U.S. higher education

   https://doi.org/10.1016/j.caeai.2025.100383  

   Lyu, Wenhan; Zhang, Shuang; Chung, Tingting; Sun, Yifan; Zhang, Yixuan (June 2025, Computers and Education: Artificial Intelligence)

   Generative AI (GenAI) has brought opportunities and challenges for higher education as it integrates into teaching and learning environments. As instructors navigate this new landscape, understanding their engagement with and attitudes toward GenAI is crucial. We surveyed 178 instructors from a single U.S. university to examine their current practices, perceptions, trust, and distrust of GenAI in higher education in March 2024. While most surveyed instructors reported moderate to high familiarity with GenAI-related concepts, their actual use of GenAI tools for direct instructional tasks remained limited. Our quantitative results show that trust and distrust in GenAI are related yet distinct; high trust does not necessarily imply low distrust, and vice versa. We also found significant differences in surveyed instructors' familiarity with GenAI across different trust and distrust groups. Our qualitative results show nuanced manifestations of trust and distrust among surveyed instructors and various approaches to support calibrated trust in GenAI. We discuss practical implications focused on (dis)trust calibration among instructors. 

   more » « less
3. Dynamical latent state computation in the male macaque posterior parietal cortex

   https://doi.org/10.1038/s41467-023-37400-4  

   Lakshminarasimhan, Kaushik J.; Avila, Eric; Pitkow, Xaq; Angelaki, Dora E. (April 2023, Nature Communications)

   Abstract Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. We hypothesized that neural populations estimate these states by processing sensory history through recurrent interactions which reflect the internal model of the world. To test this, we recorded brain activity in posterior parietal cortex (PPC) of monkeys navigating by optic flow to a hidden target location within a virtual environment, without explicit position cues. In addition to sequential neural dynamics and strong interneuronal interactions, we found that the hidden state - monkey’s displacement from the goal - was encoded in single neurons, and could be dynamically decoded from population activity. The decoded estimates predicted navigation performance on individual trials. Task manipulations that perturbed the world model induced substantial changes in neural interactions, and modified the neural representation of the hidden state, while representations of sensory and motor variables remained stable. The findings were recapitulated by a task-optimized recurrent neural network model, suggesting that task demands shape the neural interactions in PPC, leading them to embody a world model that consolidates information and tracks task-relevant hidden states. 

   more » « less
4. The Low/High Index of Pupillary Activity

   https://doi.org/10.1145/3313831.3376394  

   Duchowski, Andrew T.; Krejtz, Krzysztof; Gehrer, Nina A.; Bafna, Tanya; Bækgaard, Per (April 2020, Proceedings of CHI 2020: Human Factors in Computing)

   A novel eye-tracked measure of pupil diameter oscillation is derived as an indicator of cognitive load. The new metric, termed the Low/High Index of Pupillary Activity (LHIPA), is able to discriminate cognitive load (vis-à-vis task difficulty) in several experiments where the Index of Pupillary Activity fails to do so. Rationale for the LHIPA is tied to the functioning of the human autonomic nervous system yielding a hybrid measure based on the ratio of Low/High frequencies of pupil oscillation. The paper’s contribution is twofold. First, full documentation is provided for the calculation of the LHIPA. As with the IPA, it is possible for researchers to apply this metric to their own experiments where a measure of cognitive load is of interest. Second, robustness of the LHIPA is shown in analysis of three experiments, a restrictive fixed-gaze number counting task, a less restrictive fixed-gaze n-back task, and an applied eye-typing task. 

   more » « less
5. Investigating dimensions of instructor trust using the words of undergraduate STEM students

   https://doi.org/10.3389/feduc.2025.1617067  

   Zhang, Kathy; Gill, Julia C; Zhang, Tong; Crowley, Lia; Bennie, Juliette; Wagner, Henry; Bauer, Melanie; Hanauer, David; Chen, Xinnian; Graham, Mark J (July 2025, Frontiers in Education)

   Recent work has shown that student trust in their instructor is a key moderator of STEM student buy-in to evidence-based teaching practices (EBTs), enhancing positive student outcomes such as performance, engagement, and persistence. Although trust in instructor has been previously operationalized in related settings, a systematic classification of how undergraduate STEM students perceive trustworthiness in their instructors remains to be developed. Moreover, previous operationalizations impose a structure that often includes distinct domains, such as cognitive and affective trust, that have yet to be empirically tested in the undergraduate STEM context. MethodsTo address this gap, we engage in a multi-step qualitative approach to unify existing definitions of trust from the literature and analyze structured interviews with 57 students enrolled in undergraduate STEM classes who were asked to describe a trusted instructor. Through thematic analysis, we propose that characteristics of a trustworthy instructor can be classified into three domains. We then assess the validity of the three-domain model both qualitatively and quantitatively. First, we examine student responses to determine how traits from different domains are mentioned together. Second, we use a process-model approach to instrument design that leverages our qualitative interview codebook to develop a survey that measures student trust. We performed an exploratory factor analysis on survey responses to quantitatively test the construct validity of our proposed three-domain trust model. Results and discussionWe identified 28 instructor traits that students perceived as trustworthy, categorized into cognitive, affective, and relational domains. Within student responses, we found that there was a high degree of interconnectedness between traits in the cognitive and relational domains. When we assessed the construct validity of the three-factor model using survey responses, we found that a three-factor model did not adequately capture the underlying latent structure. Our findings align with recent calls to both closely examine long-held assumptions of trust dimensionality and to develop context-specific trust measurements. The work presented here can inform the development of a reliable measure of student trust within undergraduate STEM student environments and ultimately improve our understanding of how instructors can best leverage the effectiveness of EBTs for positive student learning outcomes. 

   more » « less