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Open Access Publications from the University of California

Scholarly Works (74 results)

  • Article
Capstone Projects (2005)

Sulu and Sulawesi Seas are in the heart of the coral triangle, the location of the world’s greatest marine biodiversity. For this reason, many non-government organizations have large conservation projects in this area, but they are working in the absence of historical baselines. Historical ecology is a discipline that addresses biological change due to human interactions with nature, and can be used as a tool to gain insight for conservation decisions. Conservation plans have not traditionally included a historical ecology study in their methodology, which is a primary reason that many marine management and conservation attempts have failed.

The scope of this project was to assess historical fishing efforts in Malaysia to provide a perspective for management and restoration of coastal marine systems. This study intends to illuminate changes in ecosystem structure, which have occurred over centuries, in order to increase the chances of success in the region. For this project, historical data was mined with the intent to uncover the ecological stories associated with centuries of marine resource use, and to provide this historical background to the World Wildlife Fund (WWF). This information will add to WWF’s existing knowledge of the area, and will help experts establish conservation goals aimed at reversing the trajectory of decline.

    Cover page: Preliminary Report to the World Wildlife Fund on the Historical Ecology of the Sulu and Sulawesi Marine Ecoregion
    • Article
    • Peer Reviewed
    Asian American Research Journal , Volume 2 (2022)

    Poor mental health is a prevalent public health issue, especially among Asian American populations. Due to cultural barriers, Asian Americans may not understand the concept of mental health and may underutilize mental health resources. With Asian Americans being the largest and fastest-growing racial group in the United States, mental health research is essential to improving the well-being of future generations of Asian American generations. Having a better understanding of the determinants for poor mental health in Asian American communities is critical for effective public health interventions. This qualitative study examined how cultural and social expectations, gender roles, intergenerational trauma, and evolving attitudes influenced the mental health outcomes of six second-generation Asian Americans, one White American, and one Latinx American. Our findings suggest that Asian Americans have a greater burden to succeed academically compared to their white and Latinx counterparts. Furthermore, our study suggests that gender roles and intergenerational trauma may increase the risk of poor mental health outcomes for cisgender females Asian Americans and Southeast Asian Americans, respectively. Finally, our findings suggest mental health is becoming normalized which would ultimately improve mental health outcomes.

      Cover page: The Risk Factors of Poor Mental Health Outcomes in Second-Generation Asian AmericansCreative Commons 'BY-NC-ND' version 4.0 license
      • Article
      • Peer Reviewed
      UC San Francisco Previously Published Works (2020)
      Collaboration within a complicated organization is inherently challenging and can be fraught with discord. Recent emphasis on interdisciplinary and collaborative teamwork in neurology has brought this issue to the forefront of daily practice. The health care system can be complex and opaque, and the stakes-human life-are high. Medical team conflict has been associated with decreased subjective effectiveness, less job satisfaction, and increase in errors. As specialists, neurologists are necessarily embedded within a network of providers and must be adept in the understanding and management of conflictual situations. For the practicing neurologist, it is important to understand team conflict dynamics. Here, management strategies are provided that illustrate how individual neurologists can serve as effective leaders who mitigate harmful effects and capitalize on benefits of team conflict on performance.
        Cover page: Team conflict and the neurologist.
        • Article
        • Peer Reviewed
        UC San Francisco Previously Published Works (2020)
        Collaboration within a complicated organization is inherently challenging and can be fraught with discord. Recent emphasis on interdisciplinary and collaborative teamwork in neurology has brought this issue to the forefront of daily practice. The health care system can be complex and opaque, and the stakes-human life-are high. Medical team conflict has been associated with decreased subjective effectiveness, less job satisfaction, and increase in errors. As specialists, neurologists are necessarily embedded within a network of providers and must be adept in the understanding and management of conflictual situations. For the practicing neurologist, it is important to understand team conflict dynamics. Here, management strategies are provided that illustrate how individual neurologists can serve as effective leaders who mitigate harmful effects and capitalize on benefits of team conflict on performance.
          Cover page: Team conflict and the neurologist.
          • Article
          • Peer Reviewed
          Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 46 (2024)
          As mental representations are standardly thought to underlie all cognitive processes, a major goal of cognitive science has been to uncover representations. Methods for representation learning from behavioral data often model choice or reaction time data alone, but not jointly, leaving out potentially useful information. Here we develop two models of choice and RT in the odd-one-out task, including one based on the Linear Ballistic Accumulator. Parameter recovery simulations show joint modeling of choice and RT with LBA recovers representations more accurately than modeling choice alone with softmax. However, on two empirical datasets of images and words, joint models performed no better than choice-only models, despite a significant correlation of reaction time with two measures of similarity and choice difficulty in both datasets. We speculate on reasons for the unrealized promise of joint modeling of RT and choice in representation learning.
            Cover page: Unrealized promise of joint modeling of choice and reaction time in improving representation learningCreative Commons 'BY' version 4.0 license
            • Article
            • Peer Reviewed
            Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 44 (2022)
            We introduce a new empirical paradigm for studying naturalistic active learning, as well as new computational tools for jointly modeling algorithmic and rational theories of information search. Subjects in our task can ask questions and learn about hundreds of everyday items, but must retrieve queried items from memory. In order to maximize information gain, subjects need to retrieve sequences of dissimilar items. We find that subjects are not able to do this. Instead, associative memory mechanisms lead to the successive retrieval of similar items, an established memory effect known as semantic congruence. The extent of semantic congruence (and thus suboptimality) is unaffected by task instructions and incentives, though subjects are able to identify efficient query sequences when given a choice. Overall, our results indicate that subjects can distinguish between optimal and suboptimal search if explicitly asked to do so, but have difficulty implementing optimal search from memory. We conclude that associative memory processes place critical restrictions on people’s ability to ask good questions in naturalistic active learning tasks.
              Cover page: Limitations to Optimal Search in Naturalistic Active LearningCreative Commons 'BY' version 4.0 license
              • Article
              • Peer Reviewed
              Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 35 (2013)
                Cover page: Modeling the Emergence of Lexicons in Homesign Systems
                • Article
                • Peer Reviewed
                Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 41 (2019)
                The complex judgments we make about the innumerable ob-jects in the world are made on the basis of our representa-tion of those objects. Thus a model of judgment should spec-ify (a) our representation of the many objects in the world,and (b) how we use this knowledge for making judgments.Here we show that word embeddings, vector representationsfor words derived from statistics of word use in corpora, proxythis knowledge, and that accurate models of judgment can betrained by regressing human judgment ratings (e.g., femininityof traits) directly on word embeddings. This method achieveshigher out-of-sample accuracy than a vector similarity-basedbaseline and compares favorably to human inter-rater relia-bility. Word embeddings can also identify the concepts mostassociated with observed judgments, and can thus shed lighton the psychological substrates of judgment. Overall, we pro-vide new methods and insights for predicting and understand-ing high-level human judgment.
                  Cover page: Distributional semantic representations predict high-level human judgment inseven diverse behavioral domains
                  • Article
                  • Peer Reviewed
                  Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 42 (2020)
                    Cover page: Dual Processes in Relational Judgment: A Computational FrameworkCreative Commons 'BY' version 4.0 license
                    • Article
                    • Peer Reviewed
                    Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 42 (2020)
                    We present a general framework for building formal models of naturalistic memory-based decision making. Our frame-work implements established theories of memory search and decision making within a single integrated cognitive system,and uses computational language models to quantify the thoughts over which memory and decision processes operate. Itcan thus describe both the content of the information that is sampled from memory, as well as the processes involved inretrieving and evaluating this information in order to make a decision. The models within our framework can be fit torecall and choice data, and can quantitatively predict choice probability, length of deliberation, retrieved thoughts, andthe effects of decision context. We showcase the power and generality of our framework by applying it to study riskperception, consumer behavior, financial decision making, ethical decision making, legal decision making, food choice,and judgments about well-being, society, and culture.
                      Cover page: Process and Content in Decisions from MemoryCreative Commons 'BY' version 4.0 license
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