Skip to main content
eScholarship
Open Access Publications from the University of California

Scholarly Works (3 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2013)

Theater, historically, has served as a site for intense debates about ontology, specifically as concerns distinctions between what is real, in Plato's sense, and what is "merely" mimetic. Similar ontological debates have attended the development of new media technologies, which are often figured as enabling shady activities like impersonation, simulation, and piracy (on the internet, after all, no one knows you're a dog, and Photoshopped images of Iranian missile tests circulate globally nearly instantly), and arousing related questions about authenticity, identity and ownership. My dissertation brings debates about realness and mimesis to three sites of twenty-first century performance: intermedial theater, specifically the Wooster Group's 2007 Hamlet and the mediatized Burton/Gielgud Hamlet it deconstructs; reality television, specifically MTV's "scripted reality" show The Hills; and alternate reality gaming, in particular the 2007 future forecasting game World Without Oil. In each of these sites I examine the ways mediatization and theatricality work, sometimes in concert and sometimes in conflict, to complicate and perform realness.

Questions of realness, authenticity and honesty have long haunted Western theater traditions, and so I use the lens of Western theatrical acting to address related questions in new media performance contexts. While only one of the dissertation's sites of analysis positions itself explicitly as theater, each is a theatrical situation that places its players in a subjunctive stance--one that depends on their acting "as if" circumstances were other than they are--and so evokes the concerns and "ontological queasiness" (Barish) that accompanies the mimetic activity of Western theater. In bringing together these disparate sites of mixed realities, I make a case for the opportunities this mixing and subjunctivity enable: mischievous interventions into supposedly stable differences between the real and its various others (the artificial, the fake, the feigned, the staged or rehearsed). An uncertain real is often described in terms of loss and threat, of radical instability that poses either overt or latent dangers. My argument, however,

prioritizes the ways in which refiguring relationships to realness might be occasion opportunity, especially the opportunity for exploration, excitement, even productive mischief.

    Cover page: Acting Real: Mimesis and Media in Performance
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2022)
    Humans appear to represent many forms of knowledge in associative networks whose nodes are multiply connected, including sensory, spatial, and semantic. Recent work has shown that explicitly augmenting artificial agents with such graph-structured representations endows them with more human-like capabilities of compositionality and transfer learning. An open question is how humans acquire these representations. Previously, it has been shown that humans can learn to navigate graph-structured conceptual spaces on the basis of direct experience with trajectories that intentionally draw the network contours (Schapiro, Kustner, & Turk-Browne, 2012; Schapiro, Turk-Browne, Botvinick, & Norman, 2016), or through direct experience with rewards that covary with the underlying associative distance (Wu, Schulz, Speekenbrink, Nelson, & Meder, 2018). Here, we provide initial evidence that this capability is more general, extending to learning to reason about shortest-path distances across a graph structure acquired across disjoint experiences with randomized edges of the graph - a form of latent learning. In other words, we show that humans can infer graph structures, assembling them from disordered experiences. We further show that the degree to which individuals learn to reason correctly and with reference to the structure of the graph corresponds to their propensity, in a separate task, to use model-based reinforcement learning to achieve rewards. This connection suggests that the correct acquisition of graph-structured relationships is a central ability underlying forward planning and reasoning, and may be a core computation across the many domains in which graph-based reasoning is advantageous.
      Cover page: Humans can navigate complex graph structures acquired during latent learning.
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2022)
      A goal of computational psychiatry is to ground symptoms in basic mechanisms. Theory suggests that avoidance in anxiety disorders may reflect dysregulated mental simulation, a process for evaluating candidate actions. If so, these covert processes should have observable consequences: choices reflecting increased and biased deliberation. In two online general population samples, we examined how self-report symptoms of social anxiety disorder predict choices in a socially framed reinforcement learning task, the patent race, in which the pattern of choices reflects the content of deliberation. Using a computational model to assess learning strategy, we found that self-report social anxiety was indeed associated with increased deliberative evaluation. This effect was stronger for a particular subset of feedback ('upward counterfactual') in one of the experiments, broadly matching the biased content of rumination in social anxiety disorder, and robust to controlling for other psychiatric symptoms. These results suggest a grounding of symptoms of social anxiety disorder in more basic neuro-computational mechanisms.
        Cover page: Increased and biased deliberation in social anxiety
        Top