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Open Access Publications from the University of California

Scholarly Works (4 results)

  • Article
  • Peer Reviewed
Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 43 (2021)
Grapheme-color synesthetes experience linguistic symbols as having a consistent color (e.g., “The letter R is burgundy.”). Intriguingly, certain letters tend to be associated with certain colors, and these biases are not random: numerous properties of letters influence which letter is associated with which color. These influences, called “Regulatory Factors” (RFs), each explain some fraction of the variation in observed associations. No comprehensive model of the influences on grapheme-color associations exists: RFs have only been measured in isolation, are not always operationalized consistently, and often make competing predictions that cannot be accounted for in a univariate model. Here, we describe a statistical framework that integrates the predictions of all RFs into a single model, and thus yields a unified account of their influence on grapheme-color associations. Our model also links these predictions to measurable properties of language, offering a window into the multifactorial contributions to letter representation in the brain.
    Cover page: A Unifying Model of Grapheme-Color Associations in Synesthetes and ControlsCreative Commons 'BY' version 4.0 license
    • Thesis
    • Peer Reviewed
    UC San Diego Electronic Theses and Dissertations (2019)

    Grapheme-color synesthesia is a neurological phenomenon in which graphemes, such as letters of the alphabet, evoke an additional, automatic, consistent sensation of color (e.g. “R is sky blue”). An as-yet unsolved question in the field of synesthesia research is what causes a particular synesthete to associate a particular grapheme with a particular color. The present work explores how properties of synesthesia and properties of language interact during development to determine which color synesthetes (and even non-synesthetes) will associate with a particular grapheme.

    Chapter One demonstrates that hypotheses about color etiology in synesthesia, which have long been confounded, can be disentangled using a multi-language dataset: language can help us understand synesthesia. Chapter Two describes a case study of a Bengali grapheme-color synesthete, the first ever report of synesthetic phenomenology in an abugida writing system. Systematic differences between synesthesia in Bengali and in English suggest ways in which Bengali grapheme representations might be structured: synesthesia can help us understand language. Chapter Three demonstrates that synesthesia research can generalize to the (much larger) non-synesthetic population by engaging with a long-standing debate about the whether synesthesia is "special" or on one end of a continuum of experience. Non-synesthetes are found to have "synesthesia-like" associations for a subset of letters, suggesting that differences between synesthetes and non-synesthetes are differences in degree, not in kind. Chapter Four probes the limits of this generalizability, demonstrating that synesthetic and non-synesthetic associations are sometimes different because synesthetes' associations are "locked in" during childhood, whereas non-synesthetes' associations remain flexible through adulthood. Together, these studies suggest that synesthesia offers a window into the brain, that lets us literally see how we think about letters.

      Cover page: What Language Tells Us About Synesthesia, What Synesthesia Tells Us About Language
      • Article
      • Peer Reviewed
      UC San Diego Previously Published Works (2019)
      In engineering, the "softness" of an object, as measured by an indenter, manifests as two measurable parameters: (i) indentation depth and (ii) contact area. For humans, softness is not well defined, although it is believed that perception depends on the same two parameters. Decoupling their relative contributions, however, has not been straightforward because most bulk-"off-the-shelf"-materials exhibit the same ratio between the indentation depth and contact area. Here, we decoupled indentation depth and contact area by fabricating elastomeric slabs with precise thicknesses and microstructured surfaces. Human subject experiments using two-alternative forced-choice and magnitude estimation tests showed that the indentation depth and contact area contributed independently to perceived softness. We found an explicit relationship between the perceived softness of an object and its geometric properties. Using this approach, it is possible to design objects for human interaction with a desired level of perceived softness.
        Cover page: Role of indentation depth and contact area on human perception of softness for haptic interfaces
        • Article
        • Peer Reviewed
        UC San Diego Previously Published Works (2020)
        Haptic devices are in general more adept at mimicking the bulk properties of materials than they are at mimicking the surface properties. This paper describes a haptic glove capable of producing sensations reminiscent of three types of near-surface properties: hardness, temperature, and roughness. To accomplish this mixed mode of stimulation, three types of haptic actuators were combined: vibrotactile motors, thermoelectric devices, and electrotactile electrodes made from a stretchable conductive polymer synthesized in our laboratory. This polymer consisted of a stretchable polyanion which served as a scaffold for the polymerization of poly(3,4-ethylenedioxythiophene) (PEDOT). The scaffold was synthesized using controlled radical polymerization to afford material of low dispersity, relatively high conductivity (0.1 S cm-1), and low impedance relative to metals. The glove was equipped with flex sensors to make it possible to control a robotic hand and a hand in virtual reality (VR). In psychophysical experiments, human participants were able to discern combinations of electrotactile, vibrotactile, and thermal stimulation in VR. Participants trained to associate these sensations with roughness, hardness, and temperature had an overall accuracy of 98%, while untrained participants had an accuracy of 85%. Sensations could similarly be conveyed using a robotic hand equipped with sensors for pressure and temperature.
          Cover page: Virtual Texture Generated Using Elastomeric Conductive Block Copolymer in a Wireless Multimodal Haptic Glove
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