The literature on perception of sound source distance reveals a wide range of lisrener accuracy. Most experiments have listeners perform unintuitive tasks, using unnatural sounds presented in impoverished acousric environments. The present experimencs implement an affordance paradigm for which listeners judge the "reachabiliry" of a natural, live sound source in a familiar acoustic environment. Resulrc reveal that listeners are quite accurate in judging whether the source is reachable and are sersitive to the advantage afforded by cwo vs. one degree of freedom reaches. Further analyses reveal that when scaled to an intrirsic bodily dimension, judgrnent differences berween listeners disappear, implicating intrinsically scaled specificational information. A follow-up experiment explores the potential informational support for these judgmencs testing the usefulness of head movements and binaural hearing' Results reveal that whereas head movements had no bearing on either judgment acculacy or consistencv, binaural information did enhance listener consistency. This could suggest that the allometric relation between interaural distance and arm length might provide a basis for auditory reachabiliry judgments.Although there is a vast lirerature on localization of sound sources in the horizontal plane (see Middlebrooks & Green, 199 1 , for a review) , relatively little research has addressed perception of sound source distance. This is surprising because ic is critical for animals to know the location of objects in both planes. For example, a bat uses distance information in timing its interceptive approach to a moth. The same is rrue of a visually impaired individual guiding his approach to switch off a radio. Sound source distance information is also used in guiding vision. For example, Guski (1992) proposed that knowing the changing distance of a looming objectRequests for reprina should be sent to
In recent years, machine learning approaches have been used to classify and extract style from media and have been used to reinforce known chronologies from classical art history. In this work we employ the first ever machine learning analysis of Australian rock art using a data efficient transfer learning approach to identify features suitable for distinguishing styles of rock art. These features are evaluated in a one-shot learning setting. Results demonstrate that known Arnhem Land Rock art styles can be resolved without knowledge of prior groupings. We then analyse the activation space of learned features and report on the relationships between styles and arrange these classes into a stylistic chronology based on distance within the activation space. By generating a stylistic chronology, it is shown that the model is sensitive to both temporal and spatial patterns in the distribution of rock art in the Arnhem Land Plateau region. More broadly, this approach is ideally suited to evaluating style within any material culture assemblage and overcomes the common constraint of small training data sets in archaeological machine learning studies.
In recent years, machine learning approaches have been used to classify and extract style from media and have been used to reinforce known chronologies from classical art history. In this work we employ the first ever machine learning analysis of Australian rock art using a data efficient transfer learning approach to identify features suitable for distinguishing styles of rock art. These features are evaluated in a one-shot learning setting. Results demonstrate that known Arnhem Land Rock art styles can be resolved without knowledge of prior groupings. We then analyse the activation space of learned features and report on the relationships between styles and arrange these classes into a stylistic chronology based on distance within the activation space. By generating a stylistic chronology, it is shown that the model is sensitive to both temporal and spatial patterns in the distribution of rock art in the Arnhem Land Plateau region. More broadly, this approach is ideally suited to evaluating style within any material culture assemblage and overcomes the common constraint of small training data sets in archaeological machine learning studies.
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