Park, Choon Seog (2009-08). Performance, Development, and Analysis of Tactile vs. Visual Receptive Fields in Texture Tasks. Doctoral Dissertation. Thesis uri icon

abstract

  • Texture segmentation is an effortless process in scene analysis, yet its neural mechanisms are not sufficiently understood. A common assumption in most current approaches is that texture segmentation is a vision problem. However, considering that texture is basically a surface property, this assumption can at times be misleading. One interesting possibility is that texture may be more intimately related with touch than with vision. Recent neurophysiological findings showed that receptive fields (RFs) for touch resemble that of vision, albeit with some subtle differences. To leverage on this, here I propose three ways to investigate the tactile receptive fields in the context of texture processing: (1) performance, (2) development, and (3) analysis. For performance, I tested how such distinct properties in tactile receptive fields can affect texture segmentation performance, as compared to that of visual receptive fields. Preliminary results suggest that touch has an advantage over vision in texture segmentation. These results support the idea that texture is fundamentally a tactile (surface) property. The next question is what drives the two types of RFs, visual and tactile, to become different during cortical development? I investigated the possibility that tactile RF and visual RF emerge based on the same cortical learning process, where the only difference is in the input type, natural-scene-like vs. texture-like. The main result is that RFs trained on natural scenes develop RFs resembling visual RFs, while those trained on texture resemble tactile RFs. These results again suggest a tight link between texture and the tactile modality, from a developmental context. To investigate further the functional properties of these RFs in texture processing, the response of tactile RFs and visual RFs were analyzed with manifold learning and with statistical approaches. The results showed that touch-based manifold seems more suitable for texture processing and desirable properties found in visual RF response can carry over to those in the tactile domain. These results are expected to shed new light on the role of tactile perception of texture; help develop more powerful, biologically inspired texture segmentation algorithms; and further clarify the differences and similarities between touch and vision.
  • Texture segmentation is an effortless process in scene analysis, yet its neural

    mechanisms are not sufficiently understood. A common assumption in most current

    approaches is that texture segmentation is a vision problem. However, considering

    that texture is basically a surface property, this assumption can at times be misleading.

    One interesting possibility is that texture may be more intimately related with

    touch than with vision. Recent neurophysiological findings showed that receptive

    fields (RFs) for touch resemble that of vision, albeit with some subtle differences. To

    leverage on this, here I propose three ways to investigate the tactile receptive fields in

    the context of texture processing: (1) performance, (2) development, and (3) analysis.

    For performance, I tested how such distinct properties in tactile receptive fields

    can affect texture segmentation performance, as compared to that of visual receptive

    fields. Preliminary results suggest that touch has an advantage over vision in texture

    segmentation. These results support the idea that texture is fundamentally a tactile

    (surface) property.

    The next question is what drives the two types of RFs, visual and tactile, to

    become different during cortical development? I investigated the possibility that

    tactile RF and visual RF emerge based on the same cortical learning process, where

    the only difference is in the input type, natural-scene-like vs. texture-like. The main result is that RFs trained on natural scenes develop RFs resembling visual RFs, while

    those trained on texture resemble tactile RFs. These results again suggest a tight

    link between texture and the tactile modality, from a developmental context.

    To investigate further the functional properties of these RFs in texture processing,

    the response of tactile RFs and visual RFs were analyzed with manifold learning

    and with statistical approaches. The results showed that touch-based manifold seems

    more suitable for texture processing and desirable properties found in visual RF response

    can carry over to those in the tactile domain.

    These results are expected to shed new light on the role of tactile perception

    of texture; help develop more powerful, biologically inspired texture segmentation

    algorithms; and further clarify the differences and similarities between touch and

    vision.

publication date

  • August 2009