Convergent evidence for global processing of shape.

There is an ongoing debate over whether there is convincing evidence in support of global contour integration in shape discrimination tasks, particularly when using radial frequency (RF) patterns as stimuli (Baldwin, Schmidtmann, Kingdom, & Hess, 2016). The objection lies in the previous use of high-threshold theory (HTT), rather than signal detection theory (SDT) to model the probability summation estimates of observer thresholds to determine whether integration of information is occurring around the contour. Here we used a discrimination at threshold method to establish evidence of global processing of two frequently used RF patterns (RF3 and RF5) that does not require mathematical modeling. To provide a bridge between current and past research we examined the two proposed methods, finding that HTT produced probability summation estimates that were more conservative than SDT (when an appropriate number of channels was used to generate estimates). We found no difference in observer thresholds when an RF pattern was presented as the only test stimulus in a block of trials or when two RF patterns were interleaved, and no evidence for a decrease in psychometric slopes with increasing numbers of stimulus elements. These findings are contrary to what is predicted by SDT for a stimulus whose detection conforms to probability summation. Therefore, our results find no evidence that supports probability summation, further demonstrating the importance of using random phase RF patterns while measuring integration around a contour and providing strong evidence for global shape processing around low frequency RF patterns.

[1]  Robert J. Green,et al.  The effect of spatiotemporal displacement on the integration of shape information. , 2018, Journal of vision.

[2]  Robert J. Green,et al.  Integration of shape information occurs around closed contours but not across them. , 2018, Journal of vision.

[3]  Vanessa K Bowden,et al.  Visual search reveals a critical component to shape. , 2018, Journal of vision.

[4]  Robert J. Green,et al.  Global processing of random-phase radial frequency patterns but not modulated lines. , 2017, Journal of vision.

[5]  D. Badcock,et al.  Dissociation of local and global contributions to detection of shape with age. , 2016, Journal of Experimental Psychology: Human Perception and Performance.

[6]  Alex R. Wade,et al.  Multivariate Patterns in the Human Object-Processing Pathway Reveal a Shift from Retinotopic to Shape Curvature Representations in Lateral Occipital Areas, LO-1 and LO-2 , 2016, The Journal of Neuroscience.

[7]  Alex S. Baldwin,et al.  Rejecting probability summation for radial frequency patterns, not so Quick! , 2016, Vision Research.

[8]  F. Kingdom,et al.  Probability, not linear summation, mediates the detection of concentric orientation-defined textures. , 2015, Journal of Vision.

[9]  Vanessa K. Bowden,et al.  Global shape processing: A behavioral and electrophysiological analysis of both contour and texture processing. , 2015, Journal of vision.

[10]  H. Wilson,et al.  Detection and recognition of angular frequency patterns , 2015, Vision Research.

[11]  Alex S. Baldwin,et al.  Modeling probability and additive summation for detection across multiple mechanisms under the assumptions of signal detection theory. , 2015, Journal of vision.

[12]  Badcock David,et al.  Tolerance for local and global differences in the integration of shape information. , 2015, Journal of vision.

[13]  Renita A. Almeida,et al.  Enhanced global integration of closed contours in individuals with high levels of autistic-like traits , 2014, Vision Research.

[14]  Geoff Cumming The New Statistics , 2014, Psychological science.

[15]  J Edwin Dickinson,et al.  Detecting shape change: characterizing the interaction between texture-defined and contour-defined borders. , 2013, Journal of vision.

[16]  D. Badcock,et al.  Near Their Thresholds for Detection, Shapes Are Discriminated by the Angular Separation of Their Corners , 2013, PloS one.

[17]  J Edwin Dickinson,et al.  Further evidence that local cues to shape in RF patterns are integrated globally. , 2012, Journal of vision.

[18]  Renita A. Almeida,et al.  Visual Search Targeting Either Local or Global Perceptual Processes Differs as a Function of Autistic-Like Traits in the Typically Developing Population , 2012, Journal of Autism and Developmental Disorders.

[19]  Gunter Loffler,et al.  Non-linear global pooling in the discrimination of circular and non-circular shapes , 2012, Vision Research.

[20]  K. Mullen,et al.  Evidence that global processing does not limit thresholds for RF shape discrimination. , 2011, Journal of vision.

[21]  Renita A. Almeida,et al.  Visual search performance in the autism spectrum II: The radial frequency search task with additional segmentation cues , 2010, Neuropsychologia.

[22]  Jason Bell,et al.  Local motion effects on form in radial frequency patterns. , 2010, Journal of vision.

[23]  Renita A. Almeida,et al.  A new step towards understanding Embedded Figures Test performance in the autism spectrum: The radial frequency search task , 2010, Neuropsychologia.

[24]  David R. Badcock,et al.  Radial frequency adaptation suggests polar-based coding of local shape cues , 2008, Vision Research.

[25]  David R. Badcock,et al.  Luminance and contrast cues are integrated in global shape detection with contours , 2008, Vision Research.

[26]  David R. Badcock,et al.  Detection of shape in radial frequency contours: Independence of local and global form information , 2007, Vision Research.

[27]  Gunter Loffler,et al.  Local and global contributions to shape discrimination , 2003, Vision Research.

[28]  H. Wilson,et al.  Detection and recognition of radial frequency patterns 1 This research was first reported at the annual meeting of the Association for Research in Vision and Ophthamology, 1996. 1 , 1998, Vision Research.

[29]  D C Van Essen,et al.  Information processing in the primate visual system: an integrated systems perspective. , 1992, Science.

[30]  D G Pelli,et al.  Uncertainty explains many aspects of visual contrast detection and discrimination. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[31]  J. Robson,et al.  Discrimination at threshold: Labelled detectors in human vision , 1981, Vision Research.

[32]  H. Wilson A transducer function for threshold and suprathreshold human vision , 1980, Biological Cybernetics.

[33]  G. Westheimer Diffraction Theory and Visual Hyperacuity* , 1976, American journal of optometry and physiological optics.

[34]  Gerald Westheimer,et al.  VISUAL ACUITY AND HYPERACUITY , 2009 .

[35]  Quick Rf A vector-magnitude model of contrast detection. , 1974 .