TY - JOUR A1 - Felisatti, Arianna A1 - Laubrock, Jochen A1 - Shaki, Samuel A1 - Fischer, Martin H. T1 - A biological foundation for spatial–numerical associations BT - the brain's asymmetric frequency tuning JF - Annals of the New York Academy of Sciences N2 - "Left" and "right" coordinates control our spatial behavior and even influence abstract thoughts. For number concepts, horizontal spatial-numerical associations (SNAs) have been widely documented: we associate few with left and many with right. Importantly, increments are universally coded on the right side even in preverbal humans and nonhuman animals, thus questioning the fundamental role of directional cultural habits, such as reading or finger counting. Here, we propose a biological, nonnumerical mechanism for the origin of SNAs on the basis of asymmetric tuning of animal brains for different spatial frequencies (SFs). The resulting selective visual processing predicts both universal SNAs and their context-dependence. We support our proposal by analyzing the stimuli used to document SNAs in newborns for their SF content. As predicted, the SFs contained in visual patterns with few versus many elements preferentially engage right versus left brain hemispheres, respectively, thus predicting left-versus rightward behavioral biases. Our "brain's asymmetric frequency tuning" hypothesis explains the perceptual origin of horizontal SNAs for nonsymbolic visual numerosities and might be extensible to the auditory domain. KW - hemispheric asymmetry KW - numerical cognition KW - SNARC effect KW - spatial KW - frequency tuning KW - spatial-numerical associations KW - spatial vision Y1 - 2020 U6 - https://doi.org/10.1111/nyas.14418 SN - 0077-8923 SN - 1749-6632 VL - 1477 IS - 1 SP - 44 EP - 53 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Schutt, Heiko Herbert A1 - Wichmann, Felix A. T1 - An image-computable psychophysical spatial vision model JF - Journal of vision N2 - A large part of classical visual psychophysics was concerned with the fundamental question of how pattern information is initially encoded in the human visual system. From these studies a relatively standard model of early spatial vision emerged, based on spatial frequency and orientation-specific channels followed by an accelerating nonlinearity and divisive normalization: contrast gain-control. Here we implement such a model in an image-computable way, allowing it to take arbitrary luminance images as input. Testing our implementation on classical psychophysical data, we find that it explains contrast detection data including the ModelFest data, contrast discrimination data, and oblique masking data, using a single set of parameters. Leveraging the advantage of an image-computable model, we test our model against a recent dataset using natural images as masks. We find that the model explains these data reasonably well, too. To explain data obtained at different presentation durations, our model requires different parameters to achieve an acceptable fit. In addition, we show that contrast gain-control with the fitted parameters results in a very sparse encoding of luminance information, in line with notions from efficient coding. Translating the standard early spatial vision model to be image-computable resulted in two further insights: First, the nonlinear processing requires a denser sampling of spatial frequency and orientation than optimal coding suggests. Second, the normalization needs to be fairly local in space to fit the data obtained with natural image masks. Finally, our image-computable model can serve as tool in future quantitative analyses: It allows optimized stimuli to be used to test the model and variants of it, with potential applications as an image-quality metric. In addition, it may serve as a building block for models of higher level processing. KW - model KW - spatial vision KW - image-computable KW - psychophysics Y1 - 2017 U6 - https://doi.org/10.1167/17.12.12 SN - 1534-7362 VL - 17 PB - Association for Research in Vision and Opthalmology CY - Rockville ER -