Motion Displaces Population Receptive Fields in the Direction Opposite to Motion

Abstract

Motion signals can bias the perceived position of visual stimuli. While the apparent position of a stimulus is biased in the direction of motion, electro-physiological studies have shown that the receptive field (RF) of neurons is shifted in the direction opposite to motion, at least in cats and macaque monkeys. In humans, it remains unclear how motion signals affect population RF (pRF) estimates. We addressed this question using psychophysical measurements and functional magnetic resonance imaging (fMRI) at 7 Tesla. We systematically varied two factors: the motion direction of the carrier pattern (inward, outward and flicker motion) and the contrast of the mapping stimulus (low and high stimulus contrast). We observed that while physical positions were identical across all conditions, presence of low-contrast motion, but not high-contrast motion, shifted perceived stimulus position in the direction of motion. Correspondingly, we found that pRF estimates in early visual cortex were shifted against the direction of motion for low-contrast stimuli but not for high stimulus contrast. We offer an explanation in form of a model for why apertures are perceptually shifted in the direction of motion even though pRFs shift in the opposite direction.Keywords visual neuroscience · position perception · population receptive fields · visual field projections 1 IntroductionAn important task of the visual system is to infer the location of objects in our environment. A wide range of psychophysical studies shows that motion signals lead to systematic localisation biases [1,2,3,4,5,6,7,8,9]. In illusions called motion-induced position shifts (MIPS), a coherent motion signal shifts the apparent location of a stimulus [1]. For example, when drifting Gabor patches are presented within a stationary aperture, the stimulus appears shifted in the direction of motion [2,6,7]. Such illusions raise the question how our visual system encodes location and how, in the case of MIPS, the apparent position shift can be explained. Furthermore, they offer a dissociation between the physical and the perceived position of a stimulus that can clarify which neuronal processes correspond to the apparent position of the stimulus. MOTION DISPLACES POPULATION RECEPTIVE FIELDSThe magnitude of MIPS is known to depend on spatial and temporal properties of the stimulus. MIPS are larger when the stimulus is shown for longer duration (tested up to 453 ms; [6]), presented at higher speed [6,9] or at higher eccentricities [10,6,9]. The magnitude of MIPS furthermore depends on spatial blurring of the presented stimulus. Blurred stimulus edges lead to larger perceptual displacements than sharp edges [4,9] and increasing the size of the Gaussian envelope of a Gabor stimulus yields larger MIPS [4]. Arnold et al. [7] have suggested that MIPS are driven by modulation of apparent contrast of the stimulus. Supporting this suggestion, they reported perceived position shifts when observers were asked to match the extremities of two contrast envelopes (low-contrast region),...

Position representations of moving objects align with real-time position in the early visual response

“…On the one hand, there is suggestive evidence that position representations in areas V4 ( Sundberg et al, 2006 ) and V5 ( Maus et al, 2013a ) are shifted for moving objects, potentially reflecting the effect of motion extrapolation in those areas. That interpretation is consistent with recent fMRI ( Schneider et al, 2019 ; Harvey and Dumoulin, 2016 ), theoretical ( Hogendoorn and Burkitt, 2019 ) and psychophysical ( van Heusden et al, 2019 ) work suggesting that motion extrapolation mechanisms operate at multiple levels of the visual system. On the other hand, shifted position representations in higher areas might simply result from those areas inheriting extrapolated information from upstream areas such as V1.…”

Motion extrapolation in the flash-lag effect depends on perceived, rather than physical speed

“…This finding is consistent with an explanation of the FLE in terms of visual motion extrapolation. It also corroborates a growing body of evidence supporting the existence of neural mechanisms involved in extrapolation in both animal models (e.g., Berry et al,1999;Jancke, Erlhagen, Schöner, & Dinse, 2004;Sunberg, Fallah, & Reynolds, 2006;Schwartz, Taylor, Fisher, Harris, & Berry, 2007;Palmer, Marre, Berry, & Bialek, 2015;Subramaniyan et al, 2018;Benvenuti et al, 2020) and human neuroimaging (e.g., Ekman, Kok, & de Lange, 2017;Schneider, Marquardt, Sengupta, De Martino, & Goebel, 2019;Hogendoorn & Burkitt, 2018;Blom, Feuerriegel, Johnson, Bode, & Hogendoorn, 2020), and is consistent with several decades investigating motion extrapolation in perception (Hubbard, 2005(Hubbard, , 2014(Hubbard, , 2018Nijhawan, 2002Nijhawan, , 2008Maus et al, 2010;Hogendoorn, 2020).…”

“…This finding is consistent with an explanation of the FLE in terms of visual motion extrapolation. It also corroborates a growing body of evidence supporting the existence of neural mechanisms involved in extrapolation in both animal models (e.g.,Benvenuti et al, 2020;Berry et al,1999;Jancke, Erlhagen, Schöner, & Dinse, 2004;Palmer, Marre, Berry, & Bialek, 2015;Schwartz, Taylor, Fisher, Harris, & Berry, 2007;Subramaniyan et al, 2018; Sunberg, Fallah, & Reynolds, 2006) and human neuroimaging (e.g.,Blom, Feuerriegel, Johnson, Bode, & Hogendoorn, 2020;Ekman, Kok, & de Lange, 2017;Hogendoorn & Burkitt, 2018;Schneider, Marquardt, Sengupta, De Martino, & Goebel, 2019), and is consistent with several decades investigating motion extrapolation in perception(Hogendoorn, 2020;Hubbard, 2005Hubbard, , 2014Hubbard, , 2018Maus et al, 2010;Nijhawan, 2002Nijhawan, , 2008.…”

Motion extrapolation in the High-Phi illusion: Analogous but dissociable effects on perceived position and perceived motion

“…This accompanies the early afferent position signal, forming a population code that represents both position and velocity, as proposed by previous theoretical (Hogendoorn & Burkitt, 2019) and computational studies (Khoei, Masson, & Perrinet, 2017;Kwon, Tadin, & Knill, 2015). These models predict that this visual motion signal causes a shift in the receptive fields of downstream neural populations in the direction opposite to the direction of motion -a prediction consistent with fMRI evidence (Harvey & Dumoulin, 2016;Liu, Ashida, Smith, & Wandell, 2006;Maus, Fischer, & Whitney, 2013;Schneider et al, 2019;Whitney et al, 2003). As a result, subsequent incoming neural signals are processed as if they originated from a position further along the trajectory, leading to a shift in the perceived position of the briefly flashed object.…”

“…Motion signals have been shown to be available to influence position signals within less than 1 ms ( Nijhawan, 2008 ; Westheimer & McKee, 1977 ), and neural extrapolation mechanisms have been identified as early as the retina ( Berry, Brivanlou, Jordan, & Meister, 1999 ; Hosoya, Baccus, & Meister, 2005 ; Schwartz, Taylor, Fisher, Harris, & Berry, 2007 ). In humans, evidence from electroencephalogram (EEG; Hogendoorn, Verstraten, & Cavanagh, 2015 ), functional magnetic resonance imaging (fMRI; Schneider, Marquardt, Sengupta, Martino, & Goebel, 2019 ), and behavioral studies ( van Heusden, Harris, Garrido, & Hogendoorn, 2019 ) converges to indicate that position information is influenced by motion signals even before that information reaches primary visual cortex.…”