Evidence and Counterevidence in Motion Perception

Duijnhouwer, Jacob; Krekelberg, Bart

doi:10.1093/cercor/bhv221

Cited by 8 publications

(19 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, conceptually area MT appears to be operate at a stage of visual processing that is more involved with the extraction of features than the combination of features into invariant representations of objects (Fujita, 2002). MT’s emphasis on extraction is consistent with computational models (Adelson & Bergen, 1985; Joukes et al, 2014) and experimental data that reveal competitive interactions (Gaudio & Huang, 2012; Krekelberg & Albright, 2005; Krekelberg & van Wezel, 2013; Xiao et al, 2014), the weighing of evidence and counter-evidence (Duijnhouwer & Krekelberg, 2015), and segmentation of figure and ground (X. Huang, Albright, & Stoner, 2007; X.…”

Section: Discussionsupporting

confidence: 62%

Testing the assumptions underlying fMRI adaptation using intracortical recordings in area MT

Kar

Krekelberg

2016

Cortex

Self Cite

View full text Add to dashboard Cite

We investigated how neural activity in the middle temporal area of the macaque monkey changes after three seconds of exposure to a visual stimulus and used this to gain insight into the assumptions underlying the fMRI adaptation method (fMRIa). We studied both changes in tuning curves following weak and strong motion stimuli (adaptation) and the differences between a first and second exposure to the same stimulus (repetition suppression). Typically, tuning curves had smaller amplitudes and narrower tuning widths after strong adaptation; this was true for single neurons, multi-unit activity, the evoked local field potential (LFP), as well as gamma band activity. Repetition typically led to reduced responses. This reduction was correlated with direction selectivity and not explained by neural fatigue. Our data, however, warn against a simplistic view of the consequences of adaptation. First, a considerable fraction of neurons and sites showed response enhancements after adaptation, especially when probed with a stimulus that moved opposite to the direction of the adapting stimulus. Second, adaptation was stimulus selective only on a time scale of ~100 ms. Third, aggregate measures of neural activity (multi-unit activity, local field potentials) had substantially different adaptation effects. Fourth, there were qualitative differences between our findings in MT and earlier findings in IT cortex. We conclude that selective adaptation effects in fMRIa are relatively easy to miss even when they exist (for instance by presenting stimuli for too long, or because neurons that enhance after adaptation cancel out the effect of neurons that suppress). Moreover, we argue that adaptation should be understood in the context of the computations that a neural circuit perform. Using fMRIa as a tool to uncover neural selectivity requires a better understanding of this circuitry and its consequences for adaptation.

show abstract

Section: Discussionsupporting

confidence: 62%

Testing the assumptions underlying fMRI adaptation using intracortical recordings in area MT

Kar

Krekelberg

2016

Cortex

Self Cite

View full text Add to dashboard Cite

show abstract

“…We speculate that the cortical eye position signal arrives first in V1 and then propagates through the cortical hierarchy along with the feedforward visual information. This would lead to progressively later lags as the combined signals reach later stages of the cortical pathway, consistent with measurements in parietal cortex 53 , while naturally maintaining the spatiotemporal alignment needed for stability within each area (and hence at every spatial scale). In parietal cortex, these signals likely interact with other mechanisms that contribute to stability, including representations of eye position and retinal position (e.g.…”

Section: Beyond V1supporting

confidence: 75%

An intrinsic neural eye tracker in primary visual cortex

Morris

Krekelberg

2018

Preprint

Self Cite

View full text Add to dashboard Cite

Humans and other primates rely on eye movements to explore visual scenes and to track moving objects. As a result, the image that is projected onto the retina -and propagated throughout the visual cortical hierarchy -is almost constantly changing and makes little sense without taking into account the momentary direction of gaze. How is this achieved in the visual system? Here we show that in primary visual cortex (V1), the earliest stage of cortical vision, neural representations carry an embedded "eye tracker" that signals the direction of gaze associated with each image. Using chronically implanted multi-electrode arrays, we recorded the activity of neurons in V1 during tasks requiring fast (exploratory) and slow (pursuit) eye movements. Neurons were stimulated with flickering, full-field luminance noise at all times. As in previous studies 1-4 , we observed neurons that were sensitive to gaze direction during fixation, despite comparable stimulation of their receptive fields. We trained a decoder to translate neural activity into metric estimates of (stationary) gaze direction. This decoded signal not only tracked the eye accurately during fixation, but also during fast and slow eye movements, even though the decoder had not been exposed to data from these behavioural states. Moreover, this signal lagged the real eye by approximately the time it took for new visual information to travel from the retina to cortex. Using simulations, we show that this V1 eye position signal could be used to take into account the sensory consequences of eye movements and map the fleeting positions of objects on the retina onto their stable position in the world.During everyday vision, fast eye movements known as 'saccades' shift the line of sight to place the high-resolution fovea onto objects of interest. These movements are complemented by slower, smooth eye rotations used to track moving objects (e.g. a bird in flight) or to lock gaze on an object during locomotion 5 . Though essential, these movements have stark consequences for the retina's view of the world: objects that are stationary appear to jump or move, and objects that are in motion travel along grossly distorted trajectories (Fig 1). These phenomena reflect the fact that visual input arrives to the nervous system in an eye-centred (or retinal) coordinate system, in which objects evoke activity within a local patch of photoreceptors and ganglion cells (i.e. as a place code). Our perception of the world, however, is entirely different: objects do not appear to change positions every time we move our eyes, and we have no trouble planning actions toward them. This suggests that the visual system uses internal knowledge of eye position (and

show abstract

“…For instance, when image contrast is reversed the between motion frames this produces a corresponding reversal in perceived motion direction (Anstis, 1970). Electrophysiological work shows that this perceptual illusion is also reflected in the responses of macaque V1 and MT neurons: their preferred direction is inverted (Duijnhouwer & Krekelberg, 2016). After verifying this behaviour in the artificial system, we explore how these changes influence the calculation of speed.…”

Section: Introductionmentioning

confidence: 84%

“…Reverse-phi motion is perceived as moving in the opposite direction to the actual movement (Anstis, 1970). The manner in which this image manipulation influences the preferred direction of neurons and the perceived direction of movement has been documented (Duijnhouwer & Krekelberg, 2016). Here we show that in addition to these effects related to direction, this manipulation may also produce biases in perceived speed.…”

Section: Discussionmentioning

confidence: 99%

“…Data in Figures 2b, 2e, 3b-c, 4a-d, 5a, 5c, 7b-d were extracted from published papers (Britten et al, 1992;Duijnhouwer & Krekelberg, 2016;Mikami et al, 1986;Movshon et al, 1986;O'Keefe et al, 1998;Priebe et al, 2003;Schütz et al, 2010) using WebPlotDigitalizer. Data in Figure 4c-d are a reanalysis of archived data (https://archive.nyu.edu/handle/2451/34281) from the published paper (Wang & Movshon, 2016).…”

Section: Data Reanalysismentioning

confidence: 99%

See 1 more Smart Citation

Exploring and explaining properties of motion processing in biological brains using a neural network

Rideaux

Welchman

2020

Preprint

View full text Add to dashboard Cite

Visual motion perception underpins behaviours ranging from navigation to depth perception and grasping. Our limited access to biological systems constrain our understanding of how motion is processed within the brain. Here we explore properties of motion perception in biological systems by training a neural network (‘MotionNetxy’) to estimate the velocity image sequences. The network recapitulates key characteristics of motion processing in biological brains, and we use our complete access to its structure explore and understand motion (mis)perception at the computational-, neural-, and perceptual-levels. First, we find that the network recapitulates the biological response to reverse-phi motion in terms of direction. We further find that it overestimates the speed of slow reverse-phi motion while underestimating the speed of fast reverse-phi motion because of the correlation between reverse-phi motion and the spatiotemporal receptive fields tuned to motion in opposite directions. Second, we find that the distribution of spatiotemporal tuning properties in the V1 and MT layers of the network are similar to those observed in biological systems. We then show that compared to MT units tuned to fast speeds, those tuned to slow speeds primarily receive input from V1 units tuned to high spatial frequency and low temporal frequency. Third, we find that there is a positive correlation between the pattern-motion and speed selectivity of MT units. Finally, we show that the network captures human underestimation of low coherence motion stimuli, and that this is due to pooling of noise and signal motion. These findings provide biologically plausible explanations for well-known phenomena, and produce concrete predictions for future psychophysical and neurophysiological experiments.

show abstract

Evidence and Counterevidence in Motion Perception

Cited by 8 publications

References 53 publications

Testing the assumptions underlying fMRI adaptation using intracortical recordings in area MT

Testing the assumptions underlying fMRI adaptation using intracortical recordings in area MT

An intrinsic neural eye tracker in primary visual cortex

Exploring and explaining properties of motion processing in biological brains using a neural network

Contact Info

Product

Resources

About