Sensitivity for reverse-phi motion

Humans use local spectrotemporal correlations to detect rising and falling pitch

To discern speech or appreciate music, the human auditory system detects how pitch increases or decreases over time. However, the algorithms used to detect changes in pitch, or pitch motion, are incompletely understood. Here, using psychophysics, computational modeling, functional neuroimaging, and analysis of recorded speech, we ask if humans detect pitch motion using computations analogous to those used by the visual system. We adapted stimuli from studies of vision to create novel auditory correlated noise stimuli that elicited robust pitch motion percepts. Crucially, these stimuli possess no persistent features across frequency or time, but do possess positive or negative local spectrotemporal correlations in intensity. In psychophysical experiments, we found clear evidence that humans judge pitch direction based on both positive and negative spectrotemporal correlations. The observed sensitivity to negative correlations is a direct analogue of illusory "reverse-phi" motion in vision, and thus constitutes a new auditory illusion. Our behavioral results and computational modeling led us to hypothesize that human auditory processing employs pitch direction opponency. fMRI measurements in auditory cortex supported this hypothesis. To link our psychophysical findings to real-world pitch perception, we analyzed recordings of English and Mandarin speech and discovered that pitch direction was robustly signaled by the same positive and negative spectrotemporal correlations used in our psychophysical tests, suggesting that sensitivity to both positive and negative correlations confers ecological benefits. Overall, this work reveals that motion detection algorithms sensitive to local correlations are deployed by the central nervous system across disparate modalities (vision and audition) and dimensions (space and frequency).

Direct comparison reveals algorithmic similarities in fly and mouse visual motion detection

Evolution has equipped vertebrates and invertebrates with neural circuits that selectively encode visual motion. While similarities in the computations performed by these circuits in mouse and fruit fly have been noted, direct experimental comparisons have been lacking. Because molecular mechanisms and neuronal morphology in the two species are distinct, we directly compared motion encoding in these two species at the algorithmic level, using matched stimuli and focusing on a pair of analogous neurons, the mouse ON starburst amacrine cell (ON SAC) and Drosophila T4 neurons. We find that the cells share similar spatiotemporal receptive field structures, sensitivity to spatiotemporal correlations, and tuning to sinusoidal drifting gratings, but differ in their responses to apparent motion stimuli. Both neuron types showed a response to summed sinusoids that deviates from models for motion processing in these cells, underscoring the similarities in their processing and identifying response features that remain to be explained.

Odour motion sensing enhances navigation of complex plumes

Odour plumes in the wild are spatially complex and rapidly fluctuating structures carried by turbulent airflows^1-4. To successfully navigate plumes in search of food and mates, insects must extract and integrate multiple features of the odour signal, including odour identity⁵, intensity⁶ and timing^6-12. Effective navigation requires balancing these multiple streams of olfactory information and integrating them with other sensory inputs, including mechanosensory and visual cues^9,12,13. Studies dating back a century have indicated that, of these many sensory inputs, the wind provides the main directional cue in turbulent plumes, leading to the longstanding model of insect odour navigation as odour-elicited upwind motion^6,8-12,14,15. Here we show that Drosophila melanogaster shape their navigational decisions using an additional directional cue-the direction of motion of odours-which they detect using temporal correlations in the odour signal between their two antennae. Using a high-resolution virtual-reality paradigm to deliver spatiotemporally complex fictive odours to freely walking flies, we demonstrate that such odour-direction sensing involves algorithms analogous to those in visual-direction sensing¹⁶. Combining simulations, theory and experiments, we show that odour motion contains valuable directional information that is absent from the airflow alone, and that both Drosophila and virtual agents are aided by that information in navigating naturalistic plumes. The generality of our findings suggests that odour-direction sensing may exist throughout the animal kingdom and could improve olfactory robot navigation in uncertain environments.

Non-preferred contrast responses in the Drosophila motion pathways reveal a receptive field structure that explains a common visual illusion

Diverse sensory systems, from audition to thermosensation, feature a separation of inputs into ON (increments) and OFF (decrements) signals. In the Drosophila visual system, separate ON and OFF pathways compute the direction of motion, yet anatomical and functional studies have identified some crosstalk between these channels. We used this well-studied circuit to ask whether the motion computation depends on ON-OFF pathway crosstalk. Using whole-cell electrophysiology, we recorded visual responses of T4 (ON) and T5 (OFF) cells, mapped their composite ON-OFF receptive fields, and found that they share a similar spatiotemporal structure. We fit a biophysical model to these receptive fields that accurately predicts directionally selective T4 and T5 responses to both ON and OFF moving stimuli. This model also provides a detailed mechanistic explanation for the directional preference inversion in response to the prominent reverse-phi illusion. Finally, we used the steering responses of tethered flying flies to validate the model's predicted effects of varying stimulus parameters on the behavioral turning inversion.

Opto-locomotor reflexes of mice to reverse-phi stimuli

In a reverse-phi stimulus, the contrast luminance of moving dots is reversed each displacement step. Under those conditions, the direction of the moving dots is perceived in the direction opposite of the displacement direction of the dots. In this study, we investigate if mice respond oppositely to phi and reverse-phi stimuli. Mice ran head-fixed on a Styrofoam ball floating on pressurized air at the center of a large dome. We projected random dot patterns that were displaced rightward or leftward, using either a phi or a reverse-phi stimulus. For phi stimuli, changes in direction caused the mice to reflexively compensate and adjust their running direction in the direction of the displaced pattern. We show that for reverse-phi stimuli mice compensate in the direction opposite to the displacement direction of the dots, in accordance with the perceived direction of displacement in humans for reverse-phi stimuli.

The Neuronal Basis of an Illusory Motion Percept Is Explained by Decorrelation of Parallel Motion Pathways

Both vertebrates and invertebrates perceive illusory motion, known as "reverse-phi," in visual stimuli that contain sequential luminance increments and decrements. However, increment (ON) and decrement (OFF) signals are initially processed by separate visual neurons, and parallel elementary motion detectors downstream respond selectively to the motion of light or dark edges, often termed ON- and OFF-edges. It remains unknown how and where ON and OFF signals combine to generate reverse-phi motion signals. Here, we show that each of Drosophila's elementary motion detectors encodes motion by combining both ON and OFF signals. Their pattern of responses reflects combinations of increments and decrements that co-occur in natural motion, serving to decorrelate their outputs. These results suggest that the general principle of signal decorrelation drives the functional specialization of parallel motion detection channels, including their selectivity for moving light or dark edges.

Neural mechanisms underlying sensitivity to reverse-phi motion in the fly

Optical illusions provide powerful tools for mapping the algorithms and circuits that underlie visual processing, revealing structure through atypical function. Of particular note in the study of motion detection has been the reverse-phi illusion. When contrast reversals accompany discrete movement, detected direction tends to invert. This occurs across a wide range of organisms, spanning humans and invertebrates. Here, we map an algorithmic account of the phenomenon onto neural circuitry in the fruit fly Drosophila melanogaster. Through targeted silencing experiments in tethered walking flies as well as electrophysiology and calcium imaging, we demonstrate that ON- or OFF-selective local motion detector cells T4 and T5 are sensitive to certain interactions between ON and OFF. A biologically plausible detector model accounts for subtle features of this particular form of illusory motion reversal, like the re-inversion of turning responses occurring at extreme stimulus velocities. In light of comparable circuit architecture in the mammalian retina, we suggest that similar mechanisms may apply even to human psychophysics.

The Primary Role of Flow Processing in the Identification of Scene-Relative Object Movement

Retinal image motion could be due to the movement of the observer through space or an object relative to the scene. Optic flow, form, and change of position cues all provide information that could be used to separate out retinal motion due to object movement from retinal motion due to observer movement. In Experiment 1, we used a minimal display to examine the contribution of optic flow and form cues. Human participants indicated the direction of movement of a probe object presented against a background of radially moving pairs of dots. By independently controlling the orientation of each dot pair, we were able to put flow cues to self-movement direction (the point from which all the motion radiated) and form cues to self-movement direction (the point toward which all the dot pairs were oriented) in conflict. We found that only flow cues influenced perceived probe movement. In Experiment 2, we switched to a rich stereo display composed of 3D objects to examine the contribution of flow and position cues. We moved the scene objects to simulate a lateral translation and counter-rotation of gaze. By changing the polarity of the scene objects (from light to dark and vice versa) between frames, we placed flow cues to self-movement direction in opposition to change of position cues. We found that again flow cues dominated the perceived probe movement relative to the scene. Together, these experiments indicate the neural network that processes optic flow has a primary role in the identification of scene-relative object movement.

SIGNIFICANCE STATEMENT Motion of an object in the retinal image indicates relative movement between the observer and the object, but it does not indicate its cause: movement of an object in the scene; movement of the observer; or both. To isolate retinal motion due to movement of a scene object, the brain must parse out the retinal motion due to movement of the eye (“flow parsing”). Optic flow, form, and position cues all have potential roles in this process. We pitted the cues against each other and assessed their influence. We found that flow parsing relies on optic flow alone. These results indicate the primary role of the neural network that processes optic flow in the identification of scene-relative object movement.

Changes in fMRI BOLD dynamics reflect anticipation to moving objects

The human brain is thought to respond differently to novel versus predictable neural input. In human visual cortex, neural response amplitude to visual input might be determined by the degree of predictability. We investigated how fMRI BOLD responses in human early visual cortex reflect the anticipation of a single moving bar's trajectory. We found that BOLD signals decreased linearly from onset to offset of the stimulus trajectory. Moreover, decreased amplitudes of BOLD responses coincided with an increased initial dip as the stimulus moved along its trajectory. Importantly, motion anticipation effects were absent, when motion coherence was disrupted by means of stimulus contrast reversals. These results show that human early visual cortex anticipates the trajectory of a coherently moving object at the initial stages of visual motion processing. The results can be explained by suppression of predictable input, plausibly underlying the formation of stable visual percepts.

Direct Measurement of Correlation Responses in Drosophila Elementary Motion Detectors Reveals Fast Timescale Tuning

Animals estimate visual motion by integrating light intensity information over time and space. The integration requires nonlinear processing, which makes motion estimation circuitry sensitive to specific spatiotemporal correlations that signify visual motion. Classical models of motion estimation weight these correlations to produce direction-selective signals. However, the correlational algorithms they describe have not been directly measured in elementary motion-detecting neurons (EMDs). Here, we employed stimuli to directly measure responses to pairwise correlations in Drosophila's EMD neurons, T4 and T5. Activity in these neurons was required for behavioral responses to pairwise correlations and was predictive of those responses. The pattern of neural responses in the EMDs was inconsistent with one classical model of motion detection, and the timescale and selectivity of correlation responses constrained the temporal filtering properties in potential models. These results reveal how neural responses to pairwise correlations drive visual behavior in this canonical motion-detecting circuit.

Evidence and Counterevidence in Motion Perception

Sensory neurons gather evidence in favor of the specific stimuli to which they are tuned, but they could improve their sensitivity by also taking counterevidence into account. The Bours-Lankheet model for motion detection uses counterevidence that relies on a specific combination of the ON and OFF channels in the early visual system. Specifically, the model detects pairs of flashes that occur separated in space and time. If the flashes have the same contrast polarity, they are interpreted as evidence in favor of the corresponding motion. But if they have opposite contrasts, they are interpreted as evidence against it. This mechanism provides an explanation for reverse-phi (the perceived reversal of an apparent motion stimulus due to periodic contrast-inversions) that is a conceptual departure from the standard explanations of the effect. Here, we investigate this counterevidence mechanism by measuring directional tuning curves of neurons in the primary visual and middle temporal cortex areas of awake, behaving macaques using constant-contrast and inverting-contrast moving dot stimuli. Our electrophysiological data support the Bours-Lankheet model and suggest that the counterevidence computation occurs at an early stage of neural processing not captured by the standard models.

Rapid Motion Adaptation Reveals the Temporal Dynamics of Spatiotemporal Correlation between ON and OFF Pathways

At the early stages of visual processing, information is processed by two major thalamic pathways encoding brightness increments (ON) and decrements (OFF). Accumulating evidence suggests that these pathways interact and merge as early as in primary visual cortex. Using regular and reverse-phi motion in a rapid adaptation paradigm, we investigated the temporal dynamics of within and across pathway mechanisms for motion processing. When the adaptation duration was short (188 ms), reverse-phi and regular motion led to similar adaptation effects, suggesting that the information from the two pathways are combined efficiently at early-stages of motion processing. However, as the adaption duration was increased to 752 ms, reverse-phi and regular motion showed distinct adaptation effects depending on the test pattern used, either engaging spatiotemporal correlation between the same or opposite contrast polarities. Overall, these findings indicate that spatiotemporal correlation within and across ON-OFF pathways for motion processing can be selectively adapted, and support those models that integrate within and across pathway mechanisms for motion processing.

Why hearts flutter: Distorted dim motions

When a display of red spots or hearts on a blue surround is moved around under dim light, the spots appear to wobble or flutter relative to the surround (the "fluttering hearts" effect). We explain this as follows: Rods and cones both respond to the hearts. Rods are more sluggish than cones, with a latency of ∼50 ms, and they are also much more sensitive to blue than to red (the Purkinje shift; Purkinje, 1825). Thus a red spot oscillating on a blue ground produces a double image: a light spot seen by the cones, followed by a trailing dark spot seen by the rods. These interacting spots of opposite luminance polarity move like "reverse phi" (Anstis, 1970) and this generates the fluttering hearts effect. We find that hearts flutter most markedly at or near mesopic equiluminance, when the red is lighter than the blue as seen by the cones, but darker than the blue as seen by the rods. These same red/blue luminance ratios give rise to two new illusions: the ghostly twin illusion, and the reversal of red/blue grating movement.

Contrasting accounts of direction and shape perception in short-range motion: Counterchange compared with motion energy detection

It has long been thought (e.g., Cavanagh & Mather, 1989) that first-order motion-energy extraction via space-time comparator-type models (e.g., the elaborated Reichardt detector) is sufficient to account for human performance in the short-range motion paradigm (Braddick, 1974), including the perception of reverse-phi motion when the luminance polarity of the visual elements is inverted during successive frames. Human observers' ability to discriminate motion direction and use coherent motion information to segregate a region of a random cinematogram and determine its shape was tested; they performed better in the same-, as compared with the inverted-, polarity condition. Computational analyses of short-range motion perception based on the elaborated Reichardt motion energy detector (van Santen & Sperling, 1985) predict, incorrectly, that symmetrical results will be obtained for the same- and inverted-polarity conditions. In contrast, the counterchange detector (Hock, Schöner, & Gilroy, 2009) predicts an asymmetry quite similar to that of human observers in both motion direction and shape discrimination. The further advantage of counterchange, as compared with motion energy, detection for the perception of spatial shape- and depth-from-motion is discussed.

Integration of motion responses underlying directional motion anisotropy in human early visual cortical areas

Recent imaging studies have reported directional motion biases in human visual cortex when perceiving moving random dot patterns. It has been hypothesized that these biases occur as a result of the integration of motion detector activation along the path of motion in visual cortex. In this study we investigate the nature of such motion integration with functional MRI (fMRI) using different motion stimuli. Three types of moving random dot stimuli were presented, showing either coherent motion, motion with spatial decorrelations or motion with temporal decorrelations. The results from the coherent motion stimulus reproduced the centripetal and centrifugal directional motion biases in V1, V2 and V3 as previously reported. The temporally decorrelated motion stimulus resulted in both centripetal and centrifugal biases similar to coherent motion. In contrast, the spatially decorrelated motion stimulus resulted in small directional motion biases that were only present in parts of visual cortex coding for higher eccentricities of the visual field. In combination with previous results, these findings indicate that biased motion responses in early visual cortical areas most likely depend on the spatial integration of a simultaneously activated motion detector chain.

Static sound timing alters sensitivity to low-level visual motion

Visual motion processing is essential to survival in a dynamic world and is probably the best-studied facet of visual perception. It has been recently discovered that the timing of brief static sounds can bias visual motion perception, an effect attributed to "temporal ventriloquism" whereby the timing of the sounds "captures" the timing of the visual events. To determine whether this cross-modal interaction is dependent on the involvement of higher-order attentive tracking mechanisms, we used near-threshold motion stimuli that isolated low-level pre-attentive visual motion processing. We found that the timing of brief sounds altered sensitivity to these visual motion stimuli in a manner that paralleled changes in the timing of the visual stimuli. Our findings indicate that auditory timing impacts visual motion processing very early in the processing hierarchy and without the involvement of higher-order attentional and/or position tracking mechanisms.

Defining the computational structure of the motion detector in Drosophila

Many animals rely on visual motion detection for survival. Motion information is extracted from spatiotemporal intensity patterns on the retina, a paradigmatic neural computation. A phenomenological model, the Hassenstein-Reichardt correlator (HRC), relates visual inputs to neural activity and behavioral responses to motion, but the circuits that implement this computation remain unknown. By using cell-type specific genetic silencing, minimal motion stimuli, and in vivo calcium imaging, we examine two critical HRC inputs. These two pathways respond preferentially to light and dark moving edges. We demonstrate that these pathways perform overlapping but complementary subsets of the computations underlying the HRC. A numerical model implementing differential weighting of these operations displays the observed edge preferences. Intriguingly, these pathways are distinguished by their sensitivities to a stimulus correlation that corresponds to an illusory percept, "reverse phi," that affects many species. Thus, this computational architecture may be widely used to achieve edge selectivity in motion detection.

Neural correlates of illusory motion perception in Drosophila

When the contrast of an image flickers as it moves, humans perceive an illusory reversal in the direction of motion. This classic illusion, called reverse-phi motion, has been well-characterized using psychophysics, and several models have been proposed to account for its effects. Here, we show that Drosophila melanogaster also respond behaviorally to the reverse-phi illusion and that the illusion is present in dendritic calcium signals of motion-sensitive neurons in the fly lobula plate. These results closely match the predictions of the predominant model of fly motion detection. However, high flicker rates cause an inversion of the reverse-phi behavioral response that is also present in calcium signals of lobula plate tangential cell dendrites but not predicted by the model. The fly's behavioral and neural responses to the reverse-phi illusion reveal unexpected interactions between motion and flicker signals in the fly visual system and suggest that a similar correlation-based mechanism underlies visual motion detection across the animal kingdom.