Dynamic Visual Input Research Articles

Behaviorally-relevant features of observed actions dominate cortical representational geometry in natural vision.

We effortlessly extract behaviorally relevant information from dynamic visual input in order to understand the actions of others. In the current study, we develop and test a number of models to better understand the neural representational geometries supporting action understanding. Using fMRI, we measured brain activity as participants viewed a diverse set of 90 different video clips depicting social and nonsocial actions in real-world contexts. We developed five behavioral models using arrangement tasks: two models reflecting behavioral judgments of the purpose (transitivity) and the social content (sociality) of the actions depicted in the video stimuli; and three models reflecting behavioral judgments of the visual content (people, objects, and scene) depicted in still frames of the stimuli. We evaluated how well these models predict neural representational geometry and tested them against semantic models based on verb and nonverb embeddings and visual models based on gaze and motion energy. Our results revealed that behavioral judgments of similarity better reflect neural representational geometry than semantic or visual models throughout much of cortex. The sociality and transitivity models in particular captured a large portion of unique variance throughout the action observation network, extending into regions not typically associated with action perception, like ventral temporal cortex. Overall, our findings expand the action observation network and indicate that the social content and purpose of observed actions are predominant in cortical representation.

Disentangling Object Category Representations Driven by Dynamic and Static Visual Input.

Humans can label and categorize objects in a visual scene with high accuracy and speed, a capacity well characterized with studies using static images. However, motion is another cue that could be used by the visual system to classify objects. To determine how motion-defined object category information is processed by the brain in the absence of luminance-defined form information, we created a novel stimulus set of "object kinematograms" to isolate motion-defined signals from other sources of visual information. Object kinematograms were generated by extracting motion information from videos of 6 object categories and applying the motion to limited-lifetime random dot patterns. Using functional magnetic resonance imaging (fMRI) (n = 15, 40% women), we investigated whether category information from the object kinematograms could be decoded within the occipitotemporal and parietal cortex and evaluated whether the information overlapped with category responses to static images from the original videos. We decoded object category for both stimulus formats in all higher-order regions of interest (ROIs). More posterior occipitotemporal and ventral regions showed higher accuracy in the static condition, while more anterior occipitotemporal and dorsal regions showed higher accuracy in the dynamic condition. Further, decoding across the two stimulus formats was possible in all regions. These results demonstrate that motion cues can elicit widespread and robust category responses on par with those elicited by static luminance cues, even in ventral regions of visual cortex that have traditionally been associated with primarily image-defined form processing.SIGNIFICANCE STATEMENT Much research on visual object recognition has focused on recognizing objects in static images. However, motion is a rich source of information that humans might also use to categorize objects. Here, we present the first study to compare neural representations of several animate and inanimate objects when category information is presented in two formats: static cues or isolated dynamic motion cues. Our study shows that, while higher-order brain regions differentially process object categories depending on format, they also contain robust, abstract category representations that generalize across format. These results expand our previous understanding of motion-derived animate and inanimate object category processing and provide useful tools for future research on object category processing driven by multiple sources of visual information.

V1-Origin Bidirectional Plasticity in Visual Thalamo-Ventral Pathway and Its Contribution to Saliency Detection of Dynamic Visual Inputs.

Visual neural plasticity and V1 saliency detection are vital for efficient coding of dynamically changing visual inputs. However, how does neural plasticity contribute to saliency detection of temporal statistically distributed visual stream remains unclear. Therefore, we adopted randomly presented but unevenly distributed stimuli with multiple orientations and examined the single-unit responses evoked by this biased orientation-adaptation protocol by single-unit recordings in the visual thalamo-ventral pathway of cats (of either sex). We found neuronal responses potentiated when the probability of biased orientation was slightly higher than other nonbiased ones and suppressed when the probability became much higher. This single neuronal short-term bidirectional plasticity is selectively induced by optimal stimuli but is interocularly transferable. It is inducible in LGN, Area 17, and Area 21a with distinct and hierarchically progressive patterns. With the results of latency analysis, receptive field structural test, cortical lesion, and simulations, we suggest this bidirectional plasticity may principally originate from the adaptation competition between excitatory and inhibitory components of V1 neuronal receptive field. In our simulation, above bidirectional plasticity could achieve saliency detection of dynamic visual inputs. These findings demonstrate a rapid probability dependent plasticity on the neural coding of visual stream and suggest its functional role in the efficient coding and saliency detection of dynamic environment.SIGNIFICANCE STATEMENT Novel elements within a dynamic visual stream can pop up from the context, which is vital for rapid response to a dynamically changing world. Saliency detection is a promising bottom-up mechanism contributing to efficient selection of visual inputs, wherein visual adaptation also plays a significant role. However, the saliency detection of dynamic visual stream is poorly understood. Here, we found a novel form of visual short-term bidirectional plasticity in multistages of the visual system that contributes to saliency detection of dynamic visual inputs. This bidirectional plasticity may principally originate from the local balance of excitation inhibition in primary visual cortex and propagates to lower and higher visual areas with progressive pattern change. Our findings suggest the excitation-inhibition balance within the visual system contributes to visual efficient coding.

Dynamic Visual Stimulations Produced in a Controlled Virtual Reality Environment Reveals Long-Lasting Postural Deficits in Children With Mild Traumatic Brain Injury.

Motor control deficits outlasting self-reported symptoms are often reported following mild traumatic brain injury (mTBI). The exact duration and nature of these deficits remains unknown. The current study aimed to compare postural responses to static or dynamic virtual visual inputs and during standard clinical tests of balance in 38 children between 9 and 18 years-of-age, at 2 weeks, 3 and 12 months post-concussion. Body sway amplitude (BSA) and postural instability (vRMS) were measured in a 3D virtual reality (VR) tunnel (i.e., optic flow) moving in the antero-posterior direction in different conditions. Measures derived from standard clinical balance evaluations (BOT-2, Timed tasks) and post-concussion symptoms (PCSS-R) were also assessed. Results were compared to those of 38 healthy non-injured children following a similar testing schedule and matched according to age, gender, and premorbid level of physical activity. Results highlighted greater postural response with BSA and vRMS measures at 3 months post-mTBI, but not at 12 months when compared to controls, whereas no differences were observed in post-concussion symptoms between mTBI and controls at 3 and 12 months. These deficits were specifically identified using measures of postural response in reaction to 3D dynamic visual inputs in the VR paradigm, while items from the BOT-2 and the 3 timed tasks did not reveal deficits at any of the test sessions. PCSS-R scores correlated between sessions and with the most challenging condition of the BOT-2 and as well as with the timed tasks, but not with BSA and vRMS. Scores obtained in the most challenging conditions of clinical balance tests also correlated weakly with BSA and vRMS measures in the dynamic conditions. These preliminary findings suggest that using 3D dynamic visual inputs such as optic flow in a controlled VR environment could help detect subtle postural impairments and inspire the development of clinical tools to guide rehabilitation and return to play recommendations.

Multisensory expectations about dynamic visual objects facilitates early sensory processing of congruent sounds

In everyday life, visual objects are often accompanied by sounds, yet little is known about how information in one sense may influence the processing of information in another sense. We examined how dynamic visual input – an object moving continuously across the visual field – influences early auditory processing of a sound that is either congruent with the object’s motion, and thus likely perceived as being part of the visual object, or incongruent with the object’s motion. We recorded EEG activity from 31 neurotypical adults who passively viewed a red ball that appeared either on the far left or right edge of the display and continuously traversed along the horizontal midline to make contact and bounce off the opposite edge. For multisensory trials, a tone accompanied the visual input the moment the ball made contact with the opposite edge (AV-synchronous), or the sound occurred 450ms before contact (AV-asynchronous). We also included audio-only and visual-only trials. Our main analysis focused on the auditory-evoked event-related potential (ERP) measured at frontal electrode sites and revealed reliable differences in the amplitude and latency of the N1-P2 auditory complex (all p’s < 0.003). Follow-up pairwise comparisons showed a reduced N1-amplitude for the AV-synchronous condition relative to the AV-asynchronous and audio-only conditions, and a delayed latency for the AV-asynchronous condition relative to the AV-synchronous and audio-only conditions. P2-peak analyses revealed greater amplitude for the audio-only relative to the AV-synchronous and AV-asynchronous and delayed latency toward AV-asynchronous relative to AV-synchronous and audio-only conditions. Overall, these results show that audio-visual synchrony elicited a faster and attenuated early auditory response relative to asynchronous or auditory-only events. This suggests that dynamic visual stimuli can help generate expectations about the timing of auditory events, which then facilitates the processing of auditory information that matches these expectations.

Interval between two sequential arrays determines their storage state in visual working memory

The visual information can be stored as either “active” representations in the active state or “activity-silent” representations in the passive state during the retention period in visual working memory (VWM). Catering to the dynamic nature of visual world, we explored how the temporally dynamic visual input was stored in VWM. In the current study, the memory arrays were presented sequentially, and the contralateral delay activity (CDA), an electrophysiological measure, was used to identify whether the memory representations were transferred into the passive state. Participants were instructed to encode two sequential arrays and retrieve them respectively, with two conditions of interval across the two arrays: 400 ms and 800 ms. These results provided strong evidence for the state-separated storage of two sequential arrays in different neural states if the interval between them was long enough, and the concurrent storage of them in the active state if the interval was relatively short. This conclusion was valid only when the participants encountered the task for the first time. Once participants have formed their mindset, they would apply the same storage mode to the subsequently extended or shortened interval condition.

Visual input shapes the auditory frequency responses in the inferior colliculus of mouse

The integration of visual and auditory information is important for humans or animals to build an accurate and coherent perception of the external world. Although some evidence has shown some principles of the audiovisual integration, little insight has been gained into its functional purpose. In this study, we investigated the functional influence of dynamic visual input on auditory frequency processing by recording single unit activity in the central nucleus of the inferior colliculus (ICc). Results showed that the auditory responses of ICc neurons to sound frequencies could be enhanced or suppressed by visual stimuli even though the same visual stimuli induced no neural responses when presented alone. For each ICc neuron, the most effective visual stimuli were located in the same azimuth as for auditory stimuli and preceded for ∼20 ms. Additionally, visual stimuli could steepen or flatten the frequency tuning curves (FTCs) of ICc neurons by various visual effects at each responsive frequency. The modulation degree of auditory FTCs was dependent on the minimal thresholds (MTs) of ICc neurons, i.e., with MTs increasing, the modulation degree decreased. Due to the non-homogeneous distribution of MTs which was lowest at 10 kHz, visual modulation of auditory FTCs exhibited a frequency-specific manner, the closer it reached the characteristic frequency (CF) of 10 kHz, the greater modulation. Thus, visual modulation of auditory frequency responses in ICc is dependent not only on the visual stimulus but also on the auditory characteristics of ICc neurons. These results suggest a moment-to-moment visual modulation of auditory frequency responses that in real time increase auditory frequency sensitivity to audiovisual stimuli. Furthermore, in the long term such modulation could serve to instruct auditory adaptive plasticity to maintain necessary and accurate auditory detection and perceptual behavior.

Work experience at height suppressed the effect of dynamic visual input on postural sway when static visual cues were visible

Background:Balance skills are challenged at the roofing worksite by dynamic visual cues in the form of clouds and trees that move with the wind; any instability may result in a fall from height.Aim:To determine if roofers are less susceptible to a dynamic visual surround (moving room) when stationary cues were available, relative to people who do not work in a similar environment.Methods:Forty male adults participated, including young roofers (N=10,26.3 years), middle-aged roofers (N=10,50.6 years), young controls (N=10,26.2 years) and middle-aged controls (N=10,55.1 years). Center of pressure root mean square (APRMS) was assessed during quiet standing and four moving room conditions when stationary cues were:(1)not in place,(2)in the foreground (inside the moving room),(3)in the background (outside the moving room, but still visible) and (4)in the foreground and background. Results:A workgroup by visual cue interaction was observed (p=0.032), where roofers had up to 18% lower APRMS relative to controls when the background references were in place. The interaction effect was independent of age. Interpretation:The roofers were able to suppress visually-induced postural sway when stationary visual cues were present in the background, likely due to the severe consequences for loss of balance when working at height. The suppression was maintained in aging workers.

Representational interactions during audiovisual speech entrainment: Redundancy in left posterior superior temporal gyrus and synergy in left motor cortex.

Integration of multimodal sensory information is fundamental to many aspects of human behavior, but the neural mechanisms underlying these processes remain mysterious. For example, during face-to-face communication, we know that the brain integrates dynamic auditory and visual inputs, but we do not yet understand where and how such integration mechanisms support speech comprehension. Here, we quantify representational interactions between dynamic audio and visual speech signals and show that different brain regions exhibit different types of representational interaction. With a novel information theoretic measure, we found that theta (3–7 Hz) oscillations in the posterior superior temporal gyrus/sulcus (pSTG/S) represent auditory and visual inputs redundantly (i.e., represent common features of the two), whereas the same oscillations in left motor and inferior temporal cortex represent the inputs synergistically (i.e., the instantaneous relationship between audio and visual inputs is also represented). Importantly, redundant coding in the left pSTG/S and synergistic coding in the left motor cortex predict behavior—i.e., speech comprehension performance. Our findings therefore demonstrate that processes classically described as integration can have different statistical properties and may reflect distinct mechanisms that occur in different brain regions to support audiovisual speech comprehension.

Stochastic Learning in Neuromorphic Hardware via Spike Timing Dependent Plasticity With RRAM Synapses

Hardware processors for neuromorphic computing are gaining significant interest as they offer the possibility of real in-memory computing, thus by-passing the limitations of speed and energy consumption of the von Neumann architecture. One of the major limitations of current neuromorphic technology is the lack of bio-realistic and scalable devices to improve the current design of artificial synapses and neurons. To overcome these limitations, the emerging technology of resistive switching memory has attracted wide interest as a nano-scaled synaptic element. This paper describes the implementation of a perceptron-like neuromorphic hardware capable of spike-timing dependent plasticity (STDP), and its operation under stochastic learning conditions. The learning algorithm of a single or multiple patterns, consisting of either static or dynamic visual input data, is described. The impact of noise is studied with respect to learning efficiency (false fire, true fire) and learning time. Finally, the impact of stochastic learning rule, such as the inversion of the time dependence of potentiation and depression in STDP, is considered. Overall, the work provides a proof of concept for unsupervised learning by STDP in memristive networks, providing insight into the dynamics of stochastic learning and supporting the understanding and design of neuromorphic networks with emerging memory devices.

Using eye tracking to investigate what bilinguals notice about linguistic landscape images

Abstract In daily life, we experience dynamic visual input referred to as the “linguistic landscape” (LL), comprised of images and text, for example, signs, and billboards (Gorter, 2013; Landry &amp; Bourhis, 1997; Shohamy, Ben-Rafael and Barni 2010). While much is known about LLs descriptively, less is known about what people notice when viewing LLs. Building upon the bilingual eye movement reading literature (e.g., Whitford, Pivneva, &amp; Titone, 2016) and the scene viewing literature (e.g., Henderson &amp; Ferreira, 2004), we report a preliminary study of French-English bilinguals’ eye movements as they viewed LL images from Montréal. These preliminary data suggest that eye tracking is a promising new method for investigating how people with different language backgrounds process real-world LL images.

Neural Representations of Observed Actions Generalize across Static and Dynamic Visual Input.

People interact with entities in the environment in distinct and categorizable ways (e.g., kicking is making contact with foot). We can recognize these action categories across variations in actors, objects, and settings; moreover, we can recognize them from both dynamic and static visual input. However, the neural systems that support action recognition across these perceptual differences are unclear. Here, we used multivoxel pattern analysis of fMRI data to identify brain regions that support visual action categorization in a format-independent way. Human participants were scanned while viewing eight categories of interactions (e.g., pulling) depicted in two visual formats: (1) visually controlled videos of two interacting actors and (2) visually varied photographs selected from the internet involving different actors, objects, and settings. Action category was decodable across visual formats in bilateral inferior parietal, bilateral occipitotemporal, left premotor, and left middle frontal cortex. In most of these regions, the representational similarity of action categories was consistent across subjects and visual formats, a property that can contribute to a common understanding of actions among individuals. These results suggest that the identified brain regions support action category codes that are important for action recognition and action understanding.SIGNIFICANCE STATEMENT Humans tend to interpret the observed actions of others in terms of categories that are invariant to incidental features: whether a girl pushes a boy or a button and whether we see it in real-time or in a single snapshot, it is still pushing Here, we investigated the brain systems that facilitate the visual recognition of these action categories across such differences. Using fMRI, we identified several areas of parietal, occipitotemporal, and frontal cortex that exhibit action category codes that are similar across viewing of dynamic videos and still photographs. Our results provide strong evidence for the involvement of these brain regions in recognizing the way that people interact physically with objects and other people.

Visual cortex responses reflect temporal structure of continuous quasi-rhythmic sensory stimulation

Neural processing of dynamic continuous visual input, and cognitive influences thereon, are frequently studied in paradigms employing strictly rhythmic stimulation. However, the temporal structure of natural stimuli is hardly ever fully rhythmic but possesses certain spectral bandwidths (e.g. lip movements in speech, gestures). Examining periodic brain responses elicited by strictly rhythmic stimulation might thus represent ideal, yet isolated cases. Here, we tested how the visual system reflects quasi-rhythmic stimulation with frequencies continuously varying within ranges of classical theta (4–7Hz), alpha (8–13Hz) and beta bands (14–20Hz) using EEG. Our findings substantiate a systematic and sustained neural phase-locking to stimulation in all three frequency ranges. Further, we found that allocation of spatial attention enhances EEG-stimulus locking to theta- and alpha-band stimulation. Our results bridge recent findings regarding phase locking (“entrainment”) to quasi-rhythmic visual input and “frequency-tagging” experiments employing strictly rhythmic stimulation. We propose that sustained EEG-stimulus locking can be considered as a continuous neural signature of processing dynamic sensory input in early visual cortices. Accordingly, EEG-stimulus locking serves to trace the temporal evolution of rhythmic as well as quasi-rhythmic visual input and is subject to attentional bias.

Specificity and timescales of cortical adaptation as inferences about natural movie statistics.

Adaptation is a phenomenological umbrella term under which a variety of temporal contextual effects are grouped. Previous models have shown that some aspects of visual adaptation reflect optimal processing of dynamic visual inputs, suggesting that adaptation should be tuned to the properties of natural visual inputs. However, the link between natural dynamic inputs and adaptation is poorly understood. Here, we extend a previously developed Bayesian modeling framework for spatial contextual effects to the temporal domain. The model learns temporal statistical regularities of natural movies and links these statistics to adaptation in primary visual cortex via divisive normalization, a ubiquitous neural computation. In particular, the model divisively normalizes the present visual input by the past visual inputs only to the degree that these are inferred to be statistically dependent. We show that this flexible form of normalization reproduces classical findings on how brief adaptation affects neuronal selectivity. Furthermore, prior knowledge acquired by the Bayesian model from natural movies can be modified by prolonged exposure to novel visual stimuli. We show that this updating can explain classical results on contrast adaptation. We also simulate the recent finding that adaptation maintains population homeostasis, namely, a balanced level of activity across a population of neurons with different orientation preferences. Consistent with previous disparate observations, our work further clarifies the influence of stimulus-specific and neuronal-specific normalization signals in adaptation.

Student Preparation and the Power of Visual Input in Veterinary Surgical Education: An Empirical Study

In recent years, veterinary educational institutions have implemented alternative teaching methods, including video demonstrations of surgical procedures. However, the power of the dynamic visual input from videos in relation to recollection of a surgical procedure has never been evaluated. The aim of this study was to investigate how veterinary surgical students perceived the influence of different educational materials on recollection of a surgical procedure. Furthermore, we investigated if surgical technique was associated with a certain method of recollection or use of educational material. During a basic surgical skills course, 112 fourth-year veterinary students participated in the study by completing a questionnaire regarding method of recollection, influence of individual types of educational input, and homework preparation. Furthermore, we observed students performing an orchiectomy in a terminal pig lab. Preparation for the pig lab consisted of homework (textbook, online material, including videos), lecture, cadaver lab, and toy animal models in a skills lab. In the instructional video, a detail was used that was not described elsewhere. Results show that 60% of the students used a visual dynamic method as their main method of recollection and that video was considered the most influential educational input with respect to recollection of a specific procedure. Observation of students' performance during the orchiectomy showed no clear association with students' method of recollection but a significant association (p=.002) with educational input. Our results illustrate the power of a visual input and support prior findings that knowledge is constructed from multiple sources of information.

Spatiotemporal features for asynchronous event-based data.

Bio-inspired asynchronous event-based vision sensors are currently introducing a paradigm shift in visual information processing. These new sensors rely on a stimulus-driven principle of light acquisition similar to biological retinas. They are event-driven and fully asynchronous, thereby reducing redundancy and encoding exact times of input signal changes, leading to a very precise temporal resolution. Approaches for higher-level computer vision often rely on the reliable detection of features in visual frames, but similar definitions of features for the novel dynamic and event-based visual input representation of silicon retinas have so far been lacking. This article addresses the problem of learning and recognizing features for event-based vision sensors, which capture properties of truly spatiotemporal volumes of sparse visual event information. A novel computational architecture for learning and encoding spatiotemporal features is introduced based on a set of predictive recurrent reservoir networks, competing via winner-take-all selection. Features are learned in an unsupervised manner from real-world input recorded with event-based vision sensors. It is shown that the networks in the architecture learn distinct and task-specific dynamic visual features, and can predict their trajectories over time.

Two objects or one? Similarity rather than complexity determines objecthood when resolving dynamic input.

The human brain is continuously confronted with dynamic visual input, and from this it must infer whether input belongs to a single versus multiple object identities across time. Object substitution masking (OSM), in which perception of a target stimulus is impaired by a temporally trailing 4-dot mask, reflects a failure to segment the target and mask as discrete objects. According to Bouvier and Treisman (2010), OSM only occurs for targets that require binding multiple separate features (e.g., color and orientation) to be identified. In contrast, a target that represents a unique feature is thought to be impervious to masking. Here, however, we show that a single orientation target (a Gabor) is susceptible to masking with an orientation-discrimination task, but only when the mask is similar in orientation to the target. That is, target-mask similarity, rather than target complexity determines masking. A reexamination of Bouvier and Treisman's (2010) results show that they can be explained within this target-mask similarity perspective. This means that the similarity of 2 objects determines whether they will be integrated or segmented across time, rather than the complexity of 1 of the objects in isolation.

Predictive coding for motion stimuli in human early visual cortex.

The current study investigates if early visual cortical areas, V1, V2 and V3, use predictive coding to process motion information. Previous studies have reported biased visual motion responses at locations where novel visual information was presented (i.e., the motion trailing edge), which is plausibly linked to the predictability of visual input. Using high-field functional magnetic resonance imaging (fMRI), we measured brain activation during predictable versus unpreceded motion-induced contrast changes during several motion stimuli. We found that unpreceded moving dots appearing at the trailing edge gave rise to enhanced BOLD responses, whereas predictable moving dots at the leading edge resulted in suppressed BOLD responses. Furthermore, we excluded biases in directional sensitivity, shifts in cortical stimulus representation, visuo-spatial attention and classical receptive field effects as viable alternative explanations. The results clearly indicate the presence of predictive coding mechanisms in early visual cortex for visual motion processing, underlying the construction of stable percepts out of highly dynamic visual input.

The influence of visual vertigo and vestibulopathy on oculomotor responses.

Dynamic visual inputs can cause visual vertigo (VV) in patients with vestibulopathy, leading to dizziness and falls. This study investigated the influence of VV on oculomotor responses. In this cross-sectional, single-blind study, with experimental and control groups, 8 individuals with vestibulopathy and VV, 10 with vestibulopathy and no VV, and 10 healthy controls participated. Oculomotor responses were examined with 2-dimensional video-oculography. Participants were exposed to dynamic visual inputs of vertical stripes sweeping across a screen at 20 deg/sec, while seated or in Romberg stance, with and without a fixed target. Responses were quantified by optokinetic nystagmus frequency (OKNf) and gain (OKNg). Seated with no target, VV participants had higher OKNf than controls (37 ± 9 vs. 24 ± 9 peaks/sec; P < 0.05). In Romberg stance with no target, they had higher OKNf than controls (41 ± 9 vs. 28 ± 10 peaks/sec; P < 0.05). With a target, OKNf was higher in VV participants compared to controls (7 ± 7 vs. 1 μ 2 peaks/sec; P < 0.05). In Romberg with no target, OKNg was higher in the VV group (0.8 ± 0.1) compared to controls (0.6 ± 0.2; P=0.024). OKNf and OKNg did not differ according to VV status. VV participants had increased OKNf and OKNg compared to healthy participants. Visual dependency should be considered in vestibular rehabilitation.

A magnocellular contribution to conscious perception via temporal object segmentation.

The human visual system is continuously confronted with dynamic visual input. One challenge that the visual system must solve, therefore, is recognizing when two distinct objects have appeared at a given location despite their brief presentation and rapid succession, that is, temporal object segmentation. Here we examined the role of magnocellular neurons in this process. We measured temporal object segmentation via object substitution masking (OSM), which reflects the failure to distinguish the target and mask as distinct objects through time. We isolated the selective role of magnocellular neurons by comparing performance under conditions of pulsed luminance pedestals, which are designed to saturate the magnocellular response, with that in a steady-pedestal condition that leaves both magnocellular and parvocellular channels available to process the target. Across two experiments, we found that OSM magnitude was enhanced under pulsed-pedestal conditions, in which the magnocellular response was impaired. This indicates that magnocellular neurons contribute to temporal object segmentation. Given that temporal object segmentation has consequences for which stimuli are consciously perceived, this demonstrates a functional mechanism via which magnocellular neurons contribute to determining the contents conscious perception. Implications for models of specialization of dorsal and ventral cortical streams are discussed.