When visual attention is divided in the flash-lag effect.

The flash-lag effect (FLE) occurs when a flash's position seems to be delayed relative to a continuously moving object, even though both are physically aligned. Although several studies have demonstrated that reduced attention increases FLE magnitude, the precise mechanism underlying these attention-dependent effects remains elusive. In this study, we investigated the influence of visual attention on the FLE by manipulating the level of attention allocated to multiple stimuli moving simultaneously in different locations.

Decoding Remapped Spatial Information in the Peri-Saccadic Period.

It has been suggested that, prior to a saccade, visual neurons predictively respond to stimuli that will fall in their receptive fields after completion of the saccade. This saccadic remapping process is thought to compensate for the shift of the visual world across the retina caused by eye movements. To map the timing of this predictive process in the brain, we recorded neural activity using electroencephalography during a saccade task.

Neural mechanisms of visual motion extrapolation.

Because neural processing takes time, the brain only has delayed access to sensory information. When localising moving objects this is problematic, as an object will have moved on by the time its position has been determined. Here, we consider predictive motion extrapolation as a fundamental delay-compensation strategy.

Same but different: The latency of a shared expectation signal interacts with stimulus attributes.

Predictive coding theories assert that perceptual inference is a hierarchical process of belief updating, wherein the onset of unexpected sensory data causes so-called prediction error responses that calibrate erroneous inferences. Given the functionally specialised organisation of visual cortex, it is assumed that prediction error propagation interacts with the specific visual attribute violating an expectation. We sought to test this within the temporal domain by applying time-resolved decoding methods to electroencephalography (EEG) data evoked by contextual trajectory violations of either brightness, size, or orientation within a bound stimulus.

Exploring the extent to which shared mechanisms contribute to motion-position illusions.

Motion-position illusions (MPIs) are visual motion illusions in which motion signals bias the perceived position of an object. Due to phenomenological similarities between these illusions, previous research has assumed that some are caused by common mechanisms. However, this assumption has yet to be directly tested.

Spike-timing dependent plasticity partially compensates for neural delays in a multi-layered network of motion-sensitive neurons.

The ability of the brain to represent the external world in real-time is impacted by the fact that neural processing takes time. Because neural delays accumulate as information progresses through the visual system, representations encoded at each hierarchical level are based upon input that is progressively outdated with respect to the external world. This 'representational lag' is particularly relevant to the task of localizing a moving object-because the object's location changes with time, neural representations of its location potentially lag behind its true location.

Visual Information Is Predictively Encoded in Occipital Alpha/Low-Beta Oscillations.

Hierarchical predictive coding networks are a general model of sensory processing in the brain. Under neural delays, these networks have been suggested to naturally generate oscillatory activity in approximately the α frequency range (∼8-12 Hz). This suggests that α oscillations, a prominent feature of EEG recordings, may be a spectral "fingerprint" of predictive sensory processing.

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

When interacting with the dynamic world, the brain receives outdated sensory information, due to the time required for neural transmission and processing. In motion perception, the brain may overcome these fundamental delays through predictively encoding the position of moving objects using information from their past trajectories. In the present study, we evaluated this proposition using multivariate analysis of high temporal resolution electroencephalographic data.

Blurred Lines: Memory, Perceptions, and Consciousness: Commentary on "Consciousness as a Memory System" by Budson et al (2022).

In the previous issue, Budson, Richman, and Kensinger (2022) put forth the intriguing proposal that consciousness may have evolved from the episodic memory system. In addition to providing a possible evolutionary trajectory for consciousness, I believe that viewing consciousness as an extension of memory in this way is particularly useful for understanding some of the puzzling temporal complexities that are inherent to consciousness. For example, due to neural transmission delays, our conscious experience must necessarily lag the outside world, which creates a paradox for both conscious perception (Do we see the past, rather than the present?) and action (How can we make rapid decisions if it takes so long to become conscious of something?).

Decoding continuous variables from event-related potential (ERP) data with linear support vector regression using the Decision Decoding Toolbox (DDTBOX).

Multivariate classification analysis for event-related potential (ERP) data is a powerful tool for predicting cognitive variables. However, classification is often restricted to categorical variables and under-utilises continuous data, such as response times, response force, or subjective ratings. An alternative approach is support vector regression (SVR), which uses single-trial data to predict continuous variables of interest.

Perception in real-time: predicting the present, reconstructing the past.

We feel that we perceive events in the environment as they unfold in real-time. However, this intuitive view of perception is impossible to implement in the nervous system due to biological constraints such as neural transmission delays. I propose a new way of thinking about real-time perception: at any given moment, instead of representing a single timepoint, perceptual mechanisms represent an entire timeline.

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

In the flash-lag effect (FLE), a flash in spatiotemporal alignment with a moving object is misperceived as lagging behind the moving object. One proposed explanation for this illusion is based on predictive motion extrapolation of trajectories. In this interpretation, the diverging effects of velocity on the perceived position of the moving object suggest that FLE might be based on the neural representation of perceived, rather than physical, velocity.

Decoding explicit and implicit representations of health and taste attributes of foods in the human brain.

Obesity has become a significant problem word-wide and is strongly linked to poor food choices. Even in healthy individuals, taste perceptions often drive dietary decisions more strongly than healthiness. This study tested whether health and taste representations can be directly decoded from brain activity, both when explicitly considered, and when implicitly processed for decision-making.

Predictive Visual Motion Extrapolation Emerges Spontaneously and without Supervision at Each Layer of a Hierarchical Neural Network with Spike-Timing-Dependent Plasticity.

The fact that the transmission and processing of visual information in the brain takes time presents a problem for the accurate real-time localization of a moving object. One way this problem might be solved is extrapolation: using an object's past trajectory to predict its location in the present moment. Here, we investigate how a simulated layered neural network might implement such extrapolation mechanisms, and how the necessary neural circuits might develop.

The time-course of prediction formation and revision in human visual motion processing.

Establishing the real-time position of a moving object poses a challenge to the visual system due to neural processing delays. While sensory information is travelling through the visual hierarchy, the object continues moving and information about its position becomes outdated. By extrapolating the position of a moving object along its trajectory, predictive mechanisms might effectively decrease the processing time associated with these objects.

Predictive activation of sensory representations as a source of evidence in perceptual decision-making.

Our brains can represent expected future states of our sensory environment. Recent work has shown that, when we expect a specific stimulus to appear at a specific time, we can predictively generate neural representations of that stimulus even before it is physically presented. These observations raise two exciting questions: Are pre-activated sensory representations used for perceptual decision-making? And, do we transiently perceive an expected stimulus that does not actually appear? To address these questions, we propose that pre-activated neural representations provide sensory evidence that is used for perceptual decision-making.

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

A range of visual illusions, including the much-studied flash-lag effect, demonstrate that neural signals coding for motion and position interact in the visual system. One interpretation of these illusions is that they are the consequence of motion extrapolation mechanisms in the early visual system. Here, we study the recently reported High-Phi illusion to investigate whether it might be caused by the same underlying mechanisms.

Visual mismatch responses index surprise signalling but not expectation suppression.

The ability to distinguish between commonplace and unusual sensory events is critical for efficient learning and adaptive behaviour. This has been investigated using oddball designs in which sequences of often-appearing (i.e.

Decoding the Temporal Dynamics of Covert Spatial Attention Using Multivariate EEG Analysis: Contributions of Raw Amplitude and Alpha Power.

Attention can be oriented in space covertly without the need of eye movements. We used multivariate pattern classification analyses (MVPA) to investigate whether the time course of the deployment of covert spatial attention leading up to the observer's perceptual decision can be decoded from both EEG alpha power and raw activity traces. Decoding attention from these signals can help determine whether raw EEG signals and alpha power reflect the same or distinct features of attentional selection.

The SSVEP tracks attention, not consciousness, during perceptual filling-in.

Research on the neural basis of conscious perception has almost exclusively shown that becoming aware of a stimulus leads to increased neural responses. By designing a novel form of perceptual filling-in (PFI) overlaid with a dynamic texture display, we frequency-tagged multiple disappearing targets as well as their surroundings. We show that in a PFI paradigm, the disappearance of a stimulus and subjective invisibility is associated with increases in neural activity, as measured with steady-state visually evoked potentials (SSVEPs), in electroencephalography (EEG).

The role of hue in visual search for texture differences: Implications for camouflage design.

The purpose of camouflage is to be inconspicuous against a given background. Colour is an important component of camouflage, and the task of designing a single camouflage pattern for use against multiple different backgrounds is particularly challenging. As it is impossible to match the colour gamut of each background exactly, the question arises which colours from the different backgrounds should be incorporated in a camouflage pattern to achieve optimal concealment.

Motion Extrapolation in Visual Processing: Lessons from 25 Years of Flash-Lag Debate.

Because of the delays inherent in neural transmission, the brain needs time to process incoming visual information. If these delays were not somehow compensated, we would consistently mislocalize moving objects behind their physical positions. Twenty-five years ago, Nijhawan used a perceptual illusion he called the flash-lag effect (FLE) to argue that the brain's visual system solves this computational challenge by extrapolating the position of moving objects (Nijhawan, 1994).

Predictions drive neural representations of visual events ahead of incoming sensory information.

The transmission of sensory information through the visual system takes time. As a result of these delays, the visual information available to the brain always lags behind the timing of events in the present moment. Compensating for these delays is crucial for functioning within dynamic environments, since interacting with a moving object (e.

Expecting the unexpected: Temporal expectation increases the flash-grab effect.

In the flash-grab effect, when a disk is flashed on a moving background at the moment it reverses direction, the perceived location of the disk is strongly displaced in the direction of the motion that follows the reversal. Here, we ask whether increased expectation of the reversal reduces its effect on the motion-induced shift, as suggested by predictive coding models with first order predictions. Across four experiments we find that when the reversal is expected, the illusion gets stronger, not weaker.