Investigating the effects of context, visual working memory, and inhibitory control in hybrid visual search.

Introduction: In real-life scenarios, individuals frequently engage in tasks that involve searching for one of the distinct items stored in memory. This combined process of visual search and memory search is known as hybrid search. To date, most hybrid search studies have been restricted to average observers looking for previously well-memorized targets in blank backgrounds.

A Comparison of Head Movement Classification Methods.

To understand human behavior, it is essential to study it in the context of natural movement in immersive, three-dimensional environments. Virtual reality (VR), with head-mounted displays, offers an unprecedented compromise between ecological validity and experimental control. However, such technological advancements mean that new data streams will become more widely available, and therefore, a need arises to standardize methodologies by which these streams are analyzed.

A Case for Studying Naturalistic Eye and Head Movements in Virtual Environments.

Using head mounted displays (HMDs) in conjunction with virtual reality (VR), vision researchers are able to capture more naturalistic vision in an experimentally controlled setting. Namely, eye movements can be accurately tracked as they occur in concert with head movements as subjects navigate virtual environments. A benefit of this approach is that, unlike other mobile eye tracking (ET) set-ups in unconstrained settings, the experimenter has precise control over the location and timing of stimulus presentation, making it easier to compare findings between HMD studies and those that use monitor displays, which account for the bulk of previous work in eye movement research and vision sciences more generally.

"Blue Sky Effect": Contextual Influences on Pupil Size During Naturalistic Visual Search.

Pupil size is influenced by cognitive and non-cognitive factors. One of the strongest modulators of pupil size is scene luminance, which complicates studies of cognitive pupillometry in environments with complex patterns of visual stimulation. To help understand how dynamic visual scene statistics influence pupil size during an active visual search task in a visually rich 3D virtual environment (VE), we analyzed the correlation between pupil size and intensity changes of image pixels in the red, green, and blue (RGB) channels within a large window (~14 degrees) surrounding the gaze position over time.

During natural viewing, neural processing of visual targets continues throughout saccades.

Relatively little is known about visual processing during free-viewing visual search in realistic dynamic environments. Free-viewing is characterized by frequent saccades. During saccades, visual processing is thought to be suppressed, yet we know that the presaccadic visual content can modulate postsaccadic processing.

Gaze Behavior During Navigation and Visual Search of an Open-World Virtual Environment.

Eye tracking has been an essential tool within the vision science community for many years. However, the majority of studies involving eye-tracking technology employ a relatively passive approach through the use of static imagery, prescribed motion, or video stimuli. This is in contrast to our everyday interaction with the natural world where we navigate our environment while actively seeking and using task-relevant visual information.

Trust and Distrust of Automated Parking in a Tesla Model X.

Objective: The present study aims to evaluate driver intervention behaviors during a partially automated parking task.

Background: Cars with partially automated parking features are becoming widely available. Although recent research explores the use of automation features in partially automated cars, none have focused on partially automated parking.

Decoding P300 Variability Using Convolutional Neural Networks.

Deep convolutional neural networks (CNN) have previously been shown to be useful tools for signal decoding and analysis in a variety of complex domains, such as image processing and speech recognition. By learning from large amounts of data, the representations encoded by these deep networks are often invariant to moderate changes in the underlying feature spaces. Recently, we proposed a CNN architecture that could be applied to electroencephalogram (EEG) decoding and analysis.

The fixation-related lambda response: Effects of saccade magnitude, spatial frequency, and ocular artifact removal.

Fixation-related potentials (FRPs) enable examination of electrophysiological signatures of visual perception under naturalistic conditions, providing a neural snapshot of the fixated scene. The most prominent FRP component, commonly referred to as the lambda response, is a large deflection over occipital electrodes (O1, Oz, O2) peaking 80-100 ms post fixation, reflecting afferent input to visual cortex. The lambda response is affected by bottom-up stimulus features and the size of the preceding saccade; however, prior research has not adequately controlled for these influences in free viewing paradigms.

Isolating Discriminant Neural Activity in the Presence of Eye Movements and Concurrent Task Demands.

A growing number of studies use the combination of eye-tracking and electroencephalographic (EEG) measures to explore the neural processes that underlie visual perception. In these studies, fixation-related potentials (FRPs) are commonly used to quantify early and late stages of visual processing that follow the onset of each fixation. However, FRPs reflect a mixture of bottom-up (sensory-driven) and top-down (goal-directed) processes, in addition to eye movement artifacts and unrelated neural activity.

EEG and Eye Tracking Signatures of Target Encoding during Structured Visual Search.

EEG and eye tracking variables are potential sources of information about the underlying processes of target detection and storage during visual search. Fixation duration, pupil size and event related potentials (ERPs) locked to the onset of fixation or saccade (saccade-related potentials, SRPs) have been reported to differ dependent on whether a target or a non-target is currently fixated. Here we focus on the question of whether these variables also differ between targets that are subsequently reported (hits) and targets that are not (misses).

Best practice for single-trial detection of event-related potentials: Application to brain-computer interfaces.

The detection of event-related potentials (ERPs) in the electroencephalogram (EEG) signal is a fundamental component in non-invasive brain-computer interface (BCI) research, and in modern cognitive neuroscience studies. Whereas the grand average response across trials provides an estimation of essential characteristics of a brain-evoked response, an estimation of the differences between trials for a particular type of stimulus can provide key insight about the brain dynamics and possible origins of the brain response. The research in ERP single-trial detection has been mainly driven by applications in biomedical engineering, with an interest from machine learning and signal processing groups that test novel methods on noisy signals.

The Impact of Task Demands on Fixation-Related Brain Potentials during Guided Search.

Recording synchronous data from EEG and eye-tracking provides a unique methodological approach for measuring the sensory and cognitive processes of overt visual search. Using this approach we obtained fixation related potentials (FRPs) during a guided visual search task specifically focusing on the lambda and P3 components. An outstanding question is whether the lambda and P3 FRP components are influenced by concurrent task demands.

A Permutation Test for Unbalanced Paired Comparisons of Global Field Power.

Global field power is a valuable summary of multi-channel electroencephalography data. However, global field power is biased by the noise typical of electroencephalography experiments, so comparisons of global field power on data with unequal noise are invalid. Here, we demonstrate the relationship between the number of trials that contribute to a global field power measure and the expected value of that global field power measure.

Common EEG features for behavioral estimation in disparate, real-world tasks.

In this study we explored the potential for capturing the behavioral dynamics observed in real-world tasks from concurrent measures of EEG. In doing so, we sought to develop models of behavior that would enable the identification of common cross-participant and cross-task EEG features. To accomplish this we had participants perform both simulated driving and guard duty tasks while we recorded their EEG.

The effect of target and non-target similarity on neural classification performance: a boost from confidence.

Brain computer interaction (BCI) technologies have proven effective in utilizing single-trial classification algorithms to detect target images in rapid serial visualization presentation tasks. While many factors contribute to the accuracy of these algorithms, a critical aspect that is often overlooked concerns the feature similarity between target and non-target images. In most real-world environments there are likely to be many shared features between targets and non-targets resulting in similar neural activity between the two classes.

Optimization of Single-Trial Detection of Event-Related Potentials Through Artificial Trials.

Goal: Many brain-computer interface (BCI) classification techniques rely on a large number of labeled brain responses to create efficient classifiers. A large database representing all of the possible variability in the signal is impossible to obtain in a short period of time, and prolonged calibration times prevent efficient BCI use. We propose to improve BCIs based on the detection of event-related potentials (ERPs) in two ways.

Estimating endogenous changes in task performance from EEG.

Brain wave activity is known to correlate with decrements in behavioral performance as individuals enter states of fatigue, boredom, or low alertness.Many BCI technologies are adversely affected by these changes in user state, limiting their application and constraining their use to relatively short temporal epochs where behavioral performance is likely to be stable. Incorporating a passive BCI that detects when the user is performing poorly at a primary task, and adapts accordingly may prove to increase overall user performance.

Usability of four commercially-oriented EEG systems.

Electroencephalography (EEG) holds promise as a neuroimaging technology that can be used to understand how the human brain functions in real-world, operational settings while individuals move freely in perceptually-rich environments. In recent years, several EEG systems have been developed that aim to increase the usability of the neuroimaging technology in real-world settings. Here, the usability of three wireless EEG systems from different companies are compared to a conventional wired EEG system, BioSemi's ActiveTwo, which serves as an established laboratory-grade 'gold standard' baseline.

Sliding HDCA: single-trial EEG classification to overcome and quantify temporal variability.

Patterns of neural data obtained from electroencephalography (EEG) can be classified by machine learning techniques to increase human-system performance. In controlled laboratory settings this classification approach works well; however, transitioning these approaches into more dynamic, unconstrained environments will present several significant challenges. One such challenge is an increase in temporal variability in measured behavioral and neural responses, which often results in suboptimal classification performance.

Multiclass classification of single-trial evoked EEG responses.

The detection of event-related potentials (ERPs) in the electroencephalogram (EEG) signal has several real-world applications, from cognitive state monitoring to brain-computer interfaces. Current systems based on the detection of ERPs only consider a single type of response to detect. Hence, the classification methods that are considered for ERP detection are binary classifiers (target vs.

Magnocellular and parvocellular influences on reflexive attention.

Previous studies have provided conflicting evidence regarding whether the magnocellular (M) or parvocellular (P) visual pathway is primarily responsible for triggering involuntary orienting. Here, we used event-related potentials (ERPs) to provide new evidence that both the M and P pathways can trigger attentional capture and bias visual processing at multiple levels. Specifically, cued-location targets elicited enhanced activity, relative to uncued-location targets, at both early sensory processing levels (indexed by the P1 component) and later higher-order processing stages (as indexed by the P300 component).

Automatic versus contingent mechanisms of sensory-driven neural biasing and reflexive attention.

Recent studies have generated debate regarding whether reflexive attention mechanisms are triggered in a purely automatic stimulus-driven manner. Behavioral studies have found that a nonpredictive "cue" stimulus will speed manual responses to subsequent targets at the same location, but only if that cue is congruent with actively maintained top-down settings for target detection. When a cue is incongruent with top-down settings, response times are unaffected, and this has been taken as evidence that reflexive attention mechanisms were never engaged in those conditions.