Pseudoneglect

Pseudoneglect refers to a phenomenon in which people exhibit a bias or tendency to allocate more attention or perceptual processing to the left side of space than the right side (or vice versa). Despite the term "neglect" in its name, pseudoneglect does not indicate an actual neglect of the opposite side but rather an asymmetry in attention allocation.

The term "pseudoneglect" was coined by Bowers and Heilman in 1980 when they observed a leftward bias in line bisection tasks, where participants were asked to mark the midpoint of a horizontal line. They found that most individuals tend to place the mark slightly to the left of the true center, indicating a bias toward the left side of the line.

This phenomenon has been attributed to the dominance of the right hemisphere in spatial attention and perception. The right hemisphere of the brain is generally considered to be more involved in processing spatial information, while the left hemisphere is typically associated with language and analytical functions. As a result, the right hemisphere's dominance may contribute to a leftward attentional bias, leading to pseudoneglect.

Pseudoneglect has been observed in various perceptual tasks, including line bisection, line length estimation, and visual search. It is thought to reflect a normal asymmetry in attentional processing rather than a pathological condition.

Understanding pseudoneglect can have implications for studying brain function and spatial cognition. Researchers have used this phenomenon to explore the mechanisms underlying spatial attention, hemispheric specialization, and disorders such as neglect syndrome, where there is a true deficit in attending to one side of space.

Rubber Hand Illusion

Body ownership, specifically arm ownership, can be experimentally manipulated using the Rubber Hand Illusion (RHI). 

RHI is a perceptual phenomenon that occurs when a person's brain is tricked into perceiving a rubber hand as part of their own body. 

In the RHI, a realistic looking rubber hand is placed next to a subject's own hand, and both hands are stroked in synchrony at the same location, with only the rubber hand being visible.

By simultaneously stroking both the rubber hand and the participant's real hand, it creates the illusion that the rubber hand is their own (attributed to their own body)

To integrate the new hand into the body representation, it is important that both the rubber hand and real hand are anatomically aligned, meaning they should be positioned in the same orientation and in parallel to each other or above one another.

Embodying the rubber hand as one's own changes the sense of location of one's own hand, and the perceived location of one's own hand typically "drifts" towards the rubber hand after inducing the illusion, which is known as proprioceptive drift.

The RHI illustrates the plasticity of our body representation, as it transiently changes how and where we perceive our hand.

RHI can also affect the foot and the entire body. RHI has been used in various research studies to investigate body perception, multisensory integration, and the brain's ability to update its body representation. It was first described by Ehrsson in 1998  paper.

Researchers have utilized the rubber hand illusion to explore various aspects of body ownership and self-perception. It has been employed in studies examining disorders like schizophrenia and body dysmorphic disorder, as well as in research related to pain perception, self-identification, and body image.

Regarding autism research, the rubber hand illusion has been employed to investigate differences in body representation and multisensory integration; whether autistics show alterations in the experience of body ownership and if their multisensory integration processes differ from NT  individuals (Casio et al, 2012)

Citation:

Ehrsson, H. H. (1998). Touching a rubber hand: feeling of body ownership is associated with the synchronous multisensory stimulation. Cognitive Brain Research, 67(2), 561-570.

Cascio, C. J., Foss-Feig, J. H., Heacock, J. L., & Newsom, C. R. (2012). Rubber Hand Illusion in Autism Spectrum Disorder: Self versus Externally Attributed Touch. Journal of Autism and Developmental Disorders, 42(11), 2420–2429. doi: 10.1007/s10803-012-1500-1

NASA-TLX - Task Load Index

 [See posts on other Screening/Assessment Tools, Psychological Measures]

The NASA-TLX (Task Load Index) questionnaire is a tool developed by NASA to assess the workload and subjective workload experienced by individuals performing a task. Though initially designed for pilots, it is widely used across various industries including autism research 

The questionnaire has 6 subscales/submeasures, that assess different dimensions of workload. 

• Mental Demand: mental effort and cognitive load required to perform the task.
• Physical Demand: physical effort and exertion involved in performing the task.
• Temporal Demand: perceived time pressure and the amount of time available to complete the task.
• Performance: individual's perception of their own performance during the task.
• Effort: perceived level of effort and energy expenditure required to complete the task.
• Frustration: degree of annoyance, stress, and dissatisfaction experienced during the task.

Scoring and Interpretation

Participants rate each submeasure on a scale of 0 to 100. Scoring and interpretation vary depending on the specific study or context. Generally, higher scores indicate a higher perceived workload in the respective submeasure. 

Researchers often analyze the individual submeasure scores and the overall workload score to gain insights into the specific dimensions of workload that are most significant in a given task or situation. The questionnaire can help identify areas where workload can be optimized or where additional support or resources may be required.

Examples of use in Autism Research in evaluating workload and cognitive demands 

Study: "Task load and verbal responses to questions in children with autism spectrum disorder"Citation: Nishida, T., Yuhi, T., Kaneoke, Y., Kurosawa, K., & Dan, I. (2014). Task load and verbal responses to questions in children with autism spectrum disorder. Frontiers in Human Neuroscience, 8, 937.
Link: https://doi.org/10.3389/fnhum.2014.00937

Study: "Measurement of cognitive workload in individuals with high-functioning autism spectrum disorder using a virtual reality task"Citation: Park, S. M., Chong, S. C., Lim, S. L., Kim, J. S., & Kim, J. S. (2020). Measurement of cognitive workload in individuals with high-functioning autism spectrum disorder using a virtual reality task. Applied Sciences, 10(2), 581.
Link: https://doi.org/10.3390/app10020581

In silico In Vivo In Vitro

In silico - performing experiments / simulations using computer models or algorithms

In vivo - in a living organism 

In vitro (in a test tube or culture dish).

Sensor Space Analysis

Sensor space analysis is a method used in neuroscience to analyze the electrical activity of the brain recorded by EEG sensors placed on the scalp. It involves analyzing the electrical signals recorded by the sensors to identify patterns of activity associated with specific cognitive or perceptual processes.

These sensors detect and measure the electrical potentials or magnetic fields generated by neural activity in the brain. Each sensor captures the neural signals from a specific location on the scalp or above the head.

The main objective of SSA is to analyze and interpret the spatiotemporal patterns of neural activity recorded by these sensors. It allows researchers to investigate various aspects of brain function, such as perceptual processing, attention, memory, language, and motor control. SSA provides insights into how different brain regions and networks contribute to specific cognitive processes.

The general steps involved in sensor space analysis include:

Preprocessing: The recorded EEG or MEG data undergoes preprocessing steps, which may include filtering, artifact removal (e.g., eye blinks, muscle artifacts), and baseline correction.


Time-Frequency Analysis: Time-frequency analysis is often applied to extract oscillatory activity in different frequency bands over time. This analysis provides information about the power or amplitude of neural oscillations at different time points and frequencies.


Sensor-level Analysis: activity recorded by individual sensors is analyzed. Various statistical techniques, such as event-related potential (ERP) analysis or time-frequency analysis, are used to examine sensor-level responses to experimental manipulations or cognitive tasks. This analysis focuses on the amplitude, latency, or spectral characteristics of neural responses at specific sensors.


Statistical Inference: Statistical tests are performed to determine the significance of observed effects or differences in neural activity across experimental conditions or groups. This step involves comparing sensor-level responses using appropriate statistical tests, such as t-tests, analysis of variance (ANOVA), or non-parametric tests.


Visualization: The results of the sensor-level analysis can be visualized using topographic maps, which illustrate the spatial distribution of neural activity across sensors. These maps help identify regions of interest and reveal the scalp distribution of neural responses.

SSA provides a detailed understanding of the dynamics of neural activity at the sensor level, allowing researchers to investigate the temporal and spatial characteristics of cognitive processes. It serves as a foundation for subsequent source-level analysis, which aims to localize the neural sources contributing to the observed sensor-level responses. By combining sensor and source space analyses, researchers can gain comprehensive insights into brain function and connectivity during various cognitive tasks or experimental manipulations

Oddball Paradigms

 [Concepts in Sensorimotor Research]

Oddball trials, also known as oddball tasks or oddball paradigms, are a type of experimental design. In oddball trials, a sequence of stimuli is presented to participants, and their task is to detect and respond to specific target stimuli embedded within a stream of more frequent, standard stimuli. The oddball paradigm has been widely used in autism research to investigate sensory processing differences, attentional deficits, and cognitive control.

The oddball paradigm typically consists of two types of stimuli:

• Standard Stimuli: These are the most common stimuli presented in the sequence and serve as the baseline / control stimuli, occurring with higher frequency. Participants are generally instructed to ignore standard stimuli and withhold any response to them
• Target Stimuli: These are the less frequent or "oddball" stimuli that differ in some way from the standard stimuli. Participants are instructed to actively detect and respond to these target stimuli. The target stimuli can be defined by various characteristics, such as a different color, shape, sound, or any other perceptual feature.

The purpose of oddball trials is to investigate how the brain processes and detects rare or deviant stimuli amidst a background of more common stimuli. By manipulating the frequency and characteristics of the target and standard stimuli, researchers can examine various aspects of cognitive processing, including attention

• Attention: how participants allocate and sustain their attention to detect infrequent target stimuli. It allows researchers to explore the mechanisms of selective attention, attentional capture, and the ability to filter out irrelevant information.
• Perception & perceptual processing: how the brain discriminates between different stimuli; how the brain detects and discriminates deviant stimuli based on sensory features, and how it forms representations and expectations about the environment
• Memory and Cognitive Control: Participants may be required to remember the occurrence or characteristics of the target stimuli and maintain this information for subsequent recall or recognition. Also sheds light on cognitive control processes, such as response inhibition and response selection when distinguishing between standard and target stimuli.

During an oddball task, researchers typically measure various physiological and behavioral responses, such as reaction times, accuracy rates, ERPs (via EEG) or fMRI (to examine neural activity patterns).