scholarly journals Personalized Biometrics of Physical Pain Agree with Psychophysics by Participants with Sensory over Responsivity

2021 ◽  
Vol 11 (2) ◽  
pp. 93
Author(s):  
Jihye Ryu ◽  
Tami Bar-Shalita ◽  
Yelena Granovsky ◽  
Irit Weissman-Fogel ◽  
Elizabeth B. Torres
Keyword(s):  
General Population ◽  
Brain Activity ◽  
A Priori ◽  
Summary Statistics ◽  
Physical Pain ◽  
New Methods ◽  
Self Reports ◽  
Theoretical Population ◽  
The Moment ◽  
Reported Data

The study of pain requires a balance between subjective methods that rely on self-reports and complementary objective biometrics that ascertain physical signals associated with subjective accounts. There are at present no objective scales that enable the personalized assessment of pain, as most work involving electrophysiology rely on summary statistics from a priori theoretical population assumptions. Along these lines, recent work has provided evidence of differences in pain sensations between participants with Sensory Over Responsivity (SOR) and controls. While these analyses are useful to understand pain across groups, there remains a need to quantify individual differences more precisely in a personalized manner. Here we offer new methods to characterize pain using the moment-by-moment standardized fluctuations in EEG brain activity centrally reflecting the person’s experiencing temperature-based stimulation at the periphery. This type of gross data is often disregarded as noise, yet here we show its utility to characterize the lingering sensation of discomfort raising to the level of pain, individually, for each participant. We show fundamental differences between the SOR group in relation to controls and provide an objective account of pain congruent with the subjective self-reported data. This offers the potential to build a standardized scale useful to profile pain levels in a personalized manner across the general population.

scholarly journals Personalized Biometrics of Physical Pain Agree with Its Psychological Perception by Participants with Sensory Over Responsivity

2020 ◽  
Author(s):  
Jihye Ryu ◽  
Tami Bar-Shalita ◽  
Yelena Granovsky ◽  
Irit Weissman-Fogel ◽  
Elizabeth B. Torres
Keyword(s):  
General Population ◽  
Brain Activity ◽  
A Priori ◽  
Summary Statistics ◽  
Physical Pain ◽  
New Methods ◽  
Self Reports ◽  
Theoretical Population ◽  
The Moment ◽  
Reported Data

The study of pain requires a balance between subjective methods that rely on self-reports and complementary objective biometrics that ascertain physical signals associated with subjective accounts. There are at present no objective scales that enable the personalized assessment of pain, as most work involving electrophysiology rely on summary statistics from a priori theoretical population assumptions. Along these lines, recent work has provided evidence of differences in pain sensations between participants with Sensory Over Responsivity (SOR) and controls. While these analyses are useful to understand pain across groups, there remains a need to quantify individual differences more precisely in a personalized manner. Here we offer new methods to characterize pain using the moment-by-moment standardized fluctuations in EEG brain activity centrally reflecting the person’s experiencing temperature-based stimulation at the periphery. This type of gross data is often disregarded as noise, yet here we show its utility to characterize the lingering sensation of discomfort raising to the level of pain, individually, for each participant. We show fundamental differences between the SOR group in relation to controls and provide an objective account of pain congruent with the subjective self-reported data. This offers the potential to build a standardized scale useful to profile pain levels in a personalized manner across the general population.

scholarly journals The Story of Taste: Using EEGs and Self-Reports to Understand Consumer Choice

2012 ◽  
Vol 2 (1) ◽  
Author(s):  
Charnetta Brown ◽  
◽  
Adriane Randolph ◽  
Janee Burkhalter ◽  
◽  
...  
Keyword(s):  
Cost Saving ◽  
Consumer Choice ◽  
Brain Activity ◽  
Private Label ◽  
Consumer Decisions ◽  
Electrical Brain Activity ◽  
Self Reports ◽  
Manufacturer Brand ◽  
Reported Data

The authors investigate consumers’ willingness to switch from a preferred manufacturer brand to an unfamiliar private-label brand if taste is perceived as identical. Consumer decisions are examined through recordings of electrical brain activity in the form of electroencephalograms (EEGs) and self-reported data captured in surveys. Results reveal a willingness of consumers to switch to a less-expensive brand when the quality is perceived to be the same as the more expensive counterpart. Cost saving options for consumers and advertising considerations for managers are discussed.

scholarly journals The influence of supraliminal priming on energy density of food selection: a randomised control trial

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Isabelle Schlegel ◽  
Sharon A. Carstairs ◽  
Gozde Ozakinci
Keyword(s):  
General Population ◽  
Lower Energy ◽  
Food Selection ◽  
Food Choices ◽  
Randomised Control Trial ◽  
Future Research ◽  
Control Group ◽  
The Moment ◽  
Overweight Adults ◽  
University Athletes

Abstract Background Many people exercise because they know it is good for their health. Although this is true, it can make us feel deserving of a reward and lead us to eat more indulgent, less healthy food than if we had not done any exercise. Generally, lower energy-dense (LED) foods are recognised as healthier choices than higher energy-dense (HED) options. Despite our intention to make healthy choices, seeing tempting higher-calorie foods on offer often side-tracks us. Priming is a psychological tool that makes specific changes to our environment that remind us of our motivation to be healthy. This makes it easier to choose a healthier option, by nudging us towards it without us even realising. However, it is currently unclear which method of priming achieves the best results. Aims Our study explores whether priming people to expect they will receive LED food leads them to make this healthier choice after exercise, even when also offered tempting less healthy HED foods at the moment of selection. Methods Our study observed the foods selected by university athletes after their sports matches. Before the match, half of the participants were primed by asking them to choose a LED snack from the options we offered, which they would receive after the match. The remaining half of participants were not asked this same question. To distract the athletes from our observation of their food choices, participants completed a task prior to choosing their snack, which was disguised as a ‘thank you’ for taking part. Results Overall, we found the priming group did not choose LED foods significantly more than the control group, hence priming did not increase LED food selection. Conclusion Importantly, our results indicate that priming must be more noticeable to achieve its goal. Additionally, we demonstrated that priming may be less successful for young athletic individuals, compared to older and more overweight adults recruited in other studies. This highlights the importance of studying a broader demographic range of individuals from the general population. We support future research into this area, which will help us to tweak priming to achieve the best outcomes. Trial registration ISRCTN Registry, ISRCTN74601698. Date registered: 02/10/2020 (retrospectively registered).

scholarly journals Jumping over baselines with new methods to predict activation maps from resting-state fMRI

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eric Lacosse ◽  
Klaus Scheffler ◽  
Gabriele Lohmann ◽  
Georg Martius
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Behavioral Differences ◽  
Large Variability ◽  
Single Vertex ◽  
Spontaneous Brain Activity ◽  
New Methods ◽  
Group Averaging ◽  
Proposed Modification

AbstractCognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI). Leveraging this fact, several recent studies have therefore aimed at predicting task activation from rsfMRI using various machine learning methods within a growing literature on ‘connectome fingerprinting’. In reviewing these results, we found lack of an evaluation against robust baselines that reliably supports a novelty of predictions for this task. On closer examination to reported methods, we found most underperform against trivial baseline model performances based on massive group averaging when whole-cortex prediction is considered. Here we present a modification to published methods that remedies this problem to large extent. Our proposed modification is based on a single-vertex approach that replaces commonly used brain parcellations. We further provide a summary of this model evaluation by characterizing empirical properties of where prediction for this task appears possible, explaining why some predictions largely fail for certain targets. Finally, with these empirical observations we investigate whether individual prediction scores explain individual behavioral differences in a task.

Acetaminophen Reduces Social Pain

2010 ◽  
Vol 21 (7) ◽  
pp. 931-937 ◽  
Author(s):  
C. Nathan DeWall ◽  
Geoff MacDonald ◽  
Gregory D. Webster ◽  
Carrie L. Masten ◽  
Roy F. Baumeister ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Regions ◽  
Daily Basis ◽  
Social Rejection ◽  
Neural Mechanisms ◽  
Anterior Cingulate ◽  
Neural Responses ◽  
Physical Pain ◽  
Social Pain ◽  
Dorsal Anterior Cingulate Cortex

Pain, whether caused by physical injury or social rejection, is an inevitable part of life. These two types of pain—physical and social—may rely on some of the same behavioral and neural mechanisms that register pain-related affect. To the extent that these pain processes overlap, acetaminophen, a physical pain suppressant that acts through central (rather than peripheral) neural mechanisms, may also reduce behavioral and neural responses to social rejection. In two experiments, participants took acetaminophen or placebo daily for 3 weeks. Doses of acetaminophen reduced reports of social pain on a daily basis (Experiment 1). We used functional magnetic resonance imaging to measure participants’ brain activity (Experiment 2), and found that acetaminophen reduced neural responses to social rejection in brain regions previously associated with distress caused by social pain and the affective component of physical pain (dorsal anterior cingulate cortex, anterior insula). Thus, acetaminophen reduces behavioral and neural responses associated with the pain of social rejection, demonstrating substantial overlap between social and physical pain.

scholarly journals Inferring brain-computational mechanisms with models of activity measurements

2016 ◽  
Vol 371 (1705) ◽  
pp. 20160278 ◽  
Author(s):  
Nikolaus Kriegeskorte ◽  
Jörn Diedrichsen
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Summary Statistics ◽  
Activity Data ◽  
Individual Measurement ◽  
Activity Measurements ◽  
The Brain ◽  
The Way

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

scholarly journals Assessing biases, relaxing moralism: On ground-truthing practices in machine learning design and application

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110135
Author(s):  
Florian Jaton
Keyword(s):  
Machine Learning ◽  
William James ◽  
A Priori ◽  
Learning Algorithms ◽  
Three Dimensional ◽  
Ground Truth ◽  
Machine Learning Algorithms ◽  
Ground Truthing ◽  
Set Up ◽  
The Moment

This theoretical paper considers the morality of machine learning algorithms and systems in the light of the biases that ground their correctness. It begins by presenting biases not as a priori negative entities but as contingent external referents—often gathered in benchmarked repositories called ground-truth datasets—that define what needs to be learned and allow for performance measures. I then argue that ground-truth datasets and their concomitant practices—that fundamentally involve establishing biases to enable learning procedures—can be described by their respective morality, here defined as the more or less accounted experience of hesitation when faced with what pragmatist philosopher William James called “genuine options”—that is, choices to be made in the heat of the moment that engage different possible futures. I then stress three constitutive dimensions of this pragmatist morality, as far as ground-truthing practices are concerned: (I) the definition of the problem to be solved (problematization), (II) the identification of the data to be collected and set up (databasing), and (III) the qualification of the targets to be learned (labeling). I finally suggest that this three-dimensional conceptual space can be used to map machine learning algorithmic projects in terms of the morality of their respective and constitutive ground-truthing practices. Such techno-moral graphs may, in turn, serve as equipment for greater governance of machine learning algorithms and systems.

Seconds Last

2022 ◽  
Vol 3 (1) ◽  
pp. 69-74
Author(s):  
P. G. Streeter ◽  
Keyword(s):  
Short Fiction ◽  
Brain Activity ◽  
Central Park ◽  
The Moment

What does it mean to be dead? If you were living in a perfect, but false, moment in time, would you choose to leave it? In this work of philosophical short fiction, Linus and Axel are sitting in Central Park on a perfect October day. They have lived in this same day, seemingly, forever. They know they are both dead. Linus died about ten years later than Axel. It occurs to Linus that if they are both seeing his vision of Central Park, it must be his reality. Linus theorizes that, at the moment of death, our brain activity speeds up dramatically, making it seem like our final moment in time lasts forever. However, it’s not real. Linus decides to end this moment in time and move on.

scholarly journals A Systematic Review Summarizing the State of Evidence on Bullying in Childhood Cancer Patients/Survivors

2018 ◽  
Vol 36 (1) ◽  
pp. 55-68 ◽  
Author(s):  
Daisy E. Collins ◽  
Sarah J. Ellis ◽  
Madeleine M. Janin ◽  
Claire E. Wakefield ◽  
Kay Bussey ◽  
...  
Keyword(s):  
General Population ◽  
Childhood Cancer ◽  
Cancer Patients ◽  
The State ◽  
Original Research ◽  
Evidence Based ◽  
Factors Associated ◽  
School Aged Children ◽  
School Absence ◽  
Self Reports

Background: One in four school-aged children is bullied. However, the risk may be greater for childhood cancer patients/survivors (diagnosed <18 years), because of symptoms of the disease and treatment that may prejudice peers. While the serious consequences of bullying are well documented in the general population, bullying may have even greater impact in children with cancer due to the myriad of challenges associated with treatment and prolonged school absence. Objective: To summarize the state of evidence on bullying in childhood cancer patients/survivors; specifically, the rate and types of bullying experienced and the associated factors. Method: We searched five electronic databases from inception to February 2018 for original research articles reporting on bullying in childhood cancer patients/survivors. Results: We identified 29 eligible articles, representing 1,078 patients/survivors ( M = 14.35 years). Self-reports from patients/survivors revealed a considerably higher rate of bullying (32.2%) compared with the general population (25%). Our review identified little information on the factors associated with bullying in patients/survivors. However, the bullying described tended to be verbal and was often related to the physical side effects of treatment, indicating that differences in appearance may prejudice peers. It was further suggested that educating the child’s classmates about cancer may prevent bullying. Conclusions: Our findings confirm that bullying is a significant challenge for many childhood cancer patients/survivors. Additional studies are needed to identify factors that may influence the risk of bullying, which will inform the development of evidence-based interventions and guidelines to prevent bullying in childhood cancer patients/survivors.

scholarly journals ERP CORE: An Open Resource for Human Event-Related Potential Research

2020 ◽  
Author(s):  
Emily S. Kappenman ◽  
Jaclyn Farrens ◽  
Wendy Zhang ◽  
Andrew X Stewart ◽  
Steven J Luck
Keyword(s):  
Data Processing ◽  
Brain Activity ◽  
A Priori ◽  
Event Related Potentials ◽  
Event Related Potential ◽  
Error Related Negativity ◽  
Wide Range ◽  
Experiment Control ◽  
Related Potentials

Event-related potentials (ERPs) are noninvasive measures of human brain activity that index a range of sensory, cognitive, affective, and motor processes. Despite their broad application across basic and clinical research, there is little standardization of ERP paradigms and analysis protocols across studies. To address this, we created ERP CORE (Compendium of Open Resources and Experiments), a set of optimized paradigms, experiment control scripts, data processing pipelines, and sample data (N = 40 neurotypical young adults) for seven widely used ERP components: N170, mismatch negativity (MMN), N2pc, N400, P3, lateralized readiness potential (LRP), and error-related negativity (ERN). This resource makes it possible for researchers to 1) employ standardized ERP paradigms in their research, 2) apply carefully designed analysis pipelines and use a priori selected parameters for data processing, 3) rigorously assess the quality of their data, and 4) test new analytic techniques with standardized data from a wide range of paradigms.

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