Frontiers in Human Neuroscience

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ISSN / EISSN : 1662-5161 / 1662-5161
Current Publisher: Frontiers Media SA (10.3389)
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Kara Brick, Janice L. Cooper, Leona Mason, Sangay Faeflen, Josiah Monmia, Janet M. Dubinsky
Frontiers in Human Neuroscience, Volume 15; doi:10.3389/fnhum.2021.664730

Abstract:
After acquiring knowledge of the neuroscience of learning, memory, stress and emotions, teachers incorporate more cognitive engagement and student-centered practices into their lessons. However, the role understanding neuroscience plays in teachers own affective and motivational competencies has not yet been investigated. The goal of this study was to investigate how learning neuroscience effected teachers’ self-efficacy, beliefs in their ability to teach effectively, self-responsibility and other components of teacher motivation. A pilot training-of-trainers program was designed and delivered in Liberia combining basic neuroscience with information on social, emotional, behavioral and mental health issues faced by students. Tier I of the professional development was a 2 weeks workshop led by a visiting neuroscientist. A subset of the 24 Tier I secondary science teachers formed a Leadership Team who adapted the content to the Liberian context and subsequently led additional workshops and follow-up sessions for the Tier II secondary science teachers. Science teachers in both tiers completed the affective-motivational scales from the internationally vetted, multiscale Innovative Teaching for Effective Learning Teacher Knowledge Survey from the OECD. Tier II teachers completed the survey in a pre-post-delayed post design. Tier I teachers completed the survey after the workshop with their attitudes at that time and separately with retrospective projections of their pre-workshop attitudes. Ten of the 92 Tier II teachers participated in structured interviews at follow-up. Statistical analysis of survey data demonstrated improved teacher self-efficacy, self-responsibility for student outcomes, and motivation to teach. Qualitatively, teachers expressed more confidence in their ability to motivate students, engage them through active learning, and manage the class through positive rather than negative reinforcement. Teachers’ own self-regulation improved as they made efforts to build supporting relationships with students. Together, these results demonstrated that (i) teacher affective-motivational attitudes can be altered with professional development, (ii) basic neuroscience, as knowledge of how students learn, can improve teacher competency, and (iii) a training-of-trainers model can be effective in a low and middle income country for disseminating neuroscience knowledge, increasing teachers’ knowledge of students’ social and emotional needs, and promoting educational improvement.
Denis Larrivee
Frontiers in Human Neuroscience, Volume 15; doi:10.3389/fnhum.2021.649544

Abstract:
“There is a demand for more and more sophisticated social robots. The ideal of many engineers is to produce machines indistinguishable from humans, on the level of behavior or appearance….” (Campa, 2016). Artificial intelligence and its companion technology robotics promise to revolutionize human machine relations through their capabilities for analyzing, interpreting, and executing human action (Institute of Electrical and Electronic Engineers, 2017). While stimulating excitement as well as concern (Bostrom, 2014), these capabilities have also invited reflection on the ethics and values guiding technology development (Calo, 2016). Factors that induce value evolution are of interest, therefore, for influencing the forms the technology may adopt. In broad terms these are seen to operate at two levels: (1) by epistemological inference, often through neuroscientific observation—humans are like machines (McCulloch and Pitts, 1943; Fodor, 1975; Marr and Poggio, 1976; Marr, 1982; Piccinini, 2004; Yuste, 2010) and (2) by ontological predication, that is, as an imputed analog of human meta properties—machines are like humans (Hornyak, 2006; Kitano, 2006; Sabanovic, 2014). Due to their design intent of reducing the onus of human intervention, AI devices are increasingly given over to servicing a spectrum of human needs, from lower order motoric assistance to higher order computational and social functions, e.g., living assistance companions and work colleagues (Sabanovic, 2014); accordingly, they invite analogy at multiple levels. Simulation of higher order cognition, especially, is regarded as driving value attribution—here understood as an intrinsic ground for rights and ethical entitlement (Rothaar, 2010)—which flows from ontological inferences about the technology's operational semblance to human cognition. That is, through replication of these uniquely human abilities, there is a growing ontological incursion in the technology, which propels value evolution under the guise of simulating ontological equivalence. Breazeale's Kismet robot, for instance, explores not merely the social gestures essential to promoting human machine interactions but also the construction of human social intelligence and even what it means to be human (Breazeal, 2002; Calo, 2016). Simulation thus challenges the traditional value hierarchy placing human beings at the apex of organismal life and grounding ethical, bioethical, and neuroethical praxis, a prioritization that has promoted human flourishing while also restricting harmful intervention into the human being. Rather than emphasizing the centrality of human value, simulation promotes a value architecture that is more inclusive, democratic, and horizontal in scope, a trend recently taken up in ethical parity models (Clark and Chalmers, 1998; Levy, 2011; Chandler, 2013). Seen through the lens of ethical parity, however, simulation poses a multidimensional challenge to an ethical system where value is contingent to the human being, a challenge mediated at the level of the ethical subject, i.e., in the siting of value contingency (Clark and Chalmers, 1998; Levy, 2011), in its theory of ethics (Latour, 1993; Connolly, 2011), i.e., in how ethics is normatively anchored (Latour, 2007), and in ethical praxis (Sgreccia, 2012). In consequence, it modifies ethical mediation as an intentionalized moral enactment, which is framed by a referential ontology. The pursuit of ethical parity between robotic technology and the human being has highlighted the symbiotic nature of human machine relations (Haraway, 2003; Rae, 2014a). Rather than the merely instrumentalist association identified in Aristotelian and scholastic philosophy, the appropriation of ontological parity motivates a physical reciprocity that lies at the intersection of the human and the machine; that is, behind the human lies hidden the machine, and behind the machine lies the human. Hence, symbiosis is understood to actuate an a priorism that is physically operative at the locus of intersection between the two (Waters, 2006; Onishi, 2011). Elucidating the philosophical roots of this a priorism is, nonetheless, infrequently considered (Rae, 2014b). While the detection of a physical ‘a priorism’ can be expected to constitute a meta valorization of the process of ontological appropriation distinguishing simulation, epistemological sources that may reveal consilience have yet to trace the physical reciprocity invoked by symbiosis to a meta-physical ground (Haraway, 2003; Rae, 2014a). Modern physics, for example, broadly views the world as consisting of individual entities embedded in space time (Esfeld, 2004), a conception rarely considered in human machine, philosophy of science guises and apparently contravened by the sort of symbiosis proposed in their chimeras. This paper will opine that standard simulation accounts like computationalism trace their understanding of ontology to Heidegger's metaphysical deconstruction of subject/object dichotomies which identified a constitutive a priorism of attribute sharing. Recent integrationist accounts of cognition, however, increasingly evidence a unity structured through the body's engagement in action (Fourneret et al., 2002; Kato et al., 2015; Noel et al., 2018; Wolpaw, 2018); that is, neural architectures reveal an a priorism grounded in the unity of their operation, a finding of relevance for ontology, where actionable behaviors qualify an emergent self. “And, in spite of the victory of the new quantum theory, and the conversion of so many physicists to indeterminism de La Mettrie's doctrine that man is a machine has perhaps more defenders than before among physicists, biologists and philosophers; especially in the form of the thesis that man is a computer.” (Popper, 1978). As Karl Popper notes (Popper, 1978), the thesis that human cognition simulates the computational abilities of machines has...
Marcello Costantini, Davide Quarona,
Frontiers in Human Neuroscience, Volume 15; doi:10.3389/fnhum.2021.628001

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How deeply does action influence perception? Does action performance affect the perception of object features directly related to action only? Or does it concern also object features such as colors, which are not held to directly afford action? The present study aimed at answering these questions. We asked participants to repeatedly grasp a handled mug hidden from their view before judging whether a visually presented mug was blue rather than cyan. The motor training impacted on their perceptual judgments, by speeding participants’ responses, when the handle of the presented mug was spatially aligned with the trained hand. The priming effect did not occur when participants were trained to merely touch the mug with their hand closed in a fist. This indicates that action performance may shape the perceptual judgment on object features, even when these features are colors and do not afford any action. How we act on surrounding objects is therefore not without consequence for how we experience them.
Frontiers in Human Neuroscience, Volume 15; doi:10.3389/fnhum.2021.639081

Abstract:
A theoretical framework for the reinforcement learning of aesthetic biases was recently proposed based on brain circuitries revealed by neuroimaging. A model grounded on that framework accounted for interesting features of human aesthetic biases. These features included individuality, cultural predispositions, stochastic dynamics of learning and aesthetic biases, and the peak-shift effect. However, despite the success in explaining these features, a potential weakness was the linearity of the value function used to predict reward. This linearity meant that the learning process employed a value function that assumed a linear relationship between reward and sensory stimuli. Linearity is common in reinforcement learning in neuroscience. However, linearity can be problematic because neural mechanisms and the dependence of reward on sensory stimuli were typically nonlinear. Here, we analyze the learning performance with models including optimal nonlinear value functions. We also compare updating the free parameters of the value functions with the delta rule, which neuroscience models use frequently, vs. updating with a new Phi rule that considers the structure of the nonlinearities. Our computer simulations showed that optimal nonlinear value functions resulted in improvements of learning errors when the reward models were nonlinear. Similarly, the new Phi rule led to improvements in these errors. These improvements were accompanied by the straightening of the trajectories of the vector of free parameters in its phase space. This straightening meant that the process became more efficient in learning the prediction of reward. Surprisingly, however, this improved efficiency had a complex relationship with the rate of learning. Finally, the stochasticity arising from the probabilistic sampling of sensory stimuli, rewards, and motivations helped the learning process narrow the range of free parameters to nearly optimal outcomes. Therefore, we suggest that value functions and update rules optimized for social and ecological constraints are ideal for learning aesthetic biases.
Margit M. Bach, Andreas Daffertshofer,
Frontiers in Human Neuroscience, Volume 15; doi:10.3389/fnhum.2021.637157

Abstract:
Muscle synergies reflect the presence of a common neural input to multiple muscles. Steering small sets of synergies is commonly believed to simplify the control of complex motor tasks like walking and running. When these locomotor patterns emerge, it is likely that synergies emerge as well. We hence hypothesized that in children learning to run the number of accompanying synergies increases and that some of the synergies’ activities display a temporal shift related to a reduced stance phase as observed in adults. We investigated the development of locomotion in 23 children aged 2–9 years of age and compared them with seven young adults. Muscle activity of 15 bilateral leg, trunk, and arm muscles, ground reaction forces, and kinematics were recorded during comfortable treadmill walking and running, followed by a muscle synergy analysis. We found that toddlers (2–3.5 years) and preschoolers (3.5–6.5 years) utilize a “walk-run strategy” when learning to run: they managed the fastest speeds on the treadmill by combining double support (DS) and flight phases (FPs). In particular the activity duration of the medial gastrocnemius muscle was weakly correlated with age. The number of synergies across groups and conditions needed to cover sufficient data variation ranged between four and eight. The number of synergies tended to be smaller in toddlers than it did in preschoolers and school-age children but the adults had the lowest number for both conditions. Against our expectations, the age groups did not differ significantly in the timing or duration of synergies. We believe that the increase in the number of muscle synergies in older children relates to motor learning and exploration. The ability to run with a FP is clearly associated with an increase in the number of muscle synergies.
, Guilherme De Albuquerque Bruneri, Guilherme Brockington, Hasan Ayaz,
Frontiers in Human Neuroscience, Volume 15; doi:10.3389/fnhum.2021.622146

Abstract:
Hyperscanning studies using functional Near-Infrared Spectroscopy (fNIRS) have been performed to understand the neural mechanisms underlying human-human interactions. In this study, we propose a novel methodological approach that is developed for fNIRS multi-brain analysis. Our method uses support vector regression (SVR) to predict one brain activity time series using another as the predictor. We applied the proposed methodology to explore the teacher-student interaction, which plays a critical role in the formal learning process. In an illustrative application, we collected fNIRS data of the teacher and preschoolers’ dyads performing an interaction task. The teacher explained to the child how to add two numbers in the context of a game. The Prefrontal cortex and temporal-parietal junction of both teacher and student were recorded. A multivariate regression model was built for each channel in each dyad, with the student’s signal as the response variable and the teacher’s ones as the predictors. We compared the predictions of SVR with the conventional ordinary least square (OLS) predictor. The results predicted by the SVR model were statistically significantly correlated with the actual test data at least one channel-pair for all dyads. Overall, 29/90 channel-pairs across the five dyads (18 channels 5 dyads = 90 channel-pairs) presented significant signal predictions withthe SVR approach. The conventional OLS resulted in only 4 out of 90 valid predictions. These results demonstrated that the SVR could be used to perform channel-wise predictions across individuals, and the teachers’ cortical activity can be used to predict the student brain hemodynamic response.
, Megan M. Herting, Lara M. Wierenga,
Frontiers in Human Neuroscience, Volume 15; doi:10.3389/fnhum.2021.663454

Abstract:
Editorial on the Research Topic Understanding the Link Between the Developing Brain and Behavior in Adolescents The transition through adolescences marks one of the most dynamic and influential periods of brain development. This period of neural maturation and reorganization drives a myriad of cognitive, social, and behavioral changes, which ultimately lay the foundations for successful adult functioning. However, this period of neurodevelopment also opens up increased vulnerability to affective and behavioral dysregulation, with a dramatic rise in the incidence of mental illness during adolescence. It is thus important to examine when, where, and why maturational changes in brain structure and function occur, in order to better understand cognition and behavior in both typical and atypical populations. In this Research Topic, we bring together a collection of the latest in neuroimaging research and reviews to understand the link between the developing brain and behavior in adolescents. We start with two articles that examine the influence of the environment we grow up in on structural brain development. Gonzalez et al. showed that socioeconomic status (SES), measured using the income-to-needs ratio (INR), was positively related to cortical surface area in 9-10-year-olds in the Adolescent Brain and Cognitive Development cohort—a large (N = 8158) diverse sample of the US population. These associations were non-linear such that incremental increases in INR had the largest effect on both surface area and cognitive function in those living in deep poverty. Moreover, access to economic and social resources contributed to better cognitive performance in these children, highlighting the importance of public health policies to invest in these resources and support healthier outcomes. McLachlan et al. also showed that SES (based on parental education and occupation) was positively related to limbic volumes in their sample of neurotypical controls aged 7-19 years old (N = 70), but failed to identify such associations in an age-matched group with perinatal alcohol exposure (PAE, N = 69). It is argued that these children and adolescents may have suffered early PAE-related injury that overwhelmed postpartum brain development, such that it reduced neural plasticity or sensitivity to SES. Thus, family SES may have varied developmental effects across different populations. Much of what is known about the adolescent brain is based on cortical gray matter, as assessed by structural morphology or task-based function. However, the connecting white matter continues to undergo a number of microscopic neurobiological processes. Beaulieu et al. advance a broad literature on the role of inefficient neuronal communication on reading ability using advanced imaging techniques that inform us about specific tissue microstructural properties of white matter. They use myelin water fraction imaging in a small sample (N = 20) of 10-18-year-olds, and provide preliminary evidence that lower myelin content differentiates poor from good readers. Findings suggest that poor myelination, and thus decreased conduction speed along axons, amongst key reading circuitry contributes to reading ability; with these findings having broader significance for cognitive and academic development in children and adolescents. In order to understand common behaviors seen in adolescence, two studies took approaches to examine hypotheses regarding the disconnect between prefrontal control systems and the limbic reward system. Kim et al. collected measures of decision-making performance on a computer-based food choice task and obesity in a sample of 71 8-22-year-olds. Specially targeting prefrontal cortical thickness and volume of amygdala nuclei, they found metrics of obesity were associated with gray-matter structure in prefrontal cognitive control regions and the central nucleus of the amygdala as part of the reward system. Furthermore, these structures were predictive of measures of dietary self-control. In a small sample (n = 19), Tymofiyeva et al. examined white-matter connectivity of the nucleus accumbens, anterior cingulate, and amygdala to elucidate potential neural correlates of smartphone dependence in adolescents. Higher structural connectivity of the amygdala with other brain regions (node centrality) was positively correlated with smartphone dependence, which the authors suggest may reflect the amygdala becoming over-sensitized with repetitive smartphone use and its associated rewards. They suggest a potential mediating link between excessive smartphone use and mental health problems through sleep problems. However, both these cross-sectional studies stress that causal direction cannot be determined and call for longitudinal studies. A couple of comprehensive reviews summarize the potential for neural changes in adolescents both in a negative capacity, such as a consequence of substance use, and in a positive capacity with training-induced plasticity. Hamidullah et al. review findings on neural, behavioral, and cognitive changes associated with substance use, focusing on the most commonly used substances (nicotine, alcohol, cannabis, and opioids) as well as their combined use. In addition to the increased risk of future substance use, drug use in adolescence can negatively impact ongoing brain development, contributing to a heightened risk of cognitive deficits and psychopathology. Tymofiyeva and Gaschler present a systematic review of neuroimaging research on training-induced changes in neural structure and function. They identified a diverse set of empirical studies examining varied developmental populations (including ADHD, autism, dyslexia, and dyscalculia) and utilizing different training interventions, study designs, and MRI modalities, showing support for the generalized effect of training-induced changes in neural plasticity in young people. The authors discuss important limitations in the literature, including the...
Michael D. Wood, Leif E. R. Simmatis, Jill A. Jacobson, Sean P. Dukelow, J. Gordon Boyd,
Frontiers in Human Neuroscience, Volume 15; doi:10.3389/fnhum.2021.652201

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Background Kinarm Standard Tests (KSTs) is a suite of upper limb tasks to assess sensory, motor, and cognitive functions, which produces granular performance data that reflect spatial and temporal aspects of behavior (>100 variables per individual). We have previously used principal component analysis (PCA) to reduce the dimensionality of multivariate data using the Kinarm End-Point Lab (EP). Here, we performed PCA using data from the Kinarm Exoskeleton Lab (EXO), and determined agreement of PCA results across EP and EXO platforms in healthy participants. We additionally examined whether further dimensionality reduction was possible by using PCA across behavioral tasks. Methods Healthy participants were assessed using the Kinarm EXO (N = 469) and EP (N = 170–200). Four behavioral tasks (six assessments in total) were performed that quantified arm sensory and motor function, including position sense [Arm Position Matching (APM)] and three motor tasks [Visually Guided Reaching (VGR), Object Hit (OH), and Object Hit and Avoid (OHA)]. The number of components to include per task was determined from scree plots and parallel analysis, and rotation type (orthogonal vs. oblique) was decided on a per-task basis. To assess agreement, we compared principal components (PCs) across platforms using distance correlation. We additionally considered inter-task interactions in EXO data by performing PCA across all six behavioral assessments. Results By applying PCA on a per task basis to data collected using the EXO, the number of behavioral parameters were substantially reduced by 58–75% while accounting for 76–87% of the variance. These results compared well to the EP analysis, and we found good-to-excellent agreement values (0.75–0.99) between PCs from the EXO and those from the EP. Finally, we were able to reduce the dimensionality of the EXO data across tasks down to 16 components out of a total of 76 behavioral parameters, which represents a reduction of 79% while accounting for 73% of the total variance. Conclusion PCA of Kinarm robotic assessment appears to capture similar relationships between kinematic features in healthy individuals and is agnostic to the robotic platform used for collection. Further work is needed to investigate the use of PCA-based data reduction for the characterization of neurological deficits in clinical populations.
, Anne Norup, Trine Schow, Tonny Elmose Andersen
Frontiers in Human Neuroscience, Volume 15; doi:10.3389/fnhum.2021.642680

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Validated self-report measures of post-stroke fatigue are lacking. The Dutch Multifactor Fatigue Scale (DMFS) was translated into Danish, and response process evidence of validity was evaluated. DMFS consists of 38 Likert-rated items distributed on five subscales: Impact of fatigue (11 items), Signs and direct consequences of fatigue (9), Mental fatigue (7), Physical fatigue (6), and Coping with fatigue (5). Response processes to DMFS were investigated using a Three-Step Test-Interview (TSTI) protocol, and data were analyzed using Framework Analysis. Response processes were indexed on the following categories: (i) “congruent,” response processes were related to the subscale construct; (ii) “incongruent,” response processes were not related to the subscale construct; (iii) “ambiguous,” response processes were both congruent and incongruent or insufficient to evaluate congruency; and (iv) “confused,” participants did not understand the item. Nine adults were recruited consecutively 10–34 months post-stroke (median = 26.5) at an outpatient brain injury rehabilitation center in 2019 [five females, mean age = 55 years (SD = 6.3)]. Problematic items were defined as <50% of response processes being congruent with the intended construct. Of the 38 items, five problematic items were identified, including four items of Physical fatigue and one of Mental fatigue. In addition, seven items posed various response difficulties to some participants due to syntactic complexity, vague terms, a presupposition, and a double-barrelled statement. In conclusion, findings elucidate the interpretative processes involved in responding to DMFS post-stroke, strengthen the evidence base of validity, and guide revisions to mitigate potential problems in item performance.
, Mariusz Szydlo, , Malgorzata Wieczorek, Krzysztof Slotwinski, Slawomir Budrewicz
Frontiers in Human Neuroscience, Volume 15; doi:10.3389/fnhum.2021.601322

Abstract:
Introduction Similarities in morphology, physiological function, and neurophysiological findings between median and ulnar nerves are not unequivocal. Our previous study confirmed differences in motor fiber parameters between these nerves in healthy persons. We made an attempt to assess and compare the physiological parameters of different sensation modalities (temperature, pain, and vibration) in median and ulnar nerves. Methods The study was performed in 31 healthy, right-handed volunteers: 17 women, 14 men, mean age 44.8 ± 15.5 years. Standard sensory conduction tests in the median and ulnar nerves were performed together with the estimation of vibratory, temperature, and warm- and cold-induced pain thresholds in the C7 and C8 dermatomes on the palm, using quantitative sensory testing. Results There were no statistically significant differences in the standard sensory conduction test in the median and ulnar nerves across the whole group: between right and left hands, and between women and men. We revealed differences in the temperature and pain thresholds between these nerves, mainly in low temperature perception. There were no differences in estimated thresholds between sides or in female and male groups. The vibratory limits did not differ significantly between nerves, and subgroups. Conclusion The study confirmed the differences in the physiological sensory perception between the median and ulnar nerves. The median nerve is more sensitive to temperature stimulation than the ulnar nerve, but simultaneously less sensitive to pain-inducing temperature stimuli. These findings should be considered during the examination of hand nerve pathology.
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