An Examination into the Putative Mechanisms Underlying Human Sensorimotor Learning and Decision Making

Sensorimotor learning can be defined as a process by which an organism benefits from its experience, such that its future behaviour is better adapted to its environment. Humans are sensorimotor learners par excellence, and neurologically intact adults possess an incredible repertoire of skilled behaviours. Nevertheless, despite the topic fascinating scientists for centuries, there remains a lack of understanding about how humans truly learn. There is a need to better understand sensorimotor learning mechanisms in order to develop treatments for individuals with movement problems, improve training regimes (e.g. surgery) and accelerate motor learning in tasks such as handwriting in children and stroke rehabilitation. This thesis set out to improve our understanding of sensorimotor learning processes and develop methodologies and tools that enable other scientists to tackle these research questions using the power of recent developments in computer science (particularly immersive technologies). Errors in sensorimotor learning are the specific focus of the experimental chapters of this thesis, where the goal is to address our understanding of error perception and correction in motor learning and provide a computational understanding of how we process different types of error to inform subsequent behaviour. A brief summary of the approaches employed, and tools developed over the course of this thesis are presented below. Chapter 1 of this thesis provides a concise overview of the literature on human sensorimotor learning. It introduces the concept of internal models of human interactions with the environment, constructed and refined by the brain in the learning process. Highlighted in this chapter are potential mechanisms for promoting learning (e.g. error augmentation, motor variability) and outstanding challenges for the field (e.g. redundancy, credit assignment). In Chapter 2 a computational model based on information acquisition is developed. The model suggests that disruptive forces applied to human movements during training could improve learning because they allow the learner to sample more information from their environment. Chapter 3 investigates whether sensorimotor learning can be accelerated through forcing participants to explore (and thus acquire more information) a novel workspace. The results imply that exploration may be a necessary component of learning but manipulating it in this way is not sufficient to accelerate learning. This work serves to highlight the critical role of error correction in learning. The process of conducting the experimental work in Chapters 2 and 3 highlighted the need for an application programme interface that would allow researchers to rapidly deploy experiments that allow one to examine learning in a controlled but ecologically relevant manner. Virtual reality systems (that measure human interactions with computer generated worlds) provide a powerful tool for exploring sensorimotor learning and their use in the study of human behaviour is now more feasible due to recent technological advances. To this end, Chapter 4 reports the development of the Unity Experiment Framework - a new tool to assist in the development of virtual reality experiments in the Unity game engine. Chapter 5 builds on the findings from Chapters 2 & 3 on learning by addressing the specific contributions of visual error. It utilises the Unity Experiment Framework to explore whether visually increasing the error signal in a novel aiming task can accelerate motor learning. A novel aiming task is developed which requires participants to learn the mapping between rotations of the handheld virtual reality controllers and the movement of a cursor in Cartesian space. The results show that the visual disturbance does not accelerate the learning of skilled movements, implying a crucial role for mechanical forces, or physical error correction, which is consistent with the findings reported in Chapter 2. Uncontrolled manifold analysis provides insight into how the variability in selected solutions related to learning and performance, as the task deliberately allowed a variety of solutions from a redundant parameter space. Chapter 6 extends the scope of this thesis by examining how error information from the sensorimotor system influences higher order action selection processes. Chapter 5 highlighted the loose definition of “error” in sensorimotor learning and here, the goal was to advance our understanding of error learning by discriminating between different sources of error to better understand their contributions to future behaviour. This issue is illustrated through the example of a tennis player who, on a given point, has the options of selecting a backhand or forehand shot available to her. If the shot is ineffective (and produces an error signal), to optimise future behaviour, the brain needs to rapidly determine whether the error was due to poor shot selection, or whether the correct shot was selected but just poorly executed. To examine these questions, a novel ‘action bandit’ task was developed where participants made reaching movements towards targets, with each target having distinct probabilities of execution and selection error. The results revealed a significant selection bias towards a target that produced a higher frequency of execution errors (rather than a target associated with more selection error) despite no difference in expected value. This behaviour may be explained by a gating mechanism, where learning from the lack of reward is discounted following sensorimotor errors. However, execution errors also increase uncertainty about the appropriateness of a selected choice and the need to reduce uncertainty could equally account for these results. Subsequent experiments test these competing hypotheses and show this putative gating mechanism can be dynamically regulated though coupling of selections and execution errors. Development of models of these processes highlighted the dynamics of the mechanisms that drive the behaviour. In Chapter 7, the motor component of the task was removed to examine whether this effect is not unique to execution errors, but a feature of any two-stage decision-making process with, multiple error types which are presumed to be dissociated. These observations highlight the complex role error plays in learning and suggest the credit assignment process is guided and modulated by internal models of the task at hand. Finally, Chapter 8 closes this thesis with a summary of the key findings and arising from this work in the context of the literature on motor learning and decision making. It is noted here that this thesis sought to cover two broad research topics of motor learning and decision making that have, until recently, been studied by separate groups of researchers, with very little overlap in literature. A key goal of this programme of research was to contribute towards bringing together these hitherto disparate fields by focussing on breadth to establish common ground. As the experimental work developed, it became clear that the processing of error required a multi-pronged approach. Within each experimental chapter, the focus on error was accordingly narrowed and definitions refined. This culminated in developing and testing how individuals discriminate between errors in the sensorimotor and cognitive domains, thus presenting a framework for understanding how motor learning and decision making interact

Learning histories, participatory methods and creative engagement for climate resilience

The potential of place-based, historically-informed approaches to drive climate action has not yet been adequately interrogated. Recent scholarly work has focussed on climate communication and the role of arts and humanities-led storytelling in engaging people in climate narratives. Far less has been said about mobilising arts and creativity to build anticipatory climate action. perNor have archival material and pre-twentieth century histories of living with water and flood been widely utilised in this endeavour. This paper reflects on our experiences delivering the UKRI-funded Risky Cities programme and specifically, of developing and utilising a learning histories approach that folds together past, present and future in productive ways so as to learn from the past and the present and rethink the future. Risky Cities uses this approach to develop engagement tools at different scales, evaluating their impact throughout using participant interviews, reflective focus groups, and surveys. Analysing this data, we consistently find that using learning histories as the foundation of arts-led and creative community engagement makes big narratives about global climate change locally meaningful. Crucially, this drives cognitive shifts, behavioural change and anticipatory action for both participants and audiences. Thus, our learning histories approach is an important participatory tool for building climate action, empowerment and resilience

Distinct biogeographic patterns for archaea, bacteria, and fungi along the vegetation gradient at the continental scale in Eastern China

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in mSystems 2 (2017): e00174-16, doi:10.1128/mSystems.00174-16.The natural forest ecosystem in Eastern China, from tropical forest to boreal forest, has declined due to cropland development during the last 300 years, yet little is known about the historical biogeographic patterns and driving processes for the major domains of microorganisms along this continental-scale natural vegetation gradient. We predicted the biogeographic patterns of soil archaeal, bacterial, and fungal communities across 110 natural forest sites along a transect across four vegetation zones in Eastern China. The distance decay relationships demonstrated the distinct biogeographic patterns of archaeal, bacterial, and fungal communities. While historical processes mainly influenced bacterial community variations, spatially autocorrelated environmental variables mainly influenced the fungal community. Archaea did not display a distance decay pattern along the vegetation gradient. Bacterial community diversity and structure were correlated with the ratio of acid oxalate-soluble Fe to free Fe oxides (Feo/Fed ratio). Fungal community diversity and structure were influenced by dissolved organic carbon (DOC) and free aluminum (Ald), respectively. The role of these environmental variables was confirmed by the correlations between dominant operational taxonomic units (OTUs) and edaphic variables. However, most of the dominant OTUs were not correlated with the major driving variables for the entire communities. These results demonstrate that soil archaea, bacteria, and fungi have different biogeographic patterns and driving processes along this continental-scale natural vegetation gradient, implying different community assembly mechanisms and ecological functions for archaea, bacteria, and fungi in soil ecosystems.This research was financially supported by the National Natural Science Foundation of China (grant number 41520104001), the 111 Project, and the Fundamental Research Funds for the Central Universities

What characteristics of primary care and patients are associated with early death in patients with lung cancer in the UK?

Background: The UK has poor lung cancer survival rates and high early mortality, compared to other countries. We aimed to identify factors associated with early death, and features of primary care that might contribute to late diagnosis. Methods: All cases of lung cancer diagnosed between 2000 and 2013 were extracted from The Health Improvement Network database. Patients who died within 90 days of diagnosis were compared with those who survived longer. Standardised chest X-ray (CXR) and lung cancer rates were calculated for each practice. Results: Of 20 142 people with lung cancer, those who died early consulted with primary care more frequently prediagnosis. Individual factors associated with early death were male sex (OR 1.17; 95% CI 1.10 to 1.24), current smoking (OR 1.43; 95% CI 1.28 to 1.61), increasing age (OR 1.80; 95% CI 1.62 to 1.99 for age ≥80 years compared to 65–69 years), social deprivation (OR 1.16; 95% CI 1.04 to 1.30 for Townsend quintile 5 vs 1) and rural versus urban residence (OR 1.22; 95% CI 1.06 to 1.41). CXR rates varied widely, and the odds of early death were highest in the practices which requested more CXRs. Lung cancer incidence at practice level did not affect early deaths. Conclusions: Patients who die early from lung cancer are interacting with primary care prediagnosis, suggesting potentially missed opportunities to identify them earlier. A general increase in CXR requests may not improve survival; rather, a more timely and appropriate targeting of this investigation using risk assessment tools needs further assessment

Improved Resolution Haplogroup G Phylogeny in the Y Chromosome, Revealed by a Set of Newly Characterized SNPs

Background: Y-SNP haplogroup G (hgG), defined by Y-SNP marker M201, is relatively uncommon in the United States general population, with only 8 additional sub-markers characterized. Many of the previously described eight sub-markers are either very rare (2–4%) or do not distinguish between major populations within this hg. In fact, prior to the current study, only 2 % of our reference Caucasian population belonged to hgG and all of these individuals were in sub-haplogroup G2a, defined by P15. Additional Y-SNPs are needed in order to differentiate between individuals within this haplogroup. Principal Findings: In this work we have investigated whether we could differentiate between a population of 63 hgG individuals using previously uncharacterized Y-SNPs. We have designed assays to test these individuals using all known hgG SNPs (n = 9) and an additional 16 unreported/undefined Y-SNPS. Using a combination of DNA sequence and genetic genealogy databases, we have uncovered a total of 15 new hgG SNPs that had been previously reported but not phylogenetically characterized. Ten of the new Y-SNPs are phylogenetically equivalent to M201, one is equivalent to P15 and, interestingly, four create new, separate haplogroups. Three of the latter are more common than many of the previously defined Y-SNPs. Y-STR data from these individuals show that DYS385*12 is present in (70%) of G2a3b1-U13 individuals while only 4 % of non-G2a3b1-U13 individuals posses the DYS385*12 allele. Conclusions: This study uncovered several previously undefined Y-SNPs by using data from several database sources. Th

Time dependence of biomarkers:non-proportional effects of immunohistochemical panels predicting relapse risk in early breast cancer

Background:We investigated the impact of follow-up duration to determine whether two immunohistochemical prognostic panels, IHC4 and Mammostrat, provide information on the risk of early or late distant recurrence using the Edinburgh Breast Conservation Series and the Tamoxifen vs Exemestane Adjuvant Multinational (TEAM) trial.Methods:The multivariable fractional polynomial time (MFPT) algorithm was used to determine which variables had possible non-proportional effects. The performance of the scores was assessed at various lengths of follow-up and Cox regression modelling was performed over the intervals of 0-5 years and >5 years.Results:We observed a strong time dependence of both the IHC4 and Mammostrat scores, with their effects decreasing over time. In the first 5 years of follow-up only, the addition of both scores to clinical factors provided statistically significant information (P<0.05), with increases in R 2 between 5 and 6% and increases in D-statistic between 0.16 and 0.21.Conclusions:Our analyses confirm that the IHC4 and Mammostrat scores are strong prognostic factors for time to distant recurrence but this is restricted to the first 5 years after diagnosis. This provides evidence for their combined use to predict early recurrence events in order to select those patients who may/will benefit from adjuvant chemotherapy

Light and Heavy Fractions of Soil Organic Matter in Response to Climate Warming and Increased Precipitation in a Temperate Steppe

Soil is one of the most important carbon (C) and nitrogen (N) pools and plays a crucial role in ecosystem C and N cycling. Climate change profoundly affects soil C and N storage via changing C and N inputs and outputs. However, the influences of climate warming and changing precipitation regime on labile and recalcitrant fractions of soil organic C and N remain unclear. Here, we investigated soil labile and recalcitrant C and N under 6 years' treatments of experimental warming and increased precipitation in a temperate steppe in Northern China. We measured soil light fraction C (LFC) and N (LFN), microbial biomass C (MBC) and N (MBN), dissolved organic C (DOC) and heavy fraction C (HFC) and N (HFN). The results showed that increased precipitation significantly stimulated soil LFC and LFN by 16.1% and 18.5%, respectively, and increased LFC∶HFC ratio and LFN∶HFN ratio, suggesting that increased precipitation transferred more soil organic carbon into the quick-decayed carbon pool. Experimental warming reduced soil labile C (LFC, MBC, and DOC). In contrast, soil heavy fraction C and N, and total C and N were not significantly impacted by increased precipitation or warming. Soil labile C significantly correlated with gross ecosystem productivity, ecosystem respiration and soil respiration, but not with soil moisture and temperature, suggesting that biotic processes rather than abiotic factors determine variations in soil labile C. Our results indicate that certain soil carbon fraction is sensitive to climate change in the temperate steppe, which may in turn impact ecosystem carbon fluxes in response and feedback to climate change

The effect of a virtual reality environment on gaze behaviour and motor skill learning

Objective: Virtual reality (VR) systems hold significant potential for training skilled behaviours and are currently receiving intense interest in the sporting domain. They offer both practical and pedagogical benefits, but there are concerns about the effect that perceptual deficiencies in VR systems (e.g. reduced haptic information, and stereoscopic display distortions) may have on learning and performance. ‘Specificity of learning’ theories suggest that VR could be ineffective (or even detrimental) if important differences (e.g. perceptual deficiencies) exist between practice and real task performance conditions. Nevertheless, ‘structural learning’ theories suggest VR could be a useful training tool, despite these deficiencies, because a trainee can still learn the underlying structure of the behaviour. We explored these theoretical predictions using golf putting as an exemplar skill. Method: In Experiment 1 we used a repeated measures design to assess putting accuracy (radial error) and quiet eye duration of expert golfers (n = 18) on real putts before and after 40 VR ‘warm up’ putts. In Experiment 2, novice golfers (n = 40) were assigned to either VR or real-world putting training. Putting accuracy and quiet eye durations were then assessed on a real-world retention test. Results: Both visual guidance (quiet eye) and putting accuracy were disrupted temporarily when moving from VR to real putting (Experiment 1). However, real-world and VR practice produced comparable improvements in putting accuracy in novice golfers (Experiment 2). Conclusion: Overall, the results suggest that: (i) underlying skill structures can be learned in VR and transferred to the real-world; (ii) perceptual deficiencies will place limits on the use of VR. These findings demonstrate the challenges and opportunities for VR as a training tool, and emphasise the need to empirically test the costs and benefits of specific systems before deploying VR training

A lightweight magnetically shielded room with active shielding

Magnetically shielded rooms (MSRs) use multiple layers of materials such as MuMetal to screen external magnetic fields that would otherwise interfere with high precision magnetic field measurements such as magnetoencephalography (MEG). Optically pumped magnetometers (OPMs) have enabled the development of wearable MEG systems which have the potential to provide a motion tolerant functional brain imaging system with high spatiotemporal resolution. Despite significant promise, OPMs impose stringent magnetic shielding requirements, operating around a zero magnetic field resonance within a dynamic range of ± 5 nT. MSRs developed for OPM-MEG must therefore effectively shield external sources and provide a low remnant magnetic field inside the enclosure. Existing MSRs optimised for OPM-MEG are expensive, heavy, and difficult to site. Electromagnetic coils are used to further cancel the remnant field inside the MSR enabling participant movements during OPM-MEG, but present coil systems are challenging to engineer and occupy space in the MSR limiting participant movements and negatively impacting patient experience. Here we present a lightweight MSR design (30% reduction in weight and 40–60% reduction in external dimensions compared to a standard OPM-optimised MSR) which takes significant steps towards addressing these barriers. We also designed a ‘window coil’ active shielding system, featuring a series of simple rectangular coils placed directly onto the walls of the MSR. By mapping the remnant magnetic field inside the MSR, and the magnetic field produced by the coils, we can identify optimal coil currents and cancel the remnant magnetic field over the central cubic metre to just |B|= 670 ± 160 pT. These advances reduce the cost, installation time and siting restrictions of MSRs which will be essential for the widespread deployment of OPM-MEG