Immersive VR for investigating threat avoidance: The VRthreat toolkit for Unity.

All animals have to respond to immediate threats in order to survive. In non-human animals, a diversity of sophisticated behaviours has been observed, but research in humans is hampered by ethical considerations. Here, we present a novel immersive VR toolkit for the Unity engine that allows assessing threat-related behaviour in single, semi-interactive, and semi-realistic threat encounters. The toolkit contains a suite of fully modelled naturalistic environments, interactive objects, animated threats, and scripted systems. These are arranged together by the researcher as a means of creating an experimental manipulation, to form a series of independent "episodes" in immersive VR. Several specifically designed tools aid the design of these episodes, including a system to allow for pre-sequencing the movement plans of animal threats. Episodes can be built with the assets included in the toolkit, but also easily extended with custom scripts, threats, and environments if required. During the experiments, the software stores behavioural, movement, and eye tracking data. With this software, we aim to facilitate the use of immersive VR in human threat avoidance research and thus to close a gap in the understanding of human behaviour under threat.

Investigating the relationship between sleep and migraine in a global sample: a Bayesian cross-sectional approach.

BACKGROUND: There is a bidirectional link between sleep and migraine, however causality is difficult to determine. This study aimed to investigate this relationship using data collected from a smartphone application. METHODS: Self-reported data from 11,166 global users (aged 18-81 years, mean: 41.21, standard deviation: 11.49) were collected from the Migraine Buddy application (Healint Pte. Ltd.). Measures included: start and end times of sleep and migraine attacks, and pain intensity. Bayesian regression models were used to predict occurrence of a migraine attack the next day based on users' deviations from average sleep, number of sleep interruptions, and hours slept the night before in those reporting ≥ 8 and < 25 migraine attacks on average per month. Conversely, we modelled whether attack occurrence and pain intensity predicted hours slept that night. RESULTS: There were 724 users (129 males, 412 females, 183 unknown, mean age = 41.88 years, SD = 11.63), with a mean monthly attack frequency of 9.94. More sleep interruptions (95% Highest Density Interval (95%HDI [0.11 - 0.21]) and deviation from a user's mean sleep (95%HDI [0.04 - 0.08]) were significant predictors of a next day attack. Total hours slept was not a significant predictor (95%HDI [-0.04 - 0.04]). Pain intensity, but not attack occurrence was a positive predictor of hours slept. CONCLUSIONS: Sleep fragmentation and deviation from typical sleep are the main drivers of the relationship between sleep and migraine. Having a migraine attack does not predict sleep duration, yet the pain associated with it does. This study highlights sleep as crucial in migraine management.

Distinct Neural Signatures of Outcome Monitoring After Selection and Execution Errors.

Losing a point in tennis could result from poor shot selection or faulty stroke execution. To explore how the brain responds to these different types of errors, we examined feedback-locked EEG activity while participants completed a modified version of a standard three-armed bandit probabilistic reward task. Our task framed unrewarded outcomes as the result of either errors of selection or errors of execution. We examined whether amplitude of a medial frontal negativity (the feedback-related negativity [FRN]) was sensitive to the different forms of error attribution. Consistent with previous reports, selection errors elicited a large FRN relative to rewards, and amplitude of this signal correlated with behavioral adjustment after these errors. A different pattern was observed in response to execution errors. These outcomes produced a larger FRN, a frontocentral attenuation in activity preceding this component, and a subsequent enhanced error positivity in parietal sites. Notably, the only correlations with behavioral adjustment were with the early frontocentral attenuation and amplitude of the parietal signal; FRN differences between execution errors and rewarded trials did not correlate with subsequent changes in behavior. Our findings highlight distinct neural correlates of selection and execution error processing, providing insight into how the brain responds to the different classes of error that determine future action.

Studying human behavior with virtual reality: The Unity Experiment Framework.

Virtual reality (VR) systems offer a powerful tool for human behavior research. The ability to create three-dimensional visual scenes and to measure responses to the visual stimuli enables the behavioral researcher to test hypotheses in a manner and scale that were previously unfeasible. For example, a researcher wanting to understand interceptive timing behavior might wish to violate Newtonian mechanics so that objects can move in novel 3-D trajectories. The same researcher might wish to collect such data with hundreds of participants outside the laboratory, and the use of a VR headset makes this a realistic proposition. The difficulty facing the researcher is that sophisticated 3-D graphics engines (e.g., Unity) have been created for game designers rather than behavioral scientists. To overcome this barrier, we have created a set of tools and programming syntaxes that allow logical encoding of the common experimental features required by the behavioral scientist. The Unity Experiment Framework (UXF) allows researchers to readily implement several forms of data collection and provides them with the ability to easily modify independent variables. UXF does not offer any stimulus presentation features, so the full power of the Unity game engine can be exploited. We use a case study experiment, measuring postural sway in response to an oscillating virtual room, to show that UXF can replicate and advance upon behavioral research paradigms. We show that UXF can simplify and speed up the development of VR experiments created in commercial gaming software and facilitate the efficient acquisition of large quantities of behavioral research data.

Getting stuck in a rut as an emergent feature of a dynamic decision-making system.

Human sensorimotor decision making has a tendency to get 'stuck in a rut', being biased towards selecting a previously implemented action structure (hysteresis). Existing explanations propose this is the consequence of an agent efficiently modifying an existing plan, rather than creating a new plan from scratch. Instead, we propose that hysteresis is an emergent property of a system learning from the consequences of its actions. To examine this, 152 participants moved a cursor to a target on a tablet device while avoiding an obstacle. Hysteresis was observed when the obstacle moved sequentially across the screen between trials, whereby the participant continued moving around the same side of the obstacle despite it now requiring a larger movement than the alternative. Two further experiments (n = 20) showed an attenuation when time and resource constraints were eased. We created a simple computational model capturing probabilistic estimate updating that showed the same patterns of results. This provides, to our knowledge, the first computational demonstration of how sensorimotor decision making can get 'stuck in a rut' through the updating of the probability estimates associated with actions.

Functional sophistication in human escape.

Animals including humans must cope with immediate threat and make rapid decisions to survive. Without much leeway for cognitive or motor errors, this poses a formidable computational problem. Utilizing fully immersive virtual reality with 13 natural threats, we examined escape decisions in N = 59 humans. We show that escape goals are dynamically updated according to environmental changes. The decision whether and when to escape depends on time-to-impact, threat identity and predicted trajectory, and stable personal characteristics. Its implementation appears to integrate secondary goals such as behavioral affordances. Perturbance experiments show that the underlying decision algorithm exhibits planning properties and can integrate novel actions. In contrast, rapid information-seeking and foraging-suppression are only partly devaluation-sensitive. Instead of being instinctive or hardwired stimulus-response patterns, human escape decisions integrate multiple variables in a flexible computational architecture. Taken together, we provide steps toward a computational model of how the human brain rapidly solves survival challenges.

Exploring disturbance as a force for good in motor learning.

Disturbance forces facilitate motor learning, but theoretical explanations for this counterintuitive phenomenon are lacking. Smooth arm movements require predictions (inference) about the force-field associated with a workspace. The Free Energy Principle (FEP) suggests that such 'active inference' is driven by 'surprise'. We used these insights to create a formal model that explains why disturbance might help learning. In two experiments, participants undertook a continuous tracking task where they learned how to move their arm in different directions through a novel 3D force field. We compared baseline performance before and after exposure to the novel field to quantify learning. In Experiment 1, the exposure phases (but not the baseline measures) were delivered under three different conditions: (i) robot haptic assistance; (ii) no guidance; (iii) robot haptic disturbance. The disturbance group showed the best learning as our model predicted. Experiment 2 further tested our FEP inspired model. Assistive and/or disturbance forces were applied as a function of performance (low surprise), and compared to a random error manipulation (high surprise). The random group showed the most improvement as predicted by the model. Thus, motor learning can be conceptualised as a process of entropy reduction. Short term motor strategies (e.g. global impedance) can mitigate unexpected perturbations, but continuous movements require active inference about external force-fields in order to create accurate internal models of the external world (motor learning). Our findings reconcile research on the relationship between noise, variability, and motor learning, and show that information is the currency of motor learning.

Robots testing robots: ALAN-Arm, a humanoid arm for the testing of robotic rehabilitation systems.

Robotics is increasing in popularity as a method of providing rich, personalized and cost-effective physiotherapy to individuals with some degree of upper limb paralysis, such as those who have suffered a stroke. These robotic rehabilitation systems are often high powered, and exoskeletal systems can attach to the person in a restrictive manner. Therefore, ensuring the mechanical safety of these devices before they come in contact with individuals is a priority. Additionally, rehabilitation systems may use novel sensor systems to measure current arm position. Used to capture and assess patient movements, these first need to be verified for accuracy by an external system. We present the ALAN-Arm, a humanoid robotic arm designed to be used for both accuracy benchmarking and safety testing of robotic rehabilitation systems. The system can be attached to a rehabilitation device and then replay generated or human movement trajectories, as well as autonomously play rehabilitation games or activities. Tests of the ALAN-Arm indicated it could recreate the path of a generated slow movement path with a maximum error of 14.2mm (mean = 5.8mm) and perform cyclic movements up to 0.6Hz with low gain (<1.5dB). Replaying human data trajectories showed the ability to largely preserve human movement characteristics with slightly higher path length and lower normalised jerk.