Biology subjects

Brookes, J.

Publications and source records attributed to Brookes, J..

6 recordsLinked to original sources

bioRxiv2025

Human escape follows a structured movement pattern shaped by threat and context

Evading danger is critical to survival. In non-human animals, escape strategies are shaped by neural, biomechanical, and ecological constraints, resulting in species-specific patterns. In humans, ethical and practical constraints have until recently hindered investigation of escape movements, such that its organising principles are commonly extrapolated from other, mostly quadruped, species. Here, we use freely moving virtual reality (fm-VR) in a large physical space to present biologically relevant threats. We discover that human escape behaviour is organized within a constrained action space shaped by threat and context, revealing a small set of stereotyped kinematic patterns that are not predicted from those reported in other mammals. Pattern selection is shaped by environment and individual preference, and is not predicted by behaviour in safe conditions. The dominant kinematic pattern includes head orientation toward the threat, body rotation beyond this angle until facing away, and escape initiation with ipsilateral foot. Alternative variants include turning away from the threat, backward movement, and misdirected flight. Certain alternative escape patterns and kinematic features reduce success, whereas specific preparatory adjustments enhance it. Our results provide a foundation for probing the neural mechanisms of human escape, and enable investigation of potential disruptions in clinical conditions.

animal behavior and cognition↗

bioRxiv2024

The global fish and invertebrate abundance value of mangroves

Mangroves are a critical coastal habitat that provides a suite of ecosystem services and supports livelihoods. We undertake the first global analysis to estimate density and abundance of 37 commercially important fish and invertebrates that are known to extensively use mangroves. Geomorphic mangrove type, sea surface salinity and temperature, and length of mangrove forest edge were important in predicting the density of commercial fish and invertebrates, with deltaic systems supporting the highest densities. The model predicted high densities throughout parts of southeast Asia, the northern coast of South America, the Red Sea, and the Caribbean and Central America. Application of our model onto the global mangrove extent, estimates that mangroves support the annual abundance of nearly 800 billion young-of-year fish and invertebrates contained in our model. Our results confirm the critical role of mangroves globally in supporting fish and fisheries, and further builds the case for their conservation and restoration.

ecology↗

bioRxiv2020

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 cannot provide a principled account of when hysteresis will occur. We propose that hysteresis is an emergent property of a dynamical system learning from the consequences of its actions. To examine this, 152 participants moved a cursor to a target on a tablet device whilst avoiding an obstacle. Hysteresis was observed when the obstacle moved sequentially across the screen between trials, but not with random obstacle placement. Two further experiments (n = 20) showed an attenuation when time and resource constraints were eased. We created a simple computational model capturing dynamic probabilistic estimate updating that showed the same patterns of results. This provides the first computational demonstration of how sensorimotor decision-making can get stuck in a rut through the dynamic updating of its probability estimates. Significance StatementHumans show a bias to select the organisational structure of a recently carried out action, even when an alternative option is available with lower costs. This hysteresis is said to be more efficient than creating a new plan and it has been interpreted as a design feature within decision-making systems. We suggest such teleological arguments are redundant, with hysteresis being a naturally emergent property of a dynamic control system that evolved to operate effectively in an uncertain and partially observable world. Empirical experimentation and simulations from a first principle computational model of decision-making were consistent with our hypothesis. The identification of such a mechanism can inform robotics research, suggesting how robotic agents can show human-like flexibility in complex dynamic environments.

neuroscience↗

bioRxiv2019

Distinct Processing of Selection and Execution Errors in Neural Signatures of Outcome Monitoring

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 either the result of 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 behavioral adjustment following 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.

neuroscience↗

bioRxiv2019

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 helps 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.

neuroscience↗

bioRxiv2018

Studying Human Behaviour with Virtual Reality: The Unity Experiment Framework

Virtual Reality systems offer a powerful tool for human behaviour research. The ability to create three-dimensional visual scenes and measure responses to the visual stimuli enables the behavioural researcher to test hypotheses in a manner and scale that were previously unfeasible. For example, a researcher wanting to understand interceptive timing behaviour might wish to violate Newtonian mechanics, so objects move in novel 3D trajectories. The same researcher may 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 3D graphics engines (e.g. Unity) have been created for game designers rather than behavioural scientists. In order 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 behavioural scientist. The Unity Experiment Framework (UXF) allows the researcher to readily implement several forms of data collection, and provides researchers 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 how UXF can replicate and advance upon behavioural research paradigms. We show that UXF can simplify and speed up development of VR experiments created in commercial gaming software and facilitate the efficient acquisition of large quantities of behavioural research data.

neuroscience↗