Luke Edward Miller

The paper tests two hypotheses about how the brain localizes touch and proprioception using Bayesian models, finding that tactile and proprioceptive localization rely on distinct prior expectations.

Sensorimotor training reduces the perceived weight of finger-extending exoskeletons, indicating improved integration with sensorimotor control.

This study shows that indirect tactile signals from a robotic thumb can be discriminated and used to spatially localise touch, even without direct haptic feedback.

The paper investigates how humans project tactile sensations onto hand-held tools using a model-driven psychophysics experiment, finding that touch is remapped into gaze-centered coordinates.

This study examines how spatial proximity, attentional focus, and anticipated action outcomes influence motor preparation and action awareness using EEG and virtual reality.

This study uses EEG to show that touch is encoded in an intrinsic, limb-specific spatial code rather than an extrinsic, limb-independent one.

This study shows that repetitive somatosensory stimulation reduces perceived finger size but does not affect body model or superficial schema.

This study investigates how the brain integrates a wearable finger-extension device into somatosensory representations across different interaction phases, finding three distinct phases of plasticity.

The paper reanalyzes behavioral experiments on tool-extended tactile localization to estimate parameters mapping sensory input to spatial perception, finding near-perfect localization accuracy.

The paper tests a Bayesian model of arm posture perception using a VR task, finding that a Gaussian prior better explains individual biases than a uniform prior.

The paper investigates whether hand-held tools can be mapped in space with the same precision as body parts and whether tool use alters these spatial maps.

The paper shows that biases in hand perception arise from Bayesian integration of somatosensory signals, not from a distorted internal hand model.

The paper investigates how neural oscillations in alpha and beta bands support touch localization on hands and tools, finding similar mechanisms for both.

The paper shows that the brain uses trilateration to localize touch on tools, similar to how it works for limbs, and demonstrates this with behavioral and computational models.

The paper determines the sensory horizon for human haptic perception, finding it extends to six meters using a rod, demonstrating the brain's adaptability in sensorimotor representations.

The paper investigates how alpha oscillations contribute to localizing touch on hand-held tools by analyzing EEG data during tactile stimulation tasks.

The paper proposes a neural network model that uses multilateration to decode touch location on the body, showing it matches psychophysical experiments.