Model Based or Model Free? Comparing Adaptive Methods for Estimating Thresholds in Neuroscience.

The quantification of human perception through the study of psychometric functions Ψ is one of the pillars of experimental psychophysics. In particular, the evaluation of the threshold is at the heart of many neuroscience and cognitive psychology studies, and a wide range of adaptive procedures has been developed to improve its estimation. However, these procedures are often implicitly based on different mathematical assumptions on the psychometric function, and unfortunately, these assumptions cannot always be validated prior to data collection. This raises questions about the accuracy of the estimator produced using the different procedures. In the study we examine in this letter, we compare five adaptive procedures commonly used in psychophysics to estimate the threshold: Dichotomous Optimistic Search (DOS), Staircase, PsiMethod, Gaussian Processes, and QuestPlus. These procedures range from model-based methods, such as the PsiMethod, which relies on strong assumptions regarding the shape of Ψ, to model-free methods, such as DOS, for which assumptions are minimal. The comparisons are performed using simulations of multiple experiments, with psychometric functions of various complexity. The results show that while model-based methods perform well when Ψ is an ideal psychometric function, model-free methods rapidly outshine them when Ψ deviates from this model, as, for instance, when Ψ is a beta cumulative distribution function. Our results highlight the importance of carefully choosing the most appropriate method depending on the context.

Identifying Fall Risk Predictors by Monitoring Daily Activities at Home Using a Depth Sensor Coupled to Machine Learning Algorithms.

Because of population ageing, fall prevention represents a human, economic, and social issue. Currently, fall-risk is assessed infrequently, and usually only after the first fall occurrence. Home monitoring could improve fall prevention. Our aim was to monitor daily activities at home in order to identify the behavioral parameters that best discriminate high fall risk from low fall risk individuals. Microsoft Kinect sensors were placed in the room of 30 patients temporarily residing in a rehabilitation center. The sensors captured the patients' movements while they were going about their daily activities. Different behavioral parameters, such as speed to sit down, gait speed or total sitting time were extracted and analyzed combining statistical and machine learning algorithms. Our algorithms classified the patients according to their estimated fall risk. The automatic fall risk assessment performed by the algorithms was then benchmarked against fall risk assessments performed by clinicians using the Tinetti test and the Timed Up and Go test. Step length, sit-stand transition and total sitting time were the most discriminant parameters to classify patients according to their fall risk. Coupling step length to the speed required to stand up or the total sitting time gave rise to an error-less classification of the patients, i.e., to the same classification as that of the clinicians. A monitoring system extracting step length and sit-stand transitions at home could complement the clinicians' assessment toolkit and improve fall prevention.

Fast and automatic assessment of fall risk by coupling machine learning algorithms with a depth camera to monitor simple balance tasks.

BACKGROUND: Falls in the elderly constitute a major health issue associated to population ageing. Current clinical tests evaluating fall risk mostly consist in assessing balance abilities. The devices used for these tests can be expensive or inconvenient to set up. We investigated whether, how and to which extent fall risk could be assessed using a low cost ambient sensor to monitor balance tasks. METHOD: Eighty four participants, forty of which were 65 or older, performed eight simple balance tasks in front of a Microsoft Kinect sensor. Custom-made algorithms coupled to the Kinect sensor were used to automatically extract body configuration parameters such as body centroid and dispersion. Participants were then classified in two groups using a clustering method. The clusters were formed based on the parameters measured by the sensor for each balance task. For each participant, fall risk was independently assessed using known risk factors as age and average physical activity, as well as the participant's performance on the Timed Up and Go clinical test. RESULTS: Standing with a normal stance and the eyes closed on a foam pad, and standing with a narrow stance and the eyes closed on regular ground were the two balance tasks for which the classification's outcome best matched fall risk as assessed by the three known risk factors. Standing on a foam pad with eyes closed was the task driving to the most robust results. CONCLUSION: Our method constitutes a simple, fast, and reliable way to assess fall risk more often with elderly people. Importantly, this method requires very little space, time and equipment, so that it could be easily and frequently used by a large number of health professionals, and in particular by family physicians. Therefore, we believe that the use of this method would substantially contribute to improve fall prevention. TRIAL REGISTRATION: CER-VD 2015-00035. Registered 7 December 2015.

Automating the Timed Up and Go Test Using a Depth Camera.

Fall prevention is a human, economic and social issue. The Timed Up and Go (TUG) test is widely used to identify individuals with a high fall risk. However, this test has been criticized because its "diagnostic" is too dependent on the conditions in which it is performed and on the healthcare professionals running it. We used the Microsoft Kinect ambient sensor to automate this test in order to reduce the subjectivity of outcome measures and to provide additional information about patient performance. Each phase of the TUG test was automatically identified from the depth images of the Kinect. Our algorithms accurately measured and assessed the elements usually measured by healthcare professionals. Specifically, average TUG test durations provided by our system differed by only 0.001 s from those measured by clinicians. In addition, our system automatically extracted several additional parameters that allowed us to accurately discriminate low and high fall risk individuals. These additional parameters notably related to the gait and turn pattern, the sitting position and the duration of each phase. Coupling our algorithms to the Kinect ambient sensor can therefore reliably be used to automate the TUG test and perform a more objective, robust and detailed assessment of fall risk.

Reaching with the sixth sense: Vestibular contributions to voluntary motor control in the human right parietal cortex.

The vestibular system constitutes the silent sixth sense: It automatically triggers a variety of vital reflexes to maintain postural and visual stability. Beyond their role in reflexive behavior, vestibular afferents contribute to several perceptual and cognitive functions and also support voluntary control of movements by complementing the other senses to accomplish the movement goal. Investigations into the neural correlates of vestibular contribution to voluntary action in humans are challenging and have progressed far less than research on corresponding visual and proprioceptive involvement. Here, we demonstrate for the first time with event-related TMS that the posterior part of the right medial intraparietal sulcus processes vestibular signals during a goal-directed reaching task with the dominant right hand. This finding suggests a qualitative difference between the processing of vestibular vs. visual and proprioceptive signals for controlling voluntary movements, which are pre-dominantly processed in the left posterior parietal cortex. Furthermore, this study reveals a neural pathway for vestibular input that might be distinct from the processing for reflexive or cognitive functions, and opens a window into their investigation in humans.

Asymmetric saccade reaction times to smooth pursuit.

Before initiating a saccade to a moving target, the brain must take into account the target's eccentricity as well as its movement direction and speed. We tested how the kinematic characteristics of the target influence the time course of this oculomotor response. Participants performed a step-ramp task in which the target object stepped from a central to an eccentric position and moved at constant velocity either to the fixation position (foveopetal) or further to the periphery (foveofugal). The step size and target speed were varied. Of particular interest were trials that exhibited an initial saccade prior to a smooth pursuit eye movement. Measured saccade reaction times were longer in the foveopetal than in the foveofugal condition. In the foveopetal (but not the foveofugal) condition, the occurrence of an initial saccade, its reaction time as well as the strength of the pre-saccadic pursuit response depended on both the target's speed and the step size. A common explanation for these results may be found in the neural mechanisms that select between oculomotor response alternatives, i.e., a saccadic or smooth response.

A key region in the human parietal cortex for processing proprioceptive hand feedback during reaching movements.

Seemingly effortless, we adjust our movements to continuously changing environments. After initiation of a goal-directed movement, the motor command is under constant control of sensory feedback loops. The main sensory signals contributing to movement control are vision and proprioception. Recent neuroimaging studies have focused mainly on identifying the parts of the posterior parietal cortex (PPC) that contribute to visually guided movements. We used event-related TMS and force perturbations of the reaching hand to test whether the same sub-regions of the left PPC contribute to the processing of proprioceptive-only and of multi-sensory information about hand position when reaching for a visual target. TMS over two distinct stimulation sites elicited differential effects: TMS applied over the posterior part of the medial intraparietal sulcus (mIPS) compromised reaching accuracy when proprioception was the only sensory information available for correcting the reaching error. When visual feedback of the hand was available, TMS over the anterior intraparietal sulcus (aIPS) prolonged reaching time. Our results show for the first time the causal involvement of the posterior mIPS in processing proprioceptive feedback for online reaching control, and demonstrate that distinct cortical areas process proprioceptive-only and multi-sensory information for fast feedback corrections.

It's not all in your feet: Improving penalty kick performance with human-avatar interaction and machine learning.

Penalty kicks are increasingly decisive in major international football competitions. Yet, over 30% of shootout kicks are missed. The outcome of the kick often relies on the ability of the penalty taker to exploit anticipatory movements of the goalkeeper to redirect the kick toward the open side of the goal. Unfortunately, this ability is difficult to train using classical methods. We used an augmented reality simulator displaying an holographic goalkeeper to test and train penalty kick performance with 13 young elite players. Machine learning algorithms were used to optimize the learning rate by maintaining an optimal level of training difficulty. Ten training sessions of 20 kicks reduced the redirection threshold by 120 ms, which constituted a 28% reduction with respect to the baseline threshold. Importantly, redirection threshold reduction was observed for all trained players, and all things being equal, it corresponded to an estimated 35% improvement of the success rate.

Saccade reaction time asymmetries during task-switching in pursuit tracking.

We investigate how smooth pursuit eye movements affect the latencies of task-switching saccades. Participants had to alternate their foveal vision between a continuous pursuit task in the display center and a discrete object discrimination task in the periphery. The pursuit task was either carried out by following the target with the eyes only (ocular) or by steering an on-screen cursor with a joystick (oculomanual). We measured participants' saccadic reaction times (SRTs) when foveal vision was shifted from the pursuit task to the discrimination task and back to the pursuit task. Our results show asymmetries in SRTs depending on the movement direction of the pursuit target: SRTs were generally shorter in the direction of pursuit. Specifically, SRTs from the pursuit target were shorter when the discrimination object appeared in the motion direction. SRTs to pursuit were shorter when the pursuit target moved away from the current fixation location. This result was independent of the type of smooth pursuit behavior that was performed by participants (ocular/oculomanual). The effects are discussed in regard to asymmetries in attention and processes that suppress saccades at the onset of pursuit.

Controlling the trajectory of a moving object substantially shortens the latency of motor responses to visual stimuli.

Motor responses to visual stimuli have shorter latencies for controlling than for initiating movement. The shorter latencies observed for movement control are notably believed to reflect the involvement of forward models when controlling moving limbs. We assessed whether controlling a moving limb is a "requisite" to observe shortened response latencies. The latency of button-press responses to a visual stimulus was compared between conditions involving or not involving the control of a moving object, but never involving any actual control of a body segment. When the motor response controlled a moving object, response latencies were significantly shorter and less variable, probably reflecting a faster sensorimotor processing (as assessed fitting a LATER model to our data). These results suggest that when the task at hand entails a control component, the sensorimotor processing of visual information is hastened, and this even if the task does not require to actually control a moving limb.

Contributions of the PPC to online control of visually guided reaching movements assessed with fMRI-guided TMS.

The posterior parietal cortex (PPC) plays an important role in controlling voluntary movements by continuously integrating sensory information about body state and the environment. We tested which subregions of the PPC contribute to the processing of target- and body-related visual information while reaching for an object, using a reaching paradigm with 2 types of visual perturbation: displacement of the visual target and displacement of the visual feedback about the hand position. Initially, functional magnetic resonance imaging (fMRI) was used to localize putative target areas involved in online corrections of movements in response to perturbations. The causal contribution of these areas to online correction was tested in subsequent neuronavigated transcranial magnetic stimulation (TMS) experiments. Robust TMS effects occurred at distinct anatomical sites along the anterior intraparietal sulcus (aIPS) and the anterior part of the supramarginal gyrus for both perturbations. TMS over neighboring sites did not affect online control. Our results support the hypothesis that the aIPS is more generally involved in visually guided control of movements, independent of body effectors and nature of the visual information. Furthermore, they suggest that the human network of PPC subregions controlling goal-directed visuomotor processes extends more inferiorly than previously thought. Our results also point toward a good spatial specificity of the TMS effects.

Influence of mental workload on motion perception: A direct comparison of luminance-based and contrast-based stimuli.

In order to study the impact of increased mental workload on motion detection, twenty-four observers performed a motion discrimination task in which they had to detect odd moving patches. Two types of moving patches were used, namely luminance-based and contrast-based patches. For both types of patches, the motion discrimination task was performed with and without an additional N-Back task aimed at increasing the mental workload. The dual task decreased discrimination performance for both types of patches, but the difference was significantly larger for contrast-based patches, i.e., for second-order motion stimuli, both as an absolute and relative increment. This suggests that motion discrimination requires larger cognitive resources for contrast-based than for luminance-based stimuli, thereby hinting at the higher complexity of the cognitive mechanisms underlying second-order motion detection.

It is all me: the effect of viewpoint on visual-vestibular recalibration.

Participants performed a visual-vestibular motor recalibration task in virtual reality. The task consisted of keeping the extended arm and hand stable in space during a whole-body rotation induced by a robotic wheelchair. Performance was first quantified in a pre-test in which no visual feedback was available during the rotation. During the subsequent adaptation phase, optical flow resulting from body rotation was provided. This visual feedback was manipulated to create the illusion of a smaller rotational movement than actually occurred, hereby altering the visual-vestibular mapping. The effects of the adaptation phase on hand stabilization performance were measured during a post-test that was identical to the pre-test. Three different groups of subjects were exposed to different perspectives on the visual scene, i.e., first-person, top view, or mirror view. Sensorimotor adaptation occurred for all three viewpoint conditions, performance in the post-test session showing a marked under-compensation relative to the pre-test performance. In other words, all viewpoints gave rise to a remapping between vestibular input and the motor output required to stabilize the arm. Furthermore, the first-person and mirror view adaptation induced a significant decrease in variability of the stabilization performance. Such variability reduction was not observed for the top view adaptation. These results suggest that even if all three viewpoints can evoke substantial adaptation aftereffects, the more naturalistic first-person view and the richer mirror view should be preferred when reducing motor variability constitutes an important issue.

Seeing the hand while reaching speeds up on-line responses to a sudden change in target position.

Goal-directed movements are executed under the permanent supervision of the central nervous system, which continuously processes sensory afferents and triggers on-line corrections if movement accuracy seems to be compromised. For arm reaching movements, visual information about the hand plays an important role in this supervision, notably improving reaching accuracy. Here, we tested whether visual feedback of the hand affects the latency of on-line responses to an external perturbation when reaching for a visual target. Two types of perturbation were used: visual perturbation consisted in changing the spatial location of the target and kinesthetic perturbation in applying a force step to the reaching arm. For both types of perturbation, the hand trajectory and the electromyographic (EMG) activity of shoulder muscles were analysed to assess whether visual feedback of the hand speeds up on-line corrections. Without visual feedback of the hand, on-line responses to visual perturbation exhibited the longest latency. This latency was reduced by about 10% when visual feedback of the hand was provided. On the other hand, the latency of on-line responses to kinesthetic perturbation was independent of the availability of visual feedback of the hand. In a control experiment, we tested the effect of visual feedback of the hand on visual and kinesthetic two-choice reaction times--for which coordinate transformation is not critical. Two-choice reaction times were never facilitated by visual feedback of the hand. Taken together, our results suggest that visual feedback of the hand speeds up on-line corrections when the position of the visual target with respect to the body must be re-computed during movement execution. This facilitation probably results from the possibility to map hand- and target-related information in a common visual reference frame.

Automatic measurement of fall risk indicators in timed up and go test.

Fall risk assessment is usually conducted in specialized centers using clinical tests. Most of the time, these tests are performed only after the occurrence of health problems potentially affecting gait and posture stability. Our aim is to define fall risk indicators that could routinely be used at home to automatically monitor the evolution of fall risk over time. We used the standard Timed Up and Go (T.U.G.) test to classify 43 individuals into two classes of fall risk, namely high- vs low- risk. Several parameters related to the gait pattern and the sitting position included in the T.U.G. test were automatically extracted using an ambient sensor (Microsoft Kinect sensor). We were able to correctly classify all individuals using machine learning on the combination of two parameters among gait speed, step length and speed to sit down. Coupled to an ambient sensor installed at home to monitor the relevant parameters in daily activities, these algorithms could therefore be used to assess the evolution of fall risk, thereby improving fall prevention.

3D feedback and observation for motor learning: Application to the roundoff movement in gymnastics.

In this paper, we assessed the efficacy of different types of visual information for improving the execution of the roundoff movement in gymnastics. Specifically, two types of 3D feedback were compared to a 3D visualization only displaying the movement of the expert (observation) as well as to a more 'traditional' video observation. The improvement in movement execution was measured using different methods, namely subjective evaluations performed by official judges, and more 'quantitative appraisals based on time series analyses. Video demonstration providing information about the expert and 3D feedback (i.e., using 3D representation of the movement in monoscopic vision) combining information about the movement of the expert and the movement of the learner were the two types of feedback giving rise to the best improvement of movement execution, as subjectively evaluated by judges. Much less conclusive results were obtained when assessing movement execution using quantification methods based on time series analysis. Correlation analyses showed that the subjective evaluation performed by the judges can hardly be predicted/ explained by the 'more objective' results of time series analyses.

Influence of the Size of the Field of View on Visual Perception While Running in a Treadmill-Mediated Virtual Environment.

We investigated how the size of the horizontal field of view (FoV) affects visual speed perception with individuals running on a treadmill. Twelve moderately trained to trained participants ran on a treadmill at two different speeds (8 and 12 km/h) in front of a moving virtual scene. Different masks were used to manipulate the visible visual field, masking either the central or the peripheral area of the virtual scene or showing the full visual field. We asked participants to match the visual speed of the scene to their actual running speed. For each trial, participants indicated whether the scene was moving faster or slower than they were running. Visual speed was adjusted according to the responses using a staircase method until the Point of Subjective Equality was reached, that is until visual and running speed were perceived as matching. For both speeds and all FoV conditions, participants underestimated visual speed relative to the actual running speed. However, this underestimation was significant only when the peripheral FoV was masked. These results confirm that the size of the FoV should absolutely be taken into account for the design of treadmill-mediated virtual environments (VEs).

A Biologically-Inspired Model to Predict Perceived Visual Speed as a Function of the Stimulated Portion of the Visual Field.

Spatial orientation relies on a representation of the position and orientation of the body relative to the surrounding environment. When navigating in the environment, this representation must be constantly updated taking into account the direction, speed, and amplitude of body motion. Visual information plays an important role in this updating process, notably via optical flow. Here, we systematically investigated how the size and the simulated portion of the field of view (FoV) affect perceived visual speed of human observers. We propose a computational model to account for the patterns of human data. This model is composed of hierarchical cells' layers that model the neural processing stages of the dorsal visual pathway. Specifically, we consider that the activity of the MT area is processed by populations of modeled MST cells that are sensitive to the differential components of the optical flow, thus producing selectivity for specific patterns of optical flow. Our results indicate that the proposed computational model is able to describe the experimental evidence and it could be used to predict expected biases of speed perception for conditions in which only some portions of the visual field are visible.

Regular physical activity modulates perceived visual speed when running in treadmill-mediated virtual environments.

In virtual reality, visual speed is usually underestimated relative to locomotor speed. Here we investigated how physical activity and fitness affect perceived visual speed when running in a treadmill-mediated virtual environment. Thirty healthy participants (ten sedentary individuals, ten team sport players and ten expert runners) ran on a treadmill at two different speeds (8, 12km/h) in front of a moving virtual scene. Participants were asked to match the speed of the visual scene to their running speed (i.e. treadmill speed), indicating for each trial whether the scene was moving slower or faster than the treadmill. The speed of the visual scene was adjusted according to the participant's response using a staircase until visual and running speeds were perceived as equivalent. More sedentary participants underestimated visual speed relative to their actual running speed. Specifically, visual speed had to exceed running speed to be perceived as equivalent. The underestimation of visual speed was speed-dependent, and it was significantly larger for sedentary participants than for team sports players and expert runners. The volume of physical activity per week was found to be the best predictor of visual speed perception for both running speeds, while the perceived effort constituted a good predictor only at 8km/h. Physical fitness, on the other hand turned out to be a poor predictor of visual speed perception. Therefore, in order to enhance users' engagement and their adherence to physical activity programs, the development of "personalized" treadmill-mediated virtual environments should take into account users' personal characteristics to provide the most natural and engaging feedback possible.

Tri-modal integration of visual, tactile and auditory signals for the perception of sequences of events.

We investigated the interactions between visual, tactile and auditory sensory signals for the perception of sequences of events. Sequences of flashes, taps and beeps were presented simultaneously. For each session, subjects were instructed to count the number of events presented in one modality (Target) and to ignore the stimuli presented in the other modalities (Background). The number of events presented in the background sequence could differ from the number of events in the target sequence. For each session, we quantified the Background-evoked bias by comparing subjects' responses with and without Background (Target presented alone). Nine combinations between vision, touch and audition were tested. In each session but two, the Background significantly biased the Target. Vision was the most susceptible to Background-evoked bias and the least efficient in biasing the other two modalities. By contrast, audition was the least susceptible to Background-evoked bias and the most efficient in biasing the other two modalities. These differences were strongly correlated to the relative reliability of each modality. In line with this, the evoked biases were larger when the Background consisted of two instead of only one modality. These results show that for the perception of sequences of events: (1) vision, touch and audition are automatically integrated; (2) the respective contributions of the three modalities to the integrated percept differ; (3) the relative contribution of each modality depends on its relative reliability (1/variability); (4) task-irrelevant stimuli have more weight when presented in two rather than only one modality.