Biology subjects

Selen, L. P. J.

Publications and source records attributed to Selen, L. P. J..

8 recordsLinked to original sources

bioRxiv2026

Stretch-induced muscle responses under threat vs safety

When in a stressful situation, making fast and accurate decisions is crucial. Previous work has shown that sensorimotor decisions can improve under threat. However, it is unclear if these improvements are achieved by improvements in perceptual or motor performance. Here, we present two hypotheses for how threat might influence motor preparation and use muscular stretch reflexes to test both. The task-unspecific hypothesis predicts that threat promotes motor preparation irrespective of the reach target, through tonic upregulation of the short latency stretch reflex. In contrast, the task-specific hypothesis predicts that threat increases sensory processing for a specific reach target, leading to direction-selective up- and down-regulation of the long latency stretch reflex. Participants were asked to reach to one of two targets that appeared shortly before a perturbation eliciting a stretch reflex, they performed this task either under threat of an electric shock or under safe circumstances. Skin conductance and heart rate results show that the threat manipulation significantly increased sympathetic activation, but not parasympathetic activation. Supporting the task-specific hypothesis, the EMG findings demonstrate a direction-selective modulation of the long-latency response of stretch reflexes, starting ~100 ms after perturbation onset. Our results suggest that stress affects action preparation through upregulation of cortical visuomotor circuits.

physiology↗

bioRxiv2024

Online interference of declarative memory on fast and slow adaptive processes in force field motor learning

Error-based motor adaptation is currently understood as a dual-rate process involving a fast adaptive process that learns quickly but also decays rapidly and a slow process that learns slowly but has good retention. While the fast process is typically categorized as procedural learning, recent evidence suggests that it relies on the declarative memory system. To test this hypothesis, we investigated in what manner a declarative memory task interferes with two processes that supposedly underly force field adaptation in reaching. This declarative memory task, which involved learning of a list of words, was assessed through either recognition or recall, and was compared to a non-declarative, vowel counting task, using a within-subject design (n=32). We employed a Bayesian hierarchical dual-rate process model to capture the observed force compensation across trials, expecting that the parameters of the fast process would be affected by the declarative memory task. We examined the 95% highest density interval of the posterior distribution of the difference between the experimental and control condition for each parameter. While most parameters remained unaffected by the declarative memory task, the retention rate of the fast process showed a hint of reduction, suggesting a complex interplay between declarative memory and ongoing motor adaptation processes.

neuroscience↗

bioRxiv2024

Visuoinertial and visual feedback in online steering control

Multisensory integration has primarily been studied in static environments, where optimal integration relies on the precision of the respective sensory modalities. However, in numerous situations, sensory information is dynamic and changes over time, due to changes in our bodily state and the surrounding environment. Given that different sensory modalities have different delays, this suggests that optimal integration may not solely depend on sensory precision but may also be affected by the delays associated with each sensory system. To investigate this hypothesis, participants (n = 22, 16 female) engaged in a continuous steering task. Participants sat on a motion platform facing a screen that displayed a cartoonish traffic scene, featuring a car traveling along a road. In the visuoinertial condition, where vestibular and somatosensory feedback were available, they were tasked with counteracting an external multi-frequency perturbation signal, which laterally perturbed the platform and the car, such that the car was kept within the center of the road. In the visual condition, the visual car was perturbed, while the motion platform remained stationary. We show that participants compensate better for the perturbation in the visuoinertial than the visual condition, particularly in the high frequency range of the perturbation. Using computational modelling, we demonstrate that this enhanced performance is partially due to the shorter delay of the vestibular modality. In this condition, participants rely more on the vestibular information, which is less delayed than the more precise but longer delayed, visual information. Author summaryNavigating the world effectively requires the continuous integration of sensory information related to our self-motion, forming a cohesive representation of our current location and direction. Although studies on multisensory integration indicate that sensory inputs are combined optimally based on their precision, these findings have largely been derived from static experimental settings. In real-life situations, the sensory environment changes continuously, as does the state of our body. In such scenarios, how are sensory inputs combined? We developed an experimental paradigm where participants were tasked with controlling a simple vehicle while counteracting a time-varying perturbation signal. To assess the role of different sensory inputs we manipulated whether visual and inertial (primarily vestibular) cues were available or solely visual cues. Our results show that the presence of both visual and inertial cues significantly improved task performance. Modeling suggests this improvement is likely due to the shorter delay associated with the vestibular system compared to the visual system. Collectively, our results suggest that sensory precision alone is insufficient for effective state estimation and control in naturalistic scenarios, where accounting for the different delays of the sensory systems is critical for optimal task performance.

neuroscience↗

bioRxiv2024

Vestibular-derived internal models in active self-motion estimation

Self-motion estimation is thought to depend on sensory information as well as on sensory predictions derived from motor feedback. In driving, the vestibular afference can in principle be predicted based on the steering motor commands if an accurate internal model of the steering dynamics is available. Here, we used a closed-loop steering experiment to examine whether participants can build such an internal model of the steering dynamics. Participants steered a motion platform on which they were seated to align their body with a memorized visual target. We varied the gain between the steering wheel angle and the velocity of the motion platform across trials in three different ways: unpredictable (white noise), moderately predictable (random walk), or highly predictable (constant gain). We examined whether participants took the across-trial predictability of the gain into account to control their steering (internal model hypothesis), or whether they simply integrated the vestibular feedback over time to estimate their travelled distance (path integration hypothesis). Results from a trial series regression analysis show that participants took the gain of the previous trial into account more when it followed a random walk across trials than when it varied unpredictably across trials. Furthermore, on interleaved trials with a large jump in the gain, participants made fast corrective responses, irrespective of gain predictability, suggesting they also rely on vestibular feedback. These findings suggest that the brain can construct an internal model of the steering dynamics to predict the vestibular reafference in driving and self-motion estimation.

neuroscience↗

bioRxiv2022

Predictive steering: Integration of artificial motor signals in self-motion estimation

The brains computations for active and passive self-motion estimation can be unified with a single model that optimally combines vestibular and visual signals with sensory predictions based on motor efference copies. It is unknown whether this theoretical framework also applies to the integration of artificial motor signals, like the motor signals that occur when driving a car. Here, we examined if training humans to control a self-motion platform would lead to the construction of an accurate internal model of the mapping between the steering movement and the vestibular reafference. Participants (n = 15) were seated on a linear motion platform and actively controlled the platforms velocity using a steering wheel to translate their body to a memorized visual target location (Motion condition). We compared their steering behavior to that of participants (n = 15) who remained stationary and instead aligned a non-visible line with the target (Stationary condition). To probe learning, the gain between the steering wheel angle and the platform velocity or line velocity changed abruptly twice during the experiment. These gain changes were virtually undetectable in the displacement error in the Motion condition, whereas clear deviations were observed in the Stationary condition. These results show that participants in the Motion condition made within-trial changes to their steering behavior immediately after the gain changes. This suggests that they continuously compared the vestibular reafference to internal predictions, and thus employed and updated an internal forward model of the mapping between the steering movement and the vestibular reafference. New & NoteworthyPerception of self-motion is known to depend on the integration of sensory signals and, when the motion is self-generated, the predicted sensory reafference based on motor efference copies. Here we show, using a closed-loop steering experiment with a direct coupling between the steering movement and the vestibular self-motion feedback, that humans are also able to integrate artificial motor signals, like the motor signals that occur when driving a car.

neuroscience↗

bioRxiv2021

Corticospinal correlates of hand preference for reaching during whole-body motion

Behavioral studies have shown that humans account for inertial acceleration in their decisions of hand choice when reaching during body motion. Physiologically, it is unclear at what stage of movement preparation information about body motion is integrated in the process of hand selection. Here, we addressed this question by applying transcranial magnetic stimulation over motor cortex (M1) of human participants who performed a preferential reach task while they were sinusoidally translated on a linear motion platform. If M1 only represents a read-out of the final hand choice, we expect the body motion not to affect the MEP amplitude. If body motion biases the hand selection process prior to target onset, we expect corticospinal excitability to modulate with the phase of the motion, with larger MEP amplitudes for phases that show a bias to using the right hand. Behavioral results replicate our earlier findings of a sinusoidal modulation of hand choice bias with motion phase. MEP amplitudes also show a sinusoidal modulation with motion phase, suggesting that body motion influences corticospinal excitability which may ultimately reflect changes of hand preference. The modulation being present prior to target onset suggests that competition between hands is represented throughout the corticospinal tract. Its phase relationship with the motion profile suggests that other processes after target onset take up time until the hand selection process has been completely resolved, and the reach is initiated. We conclude that the corticospinal correlates of hand preference are modulated by body motion.

neuroscience↗

bioRxiv2019

From competing motor plans to a single action within a single trial on the human motor periphery

Contemporary motor control theories propose competition between multiple motor plans before the winning command is executed. While most competitions are completed prior to movement onset, movements are often initiated before the competition has been resolved. An example of this is saccadic averaging, wherein the eyes land at an intermediate location between two visual targets. Behavioral and neurophysiological signatures of competing motor commands have also been reported for reaching movements, but debate remains about whether such signatures attest to an unresolved competition, arise from averaging across many trials, or reflect a strategy to optimize behavior given task constraints. Here, we recorded electromyographic activity from an upper limb muscle (m. pectoralis) while twelve (8 female) participants performed an immediate response reach task, freely choosing between one of two identical and suddenly presented visual targets. On each trial, muscle recruitment showed two distinct phases of directionally-tuned activity. In the first wave, time-locked ~100 ms of target presentation, muscle activity was clearly influenced by the non-chosen target, reflecting a competition between reach commands that was biased in favor of the ultimately chosen target. This resulted in an initial movement intermediate between the two targets. In contrast, the second wave, time-locked to voluntary reach onset, was not biased toward the non-chosen target, showing that the competition between targets was resolved. Instead, this wave of activity compensated for the averaging induced by the first wave. Thus, single-trial analysis reveals an evolution in how the non-chosen target differentially influences the first and second wave of muscle activity. SIGNIFICANCE STATEMENTContemporary theories of motor control suggest that multiple motor plans compete for selection before the winning command is executed. Evidence for this is found in intermediate reach movements towards two potential target locations, but recent findings have challenged this notion by arguing that intermediate reaching movements reflect an optimal response strategy. By examining upper limb muscle recruitment during a free-choice reach task, we show early recruitment of a sub-optimal averaged motor command to the two targets that subsequently transitions to a single motor command that compensates for the initially averaged motor command. Recording limb muscle activity permits single-trial resolution of the dynamic influence of the non-chosen target through time.

neuroscience↗

bioRxiv2017

Bayesian adaptive stimulus selection for dissociating models of psychophysical data

Comparing models facilitates testing different hypotheses regarding the computational basis of perception and action. Effective model comparison requires stimuli for which models make different predictions. Typically, experiments use a predetermined set of stimuli or sample stimuli randomly. Both methods have limitations; a predetermined set may not contain stimuli that dissociate the models whereas random sampling may be inefficient. To overcome these limitations, we expanded the psi-algorithm (Kontsevich & Tyler, 1999) from estimating the parameters of a psychometric curve to distinguishing models. To test our algorithm, we applied it to two distinct problems. First, we investigated dissociating sensory noise models. We simulated ideal observers with different noise models performing a 2-afc task. Stimuli were selected randomly or using our algorithm. We found using our algorithm improved the accuracy of model comparison. We also validated the algorithm in subjects by inferring which noise model underlies speed perception. Our algorithm converged quickly to the model previously proposed (Stocker & Simoncelli, 2006), whereas if stimuli were selected randomly model probabilities separated slower and sometimes supported alternative models. Second, we applied our algorithm to a different problem; comparing models of target selection under body acceleration. Previous work found target choice preference is modulated by whole body acceleration (Rincon-Gonzalez et al., 2016). However, the effect is subtle making model comparison difficult. We show that selecting stimuli adaptively could have led to stronger conclusions in model comparison. We conclude that our technique is more efficient and more reliable than current methods of stimulus selection for dissociating models.\n\nData AvailabilityAll data and code will be posted on our institutional repository system following acceptance. In the meantime feel free to contact the authors if you would like any of the code.

neuroscience↗