Moving onwards: an action continuation strategy in finding the way

peer-reviewedIn four studies, we examined people's strategies when deciding between multiple routes of equivalent length in way-finding tasks. The results reveal the important role of continuing behavior when faced with a choice from multiple viable routes. After affirming the existence of asymmetric preferences for alternatives (Studies 1 and 2), we observed that variations of simple known-environment mazes supported action continuation as prevailing process over alternative strategies such as preference for long initial path segments, paths with a least deviating \ud angle, and a modified hill climbing strategy (Study 3). Moreover, asymmetric preferences disappeared with the absence of initial behavior to inform subsequent decision making (Study 4). Results are discussed within the context of decision making, navigation strategies, and everyday life path finding.ACCEPTEDpeer-reviewe

Grasping Objects with Environmentally Induced Position Uncertainty

Due to noisy motor commands and imprecise and ambiguous sensory information, there is often substantial uncertainty about the relative location between our body and objects in the environment. Little is known about how well people manage and compensate for this uncertainty in purposive movement tasks like grasping. Grasping objects requires reach trajectories to generate object-fingers contacts that permit stable lifting. For objects with position uncertainty, some trajectories are more efficient than others in terms of the probability of producing stable grasps. We hypothesize that people attempt to generate efficient grasp trajectories that produce stable grasps at first contact without requiring post-contact adjustments. We tested this hypothesis by comparing human uncertainty compensation in grasping objects against optimal predictions. Participants grasped and lifted a cylindrical object with position uncertainty, introduced by moving the cylinder with a robotic arm over a sequence of 5 positions sampled from a strongly oriented 2D Gaussian distribution. Preceding each reach, vision of the object was removed for the remainder of the trial and the cylinder was moved one additional time. In accord with optimal predictions, we found that people compensate by aligning the approach direction with covariance angle to maintain grasp efficiency. This compensation results in higher probability to achieve stable grasps at first contact than non-compensation strategies in grasping objects with directional position uncertainty, and the results provide the first demonstration that humans compensate for uncertainty in a complex purposive task

Decision making in slow and rapid reaching : Sacrificing success to minimize effort

Acknowledgement This work was supported by the James S. McDonnell Foundation (Scholar Award to ARH). Supplementary Material Data available at: https://zenodo.org/record/3604284Peer reviewedPostprin

Signal Propagation in Feedforward Neuronal Networks with Unreliable Synapses

In this paper, we systematically investigate both the synfire propagation and firing rate propagation in feedforward neuronal network coupled in an all-to-all fashion. In contrast to most earlier work, where only reliable synaptic connections are considered, we mainly examine the effects of unreliable synapses on both types of neural activity propagation in this work. We first study networks composed of purely excitatory neurons. Our results show that both the successful transmission probability and excitatory synaptic strength largely influence the propagation of these two types of neural activities, and better tuning of these synaptic parameters makes the considered network support stable signal propagation. It is also found that noise has significant but different impacts on these two types of propagation. The additive Gaussian white noise has the tendency to reduce the precision of the synfire activity, whereas noise with appropriate intensity can enhance the performance of firing rate propagation. Further simulations indicate that the propagation dynamics of the considered neuronal network is not simply determined by the average amount of received neurotransmitter for each neuron in a time instant, but also largely influenced by the stochastic effect of neurotransmitter release. Second, we compare our results with those obtained in corresponding feedforward neuronal networks connected with reliable synapses but in a random coupling fashion. We confirm that some differences can be observed in these two different feedforward neuronal network models. Finally, we study the signal propagation in feedforward neuronal networks consisting of both excitatory and inhibitory neurons, and demonstrate that inhibition also plays an important role in signal propagation in the considered networks.Comment: 33pages, 16 figures; Journal of Computational Neuroscience (published

Neuromotor Noise, Error Tolerance and Velocity-Dependent Costs in Skilled Performance

In motor tasks with redundancy neuromotor noise can lead to variations in execution while achieving relative invariance in the result. The present study examined whether humans find solutions that are tolerant to intrinsic noise. Using a throwing task in a virtual set-up where an infinite set of angle and velocity combinations at ball release yield throwing accuracy, our computational approach permitted quantitative predictions about solution strategies that are tolerant to noise. Based on a mathematical model of the task expected results were computed and provided predictions about error-tolerant strategies (Hypothesis 1). As strategies can take on a large range of velocities, a second hypothesis was that subjects select strategies that minimize velocity at release to avoid costs associated with signal- or velocity-dependent noise or higher energy demands (Hypothesis 2). Two experiments with different target constellations tested these two hypotheses. Results of Experiment 1 showed that subjects chose solutions with high error-tolerance, although these solutions also had relatively low velocity. These two benefits seemed to outweigh that for many subjects these solutions were close to a high-penalty area, i.e. they were risky. Experiment 2 dissociated the two hypotheses. Results showed that individuals were consistent with Hypothesis 1 although their solutions were distributed over a range of velocities. Additional analyses revealed that a velocity-dependent increase in variability was absent, probably due to the presence of a solution manifold that channeled variability in a task-specific manner. Hence, the general acceptance of signal-dependent noise may need some qualification. These findings have significance for the fundamental understanding of how the central nervous system deals with its inherent neuromotor noise

Combining eye and hand in search is suboptimal

When performing everyday tasks, we often move our eyes and hand together: we look where we are reaching in order to better guide the hand. This coordinated pattern with the eye leading the hand is presumably optimal behaviour. But eyes and hands can move to different locations if they are involved in different tasks. To find out whether this leads to optimal performance, we studied the combination of visual and haptic search. We asked ten participants to perform a combined visual and haptic search for a target that was present in both modalities and compared their search times to those on visual only and haptic only search tasks. Without distractors, search times were faster for visual search than for haptic search. With many visual distractors, search times were longer for visual than for haptic search. For the combined search, performance was poorer than the optimal strategy whereby each modality searched a different part of the display. The results are consistent with several alternative accounts, for instance with vision and touch searching independently at the same time

On the source of human irrationality

Reasoning and decision making are error prone. This is often attributed to a fast, phylogenetically old System 1. It is striking, however, that perceptuo-motor decision making in humans and animals is rational. These results are consistent with perceptuo-motor strategies emerging in Bayesian brain theory that also appear in human data selection. People seem to have access, although limited, to unconscious generative models that can generalise to explain other verbal reasoning results. Error does not emerge predominantly from System 1, but rather seems to emerge from the later evolved System 2 that involves working memory and language. However language also sows the seeds of error correction by moving reasoning into the social domain. This reversal of roles suggests key areas of theoretical integration and new empirical directions

Motor Preparatory Activity in Posterior Parietal Cortex is Modulated by Subjective Absolute Value

For optimal response selection, the consequences associated with behavioral success or failure must be appraised. To determine how monetary consequences influence the neural representations of motor preparation, human brain activity was scanned with fMRI while subjects performed a complex spatial visuomotor task. At the beginning of each trial, reward context cues indicated the potential gain and loss imposed for correct or incorrect trial completion. FMRI-activity in canonical reward structures reflected the expected value related to the context. In contrast, motor preparatory activity in posterior parietal and premotor cortex peaked in high “absolute value” (high gain or loss) conditions: being highest for large gains in subjects who believed they performed well while being highest for large losses in those who believed they performed poorly. These results suggest that the neural activity preceding goal-directed actions incorporates the absolute value of that action, predicated upon subjective, rather than objective, estimates of one's performance