Temporal Task Research Articles

Relation between temporal fine structure processing and global processing speed

Strelcyk et al. (2019) recently found that interaural phase discrimination in older hearing-impaired listeners was correlated with both visuospatial processing speed and interaural level discrimination. This suggests that temporal fine structure (TFS) processing relies on global processing speed and/or spatial cognition, though it is possible that, generally, complex auditory discrimination engages multiple cognitive domains. Here, 50 Veterans (mean age = 48.1, range = 30–60) with normal or near-normal hearing completed batteries of temporal processing and cognitive tests. Composite cognitive test scores reflecting processing speed/executive function (PS-EX) and working memory (WM) were obtained. Temporal processing tasks included measures of envelope (ENV; gap duration discrimination, forward masking) and TFS (frequency modulation detection, interaural phase modulation detection) processing. Bayesian hierarchical regression was used to fit psychometric functions simultaneously to all ENV and TFS tasks in all subjects. Fixed effects of PS-EX and WM on thresholds and slopes were estimated for the psychometric functions in each temporal task. In general, (i) PS-EX and WM influenced both TFS and ENV thresholds, but not slopes; and (ii) TFS thresholds were best explained by PS-EX scores while ENV thresholds were best explained by WM scores. These findings suggest a specific relation between global processing speed and TFS.

Estimating the contribution of central noise from composite performance across multiple tasks

According to signal detection theory, the ability to detect a signal is limited only by internal noise, which comprises peripheral and central sources. Here, we develop a statistical approach to parse central from peripheral noise. Fifty-two Veterans (mean age = 47.8, range = 30–60) with normal or near-normal hearing performed AXB discrimination for several temporal processing tasks: gap duration discrimination, forward masking, frequency modulation detection, and interaural phase modulation detection. After training, a single adaptive run (40 reversals) was completed for each task. Subjects also completed speech-in-noise testing (“Theo-Victor-Michael") with four masker types (48 trials ea.): speech-shaped noise, speech-envelope modulated noise, one and two competing talkers. Composite speech performance was estimated using principal component analysis. Bayesian hierarchical regression was used to estimate two-parameter psychometric functions (threshold, slope) simultaneously for all temporal tasks and subjects. Crucially, fixed (group-level) thresholds were estimated per task but only a single random (subject-level) intercept was estimated (mean across-task deviation from the group thresholds). We assume central noise is the primary factor limiting across-task performance. The principal speech scores were entered as regressors on this “central threshold.” Indeed, central threshold was correlated with the principal speech scores, suggesting that central noise limits temporal processing and speech-in-noise.

Spatial–Temporal Graph Data Mining for IoT-Enabled Air Mobility Prediction

Big data analytics and mining have the potential to enable real-time decision making and control in a range of Internet of Things (IoT) application domains, such as the Internet of Vehicles, the Internet of Wings, and the Airport of Things. The prediction toward air mobility, which is essential to the studies of air traffic management, has been a challenging task due to the complex spatial and temporal dependencies in air traffic data with highly nonlinear and variational patterns. Existing works for air traffic prediction only focus on either modeling static traffic patterns of individual flight or temporal correlation, with no or limited addressing of the spatial impact, namely, the propagation of traffic perturbation among airports. In this article, we propose to leverage the concept of graph and model the airports as nodes with time-series features and conduct data mining on graph-structured data. To be specific, first, airline on-time performance (AOTP) data is preprocessed to generate a temporal graph data set, which includes three features: 1) the number; 2) average delay; and 3) average taxiing time of departure and arrival flights. Then, a spatial–temporal graph neural networks model is implemented to forecast the mobility level at each airport over time, where a combination of graph convolution and time-dimensional convolution is used to capture the spatial and temporal correlation simultaneously. Experiments on the data set demonstrate the advantage of the model on spatial–temporal air mobility prediction, together with the impact of different priors on adjacency matrices and the effectiveness of the temporal attention mechanism. Finally, we analyze the prediction performance and discuss the capability of our model. The prediction framework proposed in this work has the potential to be generalized to other spatial–temporal tasks in IoT.

Hierarchical planning with state abstractions for temporal task specifications

We often specify tasks for a robot using temporal language that can include different levels of abstraction. For example, the command “go to the kitchen before going to the second floor” contains spatial abstraction, given that “floor” consists of individual rooms that can also be referred to in isolation (“kitchen”, for example). There is also a temporal ordering of events, defined by the word “before”. Previous works have used syntactically co-safe Linear Temporal Logic (sc-LTL) to interpret temporal language (such as “before”), and Markov Decision Processes (AMDPs) to interpret hierarchical abstractions (such as “kitchen” and “second floor”), separately. To handle both types of commands at once, we introduce the Product Markov Decision Process (AP-MDP), a novel approach capable of representing non-Markovian reward functions at different levels of abstractions. The AP-MDP framework translates LTL into its corresponding automata, creates a product Markov Decision Process (MDP) of the LTL specification and the environment MDP, and decomposes the problem into subproblems to enable efficient planning with abstractions. AP-MDP performs faster than a non-hierarchical method of solving LTL problems in over 95 % of path planning tasks, and this number only increases as the size of the environment domain increases. In a cleanup world domain, AP-MDP performs faster in over 98% of tasks. We also present a neural sequence-to-sequence model trained to translate language commands into LTL expression, and a new corpus of non-Markovian language commands spanning different levels of abstraction. We test our framework with the collected language commands on two drones, demonstrating that our approach enables robots to efficiently solve temporal commands at different levels of abstraction in both indoor and outdoor environments.

Time perception of individuals with subthreshold autistic traits: the regulation of interpersonal information associations

BackgroundPeople with high subthreshold autistic traits usually share behavioral patterns similar to those of individuals on the autism spectrum, but with fewer social and cognitive changes. The effect of autistic traits on time perception and the role of interpersonal information in this effect remain unexplored.MethodsThis study used a temporal bisection task between 400 and 1600 ms to compare the time perception of individuals with higher and lower autistic traits, and to explore the regulation of interpersonal information on their time perception by establishing associations between identities and geometric shapes. Thirty-two participants with high autistic traits and thirty-one participants with low autistic traits participated in this study.ResultsIn the absence of identity information, people with high autistic traits tended to judge short durations as longer. Their subjective bisection point was lower, and the Weber ratio was higher than for those with low autistic traits, suggesting that their overestimation of short duration was due to decreased temporal sensitivity. With the involvement of interpersonal information, the proportion of long responses for no identity was significantly lower than for self, friends, and strangers, which seemed more obvious in individuals with low autistic traits although there was no significant interaction between identity and group. The Weber ratio of no identity was lower than that for other identities.ConclusionThe results suggest that individuals with high autistic traits have more conservative responses that are relatively shorter in duration, and this change is related to a decline in perceptual sensitivity. Compared to individuals with high autistic traits, the time perception of individuals with low autistic traits seemed more susceptible to interpersonal information.

Analysis of the phenomenon of "attention shift" as a marker of psycho-emotional stress

Compliance with the principles of mental health is a priority task in the education of children and adolescents. Researchers present the results of the analysis of psychophysiological studies, mostly by foreign authors, devoted to the problem of biomarkers of psycho-emotional stress (anxiety, aggression). The authors have considered the phenomenon of attention biases (attention shifts), as well as behavioral inhibition. We have analyzed the role of bioelectric asymmetry of the frontal areas of the brain, amygdala and prefrontal cortex. Scientists have shown the role of two basic motivational systems of approach/achievement ("approach system") and avoidance/withdrawal ("withdrawal system"). The use of adequate psychophysiological tests, such as: dot-probe tasks, attention shifting tasks, temporal order judgment tasks, visual search tasks allows you to best identify attention shifts and observe changes in the bioelectric activity of the brain. The asymmetry of the EEG of the frontal areas of the brain; the ratios of slow EEG waves to fast ones (theta / beta ratio; delta / beta ratio); changes in the parameters of the components of the evoked potentials P1, N1, N2 and late positive potential (LPP) with attention shifts you can consider as markers of psychoemotional tension. You can take into account the understanding of the mechanisms of shifting attention to threatening information in anxious children and adolescents in the prevention of mental health and to create corrective methods of training the system of attention to information with the opposite motivating value, allowing for adaptive behavior in the future.

Probabilistic Plan Recognition for Multi-Agent Systems under Temporal Logic Tasks

This paper studies the plan recognition problem of multi-agent systems with temporal logic tasks. The high-level temporal tasks are represented as linear temporal logic (LTL). We present a probabilistic plan recognition algorithm to predict the future goals and identify the temporal logic tasks of the agent based on the observations of their states and actions. We subsequently build a plan library composed of Nondeterministic Bu¨chi Automation to model the temporal logic tasks. We also propose a Boolean matrix generation algorithm to map the plan library to multi-agent trajectories and a task recognition algorithm to parse the Boolean matrix. Then, the probability calculation formula is proposed to calculate the posterior goal probability distribution, and the cold start situation of the plan recognition is solved using the Bayes formula. Finally, we validate the proposed algorithm via extensive comparative simulations.

Different eigenvalue distributions encode the same temporal tasks in recurrent neural networks.

Different brain areas, such as the cortex and, more specifically, the prefrontal cortex, show great recurrence in their connections, even in early sensory areas. Several approaches and methods based on trained networks have been proposed to model and describe these regions. It is essential to understand the dynamics behind the models because they are used to build different hypotheses about the functioning of brain areas and to explain experimental results. The main contribution here is the description of the dynamics through the classification and interpretation carried out with a set of numerical simulations. This study sheds light on the multiplicity of solutions obtained for the same tasks and shows the link between the spectra of linearized trained networks and the dynamics of the counterparts. The patterns in the distribution of the eigenvalues of the recurrent weight matrix were studied and properly related to the dynamics in each task.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11571-022-09802-5.

Politically Biased Time Perception and Perspective

Abstract The main objective of this study was to determine if the estimation of time could be used to reveal an implicit political bias. The study also aimed at determining if a political bias is related to a specific temporal perspective or to other personality factors. The main demonstration is based on a bisection temporal task where the participants were asked to say if the duration of the presentation of a politician’s photo is short or long. There were three independent variables of interest: the location of politicians on the left (liberal) or right (conservative) on the political axis, the emotions expressed on a politician’s photo (joy, anger or neutral), and the political allegiances of the participants. Overall, compared to conditions with neutral faces or faces expressing joy, participants overestimated the duration of faces expressing anger. This effect, however, depends on the political allegiance of the participants. Compared to the neutral face condition, liberal participants overestimated the length of presentation of politicians’ faces in the joy and anger conditions. The results also showed that, compared to the condition in which photos of conservative politicians are presented, conservative participants underestimated the presentation duration of liberal politicians’ photos; such an influence of the orientation of presented politicians was not observed with liberal participants. The results also reveal that conservative participants differed from liberal participants on the future-oriented scale and on the past-positive-oriented scale of the Zimbardo Time Perspective Inventory (ZTPI). The study shows that time perception can be used to reveal a kind of implicit political bias.

Encoding time in neural dynamic regimes with distinct computational tradeoffs.

Converging evidence suggests the brain encodes time in dynamic patterns of neural activity, including neural sequences, ramping activity, and complex dynamics. Most temporal tasks, however, require more than just encoding time, and can have distinct computational requirements including the need to exhibit temporal scaling, generalize to novel contexts, or robustness to noise. It is not known how neural circuits can encode time and satisfy distinct computational requirements, nor is it known whether similar patterns of neural activity at the population level can exhibit dramatically different computational or generalization properties. To begin to answer these questions, we trained RNNs on two timing tasks based on behavioral studies. The tasks had different input structures but required producing identically timed output patterns. Using a novel framework we quantified whether RNNs encoded two intervals using either of three different timing strategies: scaling, absolute, or stimulus-specific dynamics. We found that similar neural dynamic patterns at the level of single intervals, could exhibit fundamentally different properties, including, generalization, the connectivity structure of the trained networks, and the contribution of excitatory and inhibitory neurons. Critically, depending on the task structure RNNs were better suited for generalization or robustness to noise. Further analysis revealed different connection patterns underlying the different regimes. Our results predict that apparently similar neural dynamic patterns at the population level (e.g., neural sequences) can exhibit fundamentally different computational properties in regards to their ability to generalize to novel stimuli and their robustness to noise—and that these differences are associated with differences in network connectivity and distinct contributions of excitatory and inhibitory neurons. We also predict that the task structure used in different experimental studies accounts for some of the experimentally observed variability in how networks encode time.

Synthesis of Asynchronous Control and Synchronization for Multi-Robot System under Finite Horizon Temporal Task

This paper proposes a novel control synthesis method for cooperative multi-robot systems to fulfil a global mission given as linear temporal logic on finite traces (LTLf). Transition systems are used as the form of distributed controllers, such that the robots are driven to act asynchronously and synchronize on shared actions for necessary local coordination. An automated control synthesis method is proposed, which generates provably correct distributed controllers by two steps: (i) synthesizing satisfiable global controllers for the parallel composition of the uncontrolled system, such that the global specification is met; (ii) searching for a decomposable global controller among them over the synchronization scheme to obtain decomposed distributed controllers. The method is integrated into a closed-loop mission execution framework, of which the applicability is demonstrated with a simulation based on ROS.

The effect of movement speed on audiovisual temporal integration in streaming-bouncing illusion.

Motion perception in real situations is often stimulated by multisensory information. Speed is an essential characteristic of moving objects; however, at present, it is not clear whether speed affects the process of audiovisual temporal integration in motion perception. Therefore, this study used a streaming-bouncing task (a bistable motion perception; SB task) combined with a simultaneous judgment task (SJ task) to explore the effect of speed on audiovisual temporal integration from implicit and explicit perspectives. The experiment had a within-subjects design, two speed conditions (fast/slow), eleven audiovisual conditions [stimulus onset asynchrony (SOA): 0ms/ ± 60ms/ ± 120ms/ ± 180ms/ ± 240ms/ ± 300ms], and a visual-only condition. A total of 30 subjects were recruited for the study. These participants completed the SB task and the SJ task successively. The results showed the following outcomes: (1) the optimal times needed to induce the "bouncing" illusion and maximum audiovisual bounce-inducing effect (ABE) magnitude were much earlier than that for the optimal time of audiovisual synchrony, (2) speed as a bottom-up factor could affect the proportion of "bouncing" perception in SB illusions but did not affect the ABE magnitude, (3) speed could also affect the ability of audiovisual temporal integration in motion perception, and the main manifestation was that the point of subjective simultaneity (PSS) in fast speed conditions was earlier than that of slow speed conditions in the SJ task and (4) the SB task and SJ task were not related. In conclusion, the time to complete the maximum audiovisual integration was different from the optimal time for synchrony perception; moreover, speed could affect audiovisual temporal integration in motion perception but only in explicit temporal tasks.

Simultaneous control of natural and extra degrees of freedom by isometric force and electromyographic activity in the muscle-to-force null space

Objective. Muscle activation patterns in the muscle-to-force null space, i.e. patterns that do not generate task-relevant forces, may provide an opportunity for motor augmentation by allowing to control additional end-effectors simultaneously to natural limbs. Here we tested the feasibility of muscular null space control for augmentation by assessing simultaneous control of natural and extra degrees of freedom. Approach. We instructed eight participants to control translation and rotation of a virtual 3D end-effector by simultaneous generation of isometric force at the hand and null space activity extracted in real-time from the electromyographic signals recorded from 15 shoulder and arm muscles. First, we identified the null space components that each participant could control more naturally by voluntary co-contraction. Then, participants performed several blocks of a reaching and holding task. They displaced an ellipsoidal cursor to reach one of nine targets by generating force, and simultaneously rotated the cursor to match the target orientation by activating null space components. We developed an information-theoretic metric, an index of difficulty defined as the sum of a spatial and a temporal term, to assess individual null space control ability for both reaching and holding. Main results. On average, participants could reach the targets in most trials already in the first block (72%) and they improved with practice (maximum 93%) but holding performance remained lower (maximum 43%). As there was a high inter-individual variability in performance, we performed a simulation with different spatial and temporal task conditions to estimate those for which each individual participants would have performed best. Significance. Muscular null space control is feasible and may be used to control additional virtual or robotics end-effectors. However, decoding of motor commands must be optimized according to individual null space control ability.

Local Self-Expression Subspace Learning Network for Motion Capture Data

Deep subspace learning is an important branch of self-supervised learning and has been a hot research topic in recent years, but current methods do not fully consider the individualities of temporal data and related tasks. In this paper, by transforming the individualities of motion capture data and segmentation task as the supervision, we propose the local self-expression subspace learning network. Specifically, considering the temporality of motion data, we use the temporal convolution module to extract temporal features. To implement the local validity of self-expression in temporal tasks, we design the local self-expression layer which only maintains the representation relations with temporally adjacent motion frames. To simulate the interpolatability of motion data in the feature space, we impose a group sparseness constraint on the local self-expression layer to impel the representations only using selected keyframes. Besides, based on the subspace assumption, we propose the subspace projection loss, which is induced from distances of each frame projected to the fitted subspaces, to penalize the potential clustering errors. The superior performances of the proposed model on the segmentation task of synthetic data and three tasks of real motion capture data demonstrate the feature learning ability of our model.

Planning Autonomous Marine Inspection Tasks using SMT Encoding of Timelines

A key challenge in autonomous vehicles is the high-level decision making required to be reactive to significant changes in the environment. Timeline-based planning is a paradigm for temporal (time-aware) task planning that can provide such high-level decisions. We present an algorithm for incrementally translating a timeline-based planning problem into satisfability modulo theories (SMT). Off-the-shelf SMT solvers have become very efficient, and using such a solver keeps our planner implementation relatively small, simple, and extensible. Compared to existing SMT translations, our approach typically produces smaller SMT problems, and our system shows promising performance on several benchmark problems. Our planning software was developed in the context of research projects on inspection operations for UUVs on subsea installations and for mobile robots on marine installations.

The Influence of Emotional Awareness on Time Perception: Evidence From Event-Related Potentials

Prior studies found that participants overestimated both negative and positive emotional stimuli, compared with neutral emotion. This phenomenon can be explained by the “arousal mechanism.” Participants demonstrated individual differences in emotion perception. In other words, high emotional awareness resulted in high emotional arousal, and vice versa. This study extended existing findings by exploring the influence of emotional awareness on time perception in a temporal generalization task, while recording electroencephalographic (EEG) signals. The findings revealed that in the positive emotion condition, the high emotional awareness group made more overestimations, compared with the low emotional awareness group. However, no difference was observed in the neutral or negative emotion conditions. Moreover, the event-related potential (ERP) results showed that in the positive emotion condition, the high awareness group elicited larger vertex positive potential (VPP) amplitudes, compared with that of the low awareness group. However, no such differences were observed in the neutral and negative emotion conditions. Moreover, the contingent negative variation (CNV) (200–300, 300–490 ms) component showed that in the positive emotion, the amplitudes of the high awareness group were larger than that of the low awareness group; however, they did not show differences in the neutral condition. The findings of this study suggest that high emotional awareness produces higher physiological arousal; moreover, when participants were required to estimate the time duration of emotional pictures, they tended to make higher time overestimation. Thus, our results support the relationship between emotional awareness and time perception.

Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors

Mixed-signal analog/digital circuits emulate spiking neurons and synapses with extremely high energy efficiency, an approach known as “neuromorphic engineering”. However, analog circuits are sensitive to process-induced variation among transistors in a chip (“device mismatch”). For neuromorphic implementation of Spiking Neural Networks (SNNs), mismatch causes parameter variation between identically-configured neurons and synapses. Each chip exhibits a different distribution of neural parameters, causing deployed networks to respond differently between chips. Current solutions to mitigate mismatch based on per-chip calibration or on-chip learning entail increased design complexity, area and cost, making deployment of neuromorphic devices expensive and difficult. Here we present a supervised learning approach that produces SNNs with high robustness to mismatch and other common sources of noise. Our method trains SNNs to perform temporal classification tasks by mimicking a pre-trained dynamical system, using a local learning rule from non-linear control theory. We demonstrate our method on two tasks requiring temporal memory, and measure the robustness of our approach to several forms of noise and mismatch. We show that our approach is more robust than common alternatives for training SNNs. Our method provides robust deployment of pre-trained networks on mixed-signal neuromorphic hardware, without requiring per-device training or calibration.

The Spread of the Lengthening Time Effect of Emotions in Memory: A Test in the Setting of the Central Tendency Effect.

The aim of the present study was to test how the perception of an emotional stimulus colors the temporal context of judgment and modifies the participant’s perception of the current neutral duration. Participants were given two ready-set-go tasks consisting of a distribution of short (0.5–0.9 s) or long sample intervals (0.9–1.3 s) with an overlapping 0.9-s interval. Additional intervals were introduced in the temporal distribution. These were neutral for the two temporal tasks in a control condition and emotional for the short, but not the long temporal task in an emotion condition. The results indicated a replication of a kind of Vierordt’s law in the control condition, i.e., the temporal judgment toward the mean of the distribution of sample intervals (central tendency effect). However, there was a shift in the central tendency effect in the emotion condition indicating a general bias in the form of an overestimation of current intervals linked to the presence of a few emotional stimuli among the previous intervals. This finding is entirely consistent with timing mechanisms driven by prior duration context, particularly experience of prior emotional duration.

A novel Android app to evaluate and enhance auditory and tactile temporal thresholds.

With this work, we introduce a novel Android app designed to monitor and enhance auditory and tactile temporal sensitivity. To assess the app's reliability, we tested its technical performance evaluating stimuli production's accuracy (i.e., onset, offset, and duration of stimulation). To validate the app with participants we generated temporal intervals, using either sounds or vibratory stimuli, by implementing two versions of a Two-Alternative Forced-Choice (2AFC) task. Auditory and tactile temporal sensitivity of 12 participants was evaluated using this procedure. To investigate whether temporal abilities could be enhanced using the app, participants were then divided into two groups: one group was trained for four days on the auditory temporal task, while the other was trained for four days on the tactile temporal task. Results suggest that the app can i) effectively measure auditory and tactile temporal thresholds and ii) be used to enhance temporal abilities through perceptual learning. The accessibility of the experimental protocols, combined with our findings, fosters the app's involvement in rehabilitation programs, for example, with a specific focus on sensory disabilities that are associated with temporal deficits (e.g., deafness and Parkinson).Clinical Relevance- The current work introduces a novel app that can be used to monitor and improve temporal abilities, in both the auditory and the tactile modalities.

No reduction of between-task interference in a dual-task with a repeating sequence of SOAs

A frequent observation in dual-tasking is that spatially or conceptually (in)compatible Task 2 response features can interfere with responses in Task 1 (backward crosstalk effect; BCE). Such between-task interference is, at least to some degree, under strategic control. It has been shown that the size of the BCE can be modulated by instructions, contextual regularities, recent experience of conflict, and motivational factors. Especially large temporal task overlap (i.e., short stimulus onset asynchrony, SOA) represents a condition of potentially high levels of between-task interference. Accordingly, Fischer and Dreisbach (2015) showed that specific stimuli, associated with mostly short SOAs, were able to reduce the size of the BCE. In the present study, we investigated whether a regular sequence of SOAs can also be used for contextual regulation of the BCE. In a dual-task with spatially (in)compatible hand- and foot-responses, we implemented a repeating sequence of three SOAs. If participants learned this sequence and used it for task shielding, the BCE should decrease over time in the sequence blocks, but should increase in a subsequent random block. However, this prediction was not supported in two experiments (N = 32 each). Instead, the size of the BCE was constant across all blocks (BFs10 < 1 for the respective interactions). This is an important result, as it points at the necessity to discover the appropriate conditions allowing implicit SOA sequence learning and to further investigate whether or how the resulting implicit sequence knowledge can serve shielding against between-task interference.