Perceptual Task Research Articles

Does how I feel change how I move? The influence of anxiety, self-efficacy and resilience on movement in adults with Developmental Coordination Disorder.

Anxiety and movement consistency both influence movement in individuals with Developmental Coordination Disorder (DCD). This study investigated the influence of anxiety, self-efficacy, resilience and movement variability on perceptions and actions of adults with and without DCD. 17 adults with DCD and 17 adults with typical motor skills (TMS) (age and sex-matched) completed a questionnaire and three experimental tasks: two perceptual judgement tasks (static and dynamic conditions), and an executed action task involving judging and walking through different-sized gaps between doors. No significant relationships were detected between general or movement-specific anxiety, self-efficacy or resilience and perceptual judgement or movement behaviour; however, movement consistency did significantly relate to movement execution in both groups. Correlations showed adults with DCD with lower movement-specific self-efficacy left bigger safety margins, and indicated stronger links between perception and action in TMS adults. In the adults with DCD there was no significant correlation between the point of behaviour change (critical ratio) in the perceptual judgement and executed action tasks, suggesting a less smoothly linked perception-action cycle than in the TMS adults. Results highlight the importance of movement variability and motor control in the movement behaviour of adults with DCD, while illustrating the importance of studying perception and action together, especially when comparing populations, to elucidate how these may be constrained differently by individual-, task- and environmental-based constraints.

Neural mechanisms underlying perceptual-motor behavioral advantages in athletes: a systematic review and activation likelihood estimation meta-analysis

ABSTRACT This study aimed to uncover the neural mechanisms through which long-term exercise training influences athletes’ perceptual-motor behavior using an Activation Likelihood Estimation meta-analysis. An extensive literature search identified 14 experiments comprising 336 foci related to perceptual tasks and 16 experiments comprising 556 foci related to motor response tasks. We analyzed brain activation in athletes and novices during perceptual and motor response tasks relative to baseline and conducted direct and inverse contrasts in brain activation between the two groups within these tasks. The findings indicated that athletes exhibit neural efficiency in perceptual and motor response tasks, with fewer brain regions activated compared to novices. Notably, athletes showed greater activation in brain regions associated with sensorimotor processing and higher-order cognitive areas related to attention and memory. These results suggest that athletes extract kinematic information about opponents’ movements through sensorimotor simulation and match it with tactical and technical knowledge stored in long-term memory, thereby supporting superior perceptual-motor performance. In contrast, novices lacking athletic experience rely more on the occipital cortex for visual processing of actions. These findings highlight the differences in brain activity between athletes and novices during perceptual-motor behavior, providing valuable insights into how exercise training shapes brain function.

Perceptual discrimination and categorization of vowels in different levels of foreign language instruction

This paper reports on the development of L2 English vowel pair perception by adult L1 Spanish/Basque speakers of differing levels of English instruction. This study focuses on vowel pairs /i-ɪ/, /u-ʊ/ and /ʌ-ɔ/, with differing levels of proximity to native categories. Results of two perceptual tasks show that learners are influenced by their native categories, but that regardless of level, they can still modify their representations. However, the success in categorization varies by sound based on acoustic distance and multiple category competition.

Real-Time Registration of 3D Underwater Sonar Scans

Due to space and energy restrictions, lightweight autonomous underwater vehicles (AUVs) are usually fitted with low-power processing units, which limits the ability to run demanding applications in real time during the mission. However, several robotic perception tasks reveal a parallel nature, where the same processing routine is applied for multiple independent inputs. In such cases, leveraging parallel execution by offloading tasks to a GPU can greatly enhance processing speed. This article presents a collection of generic matrix manipulation kernels, which can be combined to develop parallelized perception applications. Taking advantage of those building blocks, we report a parallel implementation for the 3DupIC algorithm—a probabilistic scan matching method for sonar scan registration. Tests demonstrate the algorithm’s real-time performance, enabling 3D sonar scan matching to be executed in real time onboard the EVA AUV.

BSM-NET: Multi-Bandwidth, Multi-Scale and Multi-Modal Fusion Network for 3D Object Detection of 4D Radar and LiDAR

Abstract In recent years, with the rapid advancement of autonomous driving technology, the requirements for environmental perception tasks have become increasingly important. 4D millimeter-wave radar, an economical and reliable technology, has begun to attract attention. Additionally, LiDAR can measure accurately and is not easily interfered with, so it is also very popular. To overcome the limitations of single-sensor perception, this paper proposes the BSM-NET method, a multi-bandwidth, multi-scale, multi-modal fusion approach for 4D radar and LiDAR. This paper uses image technology to clean point cloud data, reducing errors and noise. BSM-NET consists of two key modules: Multi-Bandwidth Fusion (MBF) and Multi-Scale Fusion (MSF). MBF enhances data quality by capturing point cloud density and addressing issues such as gap filling and noise reduction. MSF improves accuracy and robustness through high-precision calculations. For better integration, we use the RLMF framework, which enables different types of data to learn from each other, allowing us to effectively fuse 80-line LiDAR and 4D radar data. Experimental results demonstrate that BSM-NET significantly outperforms the current state-of-the-art algorithms on Dual-Radar dataset. Compared to M2-Fusion, our fusion method achieves notable performance improvements of 2.04% and 2.21% in medium-difficulty 3D object detection and Bird's Eye View (BEV) detection for the vehicle category, respectively.

Sense of agency in gesture-based interactions: modulated by sensory modality but not feedback meaning

IntroductionGesture-based interactions provide control over a system without the need for physical contact. Mid-air haptic technology allows a user to not visually engage with the interface while receiving system information and is readily manipulable, which has positive implications for automotive environments. It is important, however, that the user still feels a sense of agency, which here refers to perceiving system changes as caused by their gesture.MethodsIn the current study, 36 participants engaged in an experimental time perception task with an automotive-themed infotainment menu, serving as an implicit quantitative measure of agency. This was supplemented with additional self-reported measures. They selected different icons via gesture poses, with sensory feedback either visually or haptically. In addition, (sensory) feedback was also the same for each icon, arbitrarily different or carried semantic information.ResultsMid-air haptics increased agency compared to visual, and this did not vary as a function of feedback meaning. Agency was also associated with general measures of trust and usability.DiscussionOur findings demonstrate positive implications for mid-air haptics in automotive contexts and highlight the general importance of user agency.

Self-beneficial transactional social dynamics for cooperation in Shwachman-Diamond syndrome: a mixed-subject analysis using computational pragmatics

BackgroundShwachman-Diamond Syndrome (SDS) is a rare genetic disorder with documented cognitive and behavioral challenges. However, its socio-pragmatic dynamics remain underexplored, particularly in cooperative interactions where social norms and economic considerations intersect.ObjectiveThis study investigates the socio-behavioral dynamics of SDS, focusing on how children with the condition navigate cooperative interactions. Using computational pragmatics, we aimed to identify the underlying principles guiding their social behavior.MethodsA cohort of 10 children (5 SDS, 5 matched controls) participated in ecological and cognitive tasks, including the WISC-V “Comprehension” subtest, NEPSY-II social perception tasks, and the Trognon Ecological Side Task for the Assessment of Speech-Act Processing (TEST-ASAP). Dialogues were analyzed using the Topological and Kinetic (2TK) model and a Recurrent Neural Network (RNN), enabling fine-grained computational insights into their interaction patterns.ResultsChildren with SDS exhibited cooperative behaviors shaped by perceived economic benefits, often at the expense of established social norms. Unlike behaviors classically observed in other pathologies such as autism spectrum disorders, where responses are influenced by the directness of communication, SDS behaviors were driven by personal gain, regardless of the indirectness of requests. Computational analyses revealed strong divergences in dialogical alignment when tasks lacked direct benefits, even with corrective prompts.ConclusionSDS children demonstrate a transactional approach to social interactions, prioritizing personal benefits over cooperative norms. Using our unique dialogic and computational frameworks, we show that perceived personal gain strongly shapes their cooperation patterns. These findings underscore the need for targeted interventions to enhance pragmatic skills and adaptive functioning in SDS, given their unique interaction profiles.

Effective connectivity predicts distributed neural coding of perceptual decision confidence, uncertainty, and speed

Abstract Decision making is often accompanied by a level of confidence regarding the accuracy of one’s decision. Previous studies have indicated neural activity associated with perceptual decision confidence during sensory stimulus presentation. Choice-based reaction time (RT) has been suggested as an indirect but more objective measure of decision confidence—generally faster RT for higher confidence. However, it is unclear whether choice confidence and RT are mediated by distinct neural pathways, and whether their neural correlates are encoded nonlinearly. Within a perceptual decision-making task, we applied functional magnetic resonance imaging-informed electroencephalography-based effective connectivity analysis via dynamic causal modelling (DCM) on event-related potentials and found the frontoparietal network for fast-vs-slow RT condition to be different from that of high-vs-low confidence rating condition. Furthermore, trial-by-trial DCM analysis predicted cortical layer-based, distributed, and nonlinear coding of RT, confidence or uncertainty. Collectively, our study suggests that decision confidence and speed are instantiated by different dynamical networks distributed across cortical layers.

Cognitive and neural underpinnings of friend-prioritization in a perceptual matching task.

Previous findings of better behavioral responses to self- over other-related stimuli suggest prioritized cognitive processes of self-related information. However, it is unclear whether the processing of information related to important others (e.g.,friends) may be prioritized over that related to the self in certain subpopulations and, if yes, whether friend-prioritization and self-prioritization engage distinct cognitive and neural mechanisms. We collected behavioral and electroencephalography (EEG) data from a large sample (N=1006) during learning associations between shapes and person labels (self or a friend). Analyses of response times and sensitivities revealed two subpopulations who performed better to friend-shape or self-shape associations, respectively (N=216 for each group). Drift diffusion model (DDM) analyses unraveled faster information acquisition for friend-shape (vs. self-shape) associations in the friend-prioritization group but an opposite pattern in the self-prioritization group . Trial-by-trial regression analyses of EEG data showed that the greater amplitudes of a frontal/central activity at 180-240 ms post-stimulus were correlated with faster information acquisition from friend-shape associations in the friend-prioritization group but from self-shape associations in the self-prioritization group. However, the frontal/central neural oscillations at 8-18 Hz during perceptual learning were specifically associated with speed of information acquisition from friend-shape associations in the friend-prioritization-group. Our findings provide evidence for friend-prioritization in perceptual learning in a subpopulation of adults and clarify the underlying cognitive and neural mechanisms.

Differences in Perceived Versus Actual Sensory Perception in Avoidant/Restrictive Food Intake Disorder.

Individuals with avoidant/restrictive food intake disorder (ARFID) self-report heightened sensitivity to taste and smell, but neither phenomenon has been systematically explored in the laboratory. We hypothesized that, compared to healthy controls (HC, n = 34), children, adolescents, and adults with full/subthreshold ARFID (n = 100; ages 9 to 23 years) would self-report heightened response to taste/smell stimuli and exhibit stronger bitter taste perception and heightened smell perception in performance-based tasks, and these differences would be especially prominent in those with the ARFID-sensory sensitivity presentation. We measured self-reported sensitivity to taste/smell with the adolescent/adult sensory profile (AASP). We measured performance-based bitter taste perception with the regional taste intensity test (RTIT) and 6-N-propylthiouracil (PROP) test, and olfactory performance with the Sniffin' Sticks test (including the odor threshold, odor detection, and odor identification subscales). As expected, the ARFID group self-reported heightened response to taste/smell on the AASP, compared to HC, with an especially large effect size in the subset with the ARFID-sensory sensitivity presentation. Contrary to hypotheses, on performance-based measures, neither the ARFID group-nor the ARFID-sensory sensitivity group specifically-demonstrated heightened sensitivity to bitter taste on the RTIT or PROP tests, nor heightened smell perception on the Sniffin' Sticks test. These first laboratory findings in a clinically diagnosed sample of individuals with full/subthreshold ARFID highlight the discrepancy between perceived versus actual sensitivity to taste/smell stimuli. Future research should explore whether this discrepancy can be replicated and therapeutically leveraged to facilitate successful food exposures.

Spontaneous slow cortical potentials and brain oscillations independently influence conscious visual perception.

Perceptual awareness results from an intricate interaction between external sensory input and the brain's spontaneous activity. Pre-stimulus ongoing activity influencing conscious perception includes both brain oscillations in the alpha (7 to 14 Hz) and beta (14 to 30 Hz) frequency ranges and aperiodic activity in the slow cortical potential (SCP, <5 Hz) range. However, whether brain oscillations and SCPs independently influence conscious perception or do so through shared mechanisms remains unknown. Here, we addressed this question in 2 independent magnetoencephalography (MEG) data sets involving near-threshold visual perception tasks in humans using low-level (Gabor patches) and high-level (objects, faces, houses, animals) stimuli, respectively. We found that oscillatory power and large-scale SCP activity influence conscious perception through independent mechanisms that do not have shared variance. In addition, through mediation analysis, we show that pre-stimulus oscillatory power and SCP activity have different relations to pupil size-an index of arousal-in their influences on conscious perception. Together, these findings suggest that oscillatory power and SCPs independently contribute to perceptual awareness, with distinct relations to pupil-linked arousal.

Resonance+: Operationalizing Protective Action Decision Model for Finding Information Useful for Public Information Officers

Microblogging platforms have been increasingly used by the public in crisis situations, enabling more participatory crisis communication between the official response channels and the affected community. However, the sheer volume of crisis-related messages on social media can make it challenging for officials to find pertinent information and understand the public’s perception of evolving risks. To address this issue, crisis informatics researchers have proposed a variety of technological solutions, but there has been limited examination of the cognitive and perceptual processes and subsequent responses of the affected population. Yet, this information is critical for the crisis response officials to gauge public’s understanding of the event, their perception of event-related risk, and perception of incident response and recovery efforts, in turn enabling the officials to craft crisis communication messaging more effectively. Taking cues from the Protective Action Decision Model, we conceptualize a metric —resonance+ — that prioritizes the cognitive and perceptual processes of the affected population, quantifying shifts in collective attention and information exposure for each tweet. Based on resonance+, we develop a principled, scalable pipeline that recommends content relating to people’s cognitive and perceptual processes. Our results suggest that resonance+ is generalizable across different types of natural hazards. We have also demonstrated its applicability for near-real time scenarios. According to the feedback from the target users, the local public information officers (PIOs) in emergency management, the messages recommended by our pipeline are useful in their tasks of understanding public perception and finding hopeful narratives, potentially leading to more effective crisis communications.

Suboptimal but intact integration of Bayesian components during perceptual decision-making in autism

BackgroundAlterations in sensory perception, a core phenotype of autism, are attributed to imbalanced integration of sensory information and prior knowledge during perceptual statistical (Bayesian) inference. This hypothesis has gained momentum in recent years, partly because it can be implemented both at the computational level, as in Bayesian perception, and at the level of canonical neural microcircuitry, as in predictive coding. However, empirical investigations have yielded conflicting results with evidence remaining limited. Critically, previous studies did not assess the independent contributions of priors and sensory uncertainty to the inference.MethodWe addressed this gap by quantitatively assessing both the independent and interdependent contributions of priors and sensory uncertainty to perceptual decision-making in autistic and non-autistic individuals (N = 126) during an orientation categorization task.ResultsContrary to common views, autistic individuals integrated the two Bayesian components into their decision behavior, and did so indistinguishably from non-autistic individuals. Both groups adjusted their decision criteria in a suboptimal manner.LimitationsThis study focuses on explicit priors in a perceptual categorization task and high-functioning adults. Thus, although the findings provide strong evidence against a general and basic alteration in prior integration in autism, they cannot rule out more specific cases of reduced prior effect – such as due to implicit prior learning, particular level of decision making (e.g., social), and level of functioning of the autistic person.ConclusionsThese results reveal intact inference for autistic individuals during perceptual decision-making, challenging the notion that Bayesian computations are fundamentally altered in autism.

DriveDiTFit: Fine-tuning Diffusion Transformers for Autonomous Driving Data Generation

In autonomous driving, deep models have shown remarkable performance across various visual perception tasks with the demand of high-quality and huge-diversity training datasets. Such datasets are expected to cover various driving scenarios with adverse weather, lighting conditions and diverse moving objects. However, manually collecting these data presents huge challenges and expensive cost. With the rapid development of large generative models, we propose DriveDiTFit, a novel method for efficiently generating autonomous Driv ing data by Fi ne- t uning pre-trained Di ffusion T ransformers (DiTs). Specifically, DriveDiTFit utilizes a gap-driven modulation technique to carefully select and efficiently fine-tune a few parameters in DiTs according to the discrepancy between the pre-trained source data and the target driving data. Additionally, DriveDiTFit develops an effective weather and lighting condition embedding module to ensure diversity in the generated data, which is initialized by a nearest-semantic-similarity initialization approach. Through progressive tuning scheme to refined the process of detail generation in early diffusion process and enlarging the weights corresponding to small objects in training loss, DriveDiTFit ensures high-quality generation of small moving objects in the generated data. Extensive experiments conducted on driving datasets confirm that our method could efficiently produce diverse real driving data.

Neuroimaging Findings for the Overnight Consolidation of Learned Non-native Speech Sounds.

Research over the past two decades has documented the importance of sleep to language learning. Sleep has been suggested to play a role in establishing new speech representations as well; however, the neural mechanisms corresponding to sleep-mediated effects on speech perception behavior are unknown. In this study, we trained monolingual English-speaking adults to perceive differences between the Hindi dental vs. retroflex speech contrast in the evening. We examined the blood oxygen level dependent signal using functional magnetic resonance imaging during perceptual tasks on both the trained talker and on an untrained talker shortly after training, and again the next morning. We also employed diffusion tensor imaging to determine if individual differences in white matter structure could predict variability in overnight consolidation. We found greater activity in cortical regions associated with language processing (e.g., left insula) on the second day. Fractional anisotropy values in the anterior thalamic radiation and the uncinate fasciculus were associated with the magnitude of overnight change in perceptual behavior on the generalization (untrained) talker, after controlling for differences in sleep duration and initial learning. Our findings suggest that speech-perceptual information is subject to an overnight transfer of information to the cortex. Moreover, neural structure appears to be linked to individual differences in efficiency of overnight consolidation.

Does smooth mean simple? The impact of tactile experience on judgments of difficulty.

People often associate roughness with difficulty, as a figure of speech. Studies have shown that there is a metaphorical connection between the concept of rough versus smooth feel and the degree of difficulty. However, it has not been determined whether rough and smooth tactile experiences influence judgments of perceived task difficulty from the perspective of physical metaphors. This study used the Stroop experimental paradigm and the metaphorical experimental paradigm to investigate the effects of rough and smooth haptic experiences on difficulty judgments of perceptual tasks in two experiments. (1) There is a psychological reality of "difficult concept-rough touch" and "easy concept-smooth touch," linking the concept of roughness to the rough/smooth touch metaphor; (2) The physical tactile experience of roughness/smoothness had an effect on perceptual task difficulty judgments. After the experience of roughness, participants tended to judge the difficulty as high, while after the experience of smoothness, participants tended to judge the difficulty as low. Rough and smooth haptics affect perceptual task difficulty judgments, and rough and smooth haptic experiences polarize difficulty judgments in the embodied condition.

Policy complexity suppresses dopamine responses.

Limits on information processing capacity impose limits on task performance. We show that male and female mice achieve performance on a perceptual decision task that is near-optimal given their capacity limits, as measured by policy complexity (the mutual information between states and actions). This behavioral profile could be achieved by reinforcement learning with a penalty on high complexity policies, realized through modulation of dopaminergic learning signals. In support of this hypothesis, we find that policy complexity suppresses midbrain dopamine responses to reward outcomes. Furthermore, neural and behavioral reward sensitivity were positively correlated across sessions. Our results suggest that policy compression shapes basic mechanisms of reinforcement learning in the brain.Significance statement Decision making relies on memory to store information about which actions to produce in which situations. This memory has limited capacity, which means that some information will be lost. The signatures of this information loss can be found in patterns of behavioral bias and randomness. However, relatively little is known about the neural mechanisms which ensure that actions achieve the highest possible reward given the limited capacity of decision memory. In this paper, we show that the neuromodulator dopamine is sensitive to the costs of memory, as predicted by a computational model of capacity-limited learning.

Eye metrics are a marker of visual conscious awareness and neural processing in cerebral blindness.

Damage to the primary visual pathway can cause vision loss. Some cerebrally blind people retain degraded vision or sensations and can perform visually guided behaviors. These cases motivate investigation and debate on blind field conscious awareness and linked residual neural processing. A key challenge in this research is that subjective measures of blind field visual conscious awareness can be misleading. Alternatively, eye metrics, including pupil size and eye movements are promising objective markers of conscious awareness and brain activity. In this study, we examined stimulus-evoked changes in pupil size, blinking, and microsaccades in the sighted and blind field of cerebrally blind participants. Using standard analysis and innovative machine learning methods, our findings support that eye metrics can infer blind field conscious awareness, even when behavioral performance on a visual perception task indicated otherwise. Furthermore, these eye metrics were linked to blind field visual stimulus-evoked occipital cortical field potentials. These findings support recording eye metrics in cerebral blindness and highlight potential clinical applications, including tracking the recovery of conscious vision and visual neural processing.

The development and validation of a Japanese hazard perception task

Objectives This study aimed to validate the hazard perception task developed for Japanese drivers with brain damage. Methods A total of 36 professional driving instructors, 67 older adult drivers, 39 young drivers, and 72 patients with brain damage participated in the study. A video-based hazard perception task measured the hazard perception skills of each group. Participants were instructed to touch the screen as soon as they identified a hazard. Outcome measures included the number of hazards identified and response latency. Comparisons were made between groups for each hazard type: environmental prediction hazard (EPH) and behavioral prediction hazard (BPH). Results Patients with brain damage demonstrated fewer hazard detections and longer response latencies than driving instructors and older adult drivers, regardless of the hazard type. The hazard perception skills of patients with brain damage were comparable to those of young drivers across both hazard types. Driving instructors exhibited the highest number of hazard detections and the fastest response latencies among all groups, with differences being more pronounced for EPH than for BPH. Conclusions Thus, the validity of the hazard perception task developed for Japanese drivers with brain damage was confirmed. Future research should examine the predictive validity of the developed task, particularly its relationship with crash rates in individuals with brain damage.

The Cost of Regulating Effort: Reward and Difficulty Cues With Longer Prediction Horizons Have a Stronger Impact on Performance.

Many theories on cognitive effort start from the assumption that cognitive effort can be expended at will, and flexibly up- or down-regulated depending on expected task demand and rewards. However, while effort regulation has been investigated across a wide range of incentive conditions, few investigated the cost of effort regulation itself. Across four experiments, we studied the effects of reward expectancy and task difficulty on effort expenditure in a perceptual decision-making task (random-dot-motion) and a cognitive control task (colour-naming Stroop), and within each task comparted cues between short (cueing the next trial) and long (cueing the next six trials) prediction horizons. We found that participants used the cue information only when it was valid for multiple trials in a row. In the random-dot-motion task, a high reward expectancy resulted in better accuracy, especially in easy trials, but only with long prediction horizon. Similarly, in the Stroop task, the reward facilitation of reaction time was only observed after reward cues with a long prediction horizon. Together, our results indicate that people experience a cost to effort regulation, and that lower adjustment frequency can compensate for this cost.