Perceptual Task Research Articles

Fast-BEV: A Fast and Strong Bird's-Eye View Perception Baseline.

Recently, perception task based on Bird's-Eye View (BEV) representation has drawn more and more attention, and BEV representation is promising as the foundation for next-generation Autonomous Vehicle (AV) perception. However, most existing BEV solutions either require considerable resources to execute on-vehicle inference or suffer from modest performance. This paper proposes a simple yet effective framework, termed Fast-BEV, which is capable of performing faster BEV perception on the on-vehicle chips. Towards this goal, we first empirically find that the BEV representation can be sufficiently powerful without expensive transformer based transformation or depth representation. Our Fast-BEV consists of five parts, we innovatively propose (1) a lightweight deployment-friendly view transformation which fast transfers 2D image features to 3D voxel space, (2) a multi-scale image encoder which leverages multi-scale information for better performance, (3) an efficient BEV encoder which is particularly designed to speed up on-vehicle inference. We further introduce (4) a strong data augmentation strategy for both image and BEV space to avoid over-fitting, (5) a multi-frame feature fusion mechanism to leverage the temporal information. Among them, (1) and (3) enable Fast-BEV to be fast inference and deployment friendly on the on-vehicle chips, (2), (4) and (5) ensure that Fast-BEV has competitive performance. All these make Fast-BEV a solution with high performance, fast inference speed, and deployment-friendly on the on-vehicle chips of autonomous driving. Through experiments, on 2080Ti platform, our R50 model can run 52.6 FPS with 47.3% NDS on the nuScenes validation set, exceeding the 41.3 FPS and 47.5% NDS of the BEVDepth-R50 model (Li et al. 2022) and 30.2 FPS and 45.7% NDS of the BEVDet4D-R50 model (J. Huang and G. Huang, 2022). Our largest model (R101@900×1600) establishes a competitive 53.5% NDS on the nuScenes validation set. We further develop a benchmark with considerable accuracy and efficiency on current popular on-vehicle chips.

Learning Omni-Dimensional Spatio-Temporal Dependencies for Millimeter-Wave Radar Perception

Reliable environmental perception capabilities are a prerequisite for achieving autonomous driving. Cameras and LiDAR are sensitive to illumination and weather conditions, while millimeter-wave radar avoids these issues. Existing models rely heavily on image-based approaches, which may not be able to fully characterize radar sensor data or efficiently further utilize them for perception tasks. This paper rethinks the approach to modeling radar signals and proposes a novel U-shaped multilayer perceptron network (U-MLPNet) that aims to enhance the learning of omni-dimensional spatio-temporal dependencies. Our method involves innovative signal processing techniques, including a 3D CNN for spatio-temporal feature extraction and an encoder–decoder framework with cross-shaped receptive fields specifically designed to capture the sparse and non-uniform characteristics of radar signals. We conducted extensive experiments using a diverse dataset of urban driving scenarios to characterize the sensor’s performance in multi-view semantic segmentation and object detection tasks. Experiments showed that U-MLPNet achieves competitive performance against state-of-the-art (SOTA) methods, improving the mAP by 3.0% and mDice by 2.7% in RD segmentation and AR and AP by 1.77% and 2.03%, respectively, in object detection. These improvements signify an advancement in radar-based perception for autonomous vehicles, potentially enhancing their reliability and safety across diverse driving conditions.

Neural dynamics of shifting attention between perception and working-memory contents

In everyday tasks, our focus of attention shifts seamlessly between contents in the sensory environment and internal memory representations. Yet, research has mainly considered external and internal attention in isolation. We used magnetoencephalography to compare the neural dynamics of shifting attention to visual contents within vs. between the external and internal domains. Participants performed a combined perception and working-memory task in which two sequential cues guided attention to upcoming (external) or memorized (internal) sensory information. Critically, the second cue could redirect attention to visual content within the same or alternative domain as the first cue. Multivariate decoding unveiled distinct patterns of human brain activity when shifting attention within vs. between domains. Brain activity distinguishing within- from between-domain shifts was broadly distributed and highly dynamic. Intriguingly, crossing domains did not invoke an additional stage prior to shifting attention. Alpha lateralization, a canonical marker of shifting spatial attention, showed no delay when cues redirected attention to the same vs. alternative domain. Instead, evidence suggested that neural states associated with a given domain linger and influence subsequent shifts of attention within vs. between domains. Our findings provide critical insights into the neural dynamics that govern attentional shifts between perception and working memory.

Motion-induced blindness shows spatial anisotropies in conscious perception.

Polar angle asymmetries (PAAs), the differences in perceptual experiences and performance across different regions of the visual field are present in various paradigms and tasks of visual perception. Currently, research in this area is sparse, particularly regarding the influence of PAAs during perceptual illusions, highlighting a gap in visual cognition studies. We aim to fill this gap by measuring PAAs across the visual field during an illusion applied to test conscious vision widely. Motion-induced blindness (MIB) is an illusion when a peripheral target disappears from consciousness as the result of a continuously moving background pattern. During MIB we separately measured the average disappearance time of peripheral targets in eight equidistant visual field positions. Our results indicate a significant variation in MIB disappearance times and frequencies as a function of target location. Specifically, we found shorter and fewer disappearances along the cardinal compared to oblique directions, and along the horizontal compared to the vertical meridian. Our results suggest specific consistencies between visual field asymmetries and conscious visual perception.

The repeatability of behavioural laterality during nest building in zebra finches.

Cerebral laterality is a widespread phenomenon across animals and refers to the specialization of the left and right hemispheres of the brain for perceptual, cognitive and behavioural tasks. Behavioural laterality occurs in several contexts, including foraging, mate selection, predator detection and tool manufacture. Behavioural laterality during nest building, however, has rarely been addressed. We conducted two experiments to examine (1) whether behavioural laterality occurs during nest building, (2) whether laterality correlates with nest-building speed, (3) whether laterality during nest building is repeatable, and (4) whether nest-building experience influences laterality. In Experiment 1, we scored individual laterality indices for 58 zebra finch (Taeniopygia guttata) males, the nest-building sex in this species, based on which eye he used to view then select the first 25 pieces of nest material. We calculated correlations between laterality strength and nest-building duration. In Experiment 2, to test the repeatability of laterality during nest building, we measured laterality for 20 males across five nests built by each male. Individuals varied both in the direction and the strength of behavioural laterality of material selection during nest building. Overall, however, males were not consistent in their laterality across the five nests. We found no correlation between laterality strength and nest-building duration in either experiment. Finally, we found evidence for building experience influencing the behavioural laterality of individuals: more building experience results in more predictable behavioural laterality during nest-material selection.

Implementing an interactive oral task to assess undergraduate psychology students’ attainment of pre-professional competencies

Finding engaging and authentic ways to assess required competencies which are resistant to academic misconduct is a challenge for tertiary psychology education. While simulation-based tasks are commonly used to assess postgraduate students’ development of professional competencies, they are less commonly used in undergraduate programs to assess pre-professional competencies. Interactive oral assessments have been suggested to achieve authenticity while inhibiting academic misconduct, and to be feasible within an online learning environment. The current study presents students’ and markers’ perceptions of an interactive oral task implemented to assess fourth-year psychology students’ counselling and reflection skills. Results suggest that students and markers perceived the interactive oral task to be an authentic assessment of knowledge and skills required for future practice, which encouraged deeper learning than written assessments or exams, and was resistant to academic misconduct. Despite feeling anxious about the new assessment format, both students and markers reported finding the engaging nature of the task enjoyable and indicated that they would like to be involved in more interactive oral assessments in the future. The extent to which these findings reflect the current literature, and the role for interactive oral tasks in the innovative, authentic assessment of pre-professional competencies which promote academic integrity are discussed.

Measuring attitudes towards ethnic minority patients: the revalidated REMP-3 instrument for graduate healthcare practitioners

ObjectiveMeasuring intercultural attitudes can aid in understanding and addressing persistent inequities in healthcare. Instead of creating new instruments, several sources call for a more rigorous revalidation of existing instruments towards a more broad population. As an example of such an existing instrument, the EMP-3 (Ethnic Minority Patients) focuses on the attitudes of physicians towards ethnic minority patients. Starting from a robust theoretical underpinning and a rigorous methodological setup, the present study revalidates the EMP-3 instrument for physicians towards the REMP-3 instrument for graduate healthcare practitioners.MethodsWe assessed the reliability and validity of the old EMP-3, which we then updated to a new REMP-3 instrument. We used structural equation modeling to model the framework of intercultural effectiveness on two waves of independent data, N2021 = 368 and N2022 = 390. Within this framework, we tested the new REMP-3 instrument as an operationalization of intercultural attitudes. We conducted a confirmatory factor analysis on the first wave, after which we made adaptations to the original EMP-3 instrument to obtain a new REMP-3 instrument. The new REMP-3 instrument was then cross-validated using the data of the second wave.ResultsThe new REMP-3 instrument is a psychometric upgrade compared to the EMP-3. The REMP-3 now has a cross-validated structure, with three subscale dimensions (i.e., task perception, background perception and the perceived need to communicate) and an overarching higher-order, full-scale dimension. Both the subscales as well as the full instrument show acceptable to good internal consistency reliability, with a reduced number of items from eighteen to ten. As theoretically predicted, the REMP-3 also functions as a measure of intercultural attitudes in an intercultural competence framework.ConclusionUltimately, the REMP-3 instrument can contribute to more equity in healthcare by concisely and reliably assessing and monitoring attitudes in healthcare practitioners. This attitude assessment represents the potential of learning new skills and knowledge to address interactions with ethnic minority patients, which is especially useful during training situations like an internship.

Perception-Action dissociations depend on factors that affect multisensory processing.

Behavioral perception-action dissociations are widely used to test models of high-level vision, but debates concerning their interpretation have underestimated the role of multisensory mechanisms in such tests. Sensorimotor tasks engage multisensory processing in fundamentally different ways than perceptual tasks, and these differences can modulate dissociations in different ways based on task features. To test this idea, we compared perception and action using a well-understood size-contrast effect, the Uznadze illusion, and manipulated both unimodal and crossmodal stimulation as well as conditions that are known to favor or hinder multisensory integration. Results demonstrated that similar or dissociable effects on perception and action can be observed depending on factors that are known to affect multisensory processing. Specifically, such factors can cause a visual task to be affected by the illusion or remain fully unaffected, whereas a visuomotor task can be affected by the illusion, remain immune from the illusion, or, unexpectedly, even show a clear reverse effect. These findings provide a novel perspective on a long standing debate in behavioral cognitive neuroscience.

The effects of prediction representations on implicit learning: evidence from sentence reading and perceptual identification

Predicting errors can facilitate implicit learning, but the long-term consequences of prediction errors are not yet fully understood. Especially when predictions are disconfirmed, it remains unclear whether initially correct prediction representations persist or are suppressed. In this study, participants first engaged in a sentence reading task and then performed a perceptual identification task after completing an N-back task or after a 24-hour delay. The perceptual identification task presented previously expected and unexpected words and previously predicted but not presented words to measure implicit memory for the critical items. This study aims to investigate the mechanisms underlying the persistence of prediction representations and the long-term effects of prediction errors on implicit learning. Our results indicate that prediction errors can promote implicit learning and can persist for more than 24 hours. Furthermore, originally correct but not seen in reality prediction representations persist to facilitate performance on the implicit memory task after 24 hours. This may reflect long-term changes in the internal representation probabilities of prediction representations.

Bayesian deep learning: An enhanced AI framework for legal reasoning alignment

The integration of artificial intelligence into the field of law has penetrated the underlying logic of legal operations. Currently, legal AI systems face difficulties in representing legal knowledge, exhibit insufficient legal reasoning capabilities, have poor explainability, and are inefficient in handling causal inference and uncertainty. In legal practice, various legal reasoning methods (deductive reasoning, inductive reasoning, abductive reasoning, etc.) are often intertwined and used comprehensively. However, the reasoning modes employed by current legal AI systems are inadequate. Identifying AI models that are more suitable for legal reasoning is crucial for advancing the development of legal AI systems.Distinguished from the current high-profile large language models, we believe that Bayesian reasoning is highly compatible with legal reasoning, as it can perferm abductive reasoning, excel at causal inference, and admits the "defeasibility" of reasoning conclusions, which is consistent with the cognitive development pattern of legal professionals from apriori to posteriori. AI models based on Bayesian methods can also become the main technological support for legal AI systems. Bayesian neural networks have advantages in uncertainty modeling, avoiding overfitting, and explainability. Legal AI systems based on Bayesian deep learning frameworks can combine the advantages of deep learning and probabilistic graphical models, facilitating the exchange and supplementation of information between perception tasks and reasoning tasks. In this paper, we take perpetrator prediction systems and legal judegment prediction systems as examples to discuss the construction and basic operation modes of the Bayesian deep learning framework. Bayesian deep learning can enhance reasoning ability, improve the explainability of models, and make the reasoning process more transparent and visualizable. Furthermore, Bayesian deep learning framework is well-suited for human-machine collaborative tasks, enabling the complementary strengths of humans and machines.

Learn to Adapt for Self-Supervised Monocular Depth Estimation.

Monocular depth estimation is one of the fundamental tasks in environmental perception and has achieved tremendous progress by virtue of deep learning. However, the performance of trained models tends to degrade or deteriorate when employed on other new datasets due to the gap between different datasets. Though some methods utilize domain adaptation technologies to jointly train different domains and narrow the gap between them, the trained models cannot generalize to new domains that are not involved in training. To boost the transferability of self-supervised monocular depth estimation models and mitigate the issue of meta-overfitting, we train the model in the pipeline of meta-learning and propose an adversarial depth estimation task. We adopt model-agnostic meta-learning (MAML) to obtain universal initial parameters for further adaptation and train the network in an adversarial manner to extract domain-invariant representations for easing meta-overfitting. In addition, we propose a constraint to impose upon cross-task depth consistency to compel the depth estimation to be identical in different adversarial tasks, which improves the performance of our method and smoothens the training process. Experiments on four new datasets demonstrate that our method adapts quite fast to new domains. Our method trained after 0.5 epoch achieves comparable results with the state-of-the-art methods trained at least 20 epochs.

Evidence for interacting but decoupled controls of decisions and movements in nonhuman primates.

Many recent studies indicate that control of decisions and actions is integrated during interactive behavior. Among these, several carried out in humans and monkeys conclude that there is a coregulation of choices and movements. Another perspective, based on human data only, proposes a decoupled control of decision duration and movement speed, allowing, for instance, trading decision duration for movement duration when time pressure increases. Crucially, it is not currently known whether this ability to flexibly dissociate decision duration from movement speed is specific to humans, whether it can vary depending on the context in which a task is performed, and whether it is stable over time. These are important questions to address, especially to rely on monkey electrophysiology to infer the neural mechanisms of decision-action coordination in humans. To do so, we trained two macaque monkeys in a perceptual decision-making task and analyzed data collected over multiple behavioral sessions. Our findings reveal a strong and complex relationship between decision duration and movement vigor. Decision duration and action duration can covary but also "compensate" each other. Such integrated but decoupled control of decisions and actions aligns with recent studies in humans, validating the monkey model in electrophysiology as a means of inferring neural mechanisms in humans. Crucially, we demonstrate for the first time that this control can evolve with experience, in an adapted manner. Together, the present findings contribute to deepening our understanding of the integrated control of decisions and actions during interactive behavior.NEW & NOTEWORTHY The mechanism by which the integrated control of decisions and actions occurs, coupled or interactive but decoupled, is debated. In the present study, we show in monkeys that decisions and actions influence each other in a decoupled way. For the first time, we also demonstrate that this control can evolve depending the subject's experience, allowing the trade of movement time for decision time and limiting the temporal discounting of reward value.

Fear among patients with Parkinson's disease and repeated falls: Analysis of skin conductance responses during simulated accidents

IntroductionThe fear of falling (FoF) usually increases in patients with Parkinson’s disease (PD); however, it can decrease despite repeated falls for unknown reasons. This phenomenon is similar to the findings that the patients with PD show lower emotional responses in the decision-making during the Iowa gambling task (IGT) than of those without PD. The decision-making during the IGT is reportedly correlated with interoceptive accuracy. This phenomenon could be associated with decreased physiological reaction and interoception deficiency. This study aimed to investigate the factors associated with decreased FoF in patients with PD who fell repeatedly. MethodsWe recorded the skin conductance responses (SCR) during the task delivered by a virtual reality system and heartbeat perception task (HPT) as an interoceptive measure. We measured emotional self-awareness using visual analog scale (VAS) scores. We recruited 28 non-demented patients with PD and 24 age-matched Non-clinical population (NC) group. All patients with PD were grouped by FoF scale score and number of falls within the previous year into repeated falls without FoF, repeated falls with FoF, and no falls groups. ResultsThe SCR during all accident videos experienced by themselves were significantly lower in the repeated falls without FoF group than in the NC group (p < 0.01). The HPT was significantly lower in the repeated falls without FoF group than in the NC group (p = 0.007). ConclusionThe repeated falls without FoF group did not recognize recurrent falls as being problematic, possibly associated with decreased physiological reactions and interoception deficiency.

Medial Temporal Lobe Damage Impairs Temporal Integration in Episodic Memory.

Although the role of the medial temporal lobe (MTL) and the hippocampus in episodic memory is well established, there is emerging evidence that these regions play a broader role in cognition, specifically in temporal processing. However, despite strong evidence that the hippocampus plays a critical role in sequential processing, the involvement of the MTL in timing per se is poorly understood. In the present study, we investigated whether patients with MTL damage exhibit differential performance on a temporal distance memory task. Critically, we manipulated context shifts, or boundaries, which have been shown to interfere with associative binding, leading to increases in subjective temporal distance. We predicted that patients with MTL damage would show impaired binding across boundaries and thus fail to show temporal expansion. Consistent with this hypothesis, unilateral patients failed to show a temporal expansion effect, and bilateral patients actually exhibited the reverse effect, suggesting a critical role for the MTL in binding temporal information across boundaries. Furthermore, patients were impaired overall on both the temporal distance memory task and recognition memory, but not on an independent, short-timescale temporal perception task. Interestingly, temporal distance performance could be independently predicted by performance on recognition memory and the short temporal perception task. Together, these data suggest that distinct mnemonic and temporal processes may influence long interval temporal memory and that damage to the MTL may impair the ability to integrate episodic and temporal information in memory.

Dual-path aggregation transformer network for super-resolution with images occlusions and variability

While Transformer-based approaches have recently achieved notable success in super-resolution, their extensive computational requirements impede widespread practical adoption. High-resolution meteorological satellite cloud imagery is essential for weather analysis and forecasting. Enhancing image resolution through super-resolution techniques facilitates the accurate identification and localization of geographic features by meteorological systems. However, current super-resolution methods fail to restore the intricacies of cloud formations and complex regions fully. This research introduces a novel dual-path aggregation Transformer network (DPAT) tailored to enhance the super-resolution of meteorological satellite cloud images. The DPAT network adeptly captures cloud imagery's subtle details and textures, effectively addressing occlusions and the variability inherent in satellite imagery. It bolsters the model's ability to manage the complex attributes of cloud images through the introduction of the Dual-path Aggregation Self-Attention (DASA) mechanism and the Multi-scale Feature Aggregation Block (MFAB), thereby enhancing performance in processing intricate cloud features. The DASA mechanism synthesizes features across spatial, depth, and channel dimensions via a dual-path approach, thoroughly exploiting feature correlations. The MFAB, designed to supplant the multilayer perceptron, incorporates shift convolution and a multi-scale interaction block to augment feature information, compensating for the deficiency in local information absorption due to fixed receptive fields. Experimental outcomes indicate that DPAT delivers superior super-resolution outcomes. With a parameter count of only 32% of the Enhanced Deep Residual Network (EDSR) or 77% of the Image Restoration using Shift Window Transformer (SwinIR), DPAT matches SwinIR's performance on the satellite cloud dataset. Moreover, DPAT balances accuracy and parameter economy across various datasets. This technology is expected to improve image super-resolution capabilities in multiple fields such as human action recognition and industrial recognition, and indirectly improve the accuracy of image perception tasks.

Learning probability distributions of sensory inputs with Monte Carlo predictive coding.

It has been suggested that the brain employs probabilistic generative models to optimally interpret sensory information. This hypothesis has been formalised in distinct frameworks, focusing on explaining separate phenomena. On one hand, classic predictive coding theory proposed how the probabilistic models can be learned by networks of neurons employing local synaptic plasticity. On the other hand, neural sampling theories have demonstrated how stochastic dynamics enable neural circuits to represent the posterior distributions of latent states of the environment. These frameworks were brought together by variational filtering that introduced neural sampling to predictive coding. Here, we consider a variant of variational filtering for static inputs, to which we refer as Monte Carlo predictive coding (MCPC). We demonstrate that the integration of predictive coding with neural sampling results in a neural network that learns precise generative models using local computation and plasticity. The neural dynamics of MCPC infer the posterior distributions of the latent states in the presence of sensory inputs, and can generate likely inputs in their absence. Furthermore, MCPC captures the experimental observations on the variability of neural activity during perceptual tasks. By combining predictive coding and neural sampling, MCPC can account for both sets of neural data that previously had been explained by these individual frameworks.

The power of one: A single flanker produces compatibility effects in the episodic flanker task

AbstractThe episodic flanker task is an episodic version of the Eriksen and Eriksen (Perception &amp; Psychophysics, 16 (1), 143–149, 1974) perceptual flanker task, showing the same compatibility and distance effects. Subjects are presented with a list followed by a probe display in which one item is cued. The task, to indicate whether the probed letter appeared in the same position in the memory list, requires focusing attention on a single item in memory. The probe display contains flanking items to be ignored. They are same as the memory list or different. Same flankers are compatible with “yes” responses and incompatible with “no” responses. Different flankers are incompatible with “yes” responses and compatible with “no” responses. Previously, we presented multiple flankers in the probe, allowing a global matching strategy. Here, we report two episodic flanker experiments with just one flanker in the probe to encourage focusing sharply on the target. We found flanker compatibility effects in both experiments when a single flanker appeared immediately adjacent to the target. Experiment 2 varied the distance between the flanker and the target in the probe and the memory list and found the compatibility effect in response time only when the flanker was immediately adjacent to the target in both the probe and the memory list. The effect in accuracy also appeared when the flanker was two positions away in both the probe and the memory list. These results show that attention is focused sharply on elements of a memory structure during retrieval, suggesting that memory retrieval is perceptual attention turned inward.

Long-term neurocognitive follow-up in children with traumatic brain injury: A literature review and monocentric cohort study

Children with mild traumatic brain injury (mTBI) may experience long-term cognitive sequelae. However, previous study results have been controversial. It remains unclear whether clinical follow-up is useful, how long patients should be followed-up, and which psychological dimensions should be investigated. Herein, we described neurocognitive evolution in a small sample of Italian children who were hospitalized for mTBI and systematically reviewed the existing evidence in this setting. In total, 15 children aged 4 &amp;ndash; 16 (median, 9) years who were evaluated for mTBI at our institution between March 2017 and September 2018 were retrospectively enrolled. All patients underwent computed tomography or magnetic resonance imaging for clinical reasons; moreover, they underwent neurocognitive evaluation within few days from the event (T0), after 3 &amp;ndash; 6 months (T1), and after 18 &amp;ndash; 24 months (T2). Neuropsychological assessment included the Child Behavior Checklist, Developmental Neuropsychology Assessment II Edition, and Wechsler Intelligence Scale for Children. An electronic search was conducted to identify studies published in the past 12 years. Neurocognitive assessments revealed low scores in memory, sensorimotor, and social perception tasks at T1 and T2. Univariate analysis of neuroradiological and clinical findings revealed no risk factors for cognitive deficits. Overall, 17 studies involving 1336 children were reviewed and analyzed. Following mTBI, psychiatric disorders were frequently newly diagnosed and were associated with significant deficits in adaptive functioning and other pre-injury psychosocial risk factors. Our study findings demonstrate that children with mTBI exhibit subtle persistent cognitive difficulties that may affect academic and social functioning. Thus, follow-up using extensive neuropsychological evaluation is essential.

Touchscreen response precision is sensitive to the explore/exploit tradeoff.

The explore/exploit tradeoff is a fundamental property of choice selection during reward-guided decision making. In perceptual decision making, higher certainty decisions are more motorically precise, even when the decision does not require motor accuracy. However, while we can parametrically control uncertainty in perceptual tasks, we do not know what variables - if any - shape motor precision and reflect subjective certainty during reward-guided decision making. Touchscreens are increasingly used across species to measure choice, but provide no tactile feedback on whether an action is precise or not, and therefore provide a valuable opportunity to determine whether actions differ in precision due to explore/exploit state, reward, or individual variables. We find all three of these factors exert independent drives towards increased precision. During exploit states, successive touches to the same choice are closer together than those made in an explore state, consistent with exploit states reflecting higher certainty and/or motor stereotypy in responding. However, exploit decisions might be expected to be rewarded more frequently than explore decisions. We find that exploit choice precision is increased independently of a separate increase in precision due to immediate past reward, suggesting multiple mechanisms regulating choice precision. Finally, we see evidence that male mice in general are less precise in their interactions with the touchscreen than females, even when exploiting a choice. These results suggest that as exploit behavior emerges in reward-guided decision making, individuals become more motorically precise reflecting increased certainty, even when decision choice does not require additional motor accuracy, but this is influenced by individual differences and prior reward. These data uncover the hidden potential for touchscreen tasks in any species to uncover the latent neural states that unite cognition and movement.

Investigating the effect of rTMS over the temporoparietal cortex on the Right Ear Advantage for perceived and imagined voices

A Right Ear Advantage (REA) is well-established in perceptual tasks but it has been found also during imagery. It is ascribed to the left temporoparietal activity for language, and it can be absent/reversed in some clinical conditions including auditory hallucinations. We applied 1-Hz repetitive TMS over TP3/TP4 (left/right language areas) identified through neuronavigation in 18 healthy participants, before administering a modified white noise (WN) speech illusion paradigm: a voice was presented at one ear, at the same or lower intensities with respect to binaural WN. In some trials the voice was not presented, but participants were anyway instructed to report in which ear they believed perceiving it in all trials. Results confirmed the REA both when the voice was present (perceptual REA) and when it was absent (imaginative REA). Interestingly, results suggested that the correct localization of the voice when the stimulus was ambiguous (presented at low intensity and “masked” by WN) was better when TMS was applied over the right/left hemisphere, in male participants with a low/high proneness to unusual experiences (e.g., auditory hallucinations), respectively. This interaction must be further explored to shed light on the relationship between hemispheric asymmetries and auditory hallucinations, in healthy and clinical samples.