Search for: All records

Abstract Previous studies have demonstrated that human participants can keep track of the magnitude and direction of their trial-to-trial errors in temporal, spatial, and numerical estimates, collectively referred to as “metric error monitoring.” These studies investigated metric error monitoring in an explicit timing/counting context. However, many of our judgments may also depend on temporal mismatches between stimuli where the temporal information is not processed explicitly, which eventually brings about the simultaneity perception. We investigated whether participants can monitor errors in their simultaneity perception. We tested participants in temporal orer judgment (TOJ) task, where they judged which of the two consecutive stimuli (one on each side of the screen) appeared first and reported their confidence rating for each TOJ. The results of all four experiments showed that the confidence judgements for correct judgments increased and for incorrect judgments decreased with longer absolute SOA. A more granular analysis showed that participants could only monitor their errors for left-first and bottom-first judgments, which suggests a metacognitive spatial–temporal association of response codes (STEARC) effect. 

Abstract Recent behavioral studies have shown that humans possess self-awareness of their individual timing ability in that they can discern the direction of their timing error. However, in these studies which included a single repeat (re-do) trial for each duration, it remains unclear whether the reduction in errors in the re-do trials was due to self-awareness of individual timing ability or because the participants used the feedback from the initial trials to improve on the re-do ones. To investigate this further, we conducted a behavioral study in which subjects were divided into two groups: one in which the “re-do” phase occurred frequently, but not always (80% of trials; called the “high-double” group), and one in which re-do trials were rare (20% of trials; called the “low-double” group). This was done to test the possibility of subjects relying on the re-do trials as a method of improvement. Subjects significantly improved in their performance on re-do trials regardless of whether re-dos were rare or frequent. Further, an unexpected finding was observed, where subjects in the low-double group also overall performed better than those in the high-double group. This finding suggests that subjects, knowing that re-do opportunities were rare, engaged better timing at the outset; yet these subjects still improved on re-do trials, suggesting humans are able to incorporate both global uncertainty and feedback. 

Abstract A longstanding issue concerns the extent to which episodic autobiographical memory (EAM) and episodic future thinking (EFT) are the expression of the same cognitive ability and may be dissociated at the neural level. Here, we provided an updated picture of overlaps and dissociations between brain networks supporting EAM and EFT, using Activation Likelihood Estimation. Moreover, we tested the hypothesis that spatial gradients characterize the transition between activations associated with the two domains, in line with accounts positing a transition in the relative predominance of their features and process components. We showed the involvement of a core network across EAM and EFT, including midline structures, the bilateral hippocampus/parahippocampus, angular gyrus and anterior middle temporal gyrus (aMTG) and the left superior frontal gyrus (SFG). Contrast analyses highlighted a cluster in the right aMTG significantly more activated during EFT compared with EAM. Finally, gradiental transitions were found in the ventromedial prefrontal cortex, left SFG, and bilateral aMTG. Results show that differences between EAM and EFT may arise at least partially through the organization of specific regions of common activation along functional gradients, and help to advocate between different theoretical accounts. 

Abstract Successful behavioral adaptation requires an ongoing assessment of rewarding outcomes based on one’s current state. A frontocentral ERP associated with reward feedback, the reward positivity (RewP), has been linked to reflect information about reward value and motivational states. It is, however, unclear if changes in the RewP are influenced by changes in reward value as a function of motivational state. To examine this, hungry participants (n= 31) completed two rounds of a modified Doors Task incorporating Pavlovian conditioning during EEG recordings and obtained feedback associated with sweet and savory food reinforcers equally matched in pleasantness and desirability. Participants underwent reinforcer devaluation, a paradigm designed to isolate inference-based behavior based on decreasing reward value, in between rounds by eating one of the foods to satiety. Prior to devaluation, participants were hungry and rated both food reinforcers equally pleasant. After devaluation, participants were sated and rated the devalued food, but not the non-devalued food, significantly less pleasant, suggesting a sensory-specific change in reward value. Logistic regression of win-stay/lose-switch behavior during the Doors Task shows participants made sensory-specific adjustments in food preferences during postdevaluation. Nonparametric permutation tests based on the tmax statistic performed revealed no significant differences in RewP amplitudes, suggesting the RewP is insensitive to reinforcer devaluation. This could not be explained by differences in perceived pleasantness or desirability. These findings suggest that affective and motivational factors such as tracking inferences based on decreases in reward value did not modulate the RewP. 

Abstract ObjectiveThe use of electronic health records (EHRs) for clinical risk prediction is on the rise. However, in many practical settings, the limited availability of task-specific EHR data can restrict the application of standard machine learning pipelines. In this study, we investigate the potential of leveraging language models (LMs) as a means to incorporate supplementary domain knowledge for improving the performance of various EHR-based risk prediction tasks. MethodsWe propose two novel LM-based methods, namely “LLaMA2-EHR” and “Sent-e-Med.” Our focus is on utilizing the textual descriptions within structured EHRs to make risk predictions about future diagnoses. We conduct a comprehensive comparison with previous approaches across various data types and sizes. ResultsExperiments across 6 different methods and 3 separate risk prediction tasks reveal that employing LMs to represent structured EHRs, such as diagnostic histories, results in significant performance improvements when evaluated using standard metrics such as area under the receiver operating characteristic (ROC) curve and precision-recall (PR) curve. Additionally, they offer benefits such as few-shot learning, the ability to handle previously unseen medical concepts, and adaptability to various medical vocabularies. However, it is noteworthy that outcomes may exhibit sensitivity to a specific prompt. ConclusionLMs encompass extensive embedded knowledge, making them valuable for the analysis of EHRs in the context of risk prediction. Nevertheless, it is important to exercise caution in their application, as ongoing safety concerns related to LMs persist and require continuous consideration. 

ABSTRACT Problem StatementDespite the recent advancements in technology, many individuals with upper-limb loss struggle to achieve stable control over multiple degrees of freedom in a prosthesis. There is an ongoing need to develop noninvasive prosthesis control modalities that could improve functional patient outcomes. Proposed SolutionUltrasound-based sensing of muscle deformation, known as sonomyography, is an emerging sensing modality for upper-limb prosthesis control with the potential to significantly improve functionality. Sonomyography enables spatiotemporal characterization of both superficial and deep muscle activity, making it possible to distinguish the contributions of individual muscles during functional movements and derive a large set of independent prosthesis control signals. Using sonomyography to control a prosthesis has shown great promise in the research literature but has not yet been fully adapted for clinical use. This article describes the implementation of sonomyography for upper-limb prosthesis control, ongoing technological development, considerations for deploying this technology in clinical settings, and recommendations for future study. Clinical RelevanceSonomyography may soon become a clinically viable modality for upper-limb prosthesis control that could offer prosthetists an additional solution when selecting optimal treatment plans for their patients. 

Abstract Should your department offer a course on how to be a scientist and a successful graduate student? We offer this course at George Mason University as a mandatory part of the graduate curriculum, but this is not common practice for graduate biomedical engineering programs. Graduate education in biomedical engineering is evolving rapidly, with an increasing demand for fundamental research skills, interdisciplinary skills, and professional development. We believe that graduate students will be more successful in their research activities if they are explicitly taught these skills at the beginning of their graduate coursework. This paper describes the design of this course in our bioengineering department. 

Abstract In this study, we ran a meta-analysis of neuroimaging studies to pinpoint the neural regions that are commonly activated across space, time, and numerosity, and we tested the existence of gradient transitions among these magnitude representations in the brain. Following PRISMA guidelines, we included in the meta-analysis 112 experiments (for space domain), 114 experiments (time domain), and 115 experiments (numerosity domain), and we used the activation likelihood estimation method. We found a system of brain regions that was commonly recruited in all the three magnitudes, which included bilateral insula, the supplementary motor area (SMA), the right inferior frontal gyrus, and bilateral intraparietal sulci. Gradiental transitions between different magnitudes were found along all these regions but insulae, with space and numbers leading to gradients mainly over parietal regions (and SMA) whereas time and numbers mainly over frontal regions. These findings provide evidence for the GradiATOM theory (Gradient Theory of Magnitude), suggesting that spatial proximity given by overlapping activations and gradients is a key aspect for efficient interactions and integrations among magnitudes. 

Abstract To navigate through the environment, humans must be able to measure both the distance traveled in space, and the interval elapsed in time. Yet, how the brain holds both of these metrics simultaneously is less well known. One possibility is that participants measure how far and how long they have traveled relative to a known reference point. To measure this, we had human participants (n = 24) perform a distance estimation task in a virtual environment in which they were cued to attend to either the spatial or temporal interval traveled while responses were measured with multiband fMRI. We observed that both dimensions evoked similar frontoparietal networks, yet with a striking rostrocaudal dissociation between temporal and spatial estimation. Multivariate classifiers trained on each dimension were further able to predict the temporal or spatial interval traveled, with centers of activation within the SMA and retrosplenial cortex for time and space, respectively. Furthermore, a cross-classification approach revealed the right supramarginal gyrus and occipital place area as regions capable of decoding the general magnitude of the traveled distance. Altogether, our findings suggest the brain uses separate systems for tracking spatial and temporal distances, which are combined together along with dimension-nonspecific estimates. 

Abstract There have been significant advances in biosignal extraction techniques to drive external biomechatronic devices or to use as inputs to sophisticated human machine interfaces. The control signals are typically derived from biological signals such as myoelectric measurements made either from the surface of the skin or subcutaneously. Other biosignal sensing modalities are emerging. With improvements in sensing modalities and control algorithms, it is becoming possible to robustly control the target position of an end-effector. It remains largely unknown to what extent these improvements can lead to naturalistic human-like movement. In this paper, we sought to answer this question. We utilized a sensing paradigm called sonomyography based on continuous ultrasound imaging of forearm muscles. Unlike myoelectric control strategies which measure electrical activation and use the extracted signals to determine the velocity of an end-effector; sonomyography measures muscle deformation directly with ultrasound and uses the extracted signals to proportionally control the position of an end-effector. Previously, we showed that users were able to accurately and precisely perform a virtual target acquisition task using sonomyography. In this work, we investigate the time course of the control trajectories derived from sonomyography. We show that the time course of the sonomyography-derived trajectories that users take to reach virtual targets reflect the trajectories shown to be typical for kinematic characteristics observed in biological limbs. Specifically, during a target acquisition task, the velocity profiles followed a minimum jerk trajectory shown for point-to-point arm reaching movements, with similar time to target. In addition, the trajectories based on ultrasound imaging result in a systematic delay and scaling of peak movement velocity as the movement distance increased. We believe this is the first evaluation of similarities in control policies in coordinated movements in jointed limbs, and those based on position control signals extracted at the individual muscle level. These results have strong implications for the future development of control paradigms for assistive technologies.