Task Domain Research Articles

The role of automation etiquette and task-criticality on performance, workload, automation reliance, and user confidence

Previous research suggests good automation etiquette can yield positive effects on user performance, trust, automation reliance, and user confidence – especially in personified or anthropomorphized technologies. The current study examined the impact of automation etiquette and task-criticality in non-personified technology. The study used a computer-based automation task to examine good and bad automation etiquette models and different domain-based task-criticality levels (between-subjects) that contained various stages of automation (stage 2 and stage 3) and automation reliability levels (60% and 80%) (within-subjects). The study found that bad automation etiquette can increase automation bias and automation reliance which improved performance in the most capable automation condition (stage 3; 80% reliable) but also heightened user subjective workload and decreased user self-confidence. The study also demonstrated task-criticality can be successfully manipulated through domain and instructions. Overall, automation etiquette influenced performance and user confidence more in highly capable automation (stage 3) and in a low-criticality task domain.

Simple actions modulate context-dependent visual size perception at late processing stages

A simple button press towards a prime stimulus enhances subsequent visual search for objects that match the prime. The present study investigated whether this action effect is a general phenomenon across different task domains, and the underlying neural mechanisms. The action effect was measured in an unspeeded size-matching task, with the presentation of the central target and the surrounding inducers of the Ebbinghaus illusion together to one eye or separately to each eye, and when repetitive TMS was applied over right primary motor cortex (M1). The results showed that a prior key-press significantly reduced the illusion effect compared to passive viewing. Notably, the action effect persisted with dichoptic presentation of the Ebbinghaus configuration, but disappeared with the right M1 disruption. These results suggest that action guides visual perception to influence human behavior, which mainly affects the late visual processing stage and probably relies on feedback projections from the motor cortex.

Transfer graph feature alignment guided multi-source domain adaptation network for machinery fault diagnosis

In recent years, the application of unsupervised multi-source domain adaptation (MSDA) techniques for fault diagnosis has gained significant traction. Current research typically overlooks or fails to effectively capture critical data structure information during feature extraction. Another challenge is optimising the integration of information from multiple source domains to diagnose the target domain while avoiding negative transfer. To address these challenges, this study proposes a transfer graph feature alignment guided multi-source domain adaptation network (MDTGAL). In the proposed method, a transfer graph sample generator module (GSG) is constructed to model the data structure between source and target domains, and multiple graph feature extractors are employed to learn the data structure information from different domain combinations. A regularisation technique is introduced to extract the domain-invariant features by aligning the parameters across multiple independent graph feature extractors. In addition, a weighted soft-voting mechanism based on the polynomial kernel-induced maximum mean difference metric (PK-MMD) is designed to fuse the outputs from multiple classifiers, to comprehensively account for the influence of each source domain. The proposed method was tested on multi-source domain transfer tasks involving various operating conditions of rotating machinery. The experimental results demonstrate that the MDTGAL exhibits superior cross-domain diagnostic performance, outperforming existing mainstream methods. In addition, this study explored the impact of varying numbers of source domains on the diagnostic accuracy of the target domain, providing insights into selecting the correct number of source domains for specific MSDA tasks.

LLMOverTab: Tabular data augmentation with language model-driven oversampling

In recent years, Large Language Model (LLM) have seen significant advancements, attracting attention for their applications in various fields. These models have shown promising results in handling tabular data, especially in cases with limited datasets, by leveraging pre-trained knowledge. However, their effectiveness in addressing imbalanced data in tabular formats is less explored. To bridge this gap, our study introduces LLMOverTab, a novel approach using LLMs for oversampling in imbalanced tabular data. We conducted comprehensive experiments on diverse tabular datasets to assess the effectiveness of LLMOverTab, demonstrating its potential in improving the handling of imbalanced data. The study also explores application of LLMOverTab in zero-shot and few-shot learning contexts, providing insights into its adaptability. Additionally, we analyze the oversampled data, offering reflections on the quality of generated samples. Our research not only showcases the utility of LLMOverTab in managing imbalanced tabular data, but also opens new avenues for the application of language models in various tasks of tabular data. This study adds to the increasing interest in applying LLMs to various task domains. It provides new perspectives for the innovative use of LLMs in structured tabular data fields, highlighting their potential in a range of applications.

Discovering the Hidden Work of Commodified Care: The Case of Early Childhood Educators

The COVID-19 pandemic has drawn attention to the care economy, including commodified early childhood education and care (ECEC). While there is some literature about the low paid, invisible, and undervalued skills among the predominantly female workforce in the ECEC sector, there is little research into what these educators do in their working day and how this contributes to quality education and care for young children. This article provides a detailed examination of ten defined domains of ECEC work tasks, derived from data generated by educators’ use of ‘intensive hour’ time-diary methodology. The results reveal that the outstanding characteristics of this occupation are multi-tasking and the rapid switching of tasks as educators manage diverse expectations arising from work with groups of very young children, families, other staff, and meeting legislated responsibilities. Drawing on William J. Baumol’s economic theory, we consider the implications for productivity and cost tensions in ECEC.

Quality of Life Assessment Four Years after a More than Ninety Percent Total Body Surface Area in Two Survivors with Burns: Case Report.

There are few reports in the literature detailing the Quality of Life (QOL) of survivors with greater than 90% total body surface area (TBSA) burns. The objective was to assess two such individuals seen at our center with the Burn Specific Health Scale-Brief (BSHS-B) survey four years after discharge. Subject responses were categorized into their respective BSHS-B health domains (heat sensitivity, affect, hand function, treatment regimens, work, sexuality, interpersonal relationships, simple abilities, and body image) and a raw score was calculated for each domain. Lower scores indicated a greater perceived decrease in QOL. The nine domains of BSHS-B survey questions were further separated into three categories: physical (hand function, work, simple abilities), treatment (treatment regimens and heat sensitivity), and psychosocial (interpersonal relationships, affect, body image, sexuality). The psychosocial category had higher scores and better outcomes, while the physical and treatment ones had lower scores and worse outcomes compared to the other categories. For both individuals, interpersonal relationships and simple tasks domains were considered important in their QOL, while work, heat sensitivity and hand function received lower scores. Family support, intense physical rehabilitation, and psychosocial management were crucial during the postoperative and follow-up periods.

Bias-accounting meta-analyses overcome cerebellar neglect to refine the cerebellar behavioral topography.

The cerebellum plays important roles in motor, cognitive, and emotional behaviors. Previous cerebellar coordinate-based meta-analyses and mappings have attributed different behaviors to cerebellar subareas, but an accurate behavioral topography is lacking. Here, we show overrepresentation of superior activation foci, which may be exacerbated by historical cerebellar neglect. Unequal foci distributions render the null hypothesis of standard activation likelihood estimation unsuitable. Our new method, cerebellum-specific activation-likelihood estimation (C-SALE), finds behavioral convergence beyond baseline activation rates. It does this by testing experimental foci versus null models sampled from a data-driven, biased probability distribution of finding foci at any cerebellar location. Cerebellar mappings were made across five BrainMap task domains and thirty-five subdomains, illustrating improved specificity of the new method. Twelve of forty (sub)domains reached convergence in specific cerebellar subregions, supporting dual motor representations and placing cognition in posterior-lateral regions. Repeated subsampling revealed that whereas action, language and working memory were relatively stable, other behaviors produced unstable meta-analytic maps. Lastly, meta-analytic connectivity modeling in the same debiased framework was used to reveal coactivation networks of cerebellar behavioral clusters. In sum, we created a new method for cerebellar meta-analysis that accounts for data biases and can be flexibly adapted to any part of the brain. Our findings provide a refined understanding of cerebellar involvement in human behaviors, highlighting regions for future investigation in both basic and clinical applications.

Speech Emotion Recognition Using Transfer Learning: Integration of Advanced Speaker Embeddings and Image Recognition Models

Automatic Speech Emotion Recognition (SER) plays a vital role in making human–computer interactions more natural and effective. A significant challenge in SER development is the limited availability of diverse emotional speech datasets, which hinders the application of advanced deep learning models. Transfer learning is a machine learning technique that helps address this issue by utilizing knowledge from pre-trained models to improve performance on a new task in a target domain, even with limited data. This study investigates the use of transfer learning from various pre-trained networks, including speaker embedding models such as d-vector, x-vector, and r-vector, and image classification models like AlexNet, GoogLeNet, SqueezeNet, ResNet-18, and ResNet-50. We also propose enhanced versions of the x-vector and r-vector models incorporating Multi-Head Attention Pooling and Angular Margin Softmax, alongside other architectural improvements. Additionally, reverberation from the Room Impulse Response datasets was added to the speech utterances to diversify and augment the available data. Notably, the enhanced r-vector model achieved classification accuracies of 74.05% Unweighted Accuracy (UA) and 73.68% Weighted Accuracy (WA) on the IEMOCAP dataset, and 80.25% UA and 79.81% WA on the CREMA-D dataset, outperforming the existing state-of-the-art methods. This study shows that using cross-domain transfer learning is beneficial for low-resource emotion recognition. The enhanced models developed in other domains (for non-emotional tasks) can further improve the accuracy of SER.

Real-world estimation taps into basic numeric abilities.

Accurately estimating and assessing real-world quantities (e.g., how long it will take to get to the train station; the calorie content of a meal) is a central skill for adaptive cognition. To date, theoretical and empirical work on the mental resources recruited by real-world estimation has focused primarily on the role of domain knowledge (e.g., knowledge of the metric and distributional properties of objects in a domain). Here we examined the role of basic numeric abilities - specifically, symbolic-number mapping - in real-world estimation. In Experiment 1 ( ) and Experiment 2 ( ), participants first completed a country-population estimation task (a task domain commonly used to study real-world estimation) and then completed a number-line task (an approach commonly used to measure symbolic-number mapping). In both experiments, participants with better performance in the number-line task made more accurate estimates in the estimation task. Moreover, Experiment 2 showed that performance in the number-line task predicts estimation accuracy independently of domain knowledge. Further, in Experiment 2 the association between estimation accuracy and symbolic-number mapping did not depend on whether the number-line task involved small numbers (up to 1000) or large numbers that matched the range of the numbers in the estimation task (up to 100,000,000). Our results show for the first time that basic numeric abilities contribute to the estimation of real-world quantities. We discuss implications for theories of real-world estimation and for interventions aiming to improve people's ability to estimate real-world quantities.

Optimal weighting strategies for maximizing contrast-to-noise ratio in photon counting CT images.

Photon counting detectors (PCDs) with energy discrimination capabilities have the potential to generate grayscale CT images with improved contrast-to-noise ratio (CNR) through optimal weighting of their spectral measurements. This study evaluates the CNR performance of grayscale CT projections and images generated from spectral measurements of PCDs using three energy-weighting strategies: pre-log weighting, post-log weighting, and material decomposition (MD) weighting. This study provides the expressions of optimal weights and maximum achievable CNR of these energy-weighting strategies, which only require the knowledge of detected bin counts and do not require information of PCD energy responses or imaging techniques. We defined and solved a generalized eigenvalue problem to obtain the maximum achievable CNR in the projection domain for low-contrast tasks using three energy-weighting strategies: pre-log weighting (weighted sum of energy bin counts), post-log weighting (weighted sum of line integrals), and MD weighting (weighted sum of basis material thicknesses, which is equivalent to virtual monoenergetic images [VMIs]). These expressions only contain energy bin counts from PCD measurements. We used a realistic PCD energy response model to simulate the detected bin counts and conducted Monte Carlo simulations of different contrast tasks and phantoms to evaluate the projection- and image-domain CNR performance of these energy-weighting strategies. Additionally, the total counts method (a special case of pre-log weighting with unity weights) was included for comparison. We also conducted Gammex head and body phantom scans on an edge-on-irradiated silicon PCCT prototype to evaluate the image-domain CNR performance of these energy-weighting strategies. The results show that pre-log, post-log, and MD weighting strategies generate approximately equal projection-domain maximum achievable CNR, with a difference of less than 2%, and outperform the total counts method. These three energy-weighting strategies also generate approximately equal image-domain maximum CNR when the contrast task is located at the center of a homogeneous phantom. Pre-log weighting generates the highest image-domain CNR for an off-center contrast task location or inhomogeneous phantoms while also outperforming the total counts method. We derived the expression of projection-domain maximum achievable CNR using three energy-weighting strategies. Our results suggest that using pre-log weighting strategies enables fast grayscale CT image generation with high CNR from spectral PCD measurements for inhomogeneous phantoms and off-center region of interests (ROIs).

Foundation models assist in human–robot collaboration assembly

Human–robot collaboration (HRC) is a novel manufacturing paradigm designed to fully leverage the advantage of humans and robots, efficiently and flexibly accomplishing customized manufacturing tasks. However, existing HRC systems lack the transfer and generalization capability for environment perception and task reasoning. These limitations manifest in: (1) current methods rely on specialized models to perceive scenes; and need retraining the model when facing unseen objects. (2) current methods only address predefined tasks, and cannot support undefined task reasoning. To avoid these limitations, this paper proposes a novel HRC approach based on Foundation Models (FMs), including Large Language models (LLMs) and Vision Foundation Models (VFMs). Specifically, a LLMs-based task reasoning method is introduced, utilizing prompt learning to transfer LLMs into the domain of HRC tasks, supporting undefined task reasoning. A VFMs-based scene semantic perception method is proposed, integrating various VFMs to achieve scene perception without training. Finally, a FMs-based HRC system is developed, comprising perception, reasoning, and execution modules for more flexible and generalized HRC. The superior performances of FMs in perception and reasoning are demonstrated by extensive experiments. Furthermore, the feasibility and effectiveness of the FMs-based HRC system are validated through an part assembly case involving a satellite component model.

Understanding factors that impact the acquisition of Independent Living Skills among young people transitioning from Out-of-Home Care

Background and AimsIndependent Living Skills (ILS) are essential to support young people as they enter adulthood. Negative outcomes are consistently observed across a variety of different independent living areas of life for care experienced young people. This study aimed to analyse ILS measure scores across eight ILS domains and overall, completed by young people from Western Australia (WA), to understand how they differ between participants who were still in-care (n = 49) and who had left-care (n = 73), and what factors (such as care experience and personal characteristics) moderate the acquisition of ILS. For this paper, the overarching concept of ILS is defined by young people’s ILS measure scores, where higher self-reported scores are interpreted as greater confidence and competence in ILS (overall and for each of the eight ILS domains). MethodsParticipants (N = 122) aged between 15 and 25 years completed an ILS measure as part of the Navigating Through Life, longitudinal, mixed-method, population-based study. This present study considers data from wave 1 (of 5) of the NTL study, where eight ILS domains were identified: Financial Management, Knowledge of Accessing Available Supports, Managing Housing, Education Planning, Job Seeking, Health Risk Management, Domestic and Self-help Task, and Managing Relationships. This study explored how dependent variables (the ILS overall scores and 8 ILS domain scores) are associated with participants’ care experience and demographic characteristics (the independent variables) via linear regression and an exploratory multivariate moderator analysis. ResultsThe in-care and left-care groups’ ILS measure scores were comparable. Of the independent variables, care status, self-determination, and regionality were significant (p-value=<.05), predicting greater ILS measure scores across the ILS domains and/or overall. Whereas the independent variables of placement stability, longest placement type, gender, taking part in an enhanced leaving care scheme (ELCS), and Aboriginal and Torres Strait Islander status were not significant predictors of higher ILS measure scores. Moderator analyses were conducted for the overall ILS scores, the Health Risk Management, and the Domestic and Self-help Task domain scores (dependent variables). No significant interaction effects were found for care status, self-determination, and regionality, which were identified as significant factors in and of themselves. Discussion and ConclusionThis study emphasises that ILS domains are interlinked, and findings should be utilised to further highlight the significance of the transition to adulthood stage, especially given the similarities in scores for both in-care and left-care groups. Future research could look at transition pathways that prioritise different ILS domains depending on young people’s different needs at different times to support their overall ILS acquisition.

Spectral domain strategies for hyperspectral super-resolution: Transfer learning and channel enhance network

As the network structures continue to innovate and evolve, significant achievements have been achieved in hyperspectral image super-resolution tasks. However, how to further explore the spectral domain potential from prior knowledge and channel-enhanced structures to achieve better performance has inspired the following two works: Firstly, to systematically compare prior knowledge of spectral with spatial domain for HSI-SR tasks, four transfer learning strategies are proposed. The superior performance of the Relevant Channel/Random Space (RCRS) strategy reveals the importance of spectral feature reconstruction in HSI-SR tasks. Meanwhile, an interesting phenomenon has been observed that even without training on real datasets, the model can already exhibit a core or even decent super-resolution capability based solely on prior knowledge of above four strategies. Secondly, a dual-branch channel network with complementary channel feature extraction (CCFE) and adjacent channel feature extraction (ACFE) module is designed for spectral feature enhancement, which demonstrate superior performance compared to state-of-the-art methods on six datasets. To conclude, the effectiveness of RCRS strategy with pseudo prior channel knowledge on seven dual-input and eight single-input networks, as well as superiority of the proposed channel-enhanced network indicate the importance of spectral properties for HSI-SR tasks.

Generalized Encoding of the Relative Subjective Value of Cognitive Effort in the Dorsal ACC.

Making choices about whether and when to engage cognitive effort are a common feature of everyday experience, with important consequences for academic, career, and health outcomes. Yet, despite their hypothesized importance, very little is understood about the underlying mechanisms that support this form of human cost-benefit decision-making. To investigate these mechanisms, we used the Cognitive Effort Discounting Paradigm (Cog-ED) during fMRI scanning to precisely quantify the neural encoding of varying cognitive effort demands relative to reward outcomes, within two distinct cognitive domains (working memory, speech comprehension). The findings provide strong evidence that the dorsal anterior cingulate cortex (dACC) plays a central and selective role in this decision-making process. Trial-by-trial modulations in dACC activation tracked the relative subjective value of the low-effort, low-reward option, with the strongest activity occurring when this was of greater value than the high-effort, high-reward option. In contrast, dACC activity was not modulated by decision difficulty, though such effects were found in other frontoparietal regions. Critically, dACC activity was also strongly correlated across the two decision-making task domains and further predicted subsequent choice behavior in both. Together, the results suggest that dACC activity modulation reflects a domain-general valuation comparison mechanism, which acts to bias participants away from decisions to engage in cognitive effort, when the perceived subjective costs of such engagement outweigh the reward-related benefits. These findings complement work in other cost domains and species by pointing to a clear role of the dACC in representing subjective value differences between choice options during cost-benefit decision-making.

Cross-Modality Medical Image Segmentation via Enhanced Feature Alignment and Cross Pseudo Supervision Learning.

Given the diversity of medical images, traditional image segmentation models face the issue of domain shift. Unsupervised domain adaptation (UDA) methods have emerged as a pivotal strategy for cross modality analysis. These methods typically utilize generative adversarial networks (GANs) for both image-level and feature-level domain adaptation through the transformation and reconstruction of images, assuming the features between domains are well-aligned. However, this assumption falters with significant gaps between different medical image modalities, such as MRI and CT. These gaps hinder the effective training of segmentation networks with cross-modality images and can lead to misleading training guidance and instability. To address these challenges, this paper introduces a novel approach comprising a cross-modality feature alignment sub-network and a cross pseudo supervised dual-stream segmentation sub-network. These components work together to bridge domain discrepancies more effectively and ensure a stable training environment. The feature alignment sub-network is designed for the bidirectional alignment of features between the source and target domains, incorporating a self-attention module to aid in learning structurally consistent and relevant information. The segmentation sub-network leverages an enhanced cross-pseudo-supervised loss to harmonize the output of the two segmentation networks, assessing pseudo-distances between domains to improve the pseudo-label quality and thus enhancing the overall learning efficiency of the framework. This method's success is demonstrated by notable advancements in segmentation precision across target domains for abdomen and brain tasks.

A closer look at how experience, task domain, and self-confidence influence reliance towards algorithms

Prior research has demonstrated experience with a forecasting algorithm decreases reliance behaviors (i.e., the action of relying on the algorithm). However, the influence of model experience on reliance intentions (i.e., an intention or willingness to rely on the algorithm) has not been explored. Additionally, other factors such as self-confidence and domain knowledge are posited to influence algorithm reliance. The objective of this research was to examine how experience with a statistical model, task domain (used car sales, college grade point average (GPA), GitHub pull requests), and self-confidence influence reliance intentions, reliance behaviors, and perceived accuracy of one's own estimates and the model's estimates. Participants (N = 347) were recruited online and completed a forecasting task. Results indicate that there was a statistically significant effect of self-confidence and task domain on reliance intentions, reliance behaviors, and perceived accuracy. However, unlike previous findings, model experience did not significantly influence reliance behavior, nor did it lead to significant changes in reliance intentions or perceived accuracy of oneself or the model. Our data suggest that factors such as task domain and self-confidence influence algorithm use more so than model experience. Individual differences and situational factors should be considered important aspects that influence forecasters' decisions to rely on predictions from a model or to instead use their own estimates, which can lead to sub-optimal performance.

Getting value out of working memory through strategic prioritisation: Implications for storage and control.

Working memory is an active system responsible for the temporary maintenance and processing of information in the support of cognition and action. In keeping with this, a growing body of research has explored the close links between working memory and attention, and how these might be harnessed to impact performance and possibly improve working memory efficiency. This is theoretically and practically important, given that working memory is a central hub in complex cognition yet is extremely capacity- and resource-limited. We review work carried out over the last 10 years or so looking at how high "value" items in working memory can be strategically prioritised through selective attention, drawing principally from visual working memory paradigms with young adult participants, while also discussing how the core effects extend to different task domains and populations. A consistent set of core findings emerges, with improved memory for items that are allocated higher value but no change in overall task performance, and a recency advantage regardless of point allocation when items are encountered sequentially. Value-directed prioritisation is effortful, under top-down strategic control, and appears to vary with perceptual distraction and executive load. It is driven by processes operating during encoding, maintenance, and retrieval, though the extent to which these are influenced by different features of the task context remains to be mapped out. We discuss implications for working memory, attention, and strategic control, and note some possible future directions of travel for this promising line of research.

Exploring different content creation and display methods for remote collaboration supported by eXtended reality: comparative analysis of distinct task scenarios

AbstractRemote collaboration using eXtended Reality (XR) has been explored to establish a common ground between physically distributed individuals. To achieve usable and impactful solutions, it is paramount to understand how different content creation and display methods contribute to the work effort of each member (remote and on-site). Additionally, explore how various task scenarios influence the collaborative process, specifically examining how activities with unique characteristics and complexities affect remote work. Hence, the question arises, ’How do different display and interaction methods impact the collaborative process within specific task domains?’ In this paper, two user studies with 30 distinct participants each are described, focusing on different content creation support (Laptop Computer; Video Wall &amp; Keyboard; Interactive Projector) and display methods (Hand-Held Device (HHD); HHD &amp; Articulated Support; Head-Mounted Display (HMD)) for remote and on-site collaborators respectively, during scenarios of remote guidance. Plus, different physical tasks were considered for each study (Lego pieces assembly; Tangram puzzle assembly; Maintenance procedures; Resource management in a map; Training activity in a laboratory classroom), to understand which method stands out according to the characteristics of the said tasks. We report the results obtained, suggesting that for the remote part of the collaborative process, using a Video Wall &amp; Keyboard appears to be more suitable for tasks associated with maintenance procedures, learning, and training activities. As for the on-site part, using HMD was considered the better option for maintenance, and learning tasks, closely followed by HHD &amp; Articulated Support.

Selective recruitment of the cerebellum evidenced by task-dependent gating of inputs

Functional magnetic resonance imaging (fMRI) studies have documented cerebellar activity across a wide array of tasks. However, the functional contribution of the cerebellum within these task domains remains unclear because cerebellar activity is often studied in isolation. This is problematic, as cerebellar fMRI activity may simply reflect the transmission of neocortical activity through fixed connections. Here, we present a new approach that addresses this problem. Rather than focus on task-dependent activity changes in the cerebellum alone, we ask if neocortical inputs to the cerebellum are gated in a task-dependent manner. We hypothesize that input is upregulated when the cerebellum functionally contributes to a task. We first validated this approach using a finger movement task, where the integrity of the cerebellum has been shown to be essential for the coordination of rapid alternating movements but not for force generation. While both neocortical and cerebellar activity increased with increasing speed and force, the speed-related changes in the cerebellum were larger than predicted by an optimized cortico-cerebellar connectivity model. We then applied the same approach in a cognitive domain, assessing how the cerebellum supports working memory. Enhanced gating was associated with the encoding of items in working memory, but not with the manipulation or retrieval of the items. Focusing on task-dependent gating of neocortical inputs to the cerebellum offers a promising approach for using fMRI to understand the specific contributions of the cerebellum to cognitive function.

Selective recruitment of the cerebellum evidenced by task-dependent gating of inputs.

Functional magnetic resonance imaging (fMRI) studies have documented cerebellar activity across a wide array of tasks. However, the functional contribution of the cerebellum within these task domains remains unclear because cerebellar activity is often studied in isolation. This is problematic, as cerebellar fMRI activity may simply reflect the transmission of neocortical activity through fixed connections. Here, we present a new approach that addresses this problem. Rather than focus on task-dependent activity changes in the cerebellum alone, we ask if neocortical inputs to the cerebellum are gated in a task-dependent manner. We hypothesize that input is upregulated when the cerebellum functionally contributes to a task. We first validated this approach using a finger movement task, where the integrity of the cerebellum has been shown to be essential for the coordination of rapid alternating movements but not for force generation. While both neocortical and cerebellar activity increased with increasing speed and force, the speed-related changes in the cerebellum were larger than predicted by an optimized cortico-cerebellar connectivity model. We then applied the same approach in a cognitive domain, assessing how the cerebellum supports working memory. Enhanced gating was associated with the encoding of items in working memory, but not with the manipulation or retrieval of the items. Focusing on task-dependent gating of neocortical inputs to the cerebellum offers a promising approach for using fMRI to understand the specific contributions of the cerebellum to cognitive function.