Task Representation Research Articles

Psychometric Evaluation of Cognitive and Positive Valence Tasks Chosen for the NIMH Research Domain Criteria Project.

The NIMH Research Domain Criteria (RDoC) initiative seeks to utilize multidimensional patterns of socio-cognitive behavior to improve understanding of mental illness. We aimed to evaluate the psychometric properties of a subset of RDoC tasks. Specifically, we investigated two positive valence tasks and five cognitive tasks. Participants (N = 320) were recruited through an online research platform. We used generalizability theory to estimate reliability, and factor analysis to examine factor structure. Reliability was average to excellent with some notable exceptions. Factor analysis results raised concerns about whether the factor structure of task scores aligns with the proposed RDoC model. Effects of cognitive manipulations generally supported the construct representation of tasks. Results indicate that the majority of RDoC task scores examined have acceptable reliability or can be made reliable through modest increases in task length. Future research in diverse populations is needed to better understand the factor structure of RDoC cognitive and positive valence measures.

Context-Based Meta-Reinforcement Learning With Bayesian Nonparametric Models.

Deep reinforcement learning agents usually need to collect a large number of interactions to solve a single task. In contrast, meta-reinforcement learning (meta-RL) aims to quickly adapt to new tasks using a small amount of experience by leveraging the knowledge from training on a set of similar tasks. State-of-the-art context-based meta-RL algorithms use the context to encode the task information and train a policy conditioned on the inferred latent task encoding. However, most recent works are limited to parametric tasks, where a handful of variables control the full variation in the task distribution, and also failed to work in non-stationary environments due to the few-shot adaptation setting. To address those limitations, we propose MEta-reinforcement Learning with Task Self-discovery (MELTS), which adaptively learns qualitatively different nonparametric tasks and adapts to new tasks in a zero-shot manner. We introduce a novel deep clustering framework (DPMM-VAE) based on an infinite mixture of Gaussians, which combines the Dirichlet process mixture model (DPMM) and the variational autoencoder (VAE), to simultaneously learn task representations and cluster the tasks in a self-adaptive way. Integrating DPMM-VAE into MELTS enables it to adaptively discover the multi-modal structure of the nonparametric task distribution, which previous methods using isotropic Gaussian random variables cannot model. In addition, we propose a zero-shot adaptation mechanism and a recurrence-based context encoding strategy to improve the data efficiency and make our algorithm applicable in non-stationary environments. On various continuous control tasks with both parametric and nonparametric variations, our algorithm produces a more structured and self-adaptive task latent space and also achieves superior sample efficiency and asymptotic performance compared with state-of-the-art meta-RL algorithms.

Speech Perception and Phonological Representation in 5 to 6 Year-Old Typically Developing Children

Objectives: This study aimed to examine the development of speech perception and phonological representation in typically developing children aged 5-6 years. It also explored the relationship between receptive vocabulary, consonant accuracy, nonword repetition performance, and speech perception and phonological representation. Methods: The study involved 77 typically developing children aged 5-6 years. The speech perception identification tasks included minimal pairs that reflected developmental error patterns. The phonological representation judgment tasks involved manipulating consonants and vowels, as well as the number of distinctive features, to examine performance differences under various speech sound manipulation conditions. Results: Performance on both speech perception and phonological representation tasks significantly increased with age. There were also significant differences in performance based on speech sound manipulation conditions. Specifically, in the speech perception task, performance was significantly higher when items focused on the stopping of fricatives rather than the fronting of velars. In the phonological representation task, performance was significantly better in vowel manipulation compared to consonant manipulation, and with two distinctive feature manipulations compared to one. Speech perception tasks showed a significant correlation only with receptive vocabulary, whereas phonological representation tasks showed significant positive correlations with receptive vocabulary, consonant accuracy, and nonword repetition performance. Conclusion: The study confirms that speech perception and phonological representation are developing between ages 5 and 6 in typically developing children. It also suggests that speech perception and phonological representation develop through a complex interplay with speech production abilities and receptive vocabulary.

Thalamocortical contributions to hierarchical cognitive control.

Cognitive flexibility relies on hierarchically structured task representations that organize task contexts, relevant environmental features, and subordinate decisions. Despite ongoing interest in the human thalamus, its role in cognitive control has been understudied. This study explored thalamic representation and thalamocortical interactions that contribute to hierarchical cognitive control in humans. We found that several thalamic nuclei, including the anterior, mediodorsal, ventrolateral, and pulvinar nuclei, exhibited stronger evoked responses when subjects switch between task contexts. Decoding analysis revealed that thalamic activity encodes task contexts within the hierarchical task representations. To determine how thalamocortical interactions contribute to task representations, we developed a thalamocortical functional interaction model to predict task-related cortical representation. This data-driven model outperformed comparison models, particularly in predicting activity patterns in cortical regions that encode context representations. Collectively, our findings highlight the significant contribution of thalamic activity and thalamocortical interactions for contextually guided hierarchical cognitive control.

Parallel maturation of rodent hippocampal memory and CA1 task representations

Hippocampal-dependent memory is known to emerge late in ontogeny, and its full development is protracted. Yet the changes in hippocampal neuronal function that underlie this delayed and gradual maturation remain relatively unexplored. To address this gap, we recorded ensembles of CA1 neurons while charting the development of hippocampal-dependent spatial working memory (WM) in rat pups (∼2-4weeks of age). We found a sharp transition in WM development, with age of inflection varying considerably between individual animals. In parallel with the sudden emergence of WM, hippocampal spatial representations became abruptly task specific, remapping between encoding and retrieval phases of the task. Further, we show how the development of task-phase remapping could partly be explained by changes in place-field size during this developmental period as well as the onset of precise temporal coordination of CA1 excitatory input. Together, these results suggest that a hallmark of hippocampal memory development may be the emergence of contextually specific CA1 representations driven by the maturation of CA1 micro-circuits.

Practice Reshapes the Geometry and Dynamics of Task-tailored Representations.

Extensive practice makes task performance more efficient and precise, leading to automaticity. However, theories of automaticity differ on which levels of task representations (e.g., low-level features, stimulus-response mappings, or high-level conjunctive memories of individual events) change with practice, despite predicting the same pattern of improvement (e.g., power law of practice). To resolve this controversy, we built on recent theoretical advances in understanding computations through neural population dynamics. Specifically, we hypothesized that practice optimizes the neural representational geometry of task representations to minimally separate the highest-level task contingencies needed for successful performance. This involves efficiently reaching conjunctive neural states that integrate task-critical features nonlinearly while abstracting over non-critical dimensions. To test this hypothesis, human participants (n = 40) engaged in extensive practice of a simple, context-dependent action selection task over 3 days while recording EEG. During initial rapid improvement in task performance, representations of the highest-level, context-specific conjunctions of task-features were enhanced as a function of the number of successful episodes. Crucially, only enhancement of these conjunctive representations, and not lower-order representations, predicted the power-law improvement in performance. Simultaneously, over sessions, these conjunctive neural states became more stable earlier in time and more aligned, abstracting over redundant task features, which correlated with offline performance gain in reducing switch costs. Thus, practice optimizes the dynamic representational geometry as task-tailored neural states that minimally tesselate the task space, taming their high-dimensionality.

Learning adaptive shift and task decoupling for discriminative one-step person search

Mainstream person search models aim to jointly optimize person detection and re-identification (ReID) in a one-step manner. Despite notable progress, existing one-step person search models still face three major challenges in extracting discriminative features: 1) incomplete feature extraction and fusion hinder the effective utilization of multiscale information, 2) the models struggle to capture critical features in complex occlusion scenarios, and 3) the optimization objectives of person detection and ReID are in conflict in the shared feature space. To address these issues, this study proposes a novel adaptive shift and task decoupling (ASTD) method that aims to enhance the accuracy and robustness of extracting discriminative features within the region of interest. In particular, we introduce a scale-aware transformer to handle scale/pose variations and occlusions. This transformer incorporates scale-aware modulation to enhance the utilization of multiscale information and adaptive shift augmentation to learn adaptation to occlusions dynamically. In addition, we design a task decoupling mechanism to hierarchically learn independent task representations using orthogonal loss to decouple two subtasks during training. Experimental results show that ASTD achieves state-of-the-art performance on the CUHK-SYSU and PRW datasets. Our code is accessible at https://github.com/zqx951102/ASTD.

Unsupervised multi-view graph representation learning with dual weight-net

Unsupervised multi-view graph representation learning (UMGRL) aims to capture the complex relationships in the multi-view graph without human annotations, so it has been widely applied in real-world applications. However, existing UMGRL methods still face the issues as follows: (i) Previous UMGRL methods tend to overlook the importance of nodes with different influences and the importance of graphs with different relationships, so that they may lose discriminative information in nodes with large influences and graphs with important relationships. (ii) Previous UMGRL methods generally ignore the heterophilic edges in the multi-view graph to possibly introduce noise from different classes into node representations. To address these issues, we propose a novel bi-level optimization UMGRL framework with dual weight-net. Specifically, the lower-level optimizes the parameters of encoders to obtain node representations of different graphs, while the upper-level optimizes the parameters of the dual weight-net to adaptively and dynamically capture the importance of node level, graph level, and edge level, thus obtaining discriminative fused representations for downstream tasks. Moreover, theoretical analysis demonstrates that the proposed method shows a better generalization ability on downstream tasks, compared to previous UMGRL methods. Extensive experimental results verify the effectiveness of the proposed method on public datasets, in terms of different downstream tasks, compared to numerous comparison methods.

UNITA’s Post-War Parliamentary Elite: From a Wartime Defeat to a Nationwide Party in Angola

Angola is governed under a presidential system characterised by a weakly institutionalised parliament ruled by the MPLA liberation movement since independence. UNITA, the largest opposition party in parliament, is also a liberation movement and former belligerent in the protracted Angolan civil war. Despite facing an uneven playing field, UNITA has seen its share of parliamentary seats increase in successive post-war elections. This makes it a relevant case study to understand patterns of elite formation and political representation more broadly. This article examines the composition of UNITA parliamentary elites in Angola in post-civil war legislatures, and how UNITA members of parliament (MPs) perceive representation tasks and engage in constituency-focused activities. We do so through a longitudinal study of post-civil war legislatures (from 2008, 2012, 2017 and 2022), drawing on new biographical data and interviews with MPs. We argue that, following the transition from rebel movement to political party, parliament became an arena in which UNITA was able to achieve a nationwide reach by integrating MPs with diverse backgrounds and by engaging with constituents. Additionally, while institutional rules favour national over local representation, MPs seem to invest in and value links with constituents. The study highlights how nationwide recruitment and connections with constituents contribute to elite formation.

Method for Developing the System Architecture of Existing Industrial Objects for Digital Representation Tasks

This paper presents a method for creating the system architecture of existing industrial objects based on Model-Based Systems Engineering (MBSE) principles. The method aims to form a digital representation of physical objects, which is crucial in the digital transformation of industrial enterprises. It allows for the accurate reflection of all components, processes, functions, and interrelationships within an object. The methodology includes stages of data collection, structuring, development of ontological models, and the integration of a comprehensive system architecture into the digital space. This method was tested using a small hydroelectric power plant, revealing its key advantages and disadvantages and identifying areas for further improvement. The main findings indicate a significant improvement in understanding the system architecture for scenario modeling and digital operation of the objects. Despite challenges such as the need for multiple iterations and high data requirements, the methodology demonstrates the potential for applying MBSE in the digital transformation of existing industrial objects.

MolMVC: Enhancing molecular representations for drug-related tasks through multi-view contrastive learning.

Effective molecular representation is critical in drug development. The complex nature of molecules demands comprehensive multi-view representations, considering 1D, 2D, and 3D aspects, to capture diverse perspectives. Obtaining representations that encompass these varied structures is crucial for a holistic understanding of molecules in drug-related contexts. In this study, we introduce an innovative multi-view contrastive learning framework for molecular representation, denoted as MolMVC. Initially, we use a Transformer encoder to capture 1D sequence information and a Graph Transformer to encode the intricate 2D and 3D structural details of molecules. Our approach incorporates a novel attention-guided augmentation scheme, leveraging prior knowledge to create positive samples tailored to different molecular data views. To align multi-view molecular positive samples effectively in latent space, we introduce an adaptive multi-view contrastive loss (AMCLoss). In particular, we calculate AMCLoss at various levels within the model to effectively capture the hierarchical nature of the molecular information. Eventually, we pre-train the encoders via minimizing AMCLoss to obtain the molecular representation, which can be used for various down-stream tasks. In our experiments, we evaluate the performance of our MolMVC on multiple tasks, including molecular property prediction (MPP), drug-target binding affinity (DTA) prediction and cancer drug response (CDR) prediction. The results demonstrate that the molecular representation learned by our MolMVC can enhance the predictive accuracy on these tasks and also reduce the computational costs. Furthermore, we showcase MolMVC's efficacy in drug repositioning across a spectrum of drug-related applications. The code and pre-trained model are publicly available at https://github.com/Hhhzj-7/MolMVC.

Semantic-Aware Dynamic Generation Networks for Few-Shot Human-Object Interaction Recognition.

Recognizing human-object interaction (HOI) aims at inferring various relationships between actions and objects. Although great progress in HOI has been made, the long-tail problem and combinatorial explosion problem are still practical challenges. To this end, we formulate HOI as a few-shot task to tackle both challenges and design a novel dynamic generation method to address this task. The proposed approach is called semantic-aware dynamic generation networks (SADG-Nets). Specifically, SADG-Net first assigns semantic-aware task representations for different batches of data, which further generates dynamic parameters. It obtains the features that highlight intercategory discriminability and intracategory commonality adaptively. In addition, we also design a dual semantic-aware encoder module (DSAE-Module), that is, verb-aware and noun-aware branches, to yield both action and object prototypes of HOI for each task space, which generalizes to novel combinations by transferring similarities among interactions. Extensive experimental results on two benchmark datasets, that is, humans interacting with common objects (HICO)-FS and trento universal HOI (TUHOI)-FS, illustrate that our SADG-Net achieves superior performance over state-of-the-art approaches, which proves its impressive effectiveness on few-shot HOI recognition.

Reducing age differences in the retrieval of verbatim and gist representations: Encoding manipulations.

Aging causes changes in episodic memory. While the ability to remember the general idea or gist of past episodes is relatively preserved from the effects of aging, the ability to remember the verbatim details of these episodes declines. The aim of the present study was to examine whether age-related differences in the quality of episodic representations could be reduced by manipulations of information encoding. Two experiments were conducted with younger adults (NExp.1 = 32, NExp.2 = 31, 18-27 years old) and older adults (NExp.1 = 31, NExp.2 = 30, 54-81 years old) in which either the retrieval of gist representations of studied items was facilitated by presenting items that could be grouped within the same category (Exp. 1) or the retrieval of verbatim details of studied items was facilitated by presenting the same items repeatedly (Exp. 2). Both manipulations proved effective in increasing retrieval of either gist or verbatim representations in a recognition task in younger and older adults. Increasing gist retrieval improved correct recognition performance for both younger and older adults but also led the latter to make more recognition errors. Increasing verbatim retrieval improved correct recognition performance for both younger and older adults, and the gain was greater for older adults. These findings support the notion that age-related changes in episodic memory can be explained by changes in the specificity of representations and suggest that simple encoding manipulations could improve episodic memory in older adults.

Domain generalization via causal fine-grained feature decomposition and learning

Domain generalization aims to accurately predict unknown data using models trained by known domain data. Learning domain-invariant representations based on causal inference is one of the popular directions in domain generalization. However, existing domain generalization models based on causal inference cannot correctly determine the invariance conditions of the data. It is because of the interdependent relationship between domain-invariant features and confounding factors that ultimately leads to difficulty in obtaining truly invariant representations by the model. In this paper, we propose a novel domain generalization method called causal fine-grained feature decomposition and learning (CFFDL), which aims to eliminate latent confounding factors and learn causal domain-invariant representations in image classification tasks. Specifically, we design a feature decomposition module based on mutual information, which can decompose deep features into fine-grained feature factors and achieve factor independence between different dimensions, thereby helping to eliminate confounding factors. Furthermore, we introduce a causal representations learning module that can effectively filter and extract relevant causal features of the prediction task while eliminating the influence of confounding factors, improving the model’s performance on domain generalization. Extensive experiments on three domain generalization datasets VLCS, PACS and Office–Home show that our method outperforms the current state-of-the-art models, proving its effectiveness and superiority on domain generalization tasks of image classification.

DPGS: Cross-cooperation guided dynamic points generation for scene text spotting

End-to-end text spotting aims to combine scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal role in designing effective spotters. While polygon or segmentation-based methods eliminate heuristic post-processing, they still face challenges such as background noise and high computational burden. In this study, we introduce DPGS, a coarse-to-fine learning framework that lets Dynamic Points Generation for text Spotting. DPGS simultaneously learns character representations for both detection and recognition tasks. Specifically, for each text instance, we represent the character sequence as ordered points and model them with learnable point queries. This approach progressively selects appropriate key points covering character and leverages group attention to associate similar information from different positions, improving detection accuracy. After passing through a single decoder, the point queries encode text semantics and locations, facilitating decoding to central line, boundary, script, and confidence of text through simple prediction heads. Additionally, we introduce an adaptive cooperative criterion to combine more useful feature knowledge, enhancing training efficiency. Extensive experiments show the superiority of our DPGS when handling scene text detection and recognition tasks. Compared to their respective top-1 methods, DPGS has significantly improved the average recognition accuracy by 3.7%, 1.9%, and 0.7% on the Total-Text, ICDAR15, and CTW1500 datasets, respectively.

Enhancing clustering representations with positive proximity and cluster dispersion learning

Contemporary deep clustering approaches often rely on contrastive or non-contrastive techniques to acquire effective representations for clustering tasks. Contrastive methods apply negative pairs to achieve homogenous representations but can introduce class collision problems, potentially compromising clustering performance. In contrast, non-contrastive techniques prevent class collisions but may produce non-uniform representations that lead to clustering collapse. In this work, we propose a novel end-to-end deep clustering approach named PIPCDR, designed to harness the strengths of both approaches while mitigating their limitations. PIPCDR incorporates a positive instance proximity loss and a cluster dispersion regularizer. The positive instance proximity loss ensures alignment between augmented views of instances and their sampled neighbors, enhancing within-cluster compactness by selecting genuinely positive pairs within the embedding space. Meanwhile, the cluster dispersion regularizer maximizes inter-cluster distances while minimizing within-cluster compactness, promoting uniformity in the learned representations. PIPCDR excels in producing well-separated clusters, generating uniform representations, avoiding class collision issues, and enhancing within-cluster compactness. We extensively validate the effectiveness of PIPCDR within an end-to-end Majorize-Minimization framework, demonstrating its competitive performance on moderate-scale clustering benchmark datasets and establishing new state-of-the-art results on large-scale datasets.

Active mutual conjoint estimation of multiple contrast sensitivity functions.

Recent advances in nonparametric contrast sensitivity function (CSF) estimation have yielded a new tradeoff between accuracy and efficiency not available to classical parametric estimators. An additional advantage of this new framework is the ability to independently tune multiple aspects of the estimator to seek further improvements. Machine learning CSF estimation with Gaussian processes allows for design optimization in the kernel, acquisition function, and underlying task representation, to name a few. This article describes a novel kernel for CSF estimation that is more flexible than a kernel based on strictly functional forms. Despite being more flexible, it can result in a more efficient estimator. Further, trial selection for data acquisition that is generalized beyond pure information gain can also improve estimator quality. Finally, introducing latent variable representations underlying general CSF shapes can enable simultaneous estimation of multiple CSFs, such as from different eyes, eccentricities, or luminances. The conditions under which the new procedures perform better than previous nonparametric estimation procedures are presented and quantified.

Inhibition of cued but not executed task sets depends on cue-task compatibility and practice

In task switching, processing a task cue is thought to activate the corresponding task representation (“task set”), thereby allowing for advance task preparation. However, the contribution of preparatory processes to the emergence of n−2 repetition costs as index of task set inhibition processes is debated. The present study investigated whether merely preparing for a task activates a corresponding task set, which needs to be inhibited in order to switch to a different task. To this end, we presented so-called task cue-only trials in trial n−2 and assessed subsequent n−2 repetition costs. The results revealed n−2 repetition costs following a task cue-only, but only for compatible cues with a transparent cue-task relation and only at the beginning of the experiment. In contrast, n−2 repetition costs following task execution in trial n−2 were absent. In a second experiment, we sought to rule out that the presence of n−2 repetition costs following a task cue-only and the corresponding absence following task execution were the consequence of a decay of task sets. This second experiment replicated the result pattern of the first experiment, with n−2 repetition costs following a task cue-only being present only at the beginning of the experiment and only for compatible cues. Hence, cue-induced task set inhibition effects depended on cue-task compatibility and practice. Furthermore, merely prepared task sets were more likely inhibited than executed task sets.

What do voters want from their legislators? evidence from ghana

Abstract Legislators make trade-offs when allocating their time and resources to their multiple tasks of representation, legislation, executive oversight, and constituency service. Furthermore, they must decide how much effort to exert or the balance to strike when undertaking a specific function. Existing research provides limited insights into citizens’ preferences over these officeholder multifaceted decisions in sub-Saharan Africa. I offer novel insights into citizens’ preferences using a conjoint survey experiment of Ghanaians to address this knowledge gap. My findings are threefold. First, I find that citizens put more ‘weight’ on constituency-related activities than parliamentary work. Second, in the constituency, citizens value political representation activities more than constituency services. Third, they weigh public-good-oriented constituency services higher than private ones. The research contributes to our understanding of citizen–legislator accountability relationships in sub-Saharan Africa.

Task switch costs scale with dissimilarity between task rules.

Cognitive flexibility enables humans to voluntarily switch tasks. Task switching requires replacing the previously active task representation with a new one, an operation that typically results in a switch cost. Thus, understanding cognitive flexibility requires understanding how tasks are represented in the brain. We hypothesize that task representations are cognitive map-like, such that the magnitude of the difference between task representations reflects their conceptual differences: The greater the distinction between the two task representations, the more updating is required. This hypothesis predicts that switch costs should increase with between task dissimilarity. To test this hypothesis, we use an experimental design that parametrically manipulates the similarity between task rules. We observe that response time scales with the dissimilarity between the task rules. The findings shed light on the organizational principles of task representations and extend the conventional binary task-switch effect (task repeat vs. switch) to a theoretical framework with parametric task switches. (PsycInfo Database Record (c) 2024 APA, all rights reserved).