Task Characteristics Research Articles

Automatic Selection and Parameter Optimization of Mathematical Models Based on Machine Learning

With the rapid progress of machine learning (ML) technology, more and more ML algorithms have emerged, and the complexity of models is also constantly increasing. This development trend brings two significant challenges in practice: how to choose appropriate algorithm models and how to optimize hyperparameters for these models. In this context, the concept of Automatic Machine Learning (AutoML) has emerged. Due to the applicability of different algorithm models to different data types and problem scenarios, it is crucial to automatically select the most suitable model based on the characteristics of specific tasks. AutoML integrates multiple ML algorithms and automatically filters based on the statistical characteristics of data and task requirements, aiming to provide users with the best model selection solution. Hyperparameters are parameters that ML models need to set before training, such as learning rate, number of iterations, regularization strength, etc., which have a significant impact on the performance of the model. AutoML integrates advanced hyperparameter optimization techniques to automatically find the optimal parameter combination, thereby improving the model's generalization ability and prediction accuracy. This article studies the automatic selection and parameter optimization of mathematical models based on ML.

Difficulty-Generating Features of Text-based Physics Tasks

Influencing the difficulty of performance tasks is of great interest in science education as in several other subjects. In the context of the VAMPS project, difficulty-generating features with respect to the cognitive demand of text-based physics tasks were systematically varied at three levels. Based on preliminary work and two pilot studies presented here briefly, a model was developed by which cognitive requirement was varied according to three features. The viability of this model was empirically tested with a sample of n = 414 secondary school students. The feature <i>cognitive activity</i> proved to be a significant factor influencing the empirically measured difficulty of tasks. With the help of the feature <i>number of information obtained from task stem</i> and <i>number of subject-specific mental procedures</i>, no systematic influence on task difficulty could be shown. The influence of the test persons’ individual prior knowledge on the actual task difficulty is generally assumed to be a confounding factor. Overall, the present study contributes to a better understanding of construct representation in assessments of subject-specific proficiency and empirically confirms that a systematic variation of the task feature <i>cognitive activity</i> on three levels affects task difficulty

Protocol for identifying and characterising critical physical tasks in the military: Development and validation.

When establishing Physical Employment Standards, validity is dependent on the correct identification and characterisation of critical job tasks. To develop and validate a standardised protocol for the identification, characterisation, and documentation of critical physical job tasks in military occupational specialities in the Swedish Armed Forces (SwAF), and propose a definition of critical physical job tasks for use in the SwAF. A protocol was drafted with three content domains, including a preliminary definition. Protocol content validity was iteratively assessed in two consecutive stages where ten subject experts rated relevance and simplicity. A consensus panel revised the protocol after each stage. Content validity index (CVI) was calculated as item-CVI (I-CVI) per each feature and as scale average (S-CVI/Ave) per content domain. Acceptable content validity thresholds were 0.78 and 0.90, respectively. The validated protocol consisted of 35 items with an I-CVI≥0.90 and≥0.80 for relevance and simplicity, respectively. The S-CVI/Ave was 0.97 for relevance and 0.98 for simplicity. The protocol was language reviewed, reorganised for easy use, and approved by the consensus panel. The final protocol includes: background and aim of the protocol, the accepted generic and critical physical job task definitions, protocol instructions, subject expert-qualifications, job task source and characteristics. A standardised protocol for identification and characterisation of critical job tasks in SwAF military occupational specialties was developed. The protocol content was rated relevant and simple by experts and will be of importance in future work establishing physical requirements in the SwAF.

Classification of Motor Imagery EEG signals using high resolution time-frequency representations and convolutional neural network

A Motor Imagery (MI) based Brain Computer Interface (BCI) system aims to provide neuro-rehabilitation for the motor disabled people and patients with brain injuries (e.g., stroke patients) etc. The aim of this work is to classify the left and right hand MI tasks by utilizing the occurrence of event related desynchronization and synchronization (ERD\\ERS) in the Electroencephalogram (EEG) during these tasks. This study proposes to use a set of Complex Morlet Wavelets (CMW) having frequency dependent widths to generate high-resolution time-frequency representations (TFR) of the MI EEG signals present in the channels C3 and C4. A novel method for the selection of the value of number of cycles relative to the center frequency of the CMW is studied here for extracting the MI task features. The generated TFRs are given as input to a Convolutional neural network (CNN) for classifying them into left or right hand MI tasks. The proposed framework attains a classification accuracy of 82.2% on the BCI Competition IV dataset 2a, showing that the TFRs generated in this work give a higher classification accuracy than the baseline methods and other existing algorithms.

Visual-guided scene-aware audio generation method based on hierarchical feature codec and rendering decision

Visually guided spatial sound generation (VGSSG) is a well-suited multimodal learning method for dealing with recorded videos. However, existing methods are difficult to be directly applied to spatial sound generation for movie clips. This is mainly due to (1) the existence of Cinematic Audiovisual Language (CAL) in movies, which makes it difficult to construct spatial sound mapping models directly through data-driven based methods. (2) The problem of the inadequate model performance, which is caused by the excessive heterogeneous gap between audiovisual modal information. To solve the aforementioned problems, we propose a VGSSG method based on CAL decision-making and hierarchical feature coding and decoding, which effectively accomplishes spatial sound generation based on the CAL of movies. Specifically, to solve the problem of CAL modeling, a multimodal information-guided movie audio rendering decision maker is established, which can decide the rendering strategy based on the CAL of the current clip. To narrow the heterogeneous gap that hinders the fusion between audiovisual modal data, we propose a codec structure based on hierarchical fusion of audiovisual features and full-scale skip-connections, which improves the efficiency of the comprehensive utilization of audiovisual modal data, and demonstrates the effectiveness of adopting shallow features in VGSSG task. We integrate both 2-channel and 6-channel spatial audio generation into a unified framework. In addition, we establish a movie audiovisual bimodal dataset with hand-crafted CAL annotations. Experimentally, we demonstrate that compared with the existing methods, our method has higher performance in terms of reducing generation distortion.

CDEST: Class Distinguishability-Enhanced Self-Training Method for Adopting Pre-Trained Models to Downstream Remote Sensing Image Semantic Segmentation

The self-supervised learning (SSL) technique, driven by massive unlabeled data, is expected to be a promising solution for semantic segmentation of remote sensing images (RSIs) with limited labeled data, revolutionizing transfer learning. Traditional ‘local-to-local’ transfer from small, local datasets to another target dataset plays an ever-shrinking role due to RSIs’ diverse distribution shifts. Instead, SSL promotes a ‘global-to-local’ transfer paradigm, in which generalized models pre-trained on arbitrarily large unlabeled datasets are fine-tuned to the target dataset to overcome data distribution shifts. However, the SSL pre-trained models may contain both useful and useless features for the downstream semantic segmentation task, due to the gap between the SSL tasks and the downstream task. To adapt such pre-trained models to semantic segmentation tasks, traditional supervised fine-tuning methods that use only a small number of labeled samples may drop out useful features due to overfitting. The main reason behind this is that supervised fine-tuning aims to map a few training samples from the high-dimensional, sparse image space to the low-dimensional, compact semantic space defined by the downstream labels, resulting in a degradation of the distinguishability. To address the above issues, we propose a class distinguishability-enhanced self-training (CDEST) method to support global-to-local transfer. First, the self-training module in CDEST introduces a semi-supervised learning mechanism to fully utilize the large amount of unlabeled data in the downstream task to increase the size and diversity of the training data, thus alleviating the problem of biased overfitting of the model. Second, the supervised and semi-supervised contrastive learning modules of CDEST can explicitly enhance the class distinguishability of features, helping to preserve the useful features learned from pre-training while adapting to downstream tasks. We evaluate the proposed CDEST method on four RSI semantic segmentation datasets, and our method achieves optimal experimental results on all four datasets compared to supervised fine-tuning as well as three semi-supervised fine-tuning methods.

Elementary Students’ Use of Mechanistic Reasoning to Explain Community-Connected Engineering Design Solutions

Mechanistic reasoning about an artifact or system involves thinking about its underlying entities and the properties, activities, and cause-effect relationships of those entities. Previous studies of children’s mechanistic reasoning about engineering solutions have mostly focused on specific mechanical systems such as gear trains. Yet there is growing interest in more contextualized, community-connected engineering design experiences for elementary students. Important questions remain about how the specific features of community contexts influence student opportunities for engineering design practice and reasoning. In this study, we explore whether comparisons in students’ mechanistic reasoning can be made across a range of five different community design contexts. For this qualitative descriptive study, we focus on interview data collected after each of five community-connected engineering-enriched science curriculum units: accessible playground design (3rd grade, N = 8, district A, schools 1 and 2), displaced animal relocation design (3rd grade, N = 10, district A, school 1), migration stopover site design (4th grade, N = 4, district A, school 2), retaining wall design (4th grade, N = 13, district B, school 1), and water filter design (5th grade, N = 9 students, district A, school 3). The findings showed that all students named entities and described entity factors for the design solutions for all five units. For the playground, displaced animals, and stopover sites units, some students described the design artifacts without explicitly expressing connections between entity factors and/or the way factors linked up to the design performance. We argue that particular features of the design tasks influenced students’ approaches to explaining their design solutions. Therefore, we can claim that comparisons can be made across different community-connected engineering design contexts in terms of children’s mechanistic reasoning.

A novel speech analysis algorithm to detect cognitive impairment in a Spanish population.

Early detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study presents preliminary data on a new screening technology using automated voice recording analysis software in a Spanish population. Data were collected from 174 Spanish-speaking individuals clinically diagnosed as cognitively normal (CN, n = 87) or impaired (mild cognitive impairment [MCI], n = 63; all-cause dementia, n = 24). Participants were recorded performing four common language tasks (Animal fluency, alternating fluency [sports and fruits], phonemic "F" fluency, and Cookie Theft Description). Recordings were processed via text-transcription and digital-signal processing techniques to capture neuropsychological variables and audio characteristics. A training sample of 122 subjects with similar demographics across groups was used to develop an algorithm to detect cognitive impairment. Speech and task features were used to develop five independent machine learning (ML) models to compute scores between 0 and 1, and a final algorithm was constructed using repeated cross-validation. A socio-demographically balanced subset of 52 participants was used to test the algorithm. Analysis of covariance (ANCOVA), covarying for demographic characteristics, was used to predict logistically-transformed algorithm scores. Mean logit algorithm scores were significantly different across groups in the testing sample (p < 0.01). Comparisons of CN with impaired (MCI + dementia) and MCI groups using the final algorithm resulted in an AUC of 0.93/0.90, with overall accuracy of 88.4%/87.5%, sensitivity of 87.5/83.3, and specificity of 89.2/89.2, respectively. Findings provide initial support for the utility of this automated speech analysis algorithm as a screening tool for cognitive impairment in Spanish speakers. Additional study is needed to validate this technology in larger and more diverse clinical populations.

Finding common features in multilingual fake news: a quantitative clustering approach

Abstract Since the Internet is a breeding ground for unconfirmed fake news, its automatic detection and clustering studies have become crucial. Most current studies focus on English texts, and the common features of multilingual fake news are not sufficiently studied. Therefore, this article uses English, Russian, and Chinese as examples and focuses on identifying the common quantitative features of fake news in different languages at the word, sentence, readability, and sentiment levels. These features are then utilized in principal component analysis, K-means clustering, hierarchical clustering, and two-step clustering experiments, which achieved satisfactory results. The common features we proposed play a greater role in achieving automatic cross-lingual clustering than the features proposed in previous studies. Simultaneously, we discovered a trend toward linguistic simplification and economy in fake news. Furthermore, fake news is easier to understand and uses negative emotional expressions in ways that real news does not. Our research provides new reference features for fake news detection tasks and facilitates research into their linguistic characteristics.

Component Identification and Depth Estimation for Structural Images Based on Multi-Scale Task Interaction Network

Component identification and depth estimation are important for detecting the integrity of post-disaster structures. However, traditional manual methods might be time-consuming, labor-intensive, and influenced by subjective judgments of inspectors. Deep-learning-based image visual inspection is a new approach to overcome these problems, but repeated modeling is required for different inspection tasks, which limits inspection accuracy and practical deployment efficiency. In this study, it is observed that the matched ratios of pixel pairs between component identification and depth estimation reach a high value, which indicates the dual tasks are highly related. Therefore, the Multi-Scale Task Interaction Network (MTI-Net) is proposed for structural images to simultaneously accomplish both tasks for accurate and efficient structural inspection. It propagates distilled task information from lower to higher scales. Then, it aggregates the refined task features from all scales to produce the final per-task predictions. Experiments show that MTI-Net delivers the full potential of multi-task learning, with a smaller memory footprint and higher efficiency compared to single-task learning. For the evaluation metrics of model performance, the mean Intersection over Union (mIoU) of component identification improves by 2.30, and root mean square error (RMSE) drops by 0.36 m with the aid of the multi-task strategy. The multi-task deep learning framework has great potential value in engineering applications.

Interpreting Tangled Program Graphs Under Partially Observable Dota 2 Invoker Tasks

Interpretable learning agents directly construct models that provide insight into the relationships learnt. Moreover, to date, there has been a lot of emphasis on interpreting reactive models developed for supervised learning tasks. In this work, we consider the case of models developed to address a suite of 6 partially observable tasks defined in the Dota 2 Online Battle Arena game engine. This means that learning agents need to make decisions based on the previous state as developed by the learning agent's memory; in addition to a 310-dimensional state vector provided by the game engine. Interpretability is addressed by adopting the tangled program graph approach to developing learning agents. Thus, decision-making is explicitly divide-and-conquer, with different parts of the resulting graph visited depending on the task context. We demonstrate that programs comprising the tangled program graph approach self-organize such that: (1) small subsets of task features are identified to define conditions under which index memory is written, and; (2) the subset of programs responsible for defining actions typically query indexed memory rather than task features. Particular preferences emerge for different tasks; thus, the blocking (or evasion) tasks result in a preference for specific actions whereas more open-ended tasks assume policies based on combinations of behaviours. In short, the ability to evolve the topology of the learning agent provides insights into how the policies are being constructed for addressing partially observable tasks.

A deep-learning-based compact method for accelerating the electrowetting lattice Boltzmann simulations

Research on the electrowetting of micro- and nanoscale droplets is essential for microfluidics and nanomaterials applications. A lattice-Boltzmann-electrostatics (LBES) method is an effective and accurate method for simulating this process. However, the electric potential field in each time step requires numerous iterative calculations to converge. Therefore, there is a trade-off dilemma between using high-density lattice fields to improve simulation refinement and low-density lattice fields to reduce computing costs in simulations. Fortunately, deep learning techniques can enhance the computing efficiency of electric potential fields, providing an efficient and accurate solution for electrowetting studies in fine-grained fields. In this study, a compact LBES (C-LBES), a computationally accelerated model for an electric potential field with spatiotemporal prediction capability, is developed by combining the advantages of a recurrent residual convolutional unit and a convolutional long-short-term memory unit. A loss function incorporating a geometric boundary constraint term and a self-cyclic prediction scheme are introduced according to the characteristics of the prediction task, which further improves the prediction accuracy of the model and the computing efficiency of the electric potential field. The model is validated with small datasets, and the results show that the C-LBES model with the self-cyclic prediction scheme improves the computing efficiency of the conventional LBES method by a factor of 10 and provides high-precision results when predicting a two-dimensional convergent electric potential field with a lattice size of (110, 160). In the generalization experiments, the average absolute error of the calculated results remains in the same order of magnitude as the accuracy experimental results.

Accessibility of working memory representations in the focus of attention: Heightened or reduced?

Many researchers agree that information residing in the focus of attention in working memory benefits from a boost in memory strength and activation, as well as heightened accessibility. However, recent studies have questioned this heightened accessibility. More specifically, these recent studies found reduced accessibility for an item in the focus of attention compared to another item in working memory, which was referred to as an "inhibition-of-return-like" effect. Our study aimed to provide a detailed examination of the accessibility of information in the focus of attention. Across a series of experiments, varying task characteristics related to the time course of the effect (Experiments 2-3) and the potential role of response inhibition (Experiments 4a-4b), we repeatedly failed to find evidence for an inhibition-of-return-like effect. Instead, we mostly found heightened accessibility for an item in the focus of attention. Given that an inhibition-of-return-like effect seems to be limited to a very specific task condition, reduced accessibility of information in the focus of attention appears to be far from a general phenomenon. Therefore, based on our findings, we propose that information in the focus of attention generally benefits from heightened accessibility, although there may be factors, most likely unrelated to the functioning of the focus of attention, that could sometimes mask this and even result in data patterns that are consistent with reduced accessibility. The theoretical implications for the focus of attention and working memory are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

재택근무가 업무 생산성에 미치는 영향과 업무 특성의 조절 효과

Purpose - This study aims to empirically identify the quantitative effects of work from home (WFH) on employee productivity using field data. Design/methodology/approach - Based on large-scale field data from a South Korean company which introduced the WFH arrangement in 2020, we conducted fixed effect and moderating effect analyses using individual-level panel data over sixty-three weeks. Findings - The empirical analysis generated several findings. It was found that overall, WFH has a positive effect on productivity. However, the productivity impact of WFH was found to vary depending on task characteristics. Specifically, WFH led to over 20% increase in productivity for simple and repetitive tasks. On the other hand, no significant productivity impact was observed for professional and knowledge-based tasks. Research implications or Originality - As the first study based on field data from South Korea, this study offers convincing causal evidence of the moderating impact of task characteristics on the relationship between WFH and productivity. Further, the above findings provide managers with practical insights concerning their work arrangement decisions.

How to Design Reinforcement Learning Methods for the Edge: An Integrated Approach toward Intelligent Decision Making

Extensive research has been carried out on reinforcement learning methods. The core idea of reinforcement learning is to learn methods by means of trial and error, and it has been successfully applied to robotics, autonomous driving, gaming, healthcare, resource management, and other fields. However, when building reinforcement learning solutions at the edge, not only are there the challenges of data-hungry and insufficient computational resources but also there is the difficulty of a single reinforcement learning method to meet the requirements of the model in terms of efficiency, generalization, robustness, and so on. These solutions rely on expert knowledge for the design of edge-side integrated reinforcement learning methods, and they lack high-level system architecture design to support their wider generalization and application. Therefore, in this paper, instead of surveying reinforcement learning systems, we survey the most commonly used options for each part of the architecture from the point of view of integrated application. We present the characteristics of traditional reinforcement learning in several aspects and design a corresponding integration framework based on them. In this process, we show a complete primer on the design of reinforcement learning architectures while also demonstrating the flexibility of the various parts of the architecture to be adapted to the characteristics of different edge tasks. Overall, reinforcement learning has become an important tool in intelligent decision making, but it still faces many challenges in the practical application in edge computing. The aim of this paper is to provide researchers and practitioners with a new, integrated perspective to better understand and apply reinforcement learning in edge decision-making tasks.

Metaheuristic optimized time–frequency features for enhancing Alzheimer’s disease identification

Background:Alzheimer’s disease (AD) is a chronic disorder characterized by progressive cognitive dysfunctions and memory loss. Electroencephalography (EEG) is a non-invasive tool to detect AD using machine learning models and signal-derived features. Feature selection enhances AD detection performance, reduces training and testing time, and builds simpler models. Metaheuristic algorithms (MHAs) have successfully selected optimum features in several classification tasks and can be valuable in enhancing AD detection. Method:In this work, an AD detection model is proposed based on a systematic investigation of adaptive signal decomposition techniques and MHAs. The four techniques comprising empirical mode decomposition, variational mode decomposition, discrete wavelet transform, and Low-Complexity Orthogonal Wavelet Filter Banks (LCOWFBs) decompose EEG signals into subbands. Thirty-four features based on signal, temporal, spectral, and entropy analysis are extracted for each subband. The salient features are selected using seven different MHAs: particle swarm algorithm, salp swarm algorithm, reptile search (RSA), Grey Wolf, dwarf mongoose optimization, snake optimizer, and Fick’s law algorithm (FLA). Results:The two publicly available AD EEG datasets validated the performance of the proposed framework. LCOWFBs achieved the highest binary classification accuracy of 99.72% using 15 salient features selected by RSA and multi-class accuracy of 88.92% with seven salient features selected by FLA. The present method investigates brain regions that are affected by AD. Conclusion:The results indicate that the combination of LCOWFBs and RSA achieved maximum AD detection performance for both datasets with shorter computational time than earlier reported methods in the literature. The present model can be extended to identify chronic disorders, including hypertension, sleep disorders, and Parkinson’s disease.

A Study on the Design and Implementation Technologies of EVA at the China Space Station

Extravehicular activity (EVA) is a key point and a difficult point for manned spaceflight tasks, as well as an inevitable trend in the development of the manned spaceflight industry. Equipment maintenance, load installation, and extravehicular routing inspection via EVA on the track are necessary to guarantee the safety and reliability of the long-term in-orbit operation of the China Space Station. In this paper, a comprehensive analysis was conducted on the features of multiple tasks, diverse working modes, and strong systematic coupling during the EVA of the China Space Station (CSS). On this basis, the design, implementation technologies’ development, and in-orbit performance evaluation during EVA were expounded. In the space station system, an extravehicular reliability verification and evaluation system suitable for the requirement for EVA under the conditions of China’s multi-mission, multi-module combination, and repairable spacecraft was constructed. Finally, the in-orbit EVA implementation of the China Space Station since the launch of the core module to the present was summarized, and the subsequent application of the extravehicular technologies in manned lunar landing projects and optical modules was anticipated.

Customized Landing Task for ACL Injury Risk Assessment: Kinematic Sex-Related Differences.

Women present a higher anterior cruciate ligament (ACL) injury rate than men, suggesting sex-related biomechanical differences. Task characteristics are often fixed for both sexes, possibly affecting the perceived difficulty. We investigated kinematic sex differences across landing tasks for ACL injury risk assessment, adjusted to participants' anthropometrics/performance, and whether different tasks affect kinematic sex comparisons. Female subjects would exhibit motion patterns more associated with ACL injury risk, and sex-related differences may depend on task type. Descriptive laboratory study. Level 3. A total of 27 female and 29 male amateur players (18-30 years) executed horizontal hop, drop jump (DJ), and DJ followed by vertical or forward jump (length, proportional to maximal forward jump; box, 20% participant's height). An optoelectronic system provided lower limb kinematics at initial contact and peaks until maximum knee flexion (KF), analyzed separately by multivariate analysis of variance (MANOVA) (α = 0.05). At initial contact, the interaction term had significant effects on hip adduction (P < 0.01) and knee abduction (KAb) (P = 0.04); female participants demonstrated higher KAb (P < 0.01) and knee internal rotation (P = 0.05). For peaks analysis, the interaction term had no significant effects on any individual variable, although significant in MANOVA; female participants had higher KAb (P = 0.01) and lower KF (P = 0.04). Task type affected hip flexion and knee angles in both analyses. All variables in which significant sex-related differences were found are potential ACL injury risk factors, and all findings indicate that the analyzed female sample exhibited higher injury-related patterns. Although customized, male and female participants showed different landing strategies depending on the task. The findings underline how female participants adopted potentially harmful kinematics while executing customized landing tasks (adjusted by subject's anthropometrics/performance), which may enhance risk of ACL injury.

Precarious employment and mental health in the Belgian service voucher system: the role of working conditions and perceived financial strain.

Jobs in domestic cleaning are often conceived as 'precarious employment' (PE)-i.e. a multidimensional concept referring to accumulated adverse characteristics of employment due to workers' weak bargaining position. Against this background, the Belgian service voucher system (SVS) was implemented aimed at creating formal and stable, subsidized domestic services jobs. The current study assesses the relationship between PE and mental health (WHO5) in the Belgian SVS, accounting for the potential mediating role of working conditions and perceived financial strain at the household level. We analysed a cross-sectional sample of 1,115 Belgian SVS domestic cleaners, collected in 2019 through an online survey. A mediation model was estimated. The crude effect of PE on adverse mental health was strong (ß 0.545-S.E. 0.063). However, 50% of the association between PE and mental well-being was mediated by work task characteristics (quantitative demands, physical demands, task variation and autonomy) and 25% by household-level perceived financial strain. The remaining direct effect of PE on adverse mental well-being is ß 0.066 (S.E. 0.032-25% of the total effect). These findings are the first based on the Belgian Employment Precariousness Scale (EPRES-BE) and are consistent with earlier-made-but seldom simultaneously tested-assumptions on the mechanisms relating PE to adverse mental health-i.e. involving direct associations and indirect associations via adverse working conditions and material deprivation. Based on the results, we recommend more democratic and higher-quality management practices in the SVS, in addition to higher wages and working time reduction.

Variable, sometimes absent, but never negative: Applying multilevel models of variability to the backward crosstalk effect to find theoretical constraints

When performing two tasks at the same time, the congruency of the second task's features influences performance in the first task. This is called the backward crosstalk effect (BCE), a phenomenon that influences both theories of binding and of dual-task capacity limitations. The question of whether the BCE is found in all participants at all times is relevant for understanding the basis of the effect. For example, if the BCE is based on strategic choices, it can be variable, but if it is automatic and involuntary, it should never vary in whether it is present or not. Variability in observed BCE sizes was already documented and discussed when the group average effect was first reported (Hommel, 1998). Yet the theories discussed at the time did not motivate a more direct analysis of this variability, nor did the readily available statistical tools permit it.Some statistical approaches recently applied in cognitive psychology allow such a variability-focused analysis and some more recent theoretical debates would benefit from this as well. We assessed the variability of the BCE as well as a BCE-like free-choice congruency effect by applying a Bayesian multilevel modeling approach to the data from a dual-tasking experiment. Trials consisted of a two- and a four-response task. We manipulated which task was presented first and whether the response to the four-choice task was free or forced choice.RT data were best predicted by a model in which the BCE is zero in part of the population and drawn from a normal distribution truncated at zero (and thus always positive) in the rest of the population. Choice congruency bias data were best predicted by a model assuming this effect to be drawn from a normal distribution truncated at zero (but, in contrast to the RT data, without the subset of the population where it is zero).The BCE is not an inflexible and universal phenomenon that is directly linked to an inherent structural trait of human cognition. We discuss theoretical constraints implied by these results with a focus on what we can infer about the traits of the factors that influence BCE size. We suggest that future research might add further major constraints by using multi-session experiments to distinguish between-person and within-person variability. Our results show that the BCE is variable. The next step is understanding along which axes it is variable and why it varies.