Task analysis

About: Task analysis is a research topic. Over the lifetime, 10432 publications have been published within this topic receiving 283481 citations.

EXPERIMENTAL DESIGNS IN SENTENCE PROCESSING RESEARCH: A Methodological Review and User’s Guide

Abstract: Since the publication of Clahsen and Felser’s (2006) keynote article on grammatical processing in language learners, the online study of sentence comprehension in adult second language (L2) learners has quickly grown into a vibrant and prolific subfield of SLA As online methods begin to establish a foothold in SLA research, it is important that researchers in our field design sentence-comprehension experiments that adhere to the fundamental principles of research design typical of sentence processing studies published in related subfields of the language sciences In this article, we discuss and review widely accepted principles of research design for sentence processing studies that are not always followed in L2 sentence processing research Particular emphasis is placed on the design of experimental items and distractors, the choice and design of the poststimulus distractor task, procedures for presenting stimuli to participants, and methods for trimming and analyzing online data, among others

Development of Speed and Accuracy in Pragmatic Comprehension in English as a Foreign Language

Abstract: This study examined development of pragmatic comprehension ability across time. Twenty native speakers and 92 Japanese college students of English completed a computerized listening task measuring ability to comprehend two types of implied meaning in dialogues: indirect refusals (k = 24) and indirect opinions (k = 24). The participants' comprehension was analyzed for accuracy (scores on the listening task) and comprehension speed (average time taken to answer each item correctly). L2 learners' accuracy and comprehension speed improved significantly over a 7-week period. However, the magnitude of effect was lower for comprehension speed than for accuracy. This study also examined the relationships among general L2 proficiency (measured on the ITP TOEFL), speed of lexical judgment (measured on a word recognition task), and pragmatic comprehension ability. There was a significant relationship between proficiency and accuracy (r = 0.39), as well as between lexical access speed and comprehension speed (r = 0.40). However, L2 proficiency bore no relationship to comprehension speed, and lexical access speed had no relationship with accuracy. Moreover, accuracy and comprehension speed were not related to each other. These findings suggest that development of pragmatic knowledge and processing capacity of using the knowledge may not coincide perfectly in L2 development.

A Complete Methodology for Generating Multi-Robot Task Solutions using ASyMTRe-D and Market-Based Task Allocation

Abstract: This paper presents an approach that enables heterogeneous robots to automatically form groups as needed to generate both strongly-cooperative and weakly-cooperative multi-robot task solutions in the same application. The fundamental contribution of this work is the layering of our low-level coalition formation algorithm for generating strongly-cooperative task solutions, with high-level, traditional task allocation methods for weakly-cooperative task solutions. At the low level, coalitions that generate strongly-cooperative multi-robot task solutions are formed using our ASyMTRe-D approach that maps environmental sensors and perceptual and motor schemas to the required flow of information in the robot team, automatically reconfiguring the connections of schemas within and across robots to form efficient solutions. At the high level, a traditional task allocation approach is used to enable individual robots and/or coalitions to compete for weakly-cooperative task assignments through task allocation. We introduce the site clearing task to motivate the work, and then formalize the problem. We then present the approach of layering ASyMTRe-D with task allocation. We validate the approach on a team of robots with the site clearing task. We believe the resulting approach is a flexible system that can handle a broad range of realistic multi-robot applications beyond what is possible using other existing approaches.

Domain Adversarial for Acoustic Emotion Recognition

Abstract: The performance of speech emotion recognition is affected by the differences in data distributions between train (source domain) and test (target domain) sets used to build and evaluate the models. This is a common problem, as multiple studies have shown that the performance of emotional classifiers drops when they are exposed to data that do not match the distribution used to build the emotion classifiers. The difference in data distributions becomes very clear when the training and testing data come from different domains, causing a large performance gap between development and testing performance. Due to the high cost of annotating new data and the abundance of unlabeled data, it is crucial to extract as much useful information as possible from the available unlabeled data. This study looks into the use of adversarial multitask training to extract a common representation between train and test domains. The primary task is to predict emotional-attribute-based descriptors for arousal, valence, or dominance. The secondary task is to learn a common representation, where the train and test domains cannot be distinguished. By using a gradient reversal layer, the gradients coming from the domain classifier are used to bring the source and target domain representations closer. We show that exploiting unlabeled data consistently leads to better emotion recognition performance across all emotional dimensions. We visualize the effect of adversarial training on the feature representation across the proposed deep learning architecture. The analysis shows that the data representations for the train and test domains converge as the data are passed to deeper layers of the network. We also evaluate the difference in performance when we use a shallow neural network versus a deep neural network and the effect of the number of shared layers used by the task and domain classifiers.