Aging and task representation updating

Older adults’ performance decrements can sometime be traced back to inferior strategic choices compared to their younger counterparts. Additionally, older adults often fail to revise their strategic choices with task experience (Bieman-Copland & Charness, 1994; Brigham & Pressley, 1988; Lovett & Schunn, 1999; Price, Dunlosky, & Hertzog, 2008; Touron & Hertzog, 2004a, 2004b; Touron, Hoyer, & Cerella, 2004). Metacognitive models of strategy selection suggests that beliefs, prior knowledge, goals, and task representation influence strategic decisions (e.g., Winne & Hadwin, 1998). No studies to date have attempted to compare task representation in older and younger adults to determine whether older adults’ poor strategic choices might be driven by an impoverished understanding of the tasks they are asked to engage in. In two studies we used a pathfinder methodology to elicit conceptual knowledge about a novel chemistry task. In both studies, more conceptual knowledge was related to superior task performance in both younger and older adults. However, we found no evidence of age-related deficits in task representation, formation, or utilization. Surprisingly, participants’ task representation scores did not improve following task practice. However, performance improved over trials, even for items that had to be learned with task practice, suggesting that task representation updating did occur. These findings provide indirect evidence of task representation updating in both younger and older adults. However, no age deficits in the ability to update task representations were found. Exploratory analyses suggest that performance in younger adults was related to motivational issues, whereas performance in older adults was driven by higher levels of processing speed and crystallized intelligence

Teaching robots parametrized executable plans through spoken interaction

While operating in domestic environments, robots will necessarily face difficulties not envisioned by their developers at programming time. Moreover, the tasks to be performed by a robot will often have to be specialized and/or adapted to the needs of specific users and specific environments. Hence, learning how to operate by interacting with the user seems a key enabling feature to support the introduction of robots in everyday environments. In this paper we contribute a novel approach for learning, through the interaction with the user, task descriptions that are defined as a combination of primitive actions. The proposed approach makes a significant step forward by making task descriptions parametric with respect to domain specific semantic categories. Moreover, by mapping the task representation into a task representation language, we are able to express complex execution paradigms and to revise the learned tasks in a high-level fashion. The approach is evaluated in multiple practical applications with a service robot

Toward A Formal Definition of Task Representation

This paper addresses the issue of how tasks within an organizational context should be represented from the perspective of a single decision maker. Based on a previous paper (Hackathorn, 1981), this paper presents a formal ism for task representation based on recent work in the Knowledge Representation area. The formalism is called Simple Associative Network (SAN). The implications of this formalism result in the discussion of several issues, such as: (a) the nature of task occurrence, (b) handling multiple task types of a task occurrence, (c) means and goals as a specialization of task types, and (d) control structures among task types

Cross-Task Representation Learning for Anatomical Landmark Detection

Recently, there is an increasing demand for automatically detecting anatomical landmarks which provide rich structural information to facilitate subsequent medical image analysis. Current methods related to this task often leverage the power of deep neural networks, while a major challenge in fine tuning such models in medical applications arises from insufficient number of labeled samples. To address this, we propose to regularize the knowledge transfer across source and target tasks through cross-task representation learning. The proposed method is demonstrated for extracting facial anatomical landmarks which facilitate the diagnosis of fetal alcohol syndrome. The source and target tasks in this work are face recognition and landmark detection, respectively. The main idea of the proposed method is to retain the feature representations of the source model on the target task data, and to leverage them as an additional source of supervisory signals for regularizing the target model learning, thereby improving its performance under limited training samples. Concretely, we present two approaches for the proposed representation learning by constraining either final or intermediate model features on the target model. Experimental results on a clinical face image dataset demonstrate that the proposed approach works well with few labeled data, and outperforms other compared approaches.Comment: MICCAI-MLMI 202

Attention allocation and task representation during joint action planning

We investigated whether people take into account an interaction partner's attentional focus and whether they represent in advance their partner's part of the task when planning to engage in a synchronous joint action. The experiment involved two participants planning and performing joint actions (i.e., synchronously lifting and clinking glasses), unimanual individual actions (i.e., lifting and moving a glass as if clinking with another person), and bimanual individual actions. EEG was recorded from one of the participants. We employed a choice reaction paradigm where a visual cue indicated the type of action to be planned, followed 1.5 sec later by a visual go stimulus, prompting the participants to act. We studied attention allocation processes by examining two lateralized EEG components, namely the anterior directing attention negativity and the late directing attention positivity. Action planning processes were examined using the late contingent negative variation and the movement-related potential. The results show that early stages of joint action planning involve dividing attention between locations in space relevant for one's own part of the joint action and locations relevant for one's partner's part of the joint action. At later stages of joint action planning, participants represented in advance their partner's upcoming action in addition to their own action, although not at an effector-specific level. Our study provides electrophysiological evidence supporting the operation of attention sharing processes and predictive self/other action representation during the planning phase of a synchronous joint task

Multi-View Multi-Task Representation Learning for Mispronunciation Detection

The disparity in phonology between learner's native (L1) and target (L2) language poses a significant challenge for mispronunciation detection and diagnosis (MDD) systems. This challenge is further intensified by lack of annotated L2 data. This paper proposes a novel MDD architecture that exploits multiple `views' of the same input data assisted by auxiliary tasks to learn more distinctive phonetic representation in a low-resource setting. Using the mono- and multilingual encoders, the model learn multiple views of the input, and capture the sound properties across diverse languages and accents. These encoded representations are further enriched by learning articulatory features in a multi-task setup. Our reported results using the L2-ARCTIC data outperformed the SOTA models, with a phoneme error rate reduction of 11.13% and 8.60% and absolute F1 score increase of 5.89%, and 2.49% compared to the single-view mono- and multilingual systems, with a limited L2 dataset.Comment: 5 page

The role of task understanding on younger and older adults’ performance

Objectives: Age-related performance decrements have been linked to inferior strategic choices. Strategy selection models argue that accurate task representations are necessary for choosing appropriate strategies. But no studies to date have compared task representations in younger and older adults. Metacognition research suggests age-related deficits in updating and utilizing strategy knowledge, but other research suggests age-related sparing when information can be consolidated into a coherent mental model.Method: Study 1 validated the use of concept mapping as a tool for measuring task representation accuracy. Study 2 measured task representations before and after a complex strategic task to test for age-related decrements in task representation formation and updating.Results: Task representation accuracy and task performance were equivalent across age groups. Better task representations were related to better performance. However, task representation scores remained fairly stable over the task with minimal evidence of updating.Discussion: Our findings mirror those in the mental model literature suggesting age-related sparing of strategy use when information can be integrated into a coherent mental model. Future research should manipulate the presence of a unifying context to better evaluate this hypothesis

Deep Multi-task Representation Learning: A Tensor Factorisation Approach

Most contemporary multi-task learning methods assume linear models. This setting is considered shallow in the era of deep learning. In this paper, we present a new deep multi-task representation learning framework that learns cross-task sharing structure at every layer in a deep network. Our approach is based on generalising the matrix factorisation techniques explicitly or implicitly used by many conventional MTL algorithms to tensor factorisation, to realise automatic learning of end-to-end knowledge sharing in deep networks. This is in contrast to existing deep learning approaches that need a user-defined multi-task sharing strategy. Our approach applies to both homogeneous and heterogeneous MTL. Experiments demonstrate the efficacy of our deep multi-task representation learning in terms of both higher accuracy and fewer design choices.Comment: 9 pages, Accepted to ICLR 2017 Conference Track. This is a conference version of the paper. For the multi-domain learning part (not in this version), please refer to https://arxiv.org/pdf/1605.06391v1.pd