Why Do Some Older Adults Avoid Memory Retrieval When Acquiring a New Skill?

Older adults are slower to acquire new cognitive skills requiring a shift from controlled (algorithmic) processing to automatic responding based on retrieving newly unitized information from memory. Research demonstrates that older adults’ delayed retrieval shift is a strategic avoidance of relying on memory when doing so would be successful, not just a function of age-related slowing in rates of associative learning. Older adults’ retrieval avoidance can be reduced by financial incentives to respond rapidly, recognition probes that demonstrate the accessibility of correct information, and other experimental manipulations. Item-level strategy reports show an exponential rise in retrieval strategy use with practice but not for all participants. A proportion of the older samples are retrieval strategy avoidant across the entire course of skill acquisition task practice. The chapter comments on the motivational nature of retrieval strategy avoidance and the possible practical consequences of a retrieval avoidance mode for older adults.

Towards an entity relation extraction framework in the cross-lingual context

Purpose Owing to the uneven distribution of annotated corpus among different languages, it is necessary to bridge the gap between low resource languages and high resource languages. From the perspective of entity relation extraction, this paper aims to extend the knowledge acquisition task from a single language context to a cross-lingual context, and to improve the relation extraction performance for low resource languages. Design/methodology/approach This paper proposes a cross-lingual adversarial relation extraction (CLARE) framework, which decomposes cross-lingual relation extraction into parallel corpus acquisition and adversarial adaptation relation extraction. Based on the proposed framework, this paper conducts extensive experiments in two tasks, i.e. the English-to-Chinese and the English-to-Arabic cross-lingual entity relation extraction. Findings The Macro-F1 values of the optimal models in the two tasks are 0.880 1 and 0.789 9, respectively, indicating that the proposed CLARE framework for CLARE can significantly improve the effect of low resource language entity relation extraction. The experimental results suggest that the proposed framework can effectively transfer the corpus as well as the annotated tags from English to Chinese and Arabic. This study reveals that the proposed approach is less human labour intensive and more effective in the cross-lingual entity relation extraction than the manual method. It shows that this approach has high generalizability among different languages. Originality/value The research results are of great significance for improving the performance of the cross-lingual knowledge acquisition. The cross-lingual transfer may greatly reduce the time and cost of the manual construction of the multi-lingual corpus. It sheds light on the knowledge acquisition and organization from the unstructured text in the era of big data.

Trudność zadania komunikacyjnego a bogactwo leksykalne wypowiedzi uczniów

Task-based language teaching has recently become a mainstream research area in second language acquisition studies. One of the underexplored areas is task design and its influence on the measures of complexity, accuracy, and fluency. While most previous research into task design focused on manipulating planning time, note-taking, or task familiarity, one of the promising lines of investigation is how task difficulty may also be conducive to L2 acquisition. Task difficulty is understood as the cognitive burden placed on a learner performing a task. In the current study learners of English as a foreign language (n=28) performed three differently designed oral communicative tasks of increasing difficulty: (1) a brainstorming task, (2) a sorting and ordering task, and (3) a problem-solving argumentative task. Task difficulty, i.e. having to employ higher-order thinking skills improved learners’ L2 lexical complexity as measured by lexical diversity, lexical density, and word-frequency counts.

Emotion Expression Recognition Using Wireless Signal (Facial Expression)

Emotion Recognition is an important area of work to improve the interaction between human and machine. Complexity of emotion makes the acquisition task more difficult. Quondam works are proposed to capture emotion through unimodal mechanism such as only facial expressions or only vocal input. More recently, inception to the idea of multimodal emotion recognition has increased the accuracy rate of the detection of the machine. Moreover, deep learning technique with neural network extended the success ratio of machine in respect of emotion recognition. Recent works with deep learning technique has been performed with different kinds of input of human behavior such as audio-visual inputs, facial expressions, body gestures, EEG signal and related brainwaves. Still many aspects in this area to work on to improve and make a robust system will detect and classify emotions more accurately. In this paper, we tried to explore the relevant significant works, their techniques, and the effectiveness of the methods and the scope of the improvement of the results.

Somatosensory Integration and Masking of Complex Tactile Information: Peripheral and Cortical Contributions

Nerve paresthesia is a sensory impairment experienced in clinical conditions such as diabetes. Paresthesia may “mask” or “compete” with meaningful tactile information in the patient’s sensory environment. The two objectives of the present study were: (1) to determine if radiating paresthesia produces a peripheral mask, a central mask, or a combination; (2) to determine if a response competition experimental design reveals changes in somatosensory integration similar to a masking design. Experiment 1 assessed the degree of masking caused by induced radiating ulnar nerve paresthesia (a concurrent non-target stimulus) on a vibrotactile Morse code letter acquisition task using both behavioral and neurophysiological measures. Experiment 2 used a response competition design by moving the radiating paresthesia to the median nerve. This move shifted the concurrent non-target stimulus to a location spatially removed from the target stimuli. The task, behavioral and neurophysiological measures remained consistent. The induced paresthesia impacted letter acquisition differentially depending on the relative location of meaningful and non-meaningful stimulation. Paresthesia acted as a peripheral mask when presented to overlapping anatomical stimulation areas, and a central mask when presented at separate anatomical areas. These findings are discussed as they relate to masking, subcortical, and centripetal gating.

Code Knowledge Acquisition for Knowledge Management Trajectory Framework

Knowledge and its related techniques cater the need of many tools and frameworks for software development, testing and management. Accurate usage of code according to the software requirement is prevalent in this regard, as inappropriate development of code with conceptual misunderstanding might lead to waste of time and effort. This paper describes a new approach to automate code acquisition task. This gives the advantage of using the coding knowledge that is already encoded which in turn reduces developer s task and their knowledge and effort could be utilized for further enhancement of their work. Using the existing coding knowledge eradicate the errors which are introduced at the time of naïve development activity. Furthermore, this technique can be utilized by software development applications. In this paper, a new knowledge execution technique to acquire code knowledge from software application is implemented. Code extraction gathers code segments from projects organize and logs it for future use. Here, Source Code Extraction algorithm is described that captures code.

Cross-domain Visual Exploration of Academic Corpora via the Latent Meaning of User-authored Keywords

Nowadays, scholars dedicate a substantial amount of their work to the querying and browsing of increasingly large collections of research papers on the Internet. In parallel, the recent surge of novel interdisciplinary approaches in science requires scholars to acquire competencies in new fields for which they may lack the necessary vocabulary to formulate adequate queries. This problem, together with the issue of information overload, poses new challenges in the fields of natural language processing (NLP) and visualization design that call for a rapid response from the scientific community. In this respect, we report on a novel visualization scheme that enables the exploration of research paper collections via the analysis of semantic proximity relationships found in author-assigned keywords. Our proposal replaces traditional string queries by a bag-of-words (BoW) extracted from a user-generated auxiliary corpus that captures the intentionality of the research. Continuing on the line established by previous works, we combine novel advances in the fields of NLP with visual network analysis techniques to offer scholars a perspective of the target corpus that better fits their research needs. To highlight the advantages of our proposal, we conduct two experiments employing a collection of visualization research papers and an auxiliary cross-domain BoW. Here, we showcase how our visualization can be used to maximize the effectiveness of a browsing session by enhancing the language acquisition task, which allows an effective extraction of knowledge that is in line with the users’ previous expectations.

Does Location of Tonic Pain Differentially Impact Motor Learning and Sensorimotor Integration?

Recent work found that experimental pain appeared to negate alterations in cortical somatosensory evoked potentials (SEPs) that occurred in response to motor learning acquisition of a novel tracing task. The goal of this experiment was to further investigate the interactive effects of pain stimulus location on motor learning acquisition, retention, and sensorimotor processing. Three groups of twelve participants (n = 36) were randomly assigned to either a local capsaicin group, remote capsaicin group or contralateral capsaicin group. SEPs were collected at baseline, post-application of capsaicin cream, and following a motor learning task. Participants performed a motor tracing acquisition task followed by a pain-free retention task 24–48 h later while accuracy data was recorded. The P25 (p < 0.001) SEP peak significantly decreased following capsaicin application for all groups. Following motor learning acquisition, the N18 SEP peak decreased for the remote capsaicin group (p = 0.02) while the N30 (p = 0.002) SEP peaks increased significantly following motor learning acquisition for all groups. The local, remote and contralateral capsaicin groups improved in accuracy following motor learning (p < 0.001) with no significant differences between the groups. Early SEP alterations are markers of the neuroplasticity that accompanies acute pain and motor learning acquisition. Improved motor learning while in acute pain may be due to an increase in arousal, as opposed to increased attention to the limb performing the task.