Irrelevant Responses Research Articles

The Impact of Listening to Background Music on Inhibition Control and Prefrontal Cortical Activation in Healthy Older Adults: A Study Using Functional Near-Infrared Spectroscopy.

Introduction Aging declines executive functions, including attentional function and inhibitory control, which is the ability to inhibit inappropriate or irrelevant responses. Certain types of background music are negatively correlated with cognitive function. The prefrontal network is correlated with task performance related to executive function. This study aimed to assess the impact of listening to background music on inhibition control and prefrontal cortical (PFC) activation measured using functional near-infrared spectroscopy (fNIRS) in healthy older people. Methods In total, 59 healthy volunteers, including 32 healthy older and 27 younger individuals (mean age ± standard deviation: 69 ± 7 and 32 ± 8 years, respectively), participated in this study. The participants completed the inhibition control task (the go/no-go task) and a similar task while listening to certain melodies of children's songs that are popular in Japan. Changes in cerebral blood flow in the PFC during each task were evaluated using multichannel fNIRS. The relative changes in oxygenated hemoglobin (oxy-Hb) levels during the no-go and go tasks under the music and no-music conditions were compared using a paired t-test. Among the channels with a significant difference in oxy-Hb levels during the go/no-go task between the music and no-music conditions in the older group, the correlation between changes in accuracy response and oxy-Hb levels was validated using Pearson's correlation test. Results The task accuracy was significantly reduced under the music condition compared with that under the no-music condition in the older group but not in the younger group. The accuracy reduction was significantly greater in the older group than in the younger group. In older people, the oxy-Hb levels in 20 channels located in the bilateral Broadman area (BA) 9 and BA46 in the dorsolateral prefrontal cortex and the bilateral BA10 in the frontal pole cortex significantly increased during the no-go tasks under the music condition. During the go/no-go task under the music condition, the decline in task accuracy was significantly correlated with increased oxy-Hb levels in six channels located in the bilateral BA10 in older people. Conclusion Background music induced the decline of inhibition control and increase of PFC activity in healthy older adults.

Optimizing biomedical information retrieval with a keyword frequency-driven prompt enhancement strategy

BackgroundMining the vast pool of biomedical literature to extract accurate responses and relevant references is challenging due to the domain's interdisciplinary nature, specialized jargon, and continuous evolution. Early natural language processing (NLP) approaches often led to incorrect answers as they failed to comprehend the nuances of natural language. However, transformer models have significantly advanced the field by enabling the creation of large language models (LLMs), enhancing question-answering (QA) tasks. Despite these advances, current LLM-based solutions for specialized domains like biology and biomedicine still struggle to generate up-to-date responses while avoiding “hallucination” or generating plausible but factually incorrect responses.ResultsOur work focuses on enhancing prompts using a retrieval-augmented architecture to guide LLMs in generating meaningful responses for biomedical QA tasks. We evaluated two approaches: one relying on text embedding and vector similarity in a high-dimensional space, and our proposed method, which uses explicit signals in user queries to extract meaningful contexts. For robust evaluation, we tested these methods on 50 specific and challenging questions from diverse biomedical topics, comparing their performance against a baseline model, BM25. Retrieval performance of our method was significantly better than others, achieving a median Precision@10 of 0.95, which indicates the fraction of the top 10 retrieved chunks that are relevant. We used GPT-4, OpenAI's most advanced LLM to maximize the answer quality and manually accessed LLM-generated responses. Our method achieved a median answer quality score of 2.5, surpassing both the baseline model and the text embedding-based approach. We developed a QA bot, WeiseEule (https://github.com/wasimaftab/WeiseEule-LocalHost), which utilizes these methods for comparative analysis and also offers advanced features for review writing and identifying relevant articles for citation.ConclusionsOur findings highlight the importance of prompt enhancement methods that utilize explicit signals in user queries over traditional text embedding-based approaches to improve LLM-generated responses for specialized queries in specialized domains such as biology and biomedicine. By providing users complete control over the information fed into the LLM, our approach addresses some of the major drawbacks of existing web-based chatbots and LLM-based QA systems, including hallucinations and the generation of irrelevant or outdated responses.

Effects of stimulus onset asynchrony on cognitive control in healthy adults.

The efficiency of cognitive control in healthy adults can be influenced by various factors, including the stimulus onset asynchrony (SOA) effect and strategy training. To address these issues, our study aims to investigate the impact of SOA on single-mode cognitive control using the Go/No-Go task, as well as the manifestation of proactive control within dual mechanisms of cognitive control through the AX-CPT task. In single-mode cognitive control, extending SOA led to significantly enhanced reaction times (RTs) during Go trials, suggesting improved task performance with increased preparation time. Moreover, the analysis revealed consistently higher accuracy rates in No-Go trials than to Go trials across all SOA levels, indicating robust inhibition processes unaffected by SOA variations. In the dual mechanisms of cognitive control, significant variations in RT and accuracy were observed among different trial types. Notably, participants exhibited superior performance in detecting targets during BY trials and shorter RTs in BX trials, indicative of efficient processing of target stimuli. Conversely, prolonged RTs in AY trials suggest proactive control strategies aimed at maintaining task-relevant information and inhibiting irrelevant responses. Overall, these findings highlight the effect of SOA on single-mode cognitive control and the emergence of proactive control within dual mechanisms of cognitive control in healthy adults.

Semi-Automated Nonresponse Detection for Open-Text Survey Data

Open-ended survey questions can enable researchers to gain insights beyond more commonly used closed-ended question formats by allowing respondents an opportunity to provide information with few constraints and in their own words. Open-ended web probes are also increasingly used to inform the design and evaluation of survey questions. However, open-ended questions are more susceptible to insufficient or irrelevant responses that can be burdensome and time-consuming to identify and remove manually, often resulting in underuse of open-ended questions and, when used, potential inclusion of poor-quality data. To address these challenges, we developed and publicly released the Semi-Automated Nonresponse Detection for Survey text (SANDS), an item nonresponse detection approach based on a Bidirectional Transformer for Language Understanding model, fine-tuned using Simple Contrastive Sentence Embedding and targeted human coding, to categorize open-ended text data as valid or likely nonresponse. This approach is powerful in that it uses natural language processing as opposed to existing nonresponse detection approaches that have relied exclusively on rules or regular expressions or used bag-of-words approaches that tend to perform less well on short pieces of text, typos, or uncommon words, often prevalent in open-text survey data. This paper presents the development of SANDS and a quantitative evaluation of its performance and potential bias using open-text responses from a series of web probes as case studies. Overall, the SANDS model performed well in identifying a dataset of likely valid results to be used for quantitative or qualitative analysis, particularly on health-related data. Developed for generalizable use and accessible to others, the SANDS model can greatly improve the efficiency of identifying inadequate and irrelevant open-text responses, offering expanded opportunities for the use of open-text data to inform question design and improve survey data quality.

Evaluating the accuracy and relevance of ChatGPT responses to frequently asked questions regarding total knee replacement

BackgroundChat Generative Pretrained Transformer (ChatGPT), a generative artificial intelligence chatbot, may have broad applications in healthcare delivery and patient education due to its ability to provide human-like responses to a wide range of patient queries. However, there is limited evidence regarding its ability to provide reliable and useful information on orthopaedic procedures. This study seeks to evaluate the accuracy and relevance of responses provided by ChatGPT to frequently asked questions (FAQs) regarding total knee replacement (TKR).MethodsA list of 50 clinically-relevant FAQs regarding TKR was collated. Each question was individually entered as a prompt to ChatGPT (version 3.5), and the first response generated was recorded. Responses were then reviewed by two independent orthopaedic surgeons and graded on a Likert scale for their factual accuracy and relevance. These responses were then classified into accurate versus inaccurate and relevant versus irrelevant responses using preset thresholds on the Likert scale.ResultsMost responses were accurate, while all responses were relevant. Of the 50 FAQs, 44/50 (88%) of ChatGPT responses were classified as accurate, achieving a mean Likert grade of 4.6/5 for factual accuracy. On the other hand, 50/50 (100%) of responses were classified as relevant, achieving a mean Likert grade of 4.9/5 for relevance.ConclusionChatGPT performed well in providing accurate and relevant responses to FAQs regarding TKR, demonstrating great potential as a tool for patient education. However, it is not infallible and can occasionally provide inaccurate medical information. Patients and clinicians intending to utilize this technology should be mindful of its limitations and ensure adequate supervision and verification of information provided.

Lightweight semantic segmentation network for tumor cell nuclei and skin lesion.

In the field of medical image segmentation, achieving fast and accurate semantic segmentation of tumor cell nuclei and skin lesions is of significant importance. However, the considerable variations in skin lesion forms and cell types pose challenges to attaining high network accuracy and robustness. Additionally, as network depth increases, the growing parameter size and computational complexity make practical implementation difficult. To address these issues, this paper proposes MD-UNet, a fast cell nucleus segmentation network that integrates Tokenized Multi-Layer Perceptron modules, attention mechanisms, and Inception structures. Firstly, tokenized MLP modules are employed to label and project convolutional features, reducing computational complexity. Secondly, the paper introduces Depthwise Attention blocks and Multi-layer Feature Extraction modules. The Depthwise Attention blocks eliminate irrelevant and noisy responses from coarse-scale extracted information, serving as alternatives to skip connections in the UNet architecture. The Multi-layer Feature Extraction modules capture a wider range of high-level and low-level semantic features during decoding and facilitate feature fusion. The proposed MD-UNet approach is evaluated on two datasets: the International Skin Imaging Collaboration (ISIC2018) dataset and the PanNuke dataset. The experimental results demonstrate that MD-UNet achieves the best performance on both datasets.

The performance of artificial intelligence chatbot large language models to address skeletal biology and bone health queries.

Artificial intelligence (AI) chatbots utilizing large language models (LLMs) have recently garnered significant interest due to their ability to generate humanlike responses to user inquiries in an interactive dialog format. While these models are being increasingly utilized to obtain medical information by patients, scientific and medical providers, and trainees to address biomedical questions, their performance may vary from field to field. The opportunities and risks these chatbots pose to the widespread understanding of skeletal health and science are unknown. Here we assess the performance of 3 high-profile LLM chatbots, Chat Generative Pre-Trained Transformer (ChatGPT) 4.0, BingAI, and Bard, to address 30 questions in 3 categories: basic and translational skeletal biology, clinical practitioner management of skeletal disorders, and patient queries to assess the accuracy and quality of the responses. Thirty questions in each of these categories were posed, and responses were independently graded for their degree of accuracy by four reviewers. While each of the chatbots was often able to provide relevant information about skeletal disorders, the quality and relevance of these responses varied widely, and ChatGPT 4.0 had the highest overall median score in each of the categories. Each of these chatbots displayed distinct limitations that included inconsistent, incomplete, or irrelevant responses, inappropriate utilization of lay sources in a professional context, a failure to take patient demographics or clinical context into account when providing recommendations, and an inability to consistently identify areas of uncertainty in the relevant literature. Careful consideration of both the opportunities and risks of current AI chatbots is needed to formulate guidelines for best practices for their use as source of information about skeletal health and biology.

Flouting Grice’s Maxims in a Jordanian TV Talk Show

This study investigates flouting Grice’s maxims in Sawt Al-Mamlakah, a Jordanian TV talk show. Itaims at identifying the types, functions, and frequency of these flouted maxims. Using descriptive qualitative research, the data were analysed within Grice’s (1975) conversational maxims.The study findings reveal that all four maxims were flouted, with quantity being the most flouted, followed by relation, manner, and quality, respectively. Strategies for flouting included evading questions, providing lengthy or irrelevant responses, and sometimes creating a fun atmosphere. Communication breakdowns were not common though they were observed in a few instances. Flouting appeared context-dependent, serving various purposes like collaboration, elaboration, maintaining social relationships, politeness, image protection, expressing agreement or disagreement, satisfaction or dissatisfaction, refraining from sharing sensitive information, and emphasizing points. The study recommends further research on the same show over other periods of time and onmaxim flouting in other Arab talk shows. Keywords: Grice’s maxims, Flouting, Cooperative principle, Implicatures, TV talk show

Mathematics on News: How do Students Interpret Percentages in Daily Life Situations?

Contexts with real-life situations can be more motivating for students, increase students' attitudes and interest, and strengthen the connection between mathematics and daily life. This study examined student interpretations of News texts consisting entirely of real-life situations and including percentage representations. In this qualitative case study, data were obtained from 30 8th grade students. Students were asked 6 News texts containing various percentage values and were expected to explain what they understood from these percentage values. The data obtained from the study were subjected to content analysis and interpreted by authors. The results indicate that students have difficulty in interpreting real-life situations involving percentage representation, and very few students make clear and understandable comments about real-life situations. It suggests that most students focused solely on the numerical growth of the percentage expression. Some students struggled with the News text, while others provided irrelevant, unclear, or meaningless responses. According to these findings, it is recommended that students' interpretations of real-world context in mathematics lessons be discussed more frequently in the classroom settings.

Contour-aware network with class-wise convolutions for 3D abdominal multi-organ segmentation.

Accurate delineation of multiple organs is a critical process for various medical procedures, which could be operator-dependent and time-consuming. Existing organ segmentation methods, which were mainly inspired by natural image analysis techniques, might not fully exploit the traits of the multi-organ segmentation task and could not accurately segment the organs with various shapes and sizes simultaneously. In this work, the characteristics of multi-organ segmentation are considered: the global count, position and scale of organs are generally predictable, while their local shape and appearance are volatile. Thus, we supplement the region segmentation backbone with a contour localization task to increase the certainty along delicate boundaries. Meantime, each organ has exclusive anatomical traits, which motivates us to deal with class variability with class-wise convolutions to highlight organ-specific features and suppress irrelevant responses at different field-of-views. To validate our method with adequate amounts of patients and organs, we constructed a multi-center dataset, which contains 110 3D CT scans with 24,528 axial slices, and provided voxel-level manual segmentations of 14 abdominal organs, which adds up to 1,532 3D structures in total. Extensive ablation and visualization studies on it validate the effectiveness of the proposed method. Quantitative analysis shows that we achieve state-of-the-art performance for most abdominal organs, and obtain 3.63 mm 95% Hausdorff Distance and 83.32% Dice Similarity Coefficient on an average.

Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery

Abstract In this paper, we conduct the first study on spurious correlations for open-domain response generation models based on a corpus CGDialog curated by ourselves. The current models indeed suffer from spurious correlations and have a tendency to generate irrelevant and generic responses. Inspired by causal discovery algorithms, we propose a novel model-agnostic method for training and inference using a conditional independence classifier. The classifier is trained by a constrained self-training method, coined ConSTrain, to overcome data sparsity. The experimental results based on both human and automatic evaluation show that our method significantly outperforms the competitive baselines in terms of relevance, informativeness, and fluency.

SPK-CG: Siamese Network based Posterior Knowledge Selection Model for Knowledge Driven Conversation Generation

Building a human-computer conversational system that can communicate with humans is a research hotspot in the field of artificial intelligence. Traditional dialogue systems tend to produce irrelevant and non-information responses, which reduce people’s interest in engaging in a conversation. This often leads to boring conversations. To alleviate this problem, many researchers use external knowledge to assist conversation generation. The accuracy of knowledge selection is the prerequisite to ensure the quality of knowledge conversation. This approach has worked positively to a certain extent, but generally only searches knowledge information based on entity words themselves, without considering the specific conversation context. Therefore, if irrelevant knowledge is retrieved, the quality of conversation generation will be reduced. Motivated by this, we propose a novel neural knowledge-based conversation generation model, named Siamese Network based Posterior Knowledge Selection Model for Knowledge Driven Conversation Generation (SPK-CG) . We have designed a novel knowledge selection mechanism to obtain knowledge information that is highly relevant to the context of the conversation. Specifically, the posterior knowledge distribution is used as a soft label to make the prior distribution consistent with the posterior distribution in the training process. At the same time, in order to narrow the gap between prior and posterior distributions and improve the accuracy of knowledge selection, we leverage siamese network and design multi-granularity matching module for knowledge selection. Compared with previous knowledge-based models, our method can select more appropriate knowledge and use the selected knowledge to generate responses that are more relevant to the conversation context. Extensive automatic and human evaluations demonstrate that our model has advantages over previous baselines.

The Relationship Between Bilingualism and Control Aspect of Intelligence: A Study of Persian-speaking Individuals.

The study of the relationship between bilingualism and the possible impact it might have on the control aspect of intelligence of adults in general and preschool children, in particular, has always been the subject of controversy for researchers. This research, following the related findings and gaps in the literature and inspired by Craik and Bialystok's (2005) framework, tries to divulge whether bilingualism could be related to the control aspect of intelligence. As our secondary goal, we also tried to see whether there are correlations between different tests assessing control. In this descriptive correlational study, via the convenience sampling method, we selected our participants. Methods: In doing so, 10 age-gender-matched Mazandarani-speaking monolinguals and the same matched bilinguals have been selected. Moreover, the literacy and socioeconomic status of subjects have been controlled. The tests for assessing subjects' executive control included day-night Stroop, the dimensional change card sort (DCCS), test of variables of attention (TOVA), and the computerized attention network test (ANT). Our participants' performance in language proficiency task as well as control tasks was demonstrated. Furthermore, via the conduction of the Pearson correlation statistics, the relationships between the participants' performance in diverse control tasks and language task were investigated. The results showed that bilinguals outperform monolinguals in all control tests except DCCS. Bilingualism could provide children with an executive control advantage promoting them in tasks demanding thought and action control. Language proficiency is not necessarily related to an advanced attentional control.Mazandarani-speaking monolinguals and bilinguals perform equally in the "knowledge" domain of intelligence.Bilinguals' performance in the control task of intelligence was better than their monolingual counterparts.It was corroborated that intelligence is not a homogeneous psychological construct.Generally, in tasks requiring suppression, bilinguals outperform monolinguals. The investigation of the relationship between control aspect of intelligence and the statue of monolingualism or bilingualism has always been the subject of controversy. "Control" aspect signifies the ability of an individual to suppress the provoking stimuli, that is, to ignore the external stimuli in favor of an intended linguistic or non-linguistic element. Our major objective in this research was to compare the performance of Mazandarani-speaking bilinguals and monolinguals in diverse control as well as vocabulary assessment tasks. Specifically, we wanted to see whether bilingual children could perform better than monolingual children in tasks requiring the inhibition capability. Furthermore, we attempted to investigate whether there were correlations between different control assessment tasks. Extending previous researches, we administered two additional cognitive tasks, namely, test of variables of attention (TOVA) and day and night task to evaluate our participants' control capability. In linguistic (vocabulary) proficiency assessment task, Peabody picture vocabulary task (PPVT), we did not find bilinguals' advantage over monolinguals. In contrast, bilinguals outperformed their monolingual counterparts in all other control tasks except dimensional change card sort (DCCS). Meanwhile, strong correlations between most control tasks were observed. The results corroborated that bilingual children outperformed their monolingual counterparts in tasks demanding strong thought and action control. Also, the conduction of most control tasks might be more challenging for monolinguals, and bilingual children, thanks to their linguistic statutes, could ignore irrelevant response or stimuli more easily, and perform better in the majority of cognitive control demanding tasks.

Assessment of Professed Service Quality of the Cafeteria Business: An Empirical Investigation

Background: It is important to note that coffee is a phenomenon that has been around for a while in Saudi Arabia. Arabica coffee cultivation on thin basins and bordering mountain slants at elevations ranging from 1200 m to 1800 m in Yemen and Saudi Arabia can be traced back four or five centuries. Aim: The researchers aimed to investigate the factors that have influenced the cafeteria's service quality and compare the effects of the determinants on cafeteria customers in the Saudi Arabian cities of Al Baha and Riyadh. Method: The cafeteria and coffee have been the primary sources of information for the study, which uses SERVQUAL to investigate the causes of perceived facility and eminence in the cafe. An online questionnaire was used to poll a total of 178 respondents. The survey includes 169 respondents who responded most efficiently after applying the pilot study, which let go of irrelevant responses. The analysis through Confirmatory Factor Analysis (CFA) identifies the compelling aspects of the cafeteria in Al Baha and Riyadh, Saudi Arabia, which is ideal for the sector's future strategies. Findings: The study's findings show that three factors significantly impact service quality, performance, convenience, and problem-solving. The other three were unimportant but still hold their importance in this research paper and can be used as sub-independent factors. The culture and habits of Saudi Arabia play a substantial part in the results' anomaly.

Academic independent directors in China: factors influencing career decision-making

PurposeThe board independence norm has shaped corporate governance globally, but research on the supply of independent directors (IDs) is limited. “New careers” studies suggest that dynamic non-conventional career paths are evolving, but empirical evidence is mostly limited to Western societies. The authors studied reasons for academics to consider taking on an ID role in firms in China.Design/methodology/approachEmploying a quantitative methodology, data were collected via a survey targeting 1,500 academics, representing a quarter of business-linked academics in China's National Key Universities. The response rate was 55% and, after eliminating irrelevant responses, the sample used comprised 581 academics, 111 of whom have taken on an ID role.FindingsCareer orientation and work attitudes play significant roles in decision-making around career success. Becoming an ID can be a rewarding and positive career path for academics who look for additional responsibility, income and impact. This new academic career path manifests in how career actors interact with each other to reach a state of equilibrium in a wider career ecosystem. Appointing academics with relevant knowledge and willingness to create impact can help organisations – both firms and academic institutions.Practical implicationsBecoming an ID can increase future collaborations and success in terms of research and corporate governance, and offer individuals another route to career success.Originality/valueThe results contribute to research on career studies and corporate governance, revealing a new source of talent for firms and a new career path for academics.

Perspectives of Nigerian Graduates on Curriculum Reengineering, Acquisition of Emerging Technologies, and Job Creation: A Descriptive Study

Curriculum reengineering, web-based technology acquisition, and job creation among Nigerian tertiary institution graduates were all examined in this research. The researchers used a descriptive survey design following the quantitative research approach. The study included all Nigerian graduates eligible for national service or its exemption who earned their diplomas or degrees between 2016 and 2021. The data was gathered via an online survey titled "Curriculum Re-engineering, Acquisition of Emerging Technologies, and Job Creation Questionnaire (CRAETJCQ). To assemble the data for this study, we used a snowball method. There were 4,874 replies countrywide after four months of data collection; however, only 4,628 responses satisfied the data analysis conditions after screening out irrelevant responses. Results indicated low curriculum reengineering in Nigerian postsecondary institutions. Nigerian graduates had a poor adoption of new web-based technologies but are increasingly using them for word processing, graphics, data science and data analysis. Nevertheless, only a few graduates employed emerging techs for other purposes (such as printing, YouTube video creation, course design and development, software development, digital marketing, online advertising, and consumer outreach). Although 58.30 per cent of graduates reported having not created any job, 41.70 per cent have done so between 2016 and 2021. Of the 1,930 graduates who owned at least one small or medium enterprise, 58.96% had no employees, whereas 41.04% had hired at least one employee between 2016 and 2021. The graduates' job creation index was estimated to be approximately 50% using a new formula. Based on these results, conclusions and recommendations were made.

Peptide Cross-Linking Using Tyrosine Residues Facilitated by an Exogenous Nickel-Histidine Complex: A Facile Approach for Enhancing Vaccine-Specific Immunogenicity.

An improved method for the generation of peptide vaccines using di-tyrosine cross-linking is described. The conserved ion channel peptide, M2e, of influenza A virus was modified with the addition of small tyrosine-rich regions (GYGY-) at both the N- and C-termini and extensively cross-linked via tyrosine-tyrosine linkages to form peptide nanoclusters. The cross-linking was catalyzed using exogenous nickel(II) ions complexed to an exogenous glycine-glycine-histidine peptide in the presence of an oxidizer. Mice that were intranasally or intramuscularly immunized with the M2e-vaccine nanoclusters induced comparable levels of M2e-specific serum antibodies. Vaccination via the intranasal or intramuscular route protected mice from subsequent lethal challenge with an influenza A virus. In comparison to our previous approach, where a histidine-rich tag was added into the peptide structure, the use of exogenous histidine reduced irrelevant off-target immune response. Additionally, the purity of the resulting nanoclusters is an attractive feature, making this approach appealing for vaccine development.

Executive functioning and verbal fluency performance in youth with Down syndrome

BackgroundExecutive functioning (EF) is an area of challenge for individuals with Down syndrome (DS) associated with a variety of downstream difficulties. Verbal fluency performance is one potential downstream effect that is commonly assessed in individuals with DS due to the measure’s utility as a predictor of dementia. Verbal fluency requires individuals to inhibit irrelevant responses, shift between groupings of related words, and monitor to prevent repetition, all skills related to EF. AimsThis study aimed to determine the association between semantic verbal fluency performance and three EF subdomains (inhibition, shifting, and working memory) in youth with DS after taking into account vocabulary and cognitive ability. Methods and proceduresNeuropsychological assessments (verbal and visuospatial), and parent reports of EF, were completed at one time point by 69 youth with DS 6–17 years old and their caregivers. Expressive and receptive vocabulary skills and cognitive ability were also assessed. Outcomes and resultsThe results revealed that verbal fluency performance was significantly associated with neuropsychological assessments of EF and parent report of inhibition even after controlling for the effects of vocabulary and cognitive ability. Conclusions and implicationsThe findings highlight the underlying importance of EF in verbal fluency tasks in youth with DS.

Learning limb-specific contingencies in early infancy.

Most research with the mobile paradigm has the underlying assumption that young infants can selectively move the limb causing the contingent feedback from the mobile while avoiding irrelevant motor responses. Contrary to this long-held belief, others have argued that such differentiation ability is not fully developed early in life. In the current study, we revisited the traditional mobile paradigm with a contemporary research approach (using high-precision motion capture techniques, a yoked-control design, and a large sample size) to investigate whether response differentiation ability emerges before 5months of age. The data collected from 76 infants (aged between 115 and 159days) revealed that infants can learn sensorimotor contingencies by increasing the movement of the connected leg relative to their baseline level. However, they did not differentially increase the movement of the leg causing an effect in the environment compared with that of other limbs. Our results illustrate that response differentiation ability emerges later than previously suggested.

Does multisensory study benefit memory for pictures and sounds?

Studies have found a multisensory memory benefit: higher recognition accuracy for unimodal test items that were studied as bimodal items than for those studied as unimodal items. This is a surprising finding because the encoding specificity principle predicts that memory performance should be better with greater overlap between processing during study and test. We used Thelen, Talsma, and Murray's (2015) method who previously found a multisensory memory benefit. Items were presented as unimodal (picture or sound) or bimodal (picture and sound) items in a continuous recognition task in which only one modality was task-relevant. In four experiments we obtained little evidence for a difference in memory performance between items studied as unimodal or bimodal stimuli, but there was a benefit of study-test overlap in format if sound was the task-relevant modality. Task-induced attention for the irrelevant modality or response bias may have played a role in previous studies. We conclude that the multisensory memory benefit may not be a general finding, but rather one that is found only under conditions that induce participants to pay attention to the task-irrelevant modality.