List Length Research Articles

Individuating and categorizing faces: The other-race effect is reduced by attending to racial but not identity cues

ABSTRACT According to social-cognitive accounts, the other-race effect in face recognition is driven by our tendency to attend to identity cues when encoding own-race faces (“individuation”) and racial cues when encoding other-race faces (“categorization”). This study employed novel one-back tasks to ensure that participants processed identity (individuation) or racial cues (categorization) when learning faces in an old/new recognition task. Categorization encoding eliminated the other-race effect in recognition sensitivity when list length was short (Experiment 2) but not when list length was long (Experiment 1B). Importantly, this elimination was driven by weaker performance for own-race faces, rather than stronger performance for other-race faces. Individuation encoding failed to reduce the other-race effect, regardless of list length (Experiment 1A and Experiment 2). We compare these findings with previous studies and propose several methodological features (e.g., presentation duration, task difficulty) that might influence the observed impacts of individuation and categorization encoding on the other-race effect.

The Interaction Between the Production Effect and Serial Position in Recognition and Recall.

In memory tasks, items read aloud are better remembered than their silently read counterparts. This production effect is often interpreted by assuming a distinctiveness benefit for produced items, but whether this benefit also comes at a cost remains up for debate. In recall tasks, when pure lists are used in which all items are produced or read silently, studies have shown a better recall of produced items at the last serial positions, but a lower recall at the first positions. This cost of production has been interpreted by assuming that production interferes with rehearsal. However, in recognition tasks, models typically assume that the distinctiveness benefit for produced items comes at no cost. Across four experiments, participants completed a 2AFC recognition test, an old-new recognition test or an immediate serial recall test. List length was also manipulated. Results show that although the production effect is larger at the last serial positions, the cross-over interaction between the production effect and serial position observed in recall was not present in recognition. These results suggest that task-related differences in the production effect may inform us about the modulation of basic memory processes by task demands.

Is Verbal Rehearsal Strategic? An Investigation into Overt Rehearsal of Nameable Pictures in 5- to 10-Year-Old Children

ABSTRACT Though verbal rehearsal is a frequently endorsed strategy for remembering short lists among adults, there is ambiguity around when children deploy it, and what circumstantial factors encourage them to rehearse. We recoded data from a recent multilab replication of a serial picture memory task in which children were observed for evidence of task-related speech or lip movements to extract finer-grained detail about how children spoke during the task. With these data, we aimed to better understand the manner in which children rehearse and the task scenarios which elicit overt rehearsal. Children in several countries from 5 to 10 years old were tasked with remembering 2–5 nameable pictures in serial order across a 15-second delay. Coders categorized children’s speech or lip movements as reflecting fixed rehearsal of the last-presented item only, cumulative rehearsal of all the items presented so far, or some attempt at cumulative rehearsal. We found that most children, regardless of age, did not overtly rehearse at all during presentation of the objects or during the delay period. However, children who sometimes overtly rehearsed recalled longer lists of items than children who did not. Though rare, cumulative rehearsal was most frequently observed for list lengths close to the participant’s demonstrated maximum recall length. Critically, on the trials where overt rehearsal was observed, recall improved. This evidence supports previous suggestions that rehearsal strategy, and possibly also its effectiveness, changes with task difficulty, and raises further questions about how verbal rehearsal affects serial recall.

GOAT: efficient and robust identification of gene set enrichment

Gene set enrichment analysis is foundational to the interpretation of high throughput biology. Identifying enriched Gene Ontology (GO) terms or disease-associated gene sets within a list of gene effect sizes that represent experimental outcomes is an everyday task in life science that crucially depends on robust and sensitive statistical tools. We here present GOAT, a parameter-free algorithm for gene set enrichment analysis of preranked gene lists. The algorithm can precompute null distributions from standardized gene scores, enabling enrichment testing of the GO database in one second. Validations using synthetic data show that estimated gene set p-values are well calibrated under the null hypothesis and invariant to gene list length and gene set size. Application to various real-world proteomics and gene expression studies demonstrates that GOAT identifies more significant GO terms as compared to current methods. GOAT is freely available as an R package and user-friendly online tool for gene set enrichment analyses that includes interactive data visualizations: https://ftwkoopmans.github.io/goat.

Evaluating the impact of COVID-19 on DXA waiting lists and osteoporosis prescription trends in England 2019-2023.

This study investigates the impact of COVID-19 on DXA scan waiting lists, service recovery and osteoporosis medication prescriptions in the NHS following the March 2020 national lockdowns and staff redeployment. Data from March 2019 to June 2023, including NHS digital diagnostics waiting times (DM01) and osteoporosis medication prescriptions from the English Prescribing Dataset (EPD), were analysed. This encompassed total waiting list data across England's seven regions and prescribing patterns for various osteoporosis medications. Analyses included total activity figures and regression analysis to estimate expected activity without COVID-19, using R for all data analysis. In England, DXA waiting lists have grown significantly, with the yearly mean waiting list length increasing from 31,851 in 2019 to 65,757 in 2023. The percentage of patients waiting over 6 weeks for DXA scans rose from 0.9% in 2019 to 40% in 2020, and those waiting over 13 weeks increased from 0.1% in 2019 to 16.7% in 2020. Prescription trends varied, with increases in denosumab, ibandronic acid and risedronate sodium and decreases in alendronic acid, raloxifene hydrochloride and teriparatide. A notable overall prescription decrease occurred in the second quarter of 2020. COVID-19 has significantly increased DXA scan waiting lists with ongoing recovery challenges. There is a noticeable disparity in DXA service access across England. Osteoporosis care, indicated by medication prescriptions, also declined during the pandemic. Addressing these issues requires focused investment and effort to improve DXA scan waiting times and overall access to osteoporosis care in England.

Visual communication design of web-based learning resources in the digital era

Since the dawn of the digital web era, web-based learning resources have become more and more significant in the field of education. To a certain extent, the visual communication design of these resources influences how well students learn. In view of this, the study proposes a deep learning-based approach to visual communication design. Convolutional neural networks are introduced to automatically construct the visual communication interface, a recommendation algorithm is used to develop the system’s recommendation function, and machine translation is used to translate the language description text. The study method’s efficacy was evaluated. According to the experimental results, the research method’s runtime in a color environment was only about 37.7 seconds at 4k resolution; in a non-color environment, the method’s F1 value was 0.87 at a recommended list length of 35, which was higher than that of other methods; and when it came to the interface solutions in real terms, the research method produced 526 at 30 buttons. The aforementioned findings demonstrate that the suggested approach can successfully increase the visual communication’s design speed and performance in online learning materials and offer a suitable answer to the needs of real-world applications.

List Length, List Content, and Gratitude List Intervention Outcomes

Gratitude lists, well-being interventions in which participants list things that they are grateful for, have recently grown in popularity. However, being tasked with generating longer gratitude lists might backfire and be associated with less felt gratitude. Furthermore, the content of these gratitude lists is rarely examined. In three studies of 3,936 participants, people assigned to shorter length conditions (three items) were relatively comparable in gratitude and life satisfaction to those assigned to longer list conditions (six and 12 items), although an exploratory analysis revealed that failure to list enough gratitude items for a given condition was associated with lower well-being. These results provided evidence that ease-of-retrieval effects may not play a strong role in the efficacy of gratitude lists. Those who listed particular content items (e.g., close relationships and health) were higher in post-intervention gratitude and well-being than those who did not.

Children's rehearsal development parallels that of self-talk in other executive functions.

Rehearsal is a form of self-talk used to support short-term memory. Historically, the study of rehearsal development has diverged from the study of self-talk more generally. The current experiment examines whether two characteristics of self-talk (impact of task difficulty and self-talk's narrative vs. planning purpose) are also observed in rehearsal. Eighty children, ages 4-7, were tasked with remembering the three-item and six-item lists over a 15-s delay. Children's spontaneous use of fixed rehearsal (i.e., immediate repetition of a just-presented item) and cumulative rehearsal (i.e., cycling through multiple items at a time) was documented from video recordings. Four-year-olds narrated item presentations using fixed rehearsal. Six- and seven-year-olds proactively planned for recall by engaging cumulative rehearsal. Five-year-olds used both forms of rehearsal, but their frequency of cumulative rehearsal was dependent on list length. Therefore, rehearsal is susceptible to task manipulations that affect other forms of self-talk. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Implementation and Application of Employment Recommendation System for College Students with Personalized Preferences

Abstract With the popularization of higher education, the competition in the employment market of college students is becoming increasingly intense. To enhance the employment efficiency and satisfaction of college students. The study first analyzes college students’ employment unit selection, attribute preference, and location preference through an employment recommendation algorithm. The collaborative filtering algorithm is utilized to complete personalized modeling and output the final recommendation results based on the acquired employment preferences and relevant data collection. Finally, the gradient descent method is used to evaluate the accuracy of college students’ employment recommendations. The results show that the overall educational requirements of enterprises for the three significant positions of short video production, account operation and anchor are not high, and the percentage of those with education of high school or below or master’s degree or above is meager, neither exceeding 3.5%. The personalized employment recommendation algorithm can provide scientific and reasonable guidance for graduates’ employment, with an accuracy rate of up to 50% and a recommendation list length of N=30. When α=0.75, the personalized employment recommendation algorithm can obtain better recommendation performance with smaller recommendation list length. This paper provides new solutions for college students’ employment and valuable references and lessons for research in related fields.

A Study of Informatization and Data-Driven Career Planning in Career Guidance in Colleges and Universities

Abstract Traditional career planning education and employment guidance is based on lectures and text reading, with a large amount of subjective coloring, which can’t achieve one person one strategy. This paper constructs a college career guidance information recommendation system based on collaborative filtering and K-means clustering to recommend suitable career information with a high potential success rate for students from both subjective and objective aspects. The weights of students’ features are objectively calculated, and the K-means algorithm is improved with the minimum spanning tree method to spontaneously calculate the initial number of categories and the class center value from the data, avoiding the problem of random generation and enhancing the effect of clustering. The system is tested against other models in the dataset to examine its clustering performance. It is found that on the optimal K-mean-square error, the errors of the model in this paper are all less than 0.96, which is the lowest among the algorithms on all terms, and its clustering performance is again verified. The best results on the dataset are achieved by the proposed algorithm, with an HR@20 of 59.31%, which is almost twice the SVD recommendation method. The experiment confirmed the significance of each module in the recommender system. Compared with the recommendation of graduates’ jobs, the system is easier to recommend industries for graduates, and the HR indicator is greater than 80% at the recommendation list lengths of 3 and 5, while the HR@5 of SVD, BPR is below 50%. This study provides useful insights into the integration of cutting-edge information technology career guidance and data mining to aid in career planning.

The modulation and elimination of temporal organization in free recall.

Experiences occur in a continual succession, and the temporal structure of those experiences is often preserved in memory. The temporal contiguity effect of free recall reveals the temporal structure of memory: when a particular item is remembered, the next response is likely to come from a nearby list position. This effect is remarkably robust, appearing across a wide variety of methodological variations of the task. The temporal contiguity effect is also central to retrieved-context models, which propose temporal organization arises from the interaction of a temporal context representation with the contents of memory. Across six experiments, we demonstrate methodological manipulations that dramatically modulate and even eliminate temporal organization in free recall. We find that temporal organization is strongly modulated and in some cases potentially eliminated by strong semantic structure, the presence of retrieval practice, and a long list length. Other factors such as orienting task, paired-associate item structure, and retention interval duration have more subtle effects on temporal organization. In an accompanying set of simulations, we show that the modulation and elimination of the temporal organization follows lawful patterns predicted by the context maintenance and retrieval (CMR) retrieved-context model. We also find cases where CMR does not specifically predict the modulation of temporal organization, and in these cases our analysis suggests how the theory might be developed to account for these effects. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Network Routing Optimization Using Tabu Search Algorithm in Dynamic Routing

Internet penetration is increasing along with the need for data packages for communication such as social media, chatting, video conferencing and others. On large-scale networks such as the Internet, dynamic routing is used to build routing protocol information in the routing table automatically. Currently, Djikstra's algorithm is used to solve the shortest path problem in dynamic routing. In this research, the optimization of the algorithm is carried out in determining the best path or trajectory. One of the optimization algorithms is the Tabu Search Algorithm which can guide heuristic local search procedures to explore the solution area outside the local optimum point. This optimization is assessed from the test parameters measured from the smallest cost. The data analyzed is in the form of bandwidth and topological flow. From the results of tracing the path of data packets sent through 9 routers using the Tabu Search algorithm with the parameters namely number of Neighbor Solutions = 50, Length of tabu list = 10, Maximum Number of Iterations = 100, the result of the path matrix value is 180.9676. The path taken is router 0-2-4-8-9

The Italian colour lexicon in Tuscany: elicited lists, cognitive salience, and semantic maps of colour terms

We investigated the Tuscan Italian colour inventory, with the aim of establishing the cognitive salience of the basic colour terms (BCTs) and most frequent non-BCTs. Native speakers from Tuscany (N = 89) completed a colour-term elicitation task lasting for 5 min. In total, 337 unique terms were elicited, with an average list length of 30.06. The frequency of each term, its mean list position and cognitive salience index (S) were calculated. The CTs with the highest S (ranked 1–13) included 10 counterparts of the Berlin and Kay BCTs listed in their 1969 seminal work and three basic ‘blue’ terms, blu, azzurro, celeste, estimated for Tuscan respondents by Del Viva et al. in 2022. S-index and Zipf-function (the terms’ “popularity”) indicated that fucsia (rank 14) is conceivably an emerging BCT in (Tuscan) Italian. Other cognitively salient non-BCTs are lilla, magenta, ocra and beige. The terms’ 3D semantic map (conceptual closeness), assessed using multidimensional scaling and cluster analysis, revealed that in the lists, closely associated CTs were arranged along three competing criteria: the term’s salience gradient; word length; and clustering of fully chromatic concepts with those defined primarily by lightness or desaturation. We also consider salient Italian non-BCTs as indicators of the ongoing process of lexical refinement in certain areas of the colour space. In conclusion, measures of elicitation productivity, as well as the augmented BCT inventory, including the Tuscan ‘triple blues’, and abundant hyponyms and derived forms all indicate (Tuscan) Italian speakers’ “cultural competence” in the colour domain and the need to communicate nuanced information about colour shades.

Changes in the Liver Transplant Waiting List after Expanding to the 'Up-to-Seven' Criteria for Hepatocellular Carcinoma.

We aimed to assess changes in the composition of the waiting list for liver transplantation (LT) after expanding from Milan to "up-to-seven" criteria in patients with hepatocellular carcinoma (HCC). A consecutive cohort of 255 LT candidates was stratified in a pre-expansion era (2016-2018; n = 149) and a post-expansion era (2019-2021; n = 106). The most frequent indication for LT was HCC in both groups (47.7% vs. 43.4%; p = 0.5). The proportion of patients exceeding the Milan criteria in the explanted liver was nearly doubled after expansion (12.5% vs. 21.1%; p = 0.25). Expanding criteria had no effect in drop-out (12.3% vs. 20.4%; p = 0.23) or microvascular invasion rates (37.8% vs. 38.7%; p = 0.93). The length on the waiting list did not increase after the expansion (172 days [IQR 74-282] vs. 118 days [IQR 67-251]; p = 0.135) and was even shortened in the post-expansion HCC subcohort (181 days [IQR 125-232] vs. 116 days [IQR 74-224]; p = 0.04). Tumor recurrence rates were reduced in the post-expansion cohort (15.4% vs. 0%; p = 0.012). In conclusion, expanding from Milan to up-to-seven criteria for LT in patients with HCC had no meaningful impact on the waiting list length and composition, thus offering the opportunity for the adoption of more liberal policies in the future.

Forward and backward recall dynamics.

Although possible to recall in both forward and backward order, recall proceeds most naturally in the order of encoding. Prior studies ask whether and how forward and backward recall differ. We reexamine this classic question by studying recall dynamics while varying the predictability and timing of forward and backward cues. Although overall accuracy did not differ by recall direction, recall dynamics highlight key distinctions. Forward recall exhibits a modest advantage for correct transitions following errors, independent of cueing predictability and list length. Without consistent directional cueing, participants initiate backward recall more accurately, but this effect reverses with predictable directional cues. Following omissions, participants commit more fill-in errors in backward recall. Our findings implicate an asymmetric, cue-dependent retrieval process underlying forward and backward recall, with relative contributions of primacy and recency depending on directional predictability. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Innovative Research on Deep Learning Algorithm in Intelligent Attractions Recommendation Technology

Abstract In order to discover users’ interest preferences from the massive travel information and meet users’ needs for personalized tourist attraction recommendations. In this paper, we construct correspondingly according to the domain knowledge and complete the design and implementation of the mapping according to the corresponding process. The item representation enhancement module is designed to obtain a more comprehensive user preference by extracting information from the user’s historical preferences and then using graph convolution to learn the correlation between the user’s historical behavioral preferences and entities to enhance the KG semantic information of items. An item representation enhancement model based on RippleNet is proposed, from the item representation enhancement module to the interest module, to establish a connection between the user and the implicit information of the item so as to consider their mutual influence and represent the missing information more comprehensively, and finally, a personalized travel recommendation model based on knowledge graph and deep learning is constructed. From the results of the study, it is obtained that the model in this paper is 4.14% and 4.6% better than the best model SASRec in the comparison method in NDCG@10, Recall@10, MRR@10 indicators on both 1.93% higher. At the recommended list length K=20, the Recall rate Recall@20 has reached 80.78%. The superiority of the performance of the model constructed in this paper is demonstrated in the experiments, which brings technical innovation to the personalized attraction recommendation system.

The List Length Effect and Immediate Free Recall of Visually Displayed Information in Various Forms

This paper investigated four main phenomena connected with the free immediate recall and visual representation of the information: list-length effect, serial position effect, horizontal versus vertical presentation of the information, expression of the information in the form of the symbol, text, and symbol with text. The number of examined respondents was 4140. The experiment revealed a different memory recall of various forms of visually displayed information and studied a dependency of list length effect on primacy and recency effect. The results showed that people recall better the combination of symbols and text than only text or symbols. The other finding indicated a significant difference between the number of recalled items in the tests of 5, 7, and 9 items. The respondents recalled the highest number of items in 5-item tests and the lowest number in 9-item tests. There was investigated that the proportions of the respondents recalling the items from the beginning is higher than those of the respondents recalling the items from the end. The results showed that the primacy effect was higher than the recency effect regardless of the list length (5, 7, or 9 items tests). This article's findings should help design visually optimized websites and applications.

177 Impact of Covid Pandemic on Cholecystectomy Waiting Lists in NHS Highland: Lessons and Opportunities

Abstract Aims The standard waiting time for elective surgery is 18 weeks. However, it continues to face pressure. We aimed to look at: Methods Retrospective data of patients listed for a cholecystectomy throughout the health board from April 2019-June 2022 were collected using electronic records. Patients removed from the list were excluded. Data was collected on patient demographics, hospital listed/performed, waiting list length, surgical approach and post-operative outcomes. Results 278 patients were included. 79% operations were listed centrally with 52% performed there. 72% were female and 28% male. The average age was 53.6 years (22-91). Of those listed 45% underwent surgery. The average waiting time was 106 days overall (5-1024), 110.5 in 2020, 156 in 2021, 57.5 in 2022, 78% being performed within target. 98.4% were laparoscopic and 1.6% converted. 88% of operations were elective and 12% emergency. 1 patient had malignancy on pathology. 15 patients had complications with 3 bile leaks (2.4%). 22 patients were admitted whilst waiting for a total of 36 days, costing an estimated £14,400. 64% received an emergency operation during admission, all which were performed centrally (p=<0.05) Length of stay was shorter at C (p=<0.05) and equivalent at B. Complications were lower at C (7.7%) and B (11%) compared to centrally (13%). Conclusions Patients are waiting longer for cholecystectomies with increased financial burden. Complications rates have increased, especially bile leaks which needs to be investigated further. Rural hospitals can be safely used to alleviate pressures.

Attention-Based Time Sequence and Distance Contexts Gated Recurrent Unit for Personalized POI Recommendation

Aiming at the problems resulting from the fact that the existing point of interest (POI) recommendation methods cannot effectively consider the personalized differences of users' mobile behavior in space and time, the author proposes a personalized POI recommendation method using attention-based time sequence and distance contexts gated recurrent unit (ATSD-GRU). First, the author combined the time sequence and distance context with the GRU to extract useful information from users, effectively alleviating the data sparsity. Second, inspired by the attention mechanism, the author introduced the attention model further into the neural network to capture the user's main mobile behavior intention. Finally, the author studied the ATSD-GRU and trained through Bayesian personalized sorting framework and back propagation algorithm. Experiments imply that the proposed method outperforms the comparison method in terms of the F1 index for any recommended number. When the recommendation list length is 15, the proposed algorithm exhibits an accuracy of 9.23% and a recall rate of 14.65%, both higher than the compared algorithm.

A study on corpus-based stopword lists in Indian language IR

We explore and evaluate the effect of different stopword lists (non-corpus-based and corpus-based) in the information retrieval (IR) tasks with different Indian languages such as Bengali, Marathi, Gujarati, Hindi and English. The issue was investigated from three viewpoints. Is there any performance difference between non-corpus-based and corpus-based stopword removal in chosen Indian languages? Can corpus-based stopword lists improve performance in Indian languages IR? If yes, to what extent? Among the different corpus-based stopword lists, which stopword list provides the best IR performance? Does the length of a corpus-based stopword list affect the retrieval performance in Indian languages? If yes, to what extent? It was observed that a corpus-based stopword list provides better retrieval performance than a non-corpus-based stopword list in different Indian languages. Among the different corpus-based stopword lists generated and experimented with, Zipf’s law-based stopword list (idf-based one) provides the best retrieval performance in various Indian languages. The aggregation1-based stopword list provides better retrieval than the aggregation2-based list in Indian languages, but in English, the aggregation2-based stopword list performs better than the aggregation1-based list. The best performing idf-based stopword list improves MAP score by 5.43% in Bengali, 1.91% in Marathi, 5.4% in Gujarati, 1.5% in Hindi and 2.12% in English, respectively, over their baseline counterparts. The probabilistic retrieval models (BM25 and TF-IDF) perform best in different Indian languages. A smaller length of corpus-based stopword lists performs better than a larger length of non-corpus-based stopword lists for all the Indian languages considered. The proposed schemes demonstrate that a stopword list can be heuristically generated in a language-independent statistical method and effectively used for IR tasks with performance comparable, to or even better than non-corpus-based stopword lists.