Semantic Retrieval Research Articles

Enhancing creative divergent thinking in older adults with a semantic retrieval strategy

ABSTRACT Creative divergent thinking involves the generation of unique ideas by pulling from semantic memory stores and exercising cognitive flexibility to shape these memories into something new. Although cognitive abilities decline with age, semantic memory tends to remain intact. This study aims to utilize that memory to investigate the effectiveness of a brief cognitive training to improve creative divergent thinking. Older adults were trained using a semantic retrieval strategy to improve creativity in the Alternate Uses Task (AUT) and the Divergent Association Task (DAT). Participants were tested on the AUT and DAT across three time points: before the strategy was introduced (T0 and T1) and afterward (T2). Results showed that the strategy enhances idea novelty in the AUT; additionally, participants that initially scored lowest on the AUT showed the greatest increase in AUT performance. This finding suggests that older adults can use a semantic retrieval strategy to enhance creative divergent thinking.

Connector hubs in semantic network contribute to creative thinking.

Semantic memory offers a rich repository of raw materials (e.g., various concepts and connections between concepts) for creative thinking, represented as a semantic network. Similar to other networks, the semantic network exhibits a modular structure characterized by modules with dense internal connections and sparse connections between them. This organizational principle facilitates the routine storage and retrieval of information but may impede creativity. The present study investigated the effect of hub concepts with varying connection patterns on creative thinking from the perspective of a modular structured semantic network. By analyzing a large-scale semantic network, connector hubs (C-hubs) and provincial hubs (P-hubs) were identified based on their intra- and intermodule connections. These hubs were used as cue words in the alternative uses task, a widely used measure of creative thinking. Across four experiments, behavioral and neural evidence indicated that C-hubs facilitate the generation of more novel and remote ideas compared to P-hubs. However, this effect is predominantly observed in the early stage of the creative thinking process, involving changes in brain activation and functional connectivity in core regions of the default mode network and the frontoparietal network, including the dorsolateral prefrontal cortex, angular gyrus, and precuneus. Neural findings suggest that the superior performance of C-hubs relies on stronger interactions between automatic spreading activation, controlled semantic retrieval, and attentional regulation of salient information. These results provide insight into how concepts with varying semantic connection patterns facilitate and constrain different stages of the creative thinking process through the modular structure of semantic network. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Differential Mnemonic Contributions of Cortical Representations during Encoding and Retrieval.

Several recent fMRI studies of episodic and working memory representations converge on the finding that visual information is most strongly represented in occipito-temporal cortex during the encoding phase but in parietal regions during the retrieval phase. It has been suggested that this location shift reflects a change in the content of representations, from predominantly visual during encoding to primarily semantic during retrieval. Yet, direct evidence on the nature of encoding and retrieval representations is lacking. It is also unclear how the representations mediating the encoding-retrieval shift contribute to memory performance. To investigate these two issues, in the current fMRI study, participants encoded pictures (e.g., picture of a cardinal) and later performed a word recognition test (e.g., word "cardinal"). Representational similarity analyses examined how visual (e.g., red color) and semantic representations (e.g., what cardinals eat) support successful encoding and retrieval. These analyses revealed two novel findings. First, successful memory was associated with representational changes in cortical location (from occipito-temporal at encoding to parietal at retrieval) but not with changes in representational content (visual vs. semantic). Thus, the representational encoding-retrieval shift cannot be easily attributed to a change in the nature of representations. Second, in parietal regions, stronger representations predicted encoding failure but retrieval success. This encoding-retrieval "flip" in representations mimics the one previously reported in univariate activation studies. In summary, by answering important questions regarding the content and contributions to the performance of the representations mediating the encoding-retrieval shift, our findings clarify the neural mechanisms of this intriguing phenomenon.

Verifiable privacy-preserving semantic retrieval scheme in the edge computing

Edge computing, with its characteristics of low latency and low transmission costs, addresses the storage and computation challenges arising from the surge in network edge traffic. It enables users to leverage nearby edge servers for data outsourcing and retrieval. However, data outsourcing poses risks to data privacy. Although searchable encryption is proposed to secure search of outsourced data, existing schemes generally cannot meet the requirements of semantic search, and they also exhibit security risks and incur high search costs. In addition, edge servers may engage in malicious activities such as data tampering or forgery. Therefore, we propose a verifiable privacy-preserving semantic retrieval scheme named VPSR suitable for edge computing environments. We utilize the Doc2Vec method to extract text feature vectors and then convert them into matrix form to reduce storage space requirements for indexes, queries, and keys. We encrypt matrices using an improved secure k-nearest neighbor (kNN) algorithm based on learning with errors (LWE) and calculate text similarity by solving the Hadamard product between matrices. Additionally, we design an aggregable signature scheme and offload part of the result verification tasks to edge servers. Security and performance analysis results demonstrate that the VPSR scheme is suitable for edge computing environments with high encryption and search efficiency and low storage cost while ensuring security.

Grandpa, tell me a story! Narrative ability in healthy aging and its relationship with cognitive functions and Theory of Mind

ABSTRACT Healthy aging affects the linguistic and cognitive abilities involved in narrative production. However, few studies provided a comprehensive assessment of narrative skills in aging and their association with cognitive and Theory of Mind (ToM) functions. We explored how aging influences narrative skills, at both micro- (i.e. lexical and grammatical) and macrolinguistic (i.e. pragmatic and discourse) levels. Sixty healthy adults divided into three age-groups (20–40, 65–75, 76–86 years old) were administered tasks assessing narrative production skills, executive functions (EFs), and ToM. A network approach was used to explore how micro- and macrolinguistic skills were associated to different cognitive and ToM functions. The analyses revealed age-related effects on various aspects of language production, including phonology, semantic retrieval, sentence complexity, coherence, cohesion, and lexical informativeness. Moreover, a significant relationship was found between linguistic measures, ToM skills and cognitive functions such as working and long-term memory and inhibition.

Cross-Lingual Short-Text Semantic Similarity for Kannada–English Language Pair

Analyzing the semantic similarity of cross-lingual texts is a crucial part of natural language processing (NLP). The computation of semantic similarity is essential for a variety of tasks such as evaluating machine translation systems, quality checking human translation, information retrieval, plagiarism checks, etc. In this paper, we propose a method for measuring the semantic similarity of Kannada–English sentence pairs that uses embedding space alignment, lexical decomposition, word order, and a convolutional neural network. The proposed method achieves a maximum correlation of 83% with human annotations. Experiments on semantic matching and retrieval tasks resulted in promising results in terms of precision and recall.

N400 or P600?—A Systematic Review of ERP Studies on Gender Stereotype Violations

ABSTRACTPsycholinguistic studies using the event‐related brain potential (ERP) technique have found both N400 and P600 effects for gender stereotype violations. The finding of a P600 effect for this type of world knowledge violation is surprizing given that this component is traditionally associated with syntactic violations. In this paper, we set out to systematically analyse design‐ and task patterns of ERP studies investigating gender stereotype violations. Based on our review, we propose a scheme that predicts a P600 effect for gender stereotype violations for stimuli comprising entire sentences, and specifically when the gender stereotype serves to establish coherence in inferences (i.e., expecting a specific referent gender or a stereotype‐appropriate behaviour of the sentence subject based on the gender stereotype information). We predict an N400 effect for gender stereotype information that does not serve to establish coherence in inferences, as well as in priming paradigms. By extension, our predictive scheme suggests that the N400 may reflect a hybrid process of semantic retrieval and integration, while the P600 may reflect a cognitive process of error monitoring, and mental revision. Our study can aid in the interpretation of previous findings and inform future studies investigating gender stereotyping.

Macroscale brain states support the control of semantic cognition

A crucial aim in neuroscience is to understand how the human brain adapts to varying cognitive demands. This study investigates network reconfiguration during controlled semantic retrieval in differing contexts. We analyze brain responses to two semantic tasks of varying difficulty – global association and feature matching judgments – which are contrasted with non-semantic tasks on the cortical surface and within a whole-brain state space. Demanding semantic association tasks elicit activation in anterior prefrontal and temporal regions, while challenging semantic feature matching and non-semantic tasks predominantly activate posterior regions. Task difficulty also modulates activation along different dimensions of functional organization, suggesting different mechanisms of cognitive control. More demanding semantic association judgments engage cognitive control and default mode networks together, while feature matching and non-semantic tasks are skewed towards cognitive control networks. These findings highlight the brain’s dynamic ability to tailor its networks to support diverse neurocognitive states, enriching our understanding of controlled cognition.

A fuzzy rough approach to analyze the significance of semantic levels for building tags in OpenStreetMap

AbstractIn the realm of volunteered geographic information (VGI), the existence of comparable tags, attributes, and values across diverse categories of geographic objects gives rise to major categorization challenges such as conceptual overlap and indiscernibility. Enhancing the semantic data retrieval of VGI relies on the semantic quality of descriptive content annotated for tagging geographic objects. The main focus of this study is analyzing the descriptive content of OpenStreetMap to assess the significance of semantic levels. The proposed methodology relies on fuzzy rough set calculations to determine the degrees of dependency and significance of semantic levels. Three indicators, namely, the significance of semantic levels, decreasing the heterogeneity of attributes, and replicability were defined and assessed for a subset of building‐related tags. Analyzing building‐related tags in OpenStreetMap unveiled the higher significance for simple object, similarity, purpose, and function levels. The value of decreasing the heterogeneity of attributes was calculated at 63%, and the average replicability indicator of important attributes was doubled. Based on the results, the significance of semantic levels was deemed fit to enhance semantic homogeneity and replicability.

Differential reorganization of episodic and semantic memory systems in epilepsy-related mesiotemporal pathology.

Declarative memory encompasses episodic and semantic divisions. Episodic memory captures singular events with specific spatiotemporal relationships, while semantic memory houses context-independent knowledge. Behavioural and functional neuroimaging studies have revealed common and distinct neural substrates of both memory systems, implicating mesiotemporal lobe (MTL) regions such as the hippocampus and distributed neocortices. Here, we explored declarative memory system reorganization in patients with unilateral temporal lobe epilepsy (TLE) as a human disease model to test the impact of variable degrees of MTL pathology on memory function. Our cohort included 31 patients with TLE as well as 60 age and sex-matched healthy controls, and all participants underwent episodic and semantic retrieval tasks during a multimodal MRI session. The functional MRI tasks were closely matched in terms of stimuli and trial design. Capitalizing on non-linear connectome gradient mapping techniques, we derived task-based functional topographies during episodic and semantic memory states, both in the MTL and in neocortical networks. Comparing neocortical and hippocampal functional gradients between TLE patients and healthy controls, we observed a marked topographic reorganization of both neocortical and MTL systems during episodic memory states. Neocortical alterations were characterized by reduced functional differentiation in TLE across lateral temporal and midline parietal cortices in both hemispheres. In the MTL, on the other hand, patients presented with a more marked functional differentiation of posterior and anterior hippocampal segments ipsilateral to the seizure focus and pathological core, indicating perturbed intrahippocampal connectivity. Semantic memory reorganization was also found in bilateral lateral temporal and ipsilateral angular regions, while hippocampal functional topographies were unaffected. Leveraging MRI proxies of MTL pathology, we furthermore observed alterations in hippocampal microstructure and morphology that were associated with TLE-related functional reorganization during episodic memory. Moreover, correlation analysis and statistical mediation models revealed that these functional alterations contributed to behavioural deficits in episodic, but again not semantic memory in patients. Altogether, our findings suggest that semantic processes rely on distributed neocortical networks, while episodic processes are supported by a network involving both the hippocampus and neocortex. Alterations of such networks can provide a compact signature of state-dependent reorganization in conditions associated with MTL damage, such as TLE.

How retrieval processes change with age: Exploring age differences in semantic network and retrieval dynamics.

This study investigated the impact of age on semantic memory networks and retrieval dynamics using a single-list free recall paradigm, involving 318 participants. The younger group, with 175 participants aged 25-55 years (M = 46.68 years; SD = 10.69), and the older group, consisting of 143 participants aged 61-88 years (M = 68.71 years; SD = 6.09), completed a word recall test to assess delayed recall performance. Semantic memory networks were constructed from recall data by analyzing the co-occurrence and sequence of recalled words. We observed significant differences in network structure, where the older group displayed higher average shortest path length and modularity values, indicative of less integrated networks, while the younger group exhibited a higher clustering coefficient, suggesting a more interconnected network. In terms of retrieval dynamics, both groups showed a temporal contiguity effect with forward asymmetry. However, this effect was less pronounced in older adults. The study also identified participants that diverted from the average dynamic curves: one subgroup relied on nontemporal mechanisms, and the other employed a backward direction in memory search. Participants utilizing forward temporal associations demonstrated the highest recall performance. Overall, our findings suggest that lower free recall performance in older adults may be related to a diminished capacity to reinstate temporal context for retrieval and distinct differences in their semantic memory network structure. Specifically, older adults appear to exhibit networks with a less flexible, small-world-like structure. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Multiple semantic X-ray medical image retrieval using efficient feature vector extracted by FPN.

Content-based medical image retrieval (CBMIR) has become an important part of computer-aided diagnostics (CAD) systems. The complex medical semantic information inherent in medical images is the most difficult part to improve the accuracy of image retrieval. Highly expressive feature vectors play a crucial role in the search process. In this paper, we propose an effective deep convolutional neural network (CNN) model to extract concise feature vectors for multiple semantic X-ray medical image retrieval. We build a feature pyramid based CNN model with ResNet50V2 backbone to extract multi-level semantic information. And we use the well-known public multiple semantic annotated X-ray medical image data set IRMA to train and test the proposed model. Our method achieves an IRMA error of 32.2, which is the best score compared to the existing literature on this dataset. The proposed CNN model can effectively extract multi-level semantic information from X-ray medical images. The concise feature vectors can improve the retrieval accuracy of multi-semantic and unevenly distributed X-ray medical images.

Application scenario design and prospect of generative artificial intelligence (AI) in intelligent manufacturing and supply chain of traditional Chinese medicine

Intelligent manufacturing technologies, including databases, mathematical modeling, and information systems have played a significant role in process control, production management, and supply chain management in traditional Chinese medicine(TCM) industry. However, their ability to process and utilize unstructured data, such as research and development reports, batch production records, quality inspection records, and supplier documents, is relatively weak. For text, images, language, and other unstructured data, generative artificial intelligence(AI) technology has shown strong potential for development in extracting information, extracting knowledge, semantic retrieval, and content generation. Generative AI is expected to provide a feasible set of tools for the utilization of unstructured data resources in the TCM industry. Based on years of research and industrial application experience in TCM intelligent manufacturing technology, this study reviewed the current situation of intelligent manufacturing in TCM and the utilization of unstructured data, analyzed the application value of generative AI in the TCM manufacturing process and supply chain, summarized four typical application scenarios, including intelligent pharmaceutical knowledge base/knowledge graph, intelligent on-the-job trai-ning, intelligent production quality control, and intelligent supply chain. Furthermore, this study also explained the data collection and processing, business process design, application potential, and value of each scenario based on industry demands. Finally, based on the integration of generative AI and TCM industrial models, the study proposed a preliminary concept of a smart industrial brain for TCM, aiming to provide a reference for the application of AI technology in the field of TCM manufacturing.

A modified Vector Space Model for semantic information retrieval

In this research, we present a modified Vector Space Model which focuses on the semantic relevance of words for retrieving documents. The modified VSM resolves the problem of the classical model performing only lexical matching of query terms to document terms for retrievals. This problem also restricts the classical model from retrieving documents that do not have exact match of query terms even if they are semantically relevant to the query. In the modified model, we introduced a Query Relevance Update technique, which pads the original query set with semantically relevant document terms for optimised semantic retrieval results. The modified model also includes a novel tf−p which replaces the tf−idf technique of the classical VSM, which is used to compute the Term Frequency weights. The replacement of the tf−idf resolves the problem of the classical model penalising terms that occur across documents with the assumption that they are stop words, which in practice, there are usually such words which carry relevant semantic information for documents’ retrieval. We also extended the cosine similarity function with a proportionality weight pqd, which moderates biases for high frequency of terms in longer documents. The pqd ensures that the frequency of query terms including the updated ones are accounted for in proportionality with documents size for the overall ranking of documents. The simulated results reveal that, the modified VSM does achieve semantic retrieval of documents beyond lexical matching of query and document terms.

Efficient Sentence Representation Learning via Knowledge Distillation with Maximum Coding Rate Reduction

Addressing the demand for effective sentence representation in natural language inference problems, this paper explores the utility of pre-trained large language models in computing such representations. Although these models generate high-dimensional sentence embeddings, a noticeable performance disparity arises when they are compared to smaller models. The hardware limitations concerning space and time necessitate the use of smaller, distilled versions of large language models. In this study, we investigate the knowledge distillation of Sentence-BERT, a sentence representation model, by introducing an additional projection layer trained on the novel Maximum Coding Rate Reduction (MCR2) objective designed for general-purpose manifold clustering. Our experiments demonstrate that the distilled language model, with reduced complexity and sentence embedding size, can achieve comparable results on semantic retrieval benchmarks, providing a promising solution for practical applications.

Meaning as mentalization

The way we establish meaning has been a profound question not only in language research but in developmental science as well. The relation between linguistic form and content has been loosened up in recent pragmatic approaches to communication, showing that code-based models of language comprehension must be augmented by context-sensitive, pragmatic-inferential mechanisms to recover the speaker’s intended meaning. Language acquisition has traditionally been thought to involve building a mental lexicon and extracting syntactic rules from noisy linguistic input, while communicative-pragmatic inferences have also been argued to be indispensable. Recent research findings exploring the electrophysiological indicator of semantic processing, the N400, have raised serious questions about the traditional separation between semantic decoding and pragmatic inferential processes. The N400 appears to be sensitive to mentalization—the ability to attribute beliefs to social partners—already from its developmental onset. This finding raises the possibility that mentalization may not simply contribute to pragmatic inferences that enrich linguistic decoding processes but that the semantic system may be functioning in a fundamentally mentalistic manner. The present review first summarizes the key contributions of pragmatic models of communication to language comprehension. Then, it provides an overview of how communicative intentions are interpreted in developmental theories of communication, with a special emphasis on mentalization. Next, it discusses the sensitivity of infants to the information-transmitting potential of language, their ability to pick up its code-like features, and their capacity to track language comprehension of social partners using mentalization. In conclusion, I argue that the recovery of meaning during linguistic communication is not adequately modeled as a process of code-based semantic retrieval complemented by pragmatic inferences. Instead, the semantic system may establish meaning, as intended, during language comprehension and acquisition through mentalistic attribution of content to communicative partners.

Improving semantic video retrieval models by training with a relevance-aware online mining strategy

To retrieve a video via a multimedia search engine, a textual query is usually created by the user and then used to perform the search. Recent state-of-the-art cross-modal retrieval methods learn a joint text–video embedding space by using contrastive loss functions, which maximize the similarity of positive pairs while decreasing that of the negative pairs. Although the choice of these pairs is fundamental for the construction of the joint embedding space, the selection procedure is usually driven by the relationships found within the dataset: a positive pair is commonly formed by a video and its own caption, whereas unrelated video-caption pairs represent the negative ones. We hypothesize that this choice results in a retrieval system with limited semantics understanding, as the standard training procedure requires the system to discriminate between groundtruth and negative even though there is no difference in their semantics. Therefore, differently from the previous approaches, in this paper we propose a novel strategy for the selection of both positive and negative pairs which takes into account both the annotations and the semantic contents of the captions. By doing so, the selected negatives do not share semantic concepts with the positive pair anymore, and it is also possible to discover new positives within the dataset. Based on our hypothesis, we provide a novel design of two popular contrastive loss functions, and explore their effectiveness on four heterogeneous state-of-the-art approaches. The extensive experimental analysis conducted on four datasets, EPIC-Kitchens-100, MSR-VTT, MSVD, and Charades, validates the effectiveness of the proposed strategy, observing, e.g., more than +20% nDCG on EPIC-Kitchens-100. Furthermore, these results are corroborated with qualitative evidence both supporting our hypothesis and explaining why the proposed strategy effectively overcomes it.

Overlapping neural correlates underpin theory of mind and semantic cognition: Evidence from a meta-analysis of 344 functional neuroimaging studies

Key unanswered questions for cognitive neuroscience include whether social cognition is underpinned by specialised brain regions and to what extent it simultaneously depends on more domain-general systems. Until we glean a better understanding of the full set of contributions made by various systems, theories of social cognition will remain fundamentally limited. In the present study, we evaluate a recent proposal that semantic cognition plays a crucial role in supporting social cognition. While previous brain-based investigations have focused on dissociating these two systems, our primary aim was to assess the degree to which the neural correlates are overlapping, particularly within two key regions, the anterior temporal lobe (ATL) and the temporoparietal junction (TPJ). We focus on activation associated with theory of mind (ToM) and adopt a meta-analytic activation likelihood approach to synthesise a large set of functional neuroimaging studies and compare their results with studies of semantic cognition. As a key consideration, we sought to account for methodological differences across the two sets of studies, including the fact that ToM studies tend to use nonverbal stimuli while the semantics literature is dominated by language-based tasks. Overall, we observed consistent overlap between the two sets of brain regions, especially in the ATL and TPJ. This supports the claim that tasks involving ToM draw upon more general semantic retrieval processes. We also identified activation specific to ToM in the right TPJ, bilateral anterior mPFC, and right precuneus. This is consistent with the view that, nested amongst more domain-general systems, there is specialised circuitry that is tuned to social processes.

Subregions of the fusiform gyrus are differentially involved in the attentional mechanism supporting visual mental imagery in depression.

Impaired visual mental imagery is an important symptom of depression and has gradually become an intervention target for cognitive behavioral therapy. Our study involved a total of 25 healthy controls (HC) and 23 individuals with moderate depressive symptoms (MD). This study explored the attentional mechanism supporting visual mental imagery impairments in depression using the Vividness of Visual Imagery Questionnaire (VVIQ), attentional network test (ANT), and resting-state functional magnetic resonance imaging (rs-fMRI). The intrinsic activity of attention-related regions relative to those supporting visual mental imagery was identified in depression patients. In addition, a meta-analysis was used to describe the cognitive function related to this intrinsic activity. The global correlation (GCOR) of the right anterior fusiform gyrus (FG) was decreased in depression patients. Attention-related areas were concentrated in the right posterior FG; the anterior and posterior functional connectivity (FC) of the FG was decreased in depression patients. Graph theoretic analysis showed that the degree of the right anterior FG was decreased, the degree of the anterior insula was increased, and the negative connection between these two regions was strengthened in depression patients. In addition, the degree of the right anterior FG, the FC between the subregions of the right FG, and the FC between the right anterior FG and insula were correlated with VVIQ scores; however, this correlation was not significant in depression patients. The meta-analysis suggested that the changes in the anterior FG in depressed patients may stem from difficulties of semantic memory retrieval. The changed intrinsic activity of subregions of the FG relative to the semantic memory retrieval may be associated with visual mental imagery impairments in depression.

Developmental refinements to neural attentional state during semantic memory retrieval through adolescence

Despite the fact that attention undergoes protracted development, little is known about how it may support memory refinements in childhood and adolescence. Here, we asked whether people differentially focus their attention on semantic or perceptual information over development during memory retrieval. First, we trained a multivoxel classifier to characterize whole-brain neural patterns reflecting semantic versus perceptual attention in a cued attention task. We then used this classifier to quantify how attention varied in a separate dataset in which children, adolescents, and adults retrieved autobiographical, semantic, and episodic memories. All age groups demonstrated a semantic attentional bias during memory retrieval, with significant age differences in this bias during the semantic task. Trials began with a preparatory picture cue followed by a retrieval question, which allowed us to ask whether attentional biases varied by trial period. Adults showed a semantic bias earlier during the picture cues, whereas adolescents showed this bias during the question. Adults and adolescents also engaged different brain regions—superior parietal cortex and ventral visual regions, respectively—during preparatory picture cues. Our results demonstrate that retrieval-related attention undergoes refinement beyond childhood. These findings suggest that alongside expanding semantic knowledge, attention-related changes may support the maturation of factual knowledge retrieval.