Representation Of Categories Research Articles

Reconciling category exceptions through representational shifts.

Real-world categories often contain exceptions that disobey the perceptual regularities followed by other members. Prominent psychological and neurobiological theories indicate that exception learning relies on the flexible modulation of object representations, but the specific representational shifts key to learning remain poorly understood. Here, we leveraged behavioral and computational approaches to elucidate the representational dynamics during the acquisition of exceptions that violate established regularity knowledge. In our study, participants (n = 42) learned novel categories in which regular and exceptional items were introduced successively; we then fitted a computational model to individuals' categorization performance to infer latent stimulus representations before and after exception learning. We found that in the representational space, exception learning not only drove confusable exceptions to be differentiated from regular items, but also led exceptions within the same category to be integrated based on shared characteristics. These shifts resulted in distinct representational clusters of regular items and exceptions that constituted hierarchically structured category representations, and the distinct clustering of exceptions from regular items was associated with a high ability to generalize and reconcile knowledge of regularities and exceptions. Moreover, by having a second group of participants (n = 42) to judge stimuli's similarity before and after exception learning, we revealed misalignment between representational similarity and behavioral similarity judgments, which further highlights the hierarchical layouts of categories with regularities and exceptions. Altogether, our findings elucidate the representational dynamics giving rise to generalizable category structures that reconcile perceptually inconsistent category members, thereby advancing the understanding of knowledge formation.

Determination of anthropometric measurements and their application in the development of clothing sizing systems for women in the regions of the Republic of Croatia

Abstract The aim of this article is to determine the differences in the physical dimensions of the female body that form the basis of the clothing size system and compare their deviations within the three regions of the Republic of Croatia. Due to the changes in the dimensions and shape of the human body, there is a need to adjust clothing in order to achieve the best possible fit. A systematic consideration of the fit of clothing includes investigation of a number of elements and factors, especially those based on anthropometric knowledge. The results of anthropometric measurements performed on a representative sample determine the body dimensions of the measured population. By sorting and grouping the data of body measurements, clothing sizes are created, with significant differences in body shape and body proportions of individual population groups. In the clothing size system, individual body dimensions define the clothing size and form a scale determined by a norm that prescribes body shape based on two dimensions of girths, where the measurement of bust girth is the basic measurement, and the body height is the dependent variable. This research was carried out on a sample of 940 women from the region of Slavonia, 1,109 women from the region of Dalmatia, and 799 women from the City of Zagreb, classified into seven age groups. The statistical processing of the results, i.e., the method of principal components, the values of the observed body measurements, and the representation of certain categories of bust girth and body size in the measured population of each region were determined.

The Role of Attention in Category Representation.

Numerous studies have found that selective attention affects category learning. However, previous research did not distinguish between the contribution of focusing and filtering components of selective attention. This study addresses this issue by examining how components of selective attention affect category representation. Participants first learned a rule-plus-similarity category structure, and then were presented with category priming followed by categorization and recognition tests. Additionally, to evaluate the involvement of focusing and filtering, we fit models with different attentional mechanisms to the data. In Experiment 1, participants received rule-based category training, with specific emphasis on a single deterministic feature (D feature). Experiment 2 added a recognition test to examine participants' memory for features. Both experiments indicated that participants categorized items based solely on the D feature, showed greater memory for the D feature, were primed exclusively by the D feature without interference from probabilistic features (P features), and were better fit by models with focusing and at least one type of filtering mechanism. The results indicated that selective attention distorted category representation by highlighting the D feature and attenuating P features. To examine whether the distorted representation was specific to rule-based training, Experiment 3 introduced training, emphasizing all features. Under such training, participants were no longer primed by the D feature, they remembered all features well, and they were better fit by the model assuming only focusing but no filtering process. The results coupled with modeling provide novel evidence that while both focusing and filtering contribute to category representation, filtering can also result in representational distortion.

Evaluating Glyph Design for Showing Large-Magnitude-Range Quantum Spins.

We present experimental results to explore a form of bivariate glyphs for representing large-magnitude-range vectors. The glyphs meet two conditions: (1) two visual dimensions are separable; and (2) one of the two visual dimensions uses a categorical representation (e.g., a categorical colormap). We evaluate how much these two conditions determine the bivariate glyphs' effectiveness. The first experiment asks participants to perform three local tasks requiring reading no more than two glyphs. The second experiment scales up the search space in global tasks when participants must look at the entire scene of hundreds of vector glyphs to get an answer. Our results support that the first condition is necessary for local tasks when a few items are compared. But it is not enough for understanding a large amount of data. The second condition is necessary for perceiving global structures of examining very complex datasets. Participants' comments reveal that the categorical features in the bivariate glyphs trigger emergent optimal viewers' behaviors. This work contributes to perceptually accurate glyph representations for revealing patterns from large scientific results. We release source code, quantum physics data, training documents, participants' answers, and statistical analyses for reproducible science at https://osf.io/4xcf5/?view_only=94123139df9c4ac984a1e0df811cd580.

Limitations Associated with Proximate Analysis-Based Gross Calorific Value Modeling for Coals

ABSTRACT This study aims at investigating the limitations associated with proximate analysis-based gross calorific value (GCV) modeling for coals. Toward this, a dataset comprising proximate analysis and GCV data of 4792 coal samples collected from various Indian coal basins was generated, and then a GCV prediction model was developed using the popular multivariate linear regression (MLR) technique. Although the developed model appeared to be acceptable in terms of prediction R2 value of 0.934, through a rigorous statistical analysis, it has been shown that grade misclassification and source specific biases are inherent limitations associated with such GCV prediction models. It was found that the grade classification accuracy associated with a GCV prediction model was inversely and linearly proportional to the model’s associated mean absolute error (MAE) value. It has further been demonstrated that even well-validated GCV prediction models available in literature may perform sub-optimally when utilized for grade classification tasks. The analysis presented in this study also confirms that a source-specific bias can be introduced in the GCV prediction models developed using coal samples from varied geographical sources. It has further been shown that the incorporation of a categorical representation of the sample sources in the GCV prediction model could successfully eliminate the source-specific biases.

Wavelet Dynamic Selection Network for Inertial Sensor Signal Enhancement

As attitude and motion sensing components, inertial sensors are widely used in various portable devices, covering consumer electronics, sports health, aerospace, etc. But the severe intrinsic errors of inertial sensors heavily restrain their function implementation, especially the advanced functionality, including motion trajectory recovery and motion semantic recognition, which attracts considerable attention. As a mainstream signal processing method, wavelet is hailed as the mathematical microscope of signal due to the plentiful and diverse wavelet basis functions. However, complicated noise types and application scenarios of inertial sensors make selecting wavelet basis perplexing. To this end, we propose a wavelet dynamic selection network (WDSNet), which intelligently selects the appropriate wavelet basis for variable inertial signals. In addition, existing deep learning architectures excel at extracting features from input data but neglect to learn the characteristics of target categories, which is essential to enhance the category awareness capability, thereby improving the selection of wavelet basis. Therefore, we propose a category representation mechanism (CRM), which enables the network to extract and represent category features without increasing trainable parameters. Furthermore, CRM transforms the common fully connected network into category representations, which provide closer supervision to the feature extractor than the far and trivial one-hot classification labels. We call this process of imposing interpretability on a network and using it to supervise the feature extractor the feature supervision mechanism, and its effectiveness is demonstrated experimentally and theoretically in this paper. The enhanced inertial signal can perform impracticable tasks with regard to the original signal, such as trajectory reconstruction. Both quantitative and visual results show that WDSNet outperforms the existing methods. Remarkably, WDSNet, as a weakly-supervised method, achieves the state-of-the-art performance of all the compared fully-supervised methods.

Variational Hybrid-Attention Framework for Multi-Label Few-Shot Aspect Category Detection

Multi-label few-shot aspect category detection (FS-ACD) is a challenging sentiment analysis task, which aims to learn a multi-label learning paradigm with limited training data. The difficulty of this task is how to use limited data to generalize effective discriminative representations for different categories. Nowadays, all advanced FS-ACD works utilize the prototypical network to learn label prototypes to represent different aspects. However, such point-based estimation methods are inherently noise-susceptible and bias-vulnerable. To this end, this paper proposes a novel Variational Hybrid-Attention Framework (VHAF) for the FS-ACD task. Specifically, to alleviate the data noise, we adopt a hybrid-attention mechanism to generate more discriminative aspect-specific embeddings. Then, based on these embeddings, we introduce the variational distribution inference to obtain the aspect-specific distribution as a more robust aspect representation, which can eliminate the scarce data bias for better inference. Moreover, we further leverage an adaptive threshold estimation to help VHAF better identify multiple relevant aspects. Extensive experiments on three datasets demonstrate the effectiveness of our VHAF over other state-of-the-art methods. Code is available at https://github.com/chengzju/VHAF.

Winnie: Task-Oriented Dialog System with Structure-Aware Contrastive Learning and Enhanced Policy Planning

Pre-trained encoder-decoder models are widely applied in Task-Oriented Dialog (TOD) systems on the session level, mainly focusing on modeling the dialog semantic information. Dialogs imply structural information indicating the interaction among user utterances, belief states, database search results, system acts and responses, which is also crucial for TOD systems. In addition, for the system acts, additional pre-training and datasets are considered to improve their accuracies, undoubtedly introducing a burden. Therefore, a novel end-to-end TOD system named Winnie is proposed in this paper to improve the TOD performance. First, to make full use of the intrinsic structural information, supervised contrastive learning is adopted to narrow the gap in the representation space between text representations of the same category and enlarge the overall continuous representation margin between text representations of different categories in dialog context. Then, a system act classification task is introduced for policy optimization during fine-tuning. Empirical results show that Winnie substantially improves the performance of the TOD system. By introducing the supervised contrastive and system act classification losses, Winnie achieves state-of-the-art results on benchmark datasets, including MultiWOZ2.2, In-Car, and Camrest676. Their end-to-end combined scores are improved by 3.2, 1.9, and 1.1 points, respectively.

Evaluating IBM’s Watson natural language processing artificial intelligence as a short-answer categorization tool for physics education research

Recent advances in publicly available natural language processors (NLP) may enhance the efficiency of analyzing student short-answer responses in physics education research (PER). We train a state-of-the-art NLP, IBM’s Watson, and test its agreement with human coders using two different studies that gathered text responses in which students explain their reasoning on physics-related questions. The first study analyzes 479 student responses to a lab data analysis question and categorizes them by main idea. The second study analyzes 732 student answers to identify the presence or absence of each of the two conceptual themes. When training Watson with approximately one-third to half of the samples, we find that samples labeled with high confidence scores have similar accuracy to human agreement; yet for lower confidence scores, humans outperform the NLP’s labeling accuracy. In addition to studying Watson’s overall accuracy, we use this analysis to better understand factors that impact how Watson categorizes. Using the data from the categorization study, we find that Watson’s algorithm does not appear to be impacted by the disproportionate representation of categories in the training set, and we examine mislabeled statements to identify vocabulary and phrasing that may increase the rate of false positives. Based on this work, we find that, with careful consideration of the research study design and an awareness of the NLP’s limitations, Watson may present a useful tool for large-scale PER studies or classroom analysis tools. Published by the American Physical Society 2024

Combining wav2vec 2.0 Fine-Tuning and ConLearnNet for Speech Emotion Recognition

Speech emotion recognition poses challenges due to the varied expression of emotions through intonation and speech rate. In order to reduce the loss of emotional information during the recognition process and to enhance the extraction and classification of speech emotions and thus improve the ability of speech emotion recognition, we propose a novel approach in two folds. Firstly, a feed-forward network with skip connections (SCFFN) is introduced to fine-tune wav2vec 2.0 and extract emotion embeddings. Subsequently, ConLearnNet is employed for emotion classification. ConLearnNet comprises three steps: feature learning, contrastive learning, and classification. Feature learning transforms the input, while contrastive learning encourages similar representations for samples from the same category and discriminative representations for different categories. Experimental results on the IEMOCAP and the EMO-DB datasets demonstrate the superiority of our proposed method compared to state-of-the-art systems. We achieve a WA and UAR of 72.86% and 72.85% on IEMOCAP, and 97.20% and 96.41% on the EMO-DB, respectively.

Children and adults privilege linguistic over visual information when creating comparison classes for prenominal gradable adjectives

Mastering the semantics of gradable adjectives is a complex acquisition task, since their interpretation requires the calculation of a threshold relative to a comparison class. Context plays a role in determining the comparison class in multiple ways; empirical research has focused on non-linguistic cues, such as world knowledge and visual context, and less on linguistic cues, such as the modified head noun. The present study is the first to examine how preschoolers and adults use the taxonomic category encoded by the modified head noun as well as visual cues to interpret relative (big, small) and absolute (clean, dirty) gradable adjectives. Using existing objects from different taxonomic categories (basic-level and superordinate-level), we developed a picture-choice task that allowed us to infer the comparison class and the threshold from participants’ choices. Forty-three children (aged 3 to 5) and 26 adults, all monolingual German-speaking, were tested. Confirming previous research, children, like adults, showed a context-sensitive interpretation of relative but not of absolute gradable adjectives. The taxonomic label was preferred to visual cues in creating the comparison class already by age three; this advantage of linguistic over non-linguistic information was also present in the adults. We explain this finding with the privileged role of information provided by the taxonomic label: the modified head noun encodes the cognitive representation of an object category and the corresponding taxonomic hierarchy. Accordingly, this representation is easily accessible while creating the comparison class, which provides further evidence that language is a powerful tool to modulate our mental computations.

Dissecting abstract, modality-specific and experience-dependent coding of affect in the human brain.

Emotion and perception are tightly intertwined, as affective experiences often arise from the appraisal of sensory information. Nonetheless, whether the brain encodes emotional instances using a sensory-specific code or in a more abstract manner is unclear. Here, we answer this question by measuring the association between emotion ratings collected during a unisensory or multisensory presentation of a full-length movie and brain activity recorded in typically developed, congenitally blind and congenitally deaf participants. Emotional instances are encoded in a vast network encompassing sensory, prefrontal, and temporal cortices. Within this network, the ventromedial prefrontal cortex stores a categorical representation of emotion independent of modality and previous sensory experience, and the posterior superior temporal cortex maps the valence dimension using an abstract code. Sensory experience more than modality affects how the brain organizes emotional information outside supramodal regions, suggesting the existence of a scaffold for the representation of emotional states where sensory inputs during development shape its functioning.

Категория кажимости и её репрезентация в новелле Герберта Уэллса “The Door in the Wall” («Дверь в стене»)

The article investigates denominations of seemingness in Herbert G. Wells’s short story “The Door in the Wall” that have not been subjected to linguistic analysis. The aim of the research is to identify the peculiarities characterizing representation of the epistemic category of seemingness in an English short story (novella) as a genre of literature. The scientific originality of the research lies in describing the specific features of the category of seemingness in the novella and revealing its essential significance in the plot-building structure. The research findings showed that the language means indicating the seeming modus are used in utterances intrinsically connected with the major plot-building event ‒ the protagonist’s casual entry to a world of beautiful unreality of a wonderful garden. The study defined a significant role played by the modal category of seemingness in the contexts inherently connected with the conflict that underlies the protagonist’s contrasting views of ordinary reality and a wonderful paradise of unreality. The results showed that the modus of seemingness in the novella considered is inseparable from evaluation, perception and the modus-dictum organization (in Ch. Bally’s terms) of an utterance. The essential role of the category of seemingness in the novella is preconditioned by the first-person narration, presenting the plot events through a prismatic view as unverified and seemingly indefinite.

Structure shapes the representation of a novel category.

Concepts contain rich structures that support flexible semantic cognition. These structures can be characterized by patterns of feature covariation: Certain features tend to cluster in the same items (e.g., feathers, wings, can fly). Existing computational models demonstrate how this kind of structure can be leveraged to slowly learn the distinctions between categories, on developmental timescales. However, it is not clear whether and how we leverage feature structure to quickly learn a novel category. We thus investigated how the internal structure of a new category is first extracted from experience, with the prediction that feature-based structure would have a rapid and broad influence on the learned category representation. Across three experiments, novel categories were designed with patterns of feature associations determined by carefully constructed graph structures, with Modular graphs-exhibiting strong clusters of feature covariation-compared against Random and Lattice graphs. In Experiment 1, a feature inference task using verbal stimuli revealed that Modular structure broadly facilitated category learning. Experiment 2 replicated this effect in visual categories. In Experiment 3, a statistical learning paradigm revealed that this Modular benefit relates to high-level structure rather than pairwise feature associations and persists even when category structure is incidental to the task. A neural network model was readily able to account for these effects, suggesting that correlational feature structure may be encoded within rapidly learned, distributed category representations. These findings constrain theories of category representation and link theories of category learning with structure learning more broadly. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Heterogeneous Feature Selection Based on Neighborhood Combination Entropy.

Feature selection aims to remove irrelevant or redundant features and thereby remain relevant or informative features so that it is often preferred for alleviating the dimensionality curse, enhancing learning performance, providing better readability and interpretability, and so on. Data that contain numerical and categorical representations are called heterogeneous data, and they exist widely in many real-world applications. Neighborhood rough set (NRS) can effectively deal with heterogeneous data by using neighborhood binary relation, which has been successfully applied to heterogeneous feature selection. In this article, the NRS model as a unified framework is used to design a feature selection method to handle categorical, numerical, and heterogeneous data. First, the concept of neighborhood combination entropy (NCE) is presented. It can reflect the probability of pairs of the neighborhood granules that are probably distinguishable from each other. Then, the conditional neighborhood combination entropy (cNCE) based on NCE is proposed under the condition of considering decision attributes. Moreover, some properties and relationships between cNCE and NCE are derived. Finally, the functions of inner and outer significances are constructed to design a feature selection algorithm based on cNCE (FScNCE). The experimental results show the effectiveness and superiority of the proposed algorithm.

On the media representation of the universal category of culture "sinfulness"

This article aims to analyze the manifestation of the universal axiological cultural category of sinfulness in the media space. The study's subject was this category's properties and functions, which are actualized when represented in digital media. In accordance with this goal, we used such methods as continuous sampling and observation of the facts of the representation of cultural categories, which allowed us to determine the linguistic material for analysis and a statistical method for quantitative analysis. The content analysis of the units selected for the study was used to identify the role, properties, and main functions of the universal axiological category of sinfulness. The novelty of the research lies in the fact that, for the first time, the universal nature of the category of sinfulness, its functions and properties, represented in media publications, were considered. Main conclusions: the study of the facts of actualization of the category of sinfulness in the media space allowed us to assert that the phenomenon under study is associated with topics such as politics, crime (in news media), and the assessment of citizens' behavior and morality (in religious media). These facts indicate that the primary function of the category of sinfulness is regulatory, aimed at evaluating and correcting the actions of members of society, as well as creating public opinion. In addition, the article proves that the category of sinfulness has transcultural properties, penetrating and being realized in all cultural layers, from local to global. In these cultural layers, the nature of the category acquires a different shade. In particular, at the global level, the category of sinfulness acquires the meaning of guilt, which is a consequence of the significant influence of Western culture on the global culture. It seems that the revealed properties of the category indicate its high importance for modern Russian society. The established regulatory function will make it possible to apply this category, including within the framework of regulating and managing public opinion and promoting basic values.

Early visual cortices reveal interrelated item and category representations in aging.

Neural dedifferentiation, the finding that neural representations tend to be less distinct in older adults compared with younger adults, has been associated with age-related declines in memory performance. Most studies assessing the relation between memory and neural dedifferentiation have evaluated how age impacts the distinctiveness of neural representations for different visual categories (e.g., scenes and objects). However, how age impacts the quality of neural representations at the level of individual items is still an open question. Here, we present data from an age-comparative fMRI study that aimed to understand how the distinctiveness of neural representations for individual stimuli differs between younger and older adults and relates to memory outcomes. Pattern similarity searchlight analyses yielded indicators of neural dedifferentiation at the level of individual items as well as at the category level in posterior occipital cortices. We asked whether age differences in neural distinctiveness at each representational level were associated with inter- and/or intraindividual variability in memory performance. While age-related dedifferentiation at both the item and category level related to between-person differences in memory, neural distinctiveness at the category level also tracked within-person variability in memory performance. Concurrently, neural distinctiveness at the item level was strongly associated with neural distinctiveness at the category level both within and across participants, elucidating a potential representational mechanism linking item- and category-level distinctiveness. In sum, we provide evidence that age-related neural dedifferentiation co-exists across multiple representational levels and is related to memory performance.Significance Statement Age-related memory decline has been associated with neural dedifferentiation, the finding that older adults have less distinctive neural representations than younger adults. This has been mostly shown for category information, while evidence for age differences in the specificity of item representations is meager. We used pattern similarity searchlight analyses to find indicators of neural dedifferentiation at both levels of representation (category and item) and linked distinctiveness to memory performance. Both item- and category-level dedifferentiation in the calcarine cortex were related to interindividual differences in memory performance, while category-level distinctiveness further tracked intraindividual variability. Crucially, neural distinctiveness was strongly tied between the item and category levels, indicating that intersecting representational properties of posterior occipital cortices reflect both individual exemplars and categories.

Is attention necessary for the representational advantage of good exemplars over bad exemplars?

Real-world (rw-) statistical regularities, or expectations about the visual world learned over a lifetime, have been found to be associated with scene perception efficiency. For example, good (i.e., highly representative) exemplars of basic scene categories, one example of an rw-statistical regularity, are detected more readily than bad exemplars of the category. Similarly, good exemplars achieve higher multivariate pattern analysis (MVPA) classification accuracy than bad exemplars in scene-responsive regions of interest, particularly in the parahippocampal place area (PPA). However, it is unclear whether the good exemplar advantages observed depend on or are even confounded by selective attention. Here, we ask whether the observed neural advantage of the good scene exemplars requires full attention. We used a dual-task paradigm to manipulate attention and exemplar representativeness while recording neural responses with functional magnetic resonance imaging (fMRI). Both univariate analysis and MVPA were adopted to examine the effect of representativeness. In the attend-to-scenes condition, our results replicated an earlier study showing that good exemplars evoke less activity but a clearer category representation than bad exemplars. Importantly, similar advantages of the good exemplars were also observed when participants were distracted by a serial visual search task demanding a high attention load. In addition, cross-decoding between attended and distracted representations revealed that attention resulted in a quantitative (increased activation) rather than qualitative (altered activity patterns) improvement of the category representation, particularly for good exemplars. We, therefore, conclude that the effect of category representativeness on neural representations does not require full attention.

Left-handed voices? Examining the perceptual learning of novel person characteristics from the voice.

We regularly form impressions of who a person is from their voice, such that we can readily categorise people as being female or male, child or adult, trustworthy or not, and can furthermore recognise who specifically is speaking. How we establish mental representations for such categories of person characteristics has, however, only been explored in detail for voice identity learning. In a series of experiments, we therefore set out to examine whether and how listeners can learn to recognise a novel person characteristic. We specifically asked how diagnostic acoustic properties underpinning category distinctions inform perceptual judgements. We manipulated recordings of voices to create acoustic signatures for a person's handedness (left-handed vs. right-handed) in their voice. After training, we found that listeners were able to successfully learn to recognise handedness from voices with above-chance accuracy, although no significant differences in accuracy between the different types of manipulation emerged. Listeners were, furthermore, sensitive to the specific distributions of acoustic properties that underpinned the category distinctions. We, however, also find evidence for perceptual biases that may reflect long-term prior exposure to how voices vary in naturalistic settings. These biases shape how listeners use acoustic information in the voices when forming representations for distinguishing handedness from voices. This study is thus a first step to examine how representations for novel person characteristics are established, outside of voice identity perception. We discuss our findings in light of theoretical accounts of voice perception and speculate about potential mechanisms that may underpin our results.

The association of Life's Simple 7 and infertility among U.S. women.

The Life's Simple 7 (LS7) metric is a comprehensive measure of cardiovascular health (CVH) that encompasses seven distinct risk factors and behaviors associated with cardiovascular disease (CVD). Some studies have shown an association between infertility and CVD. The present study aimed to explore the potential association between the LS7 factors and infertility. A cross-sectional study was conducted on a sample of 3537 women aged 18-44 years from the National Health and Nutrition Examination Survey (NHANES) spanning the years 2013-2018. The LS7 metrics encompassed various factors including physical activity, smoking habits, body mass index, blood pressure levels, dietary patterns, blood glucose levels, and total cholesterol levels. We computed a 14-point LS7 score based on participants' baseline data, classifying them as "inadequate" (3-6), "average" (7-10), or "ideal" (11-14). Infertility is defined as an affirmative answer to either of two questions on the NHANES questionnaire: "Have you tried to conceive for at least one year without success?" and "Have you sought medical help for your inability to conceive?" Logistic regression was utilized to estimate odds ratios (O.R.s) and 95% confidence intervals (C.I.s). In total, 17.66% of participants were classified as individuals who reported experiencing infertility. In the continuous analysis, each one-unit increase in LS7 score was associated with a significantly decreased odds of infertility (OR=0.88 [0.77-0.89]). Analyzing the categorical representation of LS7 score, compared to individuals with poor scores, those with ideal scores exhibited a substantial 58% reduction in the odds of infertility (OR=0.42 [0.26-0.69]). Additionally, the observed interaction suggested that the influence of age on the relationship between LS7 and infertility is not consistent across different age groups (P for interaction < 0.001). Among individuals aged 35 or younger, each unit increase in LS7 score was associated with a substantial 18% (OR=0.82 [0.76-0.89]) decrease in the odds of infertility. However, in the older age group (>35), the association was attenuated and non-significant. Our research suggests a significant inverse association between LS7 scores and infertility. Age demonstrated a varying impact on this relationship, with a more pronounced impact observed among individuals aged 35 or younger.