Contextual Cues Research Articles

Classification and risk estimation of osteoarthritis using deep learning methods

The classification of knee osteoarthritis is solely based on contextual factors, with image processing algorithms playing a significant role in computer-aided diagnosis (CAD) systems. The inconsistent real-time pre-processing, on the other hand, has a significant impact on the diagnosing process. In this work, a Densely Connected Fully Convolutional Network (DFCN) for knee osteoarthritis classifier based on multiple learning (ML) strategies effectively classify knee osteoarthritis on the basis of risk estimation. Spatial osteoarthritis contextual vectors extracted by identifying the relationship between contextual variables using a machine learning approach. The hidden convolutional layers are used to compute edge interpretation, contextual cues, and input correction. The fused layer, which is simply a concentration of derived features, supports automatic learning of contextual features of osteoarthritis classification. The standard datasets from the Osteoarthritis Initiative (OAI) and the Multicentre Osteoarthritis Study (MOST) are used for experimental purposes to validate the proposed method. The results shows that the proposed DFCN is significantly improves the feature recognition for accurate classification around 94 % which is significantly higher than existing CNN results and flexibility to real-time implementation in the CAD system. It can also be used to automatically detect osteoarthritis types using a lightweight CNN architecture.

Effects of group entitativity on young English-speaking children's interpretation of inclusive We.

The pronoun we can be used to refer to various collections of people depending on various pragmatic factors. This article reports the results of two online experiments that investigated children's interpretation of inclusive we, in which the child-listener is part of the intended referent of we. 128 2- and 4-year-olds collaborated with three partners in a coloring task. Before they played together, one partner informed participants that, e.g., "we can color!" Participants had their own markers and had to choose to how many partners to distribute (virtual) markers. In the first experiment, the partners appeared more like an aggregation of individuals than a collaborative group. The second experiment flipped this so that the partners appeared more like a collaborative group. Contrary to expectations, there was relatively little evidence for development in children's interpretation of we. Additionally, participants did not sharply distinguish their interpretations of we from those of we both or we all. Rather, participants were more likely to choose group interpretations when contextual cues indicated that their partners were a collaborative group than an aggregation of individuals. Interestingly, this interpretational distinction was sharpest for the pragmatically ambiguous we, compared to the relatively unambiguous we both and we all. These results are informative about the kinds of cues that shape young children's interpretation of pragmatically ambiguous pronominal reference.

HIV and gender identity expression among transfeminine people in the Western Cape, South Africa – a thematic analysis of data from the HPTN 071 (PopART) trial

IntroductionTransfeminine people in South Africa have a high HIV risk due to structural, behavioural, and psychosocial factors. Transfeminine people and feminine identifying men who have sex with men (MSM) are often conflated or grouped with transgender or MSM categories in HIV service programming, although they don’t necessarily identify as either. We aimed to investigate gender expression among feminine identifying people who were assigned male at birth. We examined how local conceptualizations of sexuality and gender intersect with the key population label of ‘transgender’ imported into local HIV programming.MethodsA qualitative cohort nested within the HPTN 071 (PopART) trial included longitudinal, in-depth interviews with eight transfeminine people (four who disclosed as living with HIV). Data were collected approximately every six weeks between January 2016 and October 2017. We used a combination of thematic analysis and case study descriptions to explore gender identification among participants.ResultsOf the eight participants, only one accepted ‘transgender’ as a label, and even she used varying terms at different times to describe her identity. For participants, a feminine identity included dressing in normatively feminine clothes; using feminine terms, pronouns and names; and adopting stereotypically feminine mannerisms. Participants would switch between typically feminine and masculine norms in response to contextual cues and audience. For example, some participants accepted identification as masculine gay men amongst their family members. Among peers, they expressed their identity through typically more effeminate gender characteristics, for example self-identifying as “femgay”. With partners they often also took on a feminine identity role, for example identifying as women in sexual and romantic relationships (meaning they viewed and expressed themselves as the feminine partner in the relationship).ConclusionsOur findings are amongst the first exploratory and descriptive data of transfeminine people in South Africa. We show how transfeminine people navigate fluid gender identities that could pose a challenge for accessing and utilizing HIV services that are currently set up for transgender individuals or MSM. More work needs to be done to understand and respond to the diverse and shifting ways people experience their gender identities in this high HIV burden context.

Impact of Effective Word Vectors on Deep Learning Based Subjective Classification of Online Reviews

Sentiment Analysis tasks are made considerably simpler by extracting subjective statements from online reviews, thereby reducing the overhead of the classifiers. The review dataset encompasses both subjective and objective sentences, where subjective writing expresses the author's opinions, and objective text presents factual information. Assessing the subjectivity of review statements involves categorizing them as objective or subjective. The effectiveness of word vectors plays a crucial role in this process, as they capture the semantics and contextual cues of a subjective language. This study investigates the significance of employing sophisticated word vector representations to enhance the detection of subjective reviews. Several methodologies for generating word vectors have been investigated, encompassing both conventional approaches, such as Word2Vec and Global Vectors for word representation, and recent innovations, such as like Bidirectional Encoder Representations from Transformers (BERT), ALBERT, and Embeddings from Language Models. These neural word embeddings were applied using Keras and Scikit-Learn. The analysis focuses on Cornell subjectivity review data within the restaurant domain, and metrics evaluating performance, such as accuracy, F1-score, recall, and precision, are assessed on a dataset containing subjective reviews. A wide range of conventional vector models and deep learning-based word embeddings are utilized for subjective review classification, frequently in combination with deep learning architectures like Long Short-Term Memory (LSTM). Notably, pre-trained BERT-base word embeddings exhibited exceptional accuracy of 96.4%, surpassing the performance of all other models considered in this study. It has been observed that BERT-base is expensive because of its larger structure.

Unveiling Sarcasm in Hindi: Cutting-Edge Deep Learning Framework for Sarcasm Detection

Opinion mining, also known as sentiment analysis (SA), is a pivotal computer-based approach for discerning and categorizing sentiments within textual data. With the burgeoning popularity of social media platforms and the proliferation of user-generated content in Hindi, there is an escalating need for proficient sentiment analysis techniques tailored specifically for this language. The internet's accessibility in regional languages has become imperative to accommodate a diverse user base irrespective of age or linguistic inclination. While a significant portion of SA research has been conducted in English, there exists a dearth of comprehensive studies focusing on Indian languages, notably Hindi. This paper addresses this gap by investigating sentiment analysis within the context of Hindi, delving into various sentiment categories including positive, negative, neutral, and the notably complex sentiment of sarcasm. Sarcasm, characterized by its subtle irony and ridicule, poses a formidable challenge in sentiment analysis, particularly in languages like Hindi, which boast intricate grammatical structures and nuanced contextual cues. This study aims to enhance sentiment analysis methodologies by unraveling the intricacies of sarcasm detection in Hindi textual data.

A contextual-based approach for sarcasm detection

Sarcasm is a perplexing form of human expression that presents distinct challenges in understanding. The problem of sarcasm detection has centered around analyzing individual utterances in isolation which may not provide a comprehensive understanding of the speaker’s sarcastic intent. Our work addresses this problem by exploring and understanding the specific contextual cues that contribute to sarcasm. In this paper, we propose an enhanced approach for sarcasm detection using contextual features. Our methodology involves employing pre-trained transformer models, RoBERTa and DistilBERT, and fine-tuning them on two datasets: the News Headlines and the Mustard datasets. Incorporating contextual information, the proposed approach yielded the best performance, achieving an impressive F1 score of 99% on News Headlines dataset and 90% on Mustard dataset. Moreover, we experimented summarizing the context into a concise short sentence. This enhancement reduced training time by 35.5% of the original time. We further validated the model trained on the News headlines dataset against the Reddit dataset, which resulted in 49% F1 score without context data. However, with the inclusion of context data, the F1 score surged to 75%. Proposed approach enhances the understanding of sarcasm in different contextual settings, enabling more accurate sentiment analysis and better decision-making in various applications.

Endel Tulving (1927-2023).

This article presents an obituary for Endel Tulving. Tulving's educational and professional careers are summarized. His work in the field of human memory is detailed. It is noted that Tulving's look at the field of verbal learning in the late 1950s persuaded him that the dominant associative tradition missed many important aspects of human memory. His research found that at the time of retrieval, memory for the original event may be successfully reinstated only by contextual cues that interact in a complementary fashion with the specifically encoded memory trace, a process that Tulving referred to as "synergistic ecphory". He is also known for his work on memory systems. In his book, Elements of Episodic Memory published in 1983, Tulving proposed that memory for experienced events, episodic memory, should be distinguished from general knowledge of the world, semantic memory, and from procedural memory, the learned ability to perform such skilled procedures as riding a bicycle or playing a musical instrument. He also proposed an evolutionary framework for these different but related systems, suggesting that simple animals show only procedural memory, more complex animals are consciously aware of their knowledge of the world, but only humans possess episodic memory-the ability to use "mental time travel" to consciously recreate past experiences and to imagine possible future events. Although known initially for his purely cognitive behavioral research, during the 1980s and 1990s, Tulving increasingly incorporated neuropsychological and neuroimaging approaches into his work. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The effect of foreigner talk on children’s evaluations of addressees

Children evaluate others based on how they speak, but do children evaluate others based on how they are spoken to? We examined how U.S. children and adults (N = 170 5- to 10-year-olds, 49 % female; 107 adults; in a city with a foreign population of 17.9 %) evaluated addressees of Foreigner Talk (i.e., slow, loud, simplified speech). In Study 1, children and adults evaluated Foreigner Talk addressees more negatively than Peer Talk or Teacher Talk addressees. In Study 2, adults and older children incorporated Foreigner Talk with additional contextual cues to inform their evaluations: a local peer receiving Foreigner Talk received lower evaluations than a foreign peer receiving Foreigner Talk. With medium to large effect sizes, these studies indicate the importance of speech register in children’s social inferences.

Effects of Context Cues on Chinese Consumer Cross-Border Purchase Intention: General vs. Low-Carbon Korean Goods

Purpose - Previous research has emphasized but not empirically investigated the precise effects of cross-border online shopping context cues and overall consumer perception on consumer cross-border purchase intention (CBPI). To address the gap, based on an S-O-R model and valence framework, this study investigates the structural relationship of online context cues →overall consumer perception → consumer CBPI. Design/Methodology - This study conducts a questionnaire study with 308 Chinese participants to test the all hypotheses. Findings - This study reveals that four context cues have positive effects on overall consumer perceived value and increases CBPI. On the contrary, four context cues have negative effects on overall consumer perceived uncertainty, which discourages CBPI for Korean goods. In addition, there is also a significant difference between the general and low-carbon goods shopping process in context cues → overall consumer perception relationship. Originality/value - This study focuses on online shopping context cues to elucidate Chinese consumer information processes for the CBEC marketing mix as a key to improving overall perception and CBPI. In addition, as the moderating role of good type addresses the significant different information process in general and low-carbon goods on overall consumer perception formation, this study scrutinizes how context cue effects are shaped by goods type.

Steady Hand at the Wheel: How Perceived Movement Influences Consumer Responses to Brand Failures

Paratextual communication methods, such as the use of emojis and other visual messaging cues, offer firms a unique opportunity to repair relationships with consumers after brand failures. The current research demonstrates that visual messaging cues associated with lower perceived movement lead to greater brand loyalty relative to more dynamic cues in response to brand failure. Across five studies, we show that lower perceived movement in visual messaging cues leads to more favorable consumer outcomes (e.g., subsequent real choice behavior and brand loyalty intentions). This effect is mediated by brand trust. Furthermore, we examine theoretically and practically relevant boundary conditions. The findings presented offer theoretical contributions to research on visual communication cues in brand recovery contexts. Additionally, the current research provides managerially relevant insights for communication strategies during recovery.

Exploring the Drawbacks of Using Artificial Intelligence in Studies on Architecture:NLP and Highly Contextual Postmodern Architectural Discourse

This research investigates the artificial intelligence aided architectural discursive sentiment analysis within the texts of the representants of architectural culture of the sixties and the seventies. The subject of inquiry for this study is to examine the nature and validity of sentiment analysis, using Natural Language Processing tools, on postmodernist architectural discourse. This paper searches to identify the possible drawbacks of using the NLP tool. The results show that the sentiment analysis of postmodernist texts using opinion mining techniques must be interpreted with the utmost caution. Especially in the case of unstraightforward, highly contextualized, and modernist reactive, often ironic or sarcastic, postmodern discourse. Two main reasons for that were found in this study. First, architectural postmodernists use many polarized words which might end in inconclusive results when opinion mining techniques are in use. Second, the postmodernist texts might themselves lack important contextual cues for appropriate classification of sentiment by NLP. Discrepancies and internal contradictions are characteristic for the postmodernist style of writing. and artificial intelligence still does not fully decode it.

Limitations during processing of variable reflexive anaphors and overt/null object pronouns in Turkish aphasia revealed by eye-tracking during listening studies

This study investigated how reflexives and object pronouns in Turkish are processed in aphasia as compared to a group of unimpaired controls. We aimed to understand (i) the moment-by-moment time course of processing reflexive forms, and (ii) and overt/null object pronouns in Turkish people with aphasia (PWA). We administered two eye-movement-monitoring during-listening experiments exploring resolution of reflexive variables (Experiment 1) and object pronouns (Experiment 2) in 6 PWA and their controls (n=26). Our findings showed that interpretation of reflexive anaphors in Turkish is variable, and PWA are employing a non-local interpretation of reflexive anaphors to tackle down referential ambiguity in the absence of an ability to integrate contextual cues. Furthermore, PWA inappropriately considered inaccessible local antecedents for object pronouns, and they encountered limitations processing discourse antecedents. We argue that anaphoric processing profiles in aphasia face strong limitations by variable interpretability of pronominal reference and PWA’s inability to integrate contextual cues to disambiguate the intended antecedents.

Towards Retrieval-Augmented Architectures for Image Captioning

The objective of image captioning models is to bridge the gap between the visual and linguistic modalities by generating natural language descriptions that accurately reflect the content of input images. In recent years, researchers have leveraged deep learning-based models and made advances in the extraction of visual features and the design of multimodal connections to tackle this task. This work presents a novel approach toward developing image captioning models that utilize an external k NN memory to improve the generation process. Specifically, we propose two model variants that incorporate a knowledge retriever component that is based on visual similarities, a differentiable encoder to represent input images, and a k NN-augmented language model to predict tokens based on contextual cues and text retrieved from the external memory. We experimentally validate our approach on COCO and nocaps datasets and demonstrate that incorporating an explicit external memory can significantly enhance the quality of captions, especially with a larger retrieval corpus. This work provides valuable insights into retrieval-augmented captioning models and opens up new avenues for improving image captioning at a larger scale.

Decision uncertainty as a context for motor memory

The current view of perceptual decision-making suggests that once a decision is made, only a single motor programme associated with the decision is carried out, irrespective of the uncertainty involved in decision making. In contrast, we show that multiple motor programmes can be acquired on the basis of the preceding uncertainty of the decision, indicating that decision uncertainty functions as a contextual cue for motor memory. The actions learned after making certain (uncertain) decisions are only partially transferred to uncertain (certain) decisions. Participants were able to form distinct motor memories for the same movement on the basis of the preceding decision uncertainty. Crucially, this contextual effect generalizes to novel stimuli with matched uncertainty levels, demonstrating that decision uncertainty is itself a contextual cue. These findings broaden the understanding of contextual inference in motor memory, emphasizing that it extends beyond direct motor control cues to encompass the decision-making process.

CUPID: Contextual Understanding of Prompt‐conditioned Image Distributions

AbstractWe present CUPID: a visualization method for the contextual understanding of prompt‐conditioned image distributions. CUPID targets the visual analysis of distributions produced by modern text‐to‐image generative models, wherein a user can specify a scene via natural language, and the model generates a set of images, each intended to satisfy the user's description. CUPID is designed to help understand the resulting distribution, using contextual cues to facilitate analysis: objects mentioned in the prompt, novel, synthesized objects not explicitly mentioned, and their potential relationships. Central to CUPID is a novel method for visualizing high‐dimensional distributions, wherein contextualized embeddings of objects, those found within images, are mapped to a low‐dimensional space via density‐based embeddings. We show how such embeddings allows one to discover salient styles of objects within a distribution, as well as identify anomalous, or rare, object styles. Moreover, we introduce conditional density embeddings, whereby conditioning on a given object allows one to compare object dependencies within the distribution. We employ CUPID for analyzing image distributions produced by large‐scale diffusion models, where our experimental results offer insights on language misunderstanding from such models and biases in object composition, while also providing an interface for discovery of typical, or rare, synthesized scenes.

The Influence of Sex and Gender, Gender Ontological Beliefs, and Sexism on Pain Sensitivity Judgments: Implications for Health Care Settings and Beyond

Chronic pain affects at least one in five individuals. Hence, adequate pain judgement is a vital interpersonal skill. Nonetheless, women often face treatment disparities due to biased assessments. Priorly, sex and gender (i.e., biological category vs. cultural representation) have not been investigated separately. Our study was thus designed to disentangle their distinct influence on pain judgments. In an online study, N = 679 participants (73.8% female) were presented with a novel vignettes paradigm including descriptions of fictional gender-stereotypical and counter-stereotypical women and men. Participants were instructed to rate the characters’ pain sensitivity before completing inventories measuring gender ontological beliefs and ambivalent sexism. We found significant interaction effects of observer and character sex, in that higher pain sensitivity was attributed to opposite-sex characters. Men showed a higher sex bias, which was partially mediated by lower social constructionism and higher hostile sexism. We further uncovered a gender bias across observers, in that feminine characters were assumed to be more pain sensitive, irrespective of their sex. Our research underscores the presence of sex and gender biases in pain judgments, even in scenarios with minimal contextual cues. Despite limitations, the findings offer insights for the development of intervention strategies, which might reduce treatment disparities.

Age-related contextual cueing features are more evident in reaction variability than in reaction time.

Visual-spatial contextual cueing learning underpins the daily lives of older adults, enabling them to navigate their surroundings, perform daily activities, and maintain cognitive function. While the contextual cueing effect has received increasing attention from researchers, the relationship between this cognitive ability and healthy ageing remains controversial. To investigate whether visual-spatial contextual cueing learning declines with age, we examined the contextual learning patterns of older (60-71 years old) and younger adults (18-26 years old) using a contextual-guided visual search paradigm and response variability measurements. We observed significant contextual learning effects in both age groups, impacting response speed and variability, with these effects persisting for at least 24 days. However, older adults required more repetitions and memorised fewer repeated stimuli during initial learning. Interestingly, their long-term memory maintenance appeared stronger, as their contextual facilitation persisted in both response speed and variability, while younger adults only persisted in response speed but not variability. Overall, our results suggest an age-related complex and diverse contextual cueing pattern, with older adults showing weaker learning but stronger long-term memory maintenance compared with younger adults.

Habits, Goals, and Effective Behavior Change

Why do we act on habit even when we intend to do something else? The answer lies in habit memories, or context-response associations, that form when people repeat rewarding actions in stable contexts. Although habits can form as people pursue goals, once habits develop, the perception of the context directly activates the response in mind. Because habit activation does not depend strongly on motivation, changing intentions has limited impact on habit memory. Instead, successful habit-change interventions directly impact the behavior itself: Along with classic behavior therapy interventions, habits change with (a) reward systems that form new habits, (b) disruption of context cues to forestall activation of the habit in mind, and (c) friction that makes the habitual response difficult and alternatives easier. Despite the strong evidence that habits are activated by contexts, people tend to believe that their own habits are a product of goal pursuit. This subjective reality might also explain why some researchers continue to maintain that habit performance depends on goals.

Involvement of Pre-limbic Cortex-Nucleus accumbens projections in Context-Induced alcohol seeking

Alcohol use disorder (AUD) remains a critical public health issue worldwide, characterized by high relapse rates often triggered by contextual cues. This research investigates the neural mechanisms behind context-induced reinstatement of alcohol-seeking behavior, focusing on the nucleus accumbens and its interactions with the prelimbic cortex, employing Male Long-Evans rats in an ABA renewal model. In our experimental setup, rats were trained to self-administer 10 % ethanol in Context A, followed by extinction of lever pressing in the presence of discrete cues in Context B. The context-induced reinstatement of ethanol-seeking was then assessed by re-exposing rats to Context A or B under extinction conditions, aiming to simulate the environmental cues’ influence on relapse behaviors. Three experiments were conducted: Experiment 1 utilized Fos-immunohistochemistry to examine neuronal activation in the nucleus accumbens; Experiment 2 applied the baclofen + muscimol inactivation technique to probe the functional importance of the nucleus accumbens core; Experiment 3 used Fos-immunofluorescence along with Retrobeads injection to investigate activation of neurons projecting from the prelimbic cortex to the nucleus accumbens core. Our findings revealed significant increases in Fos-immunoreactive nuclei within the nucleus accumbens core and shell during the reinstatement phase in Context A, underscoring the environment’s potent effect on ethanol-seeking behavior. Additionally, inactivation of the nucleus accumbens core markedly reduced reinstatement, and there was a notable activation of neurons from the prelimbic cortex to the nucleus accumbens core in the ethanol-associated context. These results highlight the critical role of the nucleus accumbens core and its corticostriatal projections in the neural circuitry underlying context-driven ethanol seeking.

The Impact of Scene Context on Visual Object Recognition: Comparing Humans, Monkeys, and Computational Models.

During natural vision, we rarely see objects in isolation but rather embedded in rich and complex contexts. Understanding how the brain recognizes objects in natural scenes by integrating contextual information remains a key challenge. To elucidate neural mechanisms compatible with human visual processing, we need an animal model that behaves similarly to humans, so that inferred neural mechanisms can provide hypotheses relevant to the human brain. Here we assessed whether rhesus macaques could model human context-driven object recognition by quantifying visual object identification abilities across variations in the amount, quality, and congruency of contextual cues. Behavioral metrics revealed strikingly similar context-dependent patterns between humans and monkeys. However, neural responses in the inferior temporal (IT) cortex of monkeys that were never explicitly trained to discriminate objects in context, as well as current artificial neural network models, could only partially explain this cross-species correspondence. The shared behavioral variance unexplained by context-naive neural data or computational models highlights fundamental knowledge gaps. Our findings demonstrate an intriguing alignment of human and monkey visual object processing that defies full explanation by either brain activity in a key visual region or state-of-the-art models.