Search Paradigm Research Articles

Eye-tracking analysis of attentional disengagement in phobic and non-phobic individuals.

This study investigated threat-related attention biases using a new visual search paradigm with eye tracking, which allows for measuring attentional disengagement in isolation. This is crucial as previous studies have been unable to distinguish between engagement, disengagement, and behavioral freezing. Thirty-three participants (Mage = 28.75 years, SD = 8.98; 21 women) with self-reported specific phobia (spiders, snakes, and pointed objects) and their matched controls (Mage = 28.38 years, SD = 8.66; 21 women) took part in the experiment. The participants were instructed to initially focus on a picture in the center of the screen, then search for a target picture in an outer circle consisting of six images, and respond via a button press whether the object in the target picture was oriented to the left or right. We found that phobic individuals show delayed disengagement and slower decision times compared with non-phobic individuals, regardless of whether the stimulus was threat-related or neutral. These results indicate that phobic individuals tend to exhibit poorer attentional control mechanisms and problems inhibiting irrelevant information. We also confirmed a threat-unrelated shared feature effect with complex stimuli (delayed disengagement when an attended stimulus and an unattended target share common stimulus features). This process might play a role in various experimental setups investigating attentional disengagement that has not yet been considered. These findings are important, as good attentional control may serve as a protective mechanism against anxiety disorders.

Oculomotor Behaviour in Individuals with Long COVID-19

Objective To determine, the impact of long COVID-19 on oculomotor behaviour. Design A case–control study. Setting Spanish Association of Persistent COVID. Participant Participants were 75 cases (64 women, 11 men, mean age 46.4 years ±8.9) and 42 controls (22 women, 20 men, mean age 53.5 years ±13.13). Intervention An eye-tracking test based on visual search paradigm and the Adult Developmental Eye Movement Test were used to evaluate the participants. Main measures The primary outcomes in the Adult Developmental Eye Movement Test were horizontal reading time, vertical reading time, and their ratio. And for the eye-tracking test the time to find the target, the duration, and the number of eye fixations. Results In cases and controls, eye movement test results were horizontal(Hadj) reading time 74.2 ± 22.7 s vs 52.0 ± 6.1 s (p < .0001); vertical(Vadj) reading time 67.6 ± 17.8 s vs 50.4 ± 6.9 s (p < .0001); Hadj/Vadj ratio 0.9 ± 0.1 vs 1.0 ± 0 (p = .0032), respectively; and eye-tracking test results were fixation number 11.3 ± 3.07 vs 3.51 ± 2.57 (p < .0001); fixation duration 2.01 ± 0.79 s vs 1.5 ± 0.4 s (p = .0013), and time to find target 24.5 ± 8.0 vs 18 ± 9.4 (p = .0034), respectively. Conclusions Data showed a lower performance in oculomotor behaviour in people with long COVID-19, compared to healthy individuals. It cannot be affirmed an ocular musculature dysfunction; the differentiated behaviour could be associated to cognitive alterations affected in these people. Both tests used could be an useful tool for the clinical assessment of these participants. Further studies are needed to explore the utility of these procedures.

Black-box adversarial attacks against image quality assessment models

The problem of No-Reference Image Quality Assessment (NR-IQA) is to predict the perceptual quality of an image in line with its subjective evaluation. However, the vulnerabilities of NR-IQA models to the adversarial attacks have not been thoroughly studied for model refinement. This paper aims to investigate the potential loopholes of NR-IQA models via black-box adversarial attacks. Specifically, we first formulate the attack problem as maximizing the deviation between the estimated quality scores of original and perturbed images, while restricting the perturbed image distortions for visual quality preservation. Under such formulation, we then design a Bi-directional loss function to mislead the estimated quality scores of adversarial examples towards an opposite direction with maximum deviation. On this basis, we finally develop an efficient and effective black-box attack method for NR-IQA models based on a random search paradigm. Comprehensive experiments on three benchmark datasets show that all evaluated NR-IQA models are significantly vulnerable to the proposed attack method. After being attacked, the average change rates in terms of two well-known IQA performance metrics achieved by victim models reach 97% and 101%, respectively. In addition, our attack method also outperforms a newly introduced black-box attack approach on IQA models. We also observe that the generated perturbations are not transferable, which points out a new research direction in NR-IQA community. The source code is available at https://github.com/GZHU-DVL/AttackIQA.

Speed and Reliability of Touch vs Mouse Inputs in Visual Foraging

Abstract Traditional visual attention research predominantly employs a visual search paradigm focused on identifying a single target within a scene. This approach, however, may not fully encapsulate the complexities of natural environmental interactions, which often involve multiple decisions and actions. Addressing this gap, recent advancements have introduced touch-screen-based foraging tasks, where participants are required to identify and cancel multiple targets through tactile interaction. Despite its intuitive appeal, the widespread adoption of this methodology has been limited. This article explores the computer mouse as a viable alternative for target cancellation in such foraging tasks. We conducted an experiment comparing search performance between touch and mouse inputs in a visual foraging task, requiring participants to locate and interact with a known target multiple times under various search types. Our findings indicate that while mouse responses are marginally slower, the variance in response times is consistent across both input modalities. Contrary to expectations, we observed no significant disadvantages when employing the mouse as opposed to touch screens. The results suggest that the computer mouse is a competent and underutilized tool in visual attention research, offering a comparable and effective alternative to touch-based devices for complex search tasks.

P09-08 Further exploring the automated touchscreen-based mCANTAB device: testing for motor precision and employing a self-ordered spatial search paradigm

P09-08 Further exploring the automated touchscreen-based mCANTAB device: testing for motor precision and employing a self-ordered spatial search paradigm

The potential role of robust face representations learned within families when searching for one’s child in a crowd

Own child’s face is one of the most socially salient stimuli for parents, and a faster search for it than for other children’s faces may help provide warmer and more sensitive care. However, it has not been experimentally examined whether parents find their child’s face faster. In addition, although own child’s face is specially processed, the search time for own child’s face may be similar to that for other socially salient stimuli, such as own or spouse’s faces. This study tested these possibilities using a visual search paradigm. Participants (parents) searched for their child’s, own, spouse’s, other child’s, same-sex adult’s, or opposite-sex adult’s faces as search targets. Our findings indicate that both mothers and fathers identified their child’s face more quickly than other children’s faces. Similarly, parents found their own and spouse’s faces more quickly than other adults’ faces. Moreover, the search time for family members’ faces increased with the number of faces on the search display, suggesting an attentional serial search. These results suggest that robust face representations learned within families and close relationships can support reduced search times for family members’ faces.

AILS-II: An Adaptive Iterated Local Search Heuristic for the Large-Scale Capacitated Vehicle Routing Problem

A recent study on the classical capacitated vehicle routing problem (CVRP) introduced an adaptive version of the widely used iterated local search paradigm, hybridized with a path-relinking (PR) strategy. The solution method, called adaptive iterated local search (AILS)-PR, outperformed existing meta-heuristics for the CVRP on benchmark instances. However, tests on large-scale instances suggest that PR is too slow, making AILS-PR less advantageous in this case. To overcome this challenge, this paper presents an AILS combined with mechanisms to handle large CVRP instances, called AILS-II. The computational cost of this implementation is reduced, whereas the algorithm also searches the solution space more efficiently. AILS-II is very competitive on smaller instances, outperforming the other methods from the literature with respect to the average gap to the best-known solutions. Moreover, AILS-II consistently outperforms the state of the art on larger instances with up to 30,000 vertices. History: Accepted by Ted Ralphs, Area Editor for Software Tools. This paper has been accepted for the INFORMS Journal on Computing Special Issue on Software Tools for Vehicle Routing. Funding: This work was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo [Grants 2013/07375-0, 2019/22067-6, and 2022/05803-3] and the Conselho Nacional de Desenvolvimento Científico e Tecnológico [Grants 309385/2021-0 and 403735/2021-1]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0106 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0106 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

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.

Neural Mechanisms of Visual Search and Working Memory

Visual search is a useful experimental paradigm investigating various aspects of attention. For efficient search, participants must avoid revisiting previously viewed objects. Inhibitory tagging and inhibition of return are phenomena related to this process, but their neural mechanisms are yet to be elucidated. Recent studies have shown that the rate of revisit behavior during visual search correlates with working memory capacity. This suggests that top-down signals from the frontal executive system alter the priority map that guides attention and eye movements. With this in mind, we have developed a novel visual search paradigm with many identical distractors and an evaluation model that assesses multiple parameters of working memory. The model incorporated memory capacity, memory decay, and utility rate, and when applied to data obtained from experimental animals, these parameters could be reliably evaluated. Furthermore, using the behavioral paradigm and model, we found that systemic administration of subanesthetic dose of ketamine decreased utility rate and memory capacity, while nicotine administration increased utility rate. Since our behavioral paradigm does not require complex instruction, it can be applied to a variety of patients in future clinical studies.

Lookahead Pathology in Monte-Carlo Tree Search

Monte-Carlo Tree Search (MCTS) is a search paradigm that first found prominence with its success in the domain of computer Go. Early theoretical work established the soundness and convergence bounds for Upper Confidence bounds applied to Trees (UCT), the most popular instantiation of MCTS; however, there remain notable gaps in our understanding of how UCT behaves in practice. In this work, we address one such gap by considering the question of whether UCT can exhibit lookahead pathology in adversarial settings --- a paradoxical phenomenon first observed in Minimax search where greater search effort leads to worse decision-making. We introduce a novel family of synthetic games that offer rich modeling possibilities while remaining amenable to mathematical analysis. Our theoretical and experimental results suggest that UCT is indeed susceptible to pathological behavior in a range of games drawn from this family.

Assessing the Size of the Functional Field of View in a Gaze-Contingent Search Paradigm

The functional field of view (FFV) is the part of the visual field centred around the current gaze position that the visual system can process in detail. Its size depends partly on physiological limitations, but its adaptability is largely determined by cognitive factors. For example, changes in the FFV often reflect the demands of a given task: it shrinks with high task demands and expands with easier tasks. Here, we placed an upright or inverted target among distractors. We manipulated the visibility of the search array during gaze-contingent search with small (6°), medium (12°) and large (18°) apertures. Aperture size affected performance, improving it when it increased from 6° to 12°, but not from 12° to 18°, suggesting an upper bound of the 'natural' aperture for this specific task. Furthermore, our results suggest that the FFV does not change in size in response to stimulus inversion.

Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems

In the realm of automated industry and smart production, the deployment of fault warning systems is crucial for ensuring equipment reliability and enhancing operational efficiency. Although there are a multitude of existing methodologies for fault warning, the proficiency of these systems in processing and analysing data is increasingly challenged by the progression of industrial apparatus and the escalating magnitude and intricacy of the data involved. To address these challenges, this research outlines an innovative fault warning methodology that combines a bi-directional long short-term memory (Bi-LSTM) network with an enhanced hunter–prey optimisation (EHPO) algorithm. The Bi-LSTM network is strategically utilised to outline complex temporal patterns in machinery operational data, while the EHPO algorithm is employed to meticulously fine-tune the hyperparameters of the Bi-LSTM, aiming to enhance the accuracy and generalisability of fault warning. The EHPO algorithm, building upon the foundational hunter–prey optimisation (HPO) framework, introduces an advanced population initialisation process, integrates a range of strategic exploration methodologies, and strengthens its search paradigms through the incorporation of the differential evolution (DE) algorithm. This comprehensive enhancement aims to boost the global search efficiency and accelerate the convergence speed of the algorithm. Empirical analyses, conducted using datasets from real-world industrial scenarios, have validated the improved warning performance of this proposed methodology against some benchmark techniques, as evidenced by superior metrics such as root mean square error (RMSE) and mean absolute error (MAE), albeit with a slight increase in computational resource requirements. This study not only proposes a novel paradigm for fault warning within complex industrial frameworks but also contributes to the discourse on hyperparameter optimisation within the field of machine learning algorithms.

Optimizing with Attractor: A Tutorial

This tutorial presents a novel search system—the Attractor-Based Search System (ABSS)—that can solve the Traveling Salesman Problem very efficiently with optimality guarantee. From the perspective of dynamical systems, a heuristic local search algorithm for an NP-complete combinatorial problem is a discrete dynamical system. In a local search system, an attractor drives the search trajectories into the vicinity of a globally optimal point in the solution space, and the convergence of local search trajectories makes the search system become a global and deterministic system. The attractor contains a small set of the most promising solutions to the problem. The attractor can reduce the problem size exponentially, and thus make the exhaustive search feasible. Therefore, this new search paradigm is called optimizing with attractor. The ABSS consists of two search phases: local search phase and exhaustive search phase. The local search process is used to quickly construct the attractor in the solution space, and the exhaustive search process is used to completely search the attractor to identify the optimal solution. Therefore, the exact optimal solution can be found quickly by combining local search and exhaustive search. This tutorial introduces the concept of an attractor in a local search system, and describes the process of optimizing with the attractor, using the Traveling Salesman Problem as the study platform.

Chatbots, search engines, and the sealing of knowledges

AbstractIn 2023, online search engine provider Microsoft integrated a language model that provides direct answers to search queries into its search engine Bing. Shortly afterwards, Google also introduced a similar feature to its search engine with the launch of Google Gemini. This introduction of direct answers to search queries signals an important and significant change in online search. This article explores the implications of this new search paradigm. Drawing on Donna Haraway’s theory of Situated Knowledges and Rainer Mühlhoff’s concept of Sealed Surfaces, I introduce the term Sealed Knowledges to draw attention to the increasingly difficult access to the plurality of potential answers to search queries through the output of a singular, authoritative, and plausible text paragraph. I argue that the integration of language models for the provision of direct answers into search engines is based on a de-situated and disembodied understanding of knowledge and affects the subjectivities of its users. At the same time, the sealing of knowledges can lead to an increasing spread of misinformation and may make marginalized knowledge increasingly difficult to find. The paper concludes with an outlook on how to resist the increasing sealing of knowledges in online search.

Impaired Ability in Visual-Spatial Attention in Chinese Children With Developmental Dyslexia.

A growing body of evidence suggests that children with dyslexia in alphabetic languages exhibit visual-spatial attention deficits that can obstruct reading acquisition by impairing their phonological decoding skills. However, it remains an open question whether these visual-spatial attention deficits are present in children with dyslexia in non-alphabetic languages. Chinese, with its logographic writing system, offers a unique opportunity to explore this question. The presence of visual-spatial attention deficits in Chinese children with dyslexia remains insufficiently investigated. Therefore, this study aimed to explore whether such deficits exist, employing a visual search paradigm. Three visual search tasks were conducted, encompassing two singleton feature search tasks and a serial conjunction search task. The results indicated that Chinese children with dyslexia performed as well as chronological age-matched control children in color search tasks but less effectively in orientation search, suggesting a difficulty in the rapid visual processing of orientation: a deficit potentially specific to Chinese dyslexia. Crucially, Chinese children with dyslexia also exhibited lower accuracy, longer reaction times, and steeper slopes in the reaction times by set size function in the conjunction search task compared to control children, which is indicative of a visual-spatial attention deficit.

Gaze-based human intention prediction in the hybrid foraging search task

An agent’s ability to predict a human’s intention can facilitate the effectiveness of a human-agent team. The aim of this study was to explore the feasibility of predicting human intention in the hybrid foraging search task using eye gaze data and machine learning. In the hybrid foraging search paradigm, the human performs searches in patches and decides to stay and exploit a patch or to leave and explore another patch. Researchers have modeled the “patch-leaving problem” on the collective level using the marginal value theorem. However, few have attempted to predict the exact moment when the human will leave. In the current study, 40 participants performed the hybrid foraging search task while eye gaze data were collected with an eye-tracker. The leaving intention was associated with larger pupil size, shorter average fixation duration, larger number of fixations, longer saccade amplitude, and faster saccade velocity compared to the searching intention. A cross-subject machine learning model with an artificial neural network algorithm was able to predict whether a participant would leave the current patch with up to 78% accuracy with a 2-second data analysis window. The length of the data analysis window did not significantly affect prediction accuracy. Furthermore, the earlier the behavior prediction was made, the lower the reliability of the prediction. These results demonstrate that eye gaze features and machine learning algorithms are useful in predicting human intention in visual search tasks.

Unpleasant emotion inhibits attentional focus toward a peripheral target in a visual search: an ERP study.

Previous studies have found that emotional states affect the extent of attention, and the effect has been explained by adaptive views. If the adaptive explanations are true, emotion should modulate attentional focus toward a peripheral stimulus. The present study investigated if emotion affects the focus of attention toward a peripheral target in a visual search paradigm with event-related brain potential (ERP) measurement. In each trial of the experiment, participants performed a visual search task after an emotion (unpleasant, neutral, or pleasant) was induced by presenting an international affective picture system (IAPS) image. We measured N2pc, which is an ERP index reflecting attentional focus toward a peripheral target in a visual search, and compared the amplitudes among the emotion conditions. According to the adaptive view of emotional effects on cognition, this study hypothesized that unpleasant emotion would enhance the focus of attention, and pleasant emotion would inhibit it. These hypotheses predicted that N2pc amplitude would increase with unpleasant emotion and decrease with pleasant emotion. However, this study obtained inconsistent results; N2pc amplitude decreased in the unpleasant condition, and there was no significant effect of pleasant emotion on the ERP. The results suggest that unpleasant emotion inhibited the attentional focusing process. This is the first report to examine how emotion modulates the focus of attention toward a peripheral target in a visual search by using ERP. The findings contribute to understanding the relationship between emotion and cognition.

Exploratory and directed search strategies at a social science data archive

Researchers need to be able to find, access, and use data to participate in open science. To understand how users search for research data, we analyzed textual queries issued at a large social science data archive, the Inter-university Consortium for Political and Social Research (ICPSR). We collected unique user queries from 988,475 user search sessions over four years (2012-16). Overall, we found that only 30% of site visitors entered search terms into the ICPSR website. We analyzed search strategies within these sessions by extending existing dataset search taxonomies to classify a subset of the 1,554 most popular queries. We identified five categories of commonly-issued queries: keyword-based (e.g., date, place, topic); name (e.g., study, series); identifier (e.g., study, series); author (e.g., institutional, individual); and type (e.g., file, format). While the dominant search strategy used short keywords to explore topics, directed searches for known items using study and series names were also common. We further distinguished exploratory browsing from directed search queries based on their page views, refinements, search depth, duration, and length. Directed queries were longer (i.e., they had more words), while sessions with exploratory queries had more refinements and associated page views. By comparing search interactions at ICPSR to other natural language interactions in similar web search contexts, we conclude that dataset search at ICPSR is underutilized. We envision how alternative search paradigms, such as those enabled by recommender systems, can enhance dataset search.

Rapid assessment of peripheral visual crowding.

Visual crowding, the phenomenon in which the ability to distinguish objects is hindered in cluttered environments, has critical implications for various ophthalmic and neurological disorders. Traditional methods for assessing crowding involve time-consuming and attention-demanding psychophysical tasks, making routine examination challenging. This study sought to compare trial-based Alternative Forced-Choice (AFC) paradigms using either manual or eye movement responses and a continuous serial search paradigm employing eye movement responses to evaluate their efficiency in rapidly assessing peripheral crowding. In all paradigms, we manipulated the orientation of a central Gabor patch, which could be presented alone or surrounded by six Gabor patches. We measured participants' target orientation discrimination thresholds using adaptive psychophysics to assess crowding magnitude. Depending on the paradigm, participants either made saccadic eye movements to the target location or responded manually by pressing a key or moving a mouse. We compared these paradigms in terms of crowding magnitude, assessment time, and paradigm demand. Our results indicate that employing eye movement-based paradigms for assessing peripheral visual crowding yields results faster compared to paradigms that necessitate manual responses. Furthermore, when considering similar levels of confidence in the threshold measurements, both a novel serial search paradigm and an eye movement-based 6AFC paradigm proved to be the most efficient in assessing crowding magnitude. Additionally, crowding estimates obtained through either the continuous serial search or the 6AFC paradigms were consistently higher than those obtained using the 2AFC paradigms. Lastly, participants did not report a clear difference between paradigms in terms of their perceived demand. In conclusion, both the continuous serial search and the 6AFC eye movement response paradigms enable a fast assessment of visual crowding. These approaches may potentially facilitate future routine crowding assessment. However, the usability of these paradigms in specific patient populations and specific purposes should be assessed.

Dynamic Weighted Combiner for Mixed-Modal Image Retrieval

Mixed-Modal Image Retrieval (MMIR) as a flexible search paradigm has attracted wide attention. However, previous approaches always achieve limited performance, due to two critical factors are seriously overlooked. 1) The contribution of image and text modalities is different, but incorrectly treated equally. 2) There exist inherent labeling noises in describing users' intentions with text in web datasets from diverse real-world scenarios, giving rise to overfitting. We propose a Dynamic Weighted Combiner (DWC) to tackle the above challenges, which includes three merits. First, we propose an Editable Modality De-equalizer (EMD) by taking into account the contribution disparity between modalities, containing two modality feature editors and an adaptive weighted combiner. Second, to alleviate labeling noises and data bias, we propose a dynamic soft-similarity label generator (SSG) to implicitly improve noisy supervision. Finally, to bridge modality gaps and facilitate similarity learning, we propose a CLIP-based mutual enhancement module alternately trained by a mixed-modality contrastive loss. Extensive experiments verify that our proposed model significantly outperforms state-of-the-art methods on real-world datasets. The source code is available at https://github.com/fuxianghuang1/DWC.