Feature Search Research Articles

Sequence learning is surprisingly fragile in visual search

Extensive research has shown that people are sensitive to statistical regularities of visual stimuli, such as a repeated sequence of object locations. Such learning, however, has primarily occurred for objects presented in isolation. Here, we tested whether sequence learning also manifested in complex displays. Using variants of the serial reaction time task, we asked participants to report the screen quadrant of a letter T, whose location followed a 12-trial sequence that repeated 30 times over 360 trials. In different experiments, we manipulated the nature of distractors surrounding the target. The T could appear in isolation, as a color singleton among distractors with fixed or variable locations, or as a conjunction search target. Sequence learning-expressed as elevated response time when the learned sequence was disrupted-decreased as spatial noise increased. Learning was robust when the T appeared in isolation or when it was surrounded by distractors that did not change locations. It was reduced in feature search and eliminated in conjunction search. These findings suggest that target locations are coded in relation to concurrently presented distractors. Variability in distractor locations disrupts target location sequence learning, revealing a limit to people's ability to extract and use spatiotemporal regularities in complex environments. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Attentional templates are sharpened through differential signal enhancement, not differential allocation of attention

In visual search, the internal representation of the target feature is referred to as the attentional template. The attentional template can be broad or precise depending on the task requirements. In singleton search, the attentional template is broad because the target is the only colored element in the display. In feature search, a precise attentional template is required because the target is in a specific color in an array of varied colors. To measure the precision of the attentional template, we used a cue-target paradigm where cueing benefits decrease when the cue color differs from the target color. Consistent with broad and precise attentional templates, the decrease of cueing effects was stronger in feature than in singleton search. Measurements of ERPs showed that the N2pc elicited by the cue decreased with increasing color difference, suggesting that attention was more strongly captured by cues that were similar to the target. However, the cue-elicited N2pc did not differ between feature and singleton search, making it unlikely to reflect the mechanism underlying attentional template precision. Furthermore, there was no evidence for attentional suppression as there was no cue-elicited PD, even in conditions where the cueing benefit turned into a same-location cost. However, an index of signal enhancement, the contralateral positivity, reflected attention template precision. In general, there was sensory enhancement of the stimulus appearing at the cued location in the search display. With broad attentional templates, any stimulus at the cued location was enhanced, whereas enhancement was restricted to target-matching colors with precise attentional templates.

High Level Feature Search in Autism Spectrum Disorder

High Level Feature Search in Autism Spectrum Disorder

Contextual cuing of visual search does not guide attention automatically in the presence of top-down goals.

Visual search is faster when it occurs within repeated displays, a phenomenon known as contextual cuing (CC). CC has been explained as the result of an automatic orientation of attention toward a target item driven by learned distractor-target associations. In 3 experiments we tested the specific hypothesis that CC is an automatic process of attentional guidance. Participants first searched for a T target in a standard CC procedure. Then, they experienced the same repeated configurations (with the T still present), but now searched for a Y target that was positioned either in a location on the same, or on a different side, from the old T target. Results suggested that there was no interference caused by the old T-target: target search was not affected by the relative positions of the T and Y. Instead, we found a general facilitation in search times for repeated configurations (Experiments 1 and 2). This main effect disappeared when the need for visual search was eliminated in Experiment 3 using a "feature search task". These results suggest that repeated sets of distractors did not trigger an uncontrollable response toward the position of the T; instead, CC was produced by perceptual learning processes. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Non-Local Feature Search Network for Building and Road Segmentation of Remote Sensing Image

Building and road extraction from remote sensing images is of great significance to urban planning. At present, most of building and road extraction models adopt deep learning semantic segmentation method. However, the existing semantic segmentation methods did not pay enough attention to the feature information between hidden layers, which led to the neglect of the category of context pixels in pixel classification, resulting in these two problems of large-scale misjudgment of buildings and disconnection of road extraction. In order to solve these problem, this paper proposes a Non-Local Feature Search Network (NFSNet) that can improve the segmentation accuracy of remote sensing images of buildings and roads, and to help achieve accurate urban planning. By strengthening the exploration of hidden layer feature information, it can effectively reduce the large area misclassification of buildings and road disconnection in the process of segmentation. Firstly, a Self-Attention Feature Transfer (SAFT) module is proposed, which searches the importance of hidden layer on channel dimension, it can obtain the correlation between channels. Secondly, the Global Feature Refinement (GFR) module is introduced to integrate the features extracted from the backbone network and SAFT module, it enhances the semantic information of the feature map and obtains more detailed segmentation output. The comparative experiments demonstrate that the proposed method outperforms state-of-the-art methods, and the model complexity is the lowest.

Point Cloud Based 3D Matching and Identification of Welding Seam Using Genetic Algorithm

Welding seams identification is a challenging task in robotic welding process. In this paper, we present a method for the automatic identification and location of welding seams for robotic welding using handy 3D scanner. The method uses point cloud based local feature matching and continuous feature search algorithm, which aim at achieving an adaptive seam identification process that can work on similar work pieces with different sizes. The results together with the visual verification demonstrate that the proposed algorithms have ability to identify the welding seam from point cloud acquired by handy laser scanner.

Visual Search in 3D: Effects of Monoscopic and Stereoscopic Cues to Depth on the Validity of Feature Integration Theory and Perceptual Load Theory.

Previous research has successfully used feature integration theory to operationalise the predictions of Perceptual Load Theory, while simultaneously testing the predictions of both models. Building on this work, we test the extent to which these models hold up in a 3D world. In two experiments, participants responded to a target stimulus within an array of shapes whose apparent depth was manipulated using a combination of monoscopic and stereoscopic cues. The search task was designed to test the predictions of (a) feature integration theory, as the target was identified by a single feature or a conjunction of features and embedded in search arrays of varying size, and (b) perceptual load theory, as the task included congruent and incongruent distractors presented alongside search tasks imposing high or low perceptual load. Findings from both experiments upheld the predictions of feature integration theory, regardless of 2D/3D condition. Longer search times in conditions with a combination of monoscopic and stereoscopic depth cues suggests that binding features into three-dimensional objects requires greater attentional effort. This additional effort should have implications for perceptual load theory, yet our findings did not uphold its predictions; the effect of incongruent distractors did not differ between conjunction search trials (conceptualised as high perceptual load) and feature search trials (low perceptual load). Individual differences in susceptibility to the effects of perceptual load were evident and likely explain the absence of load effects. Overall, our findings suggest that feature integration theory may be useful for predicting attentional performance in a 3D world.

Knowledge Transfer Through Mobile Application Needs: A Survey of Nurses as End Users

Dissemination of evidence through a mobile application in the nursing area is a promising area of development. The effectiveness of this method of knowledge transfer encourages the development of a user-friendly mobile application responding to the needs of the nurses. However, nurses’ needs have yet to be identified. This study aims to understand the needs for knowledge transfer through the mobile application of nurses. An online survey was conducted at two public and private hospitals in Depok City in August and September 2019. A questionnaire with four items of needs assessment and 12 items of application features importance was completed by 110 nurses. Descriptive analysis showed the majority of nurses reported the need for best evidence with an average of 5.41 for research summary, 5.49 for systematic review, 5.87 for clinical guidelines, and need for knowledge transfer through mobile application with an average of 5.65 on 7 ranged Likert scale. Most nurses rated 11 out of 12 features are important with a score of more than four out of five on the Likert scale. The highest mean is the feature search with keywords (4.68±0.06) and the lowest in the quiz/games feature (3.82±0.10). The study noted that knowledge transfer through a mobile application is needed by nurses. Future design of the mobile application based on the needs of nurses as end-users is feasible.
 
 Keywords: evidence-based practice, evidence-based nursing, knowledge translation, mobile applications, need analysis

Priming Effect of Colour on Aiding the Attentional Reorientation in Sequential Presentations of Temporal Data Visualization: Evidence From Eye-Tracking

Abstract In the present study, we focus on the priming effect of colour on mitigating the attentional reorientation cost, which is led by re-constructing the frame of reference for attention shift and visual search in sequential presentations of temporal data visualization. The study involves two experiments using complementary recordings of behavioural performance and eye-tracking events. Two aspects of colour primes are highlighted: the prime validity and the colour perceptual accessibility. A task paradigm integrating the feature search and keeping-track task was adopted in our experiments. In Experiment 1 (with a group of 16 participants), we confirmed the colour priming effect by comparing the priming condition to the neutral baseline. Furthermore, global colours that are with high perceptual accessibility generated more evident priming effects than local colours. However, more interferences in misguiding the attention to task-irrelevant regions were found when the global primes were invalid. In Experiment 2 (with another group of 15 participants), we verify the finding in Experiment 1 that global colours produced more pronounced priming effects in alleviating the attentional reorientation cost by comparing two groups of real-world visualizations with either global or local colours as the prime. Large saccades were initiated much earlier, and the search efficiency got improved when provided with global colours. We conjecture that the facilitatory effect from global colours may stem from its benefit on the pre-attentive processing of the search field. The research findings provide evidence for utilizing colours as the primes in mitigating the attentional reorientation cost and accelerating visual search in sequential presentations.

Visual attention in spatial cueing and visual search.

To characterize internal processes of an observer conducting perceptual tasks, we developed an observer model that combines the perceptual template model (PTM), the attention mechanisms in the PTM framework (Lu & Dosher, 1998), and uncertainty of signal detection theory (Green & Swets, 1966). The model was evaluated with a visual search experiment conducted in a range of external noise, signal contrast, and target-distractor similarity conditions. In each trial, eight Gabor patches were shown in each of two brief intervals, with one target at a different orientation from the distractors in one of the presentations. Subjects were precued to a subset of the stimuli (1, 2, 4, or 8) and asked to report (a) which interval contained the target and (b) where the target was. Individual roles of uncertainty and of attention in visual search were investigated by comparing models with and without an attention component. The results showed that decision uncertainty alone was sufficient to account for the set-size effect, even in conditions with high target-distractor similarity. Our theoretical model and empirical results provide a coherent picture regarding how visual information is selected and processed during feature search.

Spatial attention and spatial short term memory in PSP and Parkinson's disease

Progressive Supranuclear Palsy (PSP) is a neurodegenerative disorder characterised by deterioration in motor, oculomotor and cognitive function. A key clinical feature of PSP is the progressive paralysis of eye movements, most notably for vertical saccades. These oculomotor signs can be subtle, however, and PSP is often misdiagnosed as Parkinson’s disease (PD), in its early stages. Although some of the clinical features of PD and PSP overlap, they are distinct disorders with differing underlying pathological processes, responses to treatment and prognoses. One key difference lies in the effects the diseases have on cognition. The oculomotor system is tightly linked to cognitive processes such as spatial attention and spatial short-term memory (sSTM), and previous studies have suggested that PSP and PD experience different deficits in these domains. We therefore hypothesised that people with PSP (N = 15) would experience problems with attention (assessed with feature and conjunction visual search tasks) and sSTM (assessed with the Corsi blocks task) compared to people with PD (N = 16) and Age Matched Controls (N = 15). As predicted, feature and conjunction search were sgnificantly slower in the PSP group compared to the other groups, and this deficit was significantly worse for feature compared to conjunction search. The PD group did not differ from AMC on feature search but were significantly impaired on the conjunction search. The PSP group also had a pronounced vertical sSTM impairment that was not present in PD or AMC groups. It is argued that PSP is associated with specific impairment of visuospatial cognition which is caused by degeneration of the oculomotor structures that support exogenous spatial attention, consistent with oculomotor theories of spatial attention and memory.

Attentional Templates Are Sharpened through Differential Signal Enhancement, Not Differential Allocation of Attention.

In visual search, the internal representation of the target feature is referred to as the attentional template. The attentional template can be broad or precise depending on the task requirements. In singleton search, the attentional template is broad because the target is the only colored element in the display. In feature search, a precise attentional template is required because the target is in a specific color in an array of varied colors. To measure the precision of the attentional template, we used a cue-target paradigm where cueing benefits decrease when the cue color differs from the target color. Consistent with broad and precise attentional templates, the decrease of cueing effects was stronger in feature than in singleton search. Measurements of ERPs showed that the N2pc elicited by the cue decreased with increasing color difference, suggesting that attention was more strongly captured by cues that were similar to the target. However, the cue-elicited N2pc did not differ between feature and singleton search, making it unlikely to reflect the mechanism underlying attentional template precision. Furthermore, there was no evidence for attentional suppression as there was no cue-elicited PD, even in conditions where the cueing benefit turned into a same-location cost. However, an index of signal enhancement, the contralateral positivity, reflected attention template precision. In general, there was sensory enhancement of the stimulus appearing at the cued location in the search display. With broad attentional templates, any stimulus at the cued location was enhanced, whereas enhancement was restricted to target-matching colors with precise attentional templates.

Rapid disengagement hypothesis and signal suppression hypothesis of visual attentional capture

<p id="C3">In the traditional theory of visual attentional capture, the stimulus-driven theory and goal-driven theory were argued for nearly 20 years. Later, two new hybrid models were proposed, which combined bottom-up capture and top-down control settings, called the rapid disengagement hypothesis and the signal suppression hypothesis. The main content of the rapid disengagement hypothesis is that a salient distractor can always capture attention in a bottom-up manner, but attention is immediately disengaged when the distractor does not contain target’s defining attribute. Signal suppression hypothesis posits that a salient distractor can automatically produce a bottom-up “attend-to-me” signal, but this signal can be suppressed via top-down control processes so that it does not actually capture attention. The empirical evidence of the rapid disengagement hypothesis indicated that the spatial-cuing paradigm and oculomotor disengagement paradigm were most often adopted, and participants took the singleton search strategy. The empirical evidence of the signal suppression hypothesis indicated that the additional singleton paradigm was most often adopted, and participants were forced to take the feature search strategy. In the future, more studies adopting different stimuli and experimental methods are needed to support those two hybrid models. The effects of reward and training on “attentional capture-disengagement” and “signal-suppression” should also be explored in future research.

Age-related changes in visual search: manipulation of colour cues based on cone contrast and opponent modulation space

Reduced retinal illuminance affects colour perception in older adults, and studies show that they exhibit deficiencies in yellow-blue (YB) discrimination. However, the influence of colour cues on the visual attention in older individuals remains unclarified. Visual attention refers to the cognitive model by which we prioritise regions within the visual space and selectively process information. The present study aimed to explore the effect of colour on visual search performance in older observers. In our experiment, younger observers wearing glasses with a filter that simulated the spectral transmittance of the aging human lens and older observers performed two types of search tasks, feature search (FS) and conjunction search (CS), under three colour conditions: red-green, YB, and luminance. Targets and distractors were designed on the basis of the Derrington–Krauskopf–Lennie colour representation. In FS tasks, reaction times changed according to colour in all groups, especially under the YB condition, regardless of the presence or absence of distractors. In CS tasks with distractors, older participants and younger participants wearing glasses showed slower responses under chromatic conditions than under the achromatic condition. These results provide preliminary evidence that, for older observers, visual search performance may be affected by impairments in chromatic colour discrimination.

Trend Analyses Methodologies in Hydro-meteorological Records

In recent years, global warming and climate change impacts on hydro-meteorological variables and water resources triggered extensive focus on trend analyses. Especially, in historical records and climate change model scenario projections, trend feature searches help for better predictions prior to mitigation and adaptation activities. Each trend identification technique has a set of restrictive assumptions and limitations, but they are not cared for by many researchers. The major problem with trend research is that the researchers do not care for the basic assumptions of any methodology but use ready software to solve their problems. Among these assumptions, the most significant ones are the normal (Gaussian) probability distribution function (PDF) and serially independent structure of a given time series. It is the main objective of this review paper to present each trend identification methodology including classical ones with the new alternatives so that any researcher in need of trend analysis can have concise and clear interpretations for the choice of the most convenient trend method. In general, parametric, non-parametric, classical and innovative trend methods are explained comparatively including the linear regression, Mann–Kendall (MK) trend test with Sen slope estimation, Spearman’s rho, innovative trend analysis (ITA), partial trend analysis (PTA) and crossing trend analysis (CTA). Pros and cons are given for each methodology. In addition, for improvement of serial independence requirement of the classical trend analyses, methods are introduced briefly by pre- and over-whitening processes. Finally, a set of recommendations is suggested for future research possibilities.

Context isn't everything: Search performance is influenced by the nature of the task but not the background.

AbstractIt has been demonstrated in the literature that cues in the environment that are predictive of how a task ought to be performed can influence performance. In an extension of this general notion, Cosman and Vecera (Journal of Experimental Psychology: Human Perception and Performance, 39(3), 836-848, 2013) reported that simply performing singleton and feature search tasks when irrelevant scenes were displayed in the background automatically modulated the search strategies adopted by participants when these scenes were reinstated at a later time. While intriguing, this result was also somewhat surprising given that an adaptive system (like the human brain) should disregard irrelevant information so task competencies generalize across environments. To investigate this issue further, we replicated the experimental procedures of Cosman and Vecera, while varying whether the test phase was either a singleton search (Experiments 1 and 3) or a feature search (Experiment 2) task. While it was observed that the nature of the search task varied whether a color singleton distractor influenced performance, there was no evidence that background scenes modulated the search strategies adopted by participants, contrasting the results of Cosman and Vecera. Overall, the findings here support the conclusion that the visual system prioritizes task-relevant information while disregarding irrelevant background information.

Feature indexing and search optimization for enhancing the forensic analysis of mobile cloud environment

ABSTRACT The increased utilization of Mobile Cloud Computing (MCC) technology creates an opportunity for cybercrimes. Modeling the suitable methods for mobile cloud forensic examination and analysis is essential to improve the investigation performance. This paper incorporates data mining and optimization methods to enforce precise handling of the mobile cloud evidence in examination and analysis to improve the investigation performance. It enhances the analysis of the mobile cloud forensics with the incorporation of the evidence indexing, cross-referencing, and keyword searching as the sub-processes. The proposed Forensic Examination and analysis methodology using the Data mining and Optimization (FEDO) approach examines the key features of the evidence and indexes the pieces of evidence with key features to facilitate the investigation over the massive cloud evidence. By analyzing the temporal and geo-information, it applies cross-referencing to alleviate the evidence toward the case-specific evidence. The proposed methodology improves the searching capability of the investigation through the Linearly Decreasing Weight (LDW) strategy based Particle Swarm Optimization (PSO) algorithm. Thus, the experimental results demonstrate that the proposed forensic methodology yields better investigation performance in terms of accuracy of evidence detection.

Learning Class-Specific Features with Class Regularization for Videos

One of the main principles of Deep Convolutional Neural Networks (CNNs) is the extraction of useful features through a hierarchy of kernels operations. The kernels are not explicitly tailored to address specific target classes but are rather optimized as general feature extractors. Distinction between classes is typically left until the very last fully-connected layers. Consequently, variances between classes that are relatively similar are treated the same way as variations between classes that exhibit great dissimilarities. In order to directly address this problem, we introduce Class Regularization, a novel method that can regularize feature map activations based on the classes of the examples used. Essentially, we amplify or suppress activations based on an educated guess of the given class. We can apply this step to each minibatch of activation maps, at different depths in the network. We demonstrate that this improves feature search during training, leading to systematic improvement gains on the Kinetics, UCF-101, and HMDB-51 datasets. Moreover, Class Regularization establishes an explicit correlation between features and class, which makes it a perfect tool to visualize class-specific features at various network depths.

Categorical grouping is not required for guided conjunction search.

Knowledge of target features can guide attention in many conjunction searches in a top-down manner. For example, in search of a red vertical line among blue vertical and red horizontal lines, observers can guide attention toward all red items and all vertical items. In typical conjunction searches, distractors often form perceptually vivid, categorical groups of identical objects. This could favor the efficient search via guidance of attention to these “segmentable” groups. Can attention be guided if the distractors are not neatly segmentable (e.g., if colors vary continuously from red through purple to blue)? We tested search for conjunctions of color × orientation (Experiments 1, 3, 4, 5) or length × orientation (Experiment 2). In segmentable conditions, distractors could form two clear groups (e.g., blue steep and red flat). In non-segmentable conditions, distractors varied smoothly from red to blue and/or steep to flat; thus, discouraging grouping and increasing overall heterogeneity. We found that the efficiency of conjunction search was reasonably high and unaffected by segmentability. The same lack of segmentability had a detrimental effect on feature search (Experiment 4) and on conjunction search, if target information was limited to one feature (e.g., find the odd item in the red set, “subset search,” Experiment 3). Guidance in conjunction search may not require grouping and segmentation cues that are very important in other tasks like texture discrimination. Our results support an idea of simultaneous, parallel top-down guidance by multiple features and argue against models suggesting sequential guidance by each feature in turn.

Online tuning of predictor weights for relevant data selection in just-in-time-learning

Just-in-Time-Learning (JITL) is one of the most frequently used adaptive methods in data-based soft sensor design for chemical processes. While JITL is an effective method to combat concept drifts, samples selected via similarity metric in a Euclidean space consisting of a large number of equally-weighted predictors may diminish the performance of the JITL, due to curse of dimensionality and the varying degrees of nonlinearity between the predictor and response variables. Algorithms involving offline tuning of predictor weights were developed to tackle this issue, but changes in process conditions may depreciate a currently used set of weights for measuring similarity. In the current study, an adaptive method is developed for adjusting predictor weights in the Euclidean space used in measuring similarity between samples, named JITL via Online Weighted Euclidean Distance (JITL-OWED). JITL-OWED mainly consists of four steps: i) Relevant data is selected from an online weighted Euclidean space, and multiple models using different weights in their similarity measures are simultaneously constructed. ii) Control charts are used to detect changes in the prediction accuracy of the multiple models constructed online, hence triggering new subset searches. iii) A small number of search steps is used in subset selection, akin to early stopping in gradient search, with the “modest” aim of moving towards a local minimum for feasible online implementation. Exponentially Weighted Moving Average (EWMA) filtering is employed to stabilize the results from feature searches. iv) Final predictions are obtained via stacking multiple models. Employing JITL-OWED on one synthetic and four publicly available real datasets shows that online predictor weighting may indeed improve the prediction accuracy of the traditional JITL up to 80%, and JITL-OWED is both easy to implement and tune.