Target Cue Research Articles

Alpha-band fluctuations represent behaviorally relevant excitability changes as a consequence of top–down guided spatial attention in a probabilistic spatial cueing design

Abstract Spatial attention is a key function enabling the selection of relevant information and meaningful behavioral responses and is likely implemented by different neural mechanisms. In previous work, attention led to robust but uncorrelated modulations of Steady-State-Visual-Evoked-Potentials (SSVEPs) as a marker of early sensory gain and visual as well as motor alpha-band activity. We probed the behavioral relevance of attention-modulated trial-by-trial fluctuations of these measures. For this purpose, in an experiment with a classical probabilistic visuospatial attention cueing task, a to-be-discriminated target stimulus was validly, neutrally, or invalidly cued, while behavioral responses and EEG were recorded. Single-trial flicker-driven SSVEPs, visual and motor alpha-band activity were measured and the relationship between their amplitudes and reaction times was modeled via Bayesian regression models, respectively. We replicated previous findings that these neural measures and behavioral responses were overall modulated by the attentional cue. Beyond that, SSVEP amplitudes were not associated with behavior, while single-trial alpha-band amplitudes were predictive of reaction times: For trials with a valid or neutral cue, lower visual and motor alpha-band amplitudes measured contralateral to the target in the cue–target interval were associated with faster responses (and for valid cues also higher amplitudes ipsilateral to the target). For invalid cues, which required attentional reallocating to the uncued side, no such relationship was found. We argue that behavioral relevance of alpha-band modulations is a consequence but not a mechanism of top–down guided spatial attention, representing neural excitability in cortical areas activated by the attentional shift.

Mentalistic attention orienting triggered by android eyes

The eyes play a special role in human communications. Previous psychological studies have reported reflexive attention orienting in response to another individual’s eyes during live interactions. Although robots are expected to collaborate with humans in various social situations, it remains unclear whether robot eyes have the potential to trigger attention orienting similarly to human eyes, specifically based on mental attribution. We investigated this issue in a series of experiments using a live gaze-cueing paradigm with an android. In Experiment 1, the non-predictive cue was the eyes and head of an android placed in front of human participants. Light-emitting diodes in the periphery served as target signals. The reaction times (RTs) required to localize the valid cued targets were faster than those for invalid cued targets for both types of cues. In Experiment 2, the gaze direction of the android eyes changed before the peripheral target lights appeared with or without barriers that made the targets non-visible, such that the android did not attend to them. The RTs were faster for validly cued targets only when there were no barriers. In Experiment 3, the targets were changed from lights to sounds, which the android could attend to even in the presence of barriers. The RTs to the target sounds were faster with valid cues, irrespective of the presence of barriers. These results suggest that android eyes may automatically induce attention orienting in humans based on mental state attribution.

FRCE: Transformer-based feature reconstruction and cross-enhancement for occluded person re-identification

Occluded person re-identification (ReID) aims to capture the same person from different camera views with partial occlusion. In real-world scenarios, any object can become a random occluder, which is still an unsettled challenge for person ReID. Previous methods used auxiliary models to locate or reconstruct the occluded parts for ReID, but these methods are affected by external models or unable to model well, leaving the difficulty unresolved. Currently, Transformer has been widely applied to occluded person ReID tasks, but due to the lack of attention to local features, these methods still have limitations. Therefore, we propose a Feature Reconstruction and Cross-Enhancement (FRCE) network. Firstly, a Cross-level Feature Reconstruction Distiller (CFRD) is designed to improve the feature richness in the recognition scope and generate higher quality identity feature mapping by reconstructing the effective recognition region of the target. Secondly, we introduce a Multi-Dimensional Local Refinement (MDLR) module to deeply excavate local details in spatial and channel dimensions, seek subtle target cues and similarity relationships at local angles, and achieve more efficient ReID. Extensive experiments on five challenging occluded and holistic person ReID datasets demonstrate the effectiveness of our approach.

Absence of differential protection from extinction in human causal learning.

Elemental models of associative learning typically employ a common prediction-error term. Following a conditioning trial, they predict that the change in the strength of an association between a cue and an outcome is dependent upon how well the outcome was predicted. When multiple cues are present, they each contribute to that prediction. The same rule applies both to increases in associative strength during excitatory conditioning and the loss of associative strength during extinction. In five experiments using an allergy prediction task, we tested the involvement of a common error term in the extinction of causal learning. Two target cues were each paired with an outcome prior to undergoing extinction in compound either with a second excitatory cue or with a cue that had previously undergone extinction in isolation. At test, there was no difference in the causal ratings of the two target cues. Manipulations designed to bias participants toward elemental processing of cue compounds, to promote the acquisition of inhibitory associations, or to reduce generalization decrement between training and test were each without effect. These results are not consistent with common error term models of associative learning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Target search in the CRISPR/Cas9 system: facilitated diffusion with target cues

We study how Cas9, a central component of the CRISPR/Cas9 system, searches for a target sequence on the DNA. We propose a model that includes as key ingredients 3D diffusion, 1D sliding along the DNA, and the effect of short binding sequences preceding the target (protospacer adjacent motifs—PAMs). This last aspect constitutes the main difference with traditional facilitated diffusion of transcription factors. We solve our model, obtaining an expression for the average search time of Cas9 for its target. We find that experimentally measured kinetic parameters are close to the values yielding an optimal search time. Our results rationalize the role of PAMs in guiding the search process, and show that Cas9 searches for its targets in a nearly optimal way.

Association among childhood adversity and susceptibility to interference during varying salience: two studies in healthy males

Childhood adversity, a prevalent experience, is related to a higher risk for externalizing and internalizing psychopathology. Alterations in the development of cognitive processes, for example in the attention-interference domain may link childhood adversity and psychopathology. Interfering stimuli can vary in their salience, i.e. ability to capture attentional focus, and valence. However, it is not known if interference by salience or valence is associated with self-reported adversity. In two independent study samples of healthy men (Study 1: n = 44; mean age [standard deviation (SD)] = 25.9 [3.4] years; Study 2: n = 37; 43.5 [9.7] years) we used the attention modulation task (AMT) that probed interference by two attention-modulating conditions, salience and valence separately across repeated target stimuli. The AMT measures the effects of visual distractors (pictures) on the performance of auditory discrimination tasks (target stimuli). We hypothesized that participants reporting higher levels of childhood adversity, measured with the childhood trauma questionnaire, would show sustained interference in trials with lower salience. Due to conflicting reports on the valence-modulation, we tested the valence condition in an exploratory manner. Linear mixed models revealed an interaction between reported childhood adversity and the salience condition across tone presentations in both study samples (Sample 1: p = .03; Sample 2: p = .04), while there were no effects for the valence condition across both studies. Our study suggests that higher self-reported childhood adversity is related to faster processing of target cues during high salience, but slower during low salience conditions. These results hint to the mechanisms linking childhood adversity and psychopathological symptoms in the attentional domain.

M3SOT: Multi-Frame, Multi-Field, Multi-Space 3D Single Object Tracking

3D Single Object Tracking (SOT) stands a forefront task of computer vision, proving essential for applications like autonomous driving. Sparse and occluded data in scene point clouds introduce variations in the appearance of tracked objects, adding complexity to the task. In this research, we unveil M3SOT, a novel 3D SOT framework, which synergizes multiple input frames (template sets), multiple receptive fields (continuous contexts), and multiple solution spaces (distinct tasks) in ONE model. Remarkably, M3SOT pioneers in modeling temporality, contexts, and tasks directly from point clouds, revisiting a perspective on the key factors influencing SOT. To this end, we design a transformer-based network centered on point cloud targets in the search area, aggregating diverse contextual representations and propagating target cues by employing historical frames. As M3SOT spans varied processing perspectives, we've streamlined the network—trimming its depth and optimizing its structure—to ensure a lightweight and efficient deployment for SOT applications. We posit that, backed by practical construction, M3SOT sidesteps the need for complex frameworks and auxiliary components to deliver sterling results. Extensive experiments on benchmarks such as KITTI, nuScenes, and Waymo Open Dataset demonstrate that M3SOT achieves state-of-the-art performance at 38 FPS. Our code and models are available at https://github.com/ywu0912/TeamCode.git.

Latent inhibition in humans from simple stimulus exposure.

Two experiments observed an effect consistent with a latent-inhibition (LI) effect in humans that (a) did not depend on masking or instruction-generated expectations and (b) suggested that the effect results from a change in processing of the predictive cue. Participants viewed a video of a superhero character flying through three different contexts past a different stimulus in each context. In conditioning, The superhero flew past a target cue that was either Novel (Group No Exposure), had been preexposed in the Same context as where conditioning was occurring (Group Same), or was preexposed in a Different context (Group Different). Each time the superhero flew past the target cue his Hands Glowed (outcome). On test (E1), an image of the superhero flying in the context with normal Hands and the target cue was present. Participants were asked if anything was missing. Experiment 2 tested participants with the superhero present and his Hands Glowing to test outcome-cue associations (Test 1) or just the superhero in the context (Test 2, counterbalanced) to assess contextual associations. In E1 fewer people in Group Same reported the outcome missing than Group No Exposure or Group Different. In E2 fewer people in Group Same reported the target cue missing when presented with the outcome than in the other groups, a result inconsistent with interference accounts of LI. When presented only with contextual cues, reports of the stimulus missing showed that the context was associated with the stimuli presented within it. Results are discussed with respect to theories and demonstrations of human LI. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Increasing previous but not concurrent extinction attenuates the "extinction makes acquisition context specific" effect in human predictive learning.

Four experiments in human predictive learning evaluated whether the extinction makes the acquisition context specific (EMACS) effect is attenuated when the increase in prediction error that extinction produces disappears. Participants had to evaluate the relationship between a given food (cue) that was ingested by an imaginary client of a given restaurant (context) and a potential gastric illness (outcome). The task was implemented using Gorilla online software. All participants received the relevant training in context A, and equivalent exposure to context B. Cue E was presented paired with the outcome in all groups. Cue E was then either extinguished (group E) or not extinguished (group NE), either previously or concurrently to training of the target cue (P). P was then tested in contexts A and B. When extinction was conducted concurrently, performance to P became context-dependent regardless of the number of extinction trials (12 or 24)-the EMACS effect. The EMACS effect disappeared when extinction was elongated to 24 trials, and conducted before acquisition of P. Implications of these results for attentional explanations of context processing are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Associative interference and nonreinforcement in human contingency learning.

This article reports three experiments comparing the impact on contingency assessment of associative cue interference (proactive, interspersed, and retroactive) and nonreinforcement (latent inhibition, partial reinforcement, and extinction). All three experiments used variants of the rapid trial streaming procedure developed by Allan and collaborators. Participants were exposed to stimulus streams and then asked how likely it was for a target cue to be accompanied (Experiment 1) or to be followed (Experiments 2 and 3) by a target outcome. Experiments 1 and 2 looked at interference and found that when the objective target cue-outcome contingency is positive, interspersed interference is more effective than either proactive or retroactive interference. Experiment 2 additionally showed that this conclusion was a function of the target cue-outcome contingency: when the number of cue-outcome pairings was low, retroactive interference was more efficient than interspersed interference. Experiment 3 examined nonreinforcement and found that the efficacies of latent inhibition, partial reinforcement, and extinction are also a function of the target cue-outcome contingency, but the pattern differed greatly from what was observed in Experiment 2. When the number of cue-outcome pairings was high, there was no difference between latent inhibition, partial reinforcement, and extinction. When the number of cue-outcome pairings was low, extinction did not lower the contingency judgement, whereas latent inhibition and partial reinforcement did.

Density-based ship detection in SAR images: Extension to a self-similarity perspective

Nonlocal self-similarity is an important property of Synthetic Aperture Radar (SAR) images to characterize the repetitiveness of features embodied by SAR images within nonlocal areas and has been used for enhancement of SAR images. Existing SAR ship detectors often independently handle small sub-images cropped from a large marine SAR image and do not exploit the nonlocal self-similarity therein. In this paper, we propose a new ship detector from the perspective of nonlocal self-similarity in SAR images to improve the ship detection performance, basically including three stages: prescreening, intra-cue calculation, and inter-cue calculation. In the pre-screening stage, we design a new Histogram-based Density (HD) feature to rapidly select candidate sub-images potentially containing ship targets from a large SAR image. In the intra-cue calculation stage, target cues within a single candidate sub-image are extracted. In the inter-cue calculation stage, thanks to the nonlocal self-similarity among different candidate sub-images in terms of density features, we innovatively extract a weighted superpixel-HD map to obtain accumulated intra-cues across all the candidate sub-images. Finally, for each candidate sub-image, we fuse its inter-cue and intra-cue to obtain final detection results. Experimental results based on real SAR images show that our newly proposed method provides a better target-to-clutter contrast and ship detection performance than those of other state-of-the-art detection approaches.

The relation between implicit statistical learning and proactivity as revealed by EEG

Environmental events often occur on a probabilistic basis but can sometimes be predicted based on specific cues and thus approached proactively. Incidental statistical learning enables the acquisition of knowledge about probabilistic cue-target contingencies. However, the neural mechanisms of statistical learning about contingencies (SLC), the required conditions for successful learning, and the role of implicit processes in the resultant proactive behavior are still debated. We examined changes in behavior and cortical activity during an SLC task in which subjects responded to visual targets. Unbeknown to them, there were three types of target cues associated with high-, low-, and zero target probabilities. About half of the subjects spontaneously gained explicit knowledge about the contingencies (contingency-aware group), and only they showed evidence of proactivity: shortened response times to predictable targets and enhanced event-related brain responses (cue-evoked P300 and contingent negative variation, CNV) to high probability cues. The behavioral and brain responses were strictly associated on a single-trial basis. Source reconstruction of the brain responses revealed activation of fronto-parietal brain regions associated with cognitive control, particularly the anterior cingulate cortex and precuneus. We also found neural correlates of SLC in the contingency-unaware group, but these were restricted to post-target latencies and visual association areas. Our results document a qualitative difference between explicit and implicit learning processes and suggest that in certain conditions, proactivity may require explicit knowledge about contingencies.

Further evidence for the role of temporal contiguity as a determinant of overshadowing.

Three experiments explored whether weakening temporal contiguity between auditory cues and an aversive outcome attenuated cue competition in an avoidance learning task with human participants. Overall, with strong temporal contiguity between auditory cues and the outcome during training (the offset of the predictive auditory signals concurred with the onset of the outcome), the target cue trained as part of a compound yielded less avoidance behaviour than the control cue trained alone, an instance of overshadowing. However, weakening temporal contiguity during training (inserting a 5-s trace) attenuated overshadowing, resulting in similar avoidance behaviour in response to the control and target cues. These results provide evidence that, as predicted by a recent modification of Pearce's configural theory, temporal contiguity is critical for determining cue competition.

Explicit attentional goals unlock implicit spatial statistical learning.

People can quickly learn spatial distributions of targets and direct attention to likely regions of targets. These implicitly learned spatial biases have been shown to be persistent, transferring to other similar visual search tasks. However, a persistent attentional bias is incompatible with frequently changing goals in our typical daily environment. We propose a flexible goal-specific probability cueing mechanism to address this discrepancy. We examined whether participants could learn and utilize target-specific spatial priority maps across five experiments (each N = 24). In Experiment 1, participants were faster to find the target at the target-specific high-probability location, in line with a goal-specific probability cueing effect. This demonstrated that separate spatial priorities derived from statistical learning can be flexibly activated based on the current goal. In Experiment 2, we ensured the results were not driven solely by intertrial priming. In Experiment 3, we ensured the results were driven by early attentional guidance effects. In Experiment 4, we extended our findings to a complex spatial distribution including four locations, supporting a sophisticated representation of target likelihood in the activated spatial priority maps. Finally, in Experiment 5, we confirmed that the effect was driven by the activation of an attentional template and not associative learning between the target cue and a spatial location. Our findings demonstrate a previously unrecognized mechanism for flexibility within statistical learning. The goal-specific probability cueing effect relies on coordination of feature-based and location-based attention, utilizing information that crosses traditional boundaries between top-down control and selection history. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Cue-reactivity to distal cues in individuals at risk for gaming disorder

BackgroundGaming disorder (GD) is a disorder due to addictive behaviors (ICD-11). Cue-reactivity and craving are relevant mechanisms in the development and maintenance of addictive behaviors. When confronted with cues showing in-game content (proximal cues) individuals with higher symptom severity show increased cue-reactivity. Based on conditioning and addiction theories on incentive sensitization, cue-reactivity responses may generalize to more distal cues, e.g. when individuals at risk of developing a GD are confronted with a starting page of an online game. In cue-reactivity paradigms so far, only proximal gaming cues have been used. MethodsWe investigated the effect of distal gaming cues compared to gaming-unrelated control cues on cue-reactivity and craving in 88 individuals with non-problematic use of online games (nPGU) and 69 individuals at risk for GD (rGD). The distal cues showed the use of an electronic device (e.g., desktop PC or smartphone) whose screen showed starting pages of either games (target cues), shopping- or pornography sites (control cues) from a first-person perspective. FindingsWe found significantly higher urge and arousal ratings as well as longer viewing times for gaming-related compared to gaming-unrelated control cues in rGD compared to nPGU. Valence ratings did not differ between groups. InterpretationThe results demonstrate that already distal gaming-specific cues lead to cue-reactivity and craving in rGD. This finding indicates that based on conditioning processes, cue-reactivity and craving develop during the course of GD and generalize to cues that are only moderately related to the specific gaming activity.

Spatial attention modulates multisensory integration: The dissociation between exogenous and endogenous orienting.

Previous studies have separately found that exogenous orienting decreases multisensory integration (MSI), while endogenous orienting enhances MSI. It is currently unclear, however, why the two orientations have opposite effects on MSI. In the current study, we investigated the interaction between spatial attention and MSI in two experiments based on the cue-target paradigm. Experiment 1 separated exogenous and endogenous orienting to investigate the effect of spatial attention on MSI by varying the predictability of the cue. Experiment 2 further explored the effect of endogenous orienting on MSI. We found that exogenous orienting induced by the directionality of the cue decreased MSI, while endogenous orienting induced by the predictability of the cue enhanced MSI. The role of spatial orienting need and spatial attention bias in the modulation of MSI by exogenous and endogenous orienting was discussed. The present study sheds new light on how spatial attention modulates MSI processes.

AARN: Anchor‐guided attention refinement network for inshore ship detection

AbstractInshore ship detection is an important task in several fields, for example, maritime transportation, maritime supervision, and port management. However, due to the diversified categories and locations of different ships and interference of complex surroundings, capturing discriminative characteristics of multi‐scale inshore ships for accurate detection is still challenging. Here, an anchor‐guided attention refinement network (AARN) is proposed to alleviate the problems by prominently designing an attention feature filter module (AFFM) and an anchor‐guided alignment detection module (AADM). In AFFM, of which the attention supervision generated from high‐level semantic features, is used to highlight informative target cues and suppress background interference when establishing a four‐layer feature pyramid. In AADM, the anchor‐aligned features are adopted to eventually identify potential inshore ships, which both alleviates misalignment between refined anchors and pyramidal features and improves the performance further. Extensive experiments conducted on the public Seaships7000 dataset verify the contributions of the proposed modules and the effectiveness of our method for detecting multi‐scale inshore ships in comparison to both domain‐specific and general CNN‐based methods.

Prototypical Matching Networks for Video Object Segmentation.

Semi-supervised video object segmentation is the task of segmenting the target in sequential frames given the ground truth mask in the first frame. The modern approaches usually utilize such a mask as pixel-level supervision and typically exploit pixel-to-pixel matching between the reference frame and current frame. However, the matching at pixel level, which overlooks the high-level information beyond local areas, often suffers from confusion caused by similar local appearances. In this paper, we present Prototypical Matching Networks (PMNet) - a novel architecture that integrates prototypes into matching-based video objection segmentation frameworks as high-level supervision. Specifically, PMNet first divides the foreground and background areas into several parts according to the similarity to the global prototypes. The part-level prototypes and instance-level prototypes are generated by encapsulating the semantic information of identical parts and identical instances, respectively. To model the correlation between prototypes, the prototype representations are propagated to each other by reasoning on a graph structure. Then, PMNet stores both the pixel-level features and prototypes in the memory bank as the target cues. Three affinities, i.e., pixel-to-pixel affinity, prototype-to-pixel affinity, and prototype-to-prototype affinity, are derived to measure the similarity between the query frame and the features in the memory bank. The features aggregated from the memory bank using these affinities provide powerful discrimination from both the pixel-level and prototype-level perspectives. Extensive experiments conducted on four benchmarks demonstrate superior results than the state-of-the-art video object segmentation techniques.

Capsule-based Regression Tracking via Background Inpainting.

Background cues play an accompanying role in most regression trackers, where they directly learn a mapping from dense sampling to soft label by giving a search area. In essence, the trackers need to identify a large amount of background information (i.e., other objects and distractor objects) under the circumstance of extreme target-background data imbalance. Therefore, we believe that it is more worth performing regression tracking depending on the informative background cues and using target cues as supplementary. To do this, we propose a capsule-based approach, referred to as CapsuleBI, which performs regression tracking based on a background inpainting network and a target-aware network. The background inpainting network explores the background representations by restoring the region of the target with all available scenes, and a target-aware network captures the target representations by focusing on the target itself only. To explore the subjects/distractors in the whole scene, we propose a global-guided feature construction module, which helps enhance the local features with global information. Both the background and target are encoded in capsules, which can model the relationships between objects or object parts in the background scene. Apart from this, the target-aware network assists the background inpainting network with a novel background-target routing algorithm that guides the background and target capsules to estimate the target location with multi-video relationships information precisely. Extensive experimental results show that the proposed tracker achieves favorably against state-of-the-art methods.

Accurate Object Tracking by Utilizing Diverse Prior Information

Siamese trackers draw continuous attention in the object tracking community due to their proper balance between performance and inference speed. Despite that, it remains unclear how to effectively exploit the target appearance cues and motion cues involved in videos to improve trackers’ performance. To address this problem, we develop a Siamese network with diverse prior information integrated, namely DPINet, by extending two novel blocks to a powerful anchor-free Siamese network. First, we design a channel- and space-aware feature enhancement (CSE) block to highlight target-specific feature weights in two aspects (channel and spatial dimensions). It is devoted to making full use of the target cues in the initial frame by considering them as guidances, in which way target-related representation in feature maps can be improved. It also facilitates the interplay between two input branches. Second, we advance a cross-correlation block with multi-dimensional information fusion (MDI-XCorr). In this block, target motion cues within adjacent frames can be mined and treated as supervisions to refine the response map in the current frame during inference. Hence, both tracking quality and stabilization can be enhanced. Evaluations on four popular benchmarks are conducted, showing that DPINet achieves 0.702 (AUC), 0.474 (EAO), 0.336 (EAO), 0.613 (AO), and 0.527 (AUC) on OTB100, VOT2018, VOT2019, GOT-10k, and LaSOT, respectively.