Visual Signals Research Articles

Interactions between sexual signaling and wing size drive ecology and evolution of wing colors in Odonata

Insect coloration has evolved in response to multiple pressures, and in Odonata (dragonflies and damselflies) a body of work supports a role of wing color in a variety of visual signals and potentially in thermoregulation. Previous efforts have focused primarily on melanistic coloration even though wings are often multicolored, and there has yet to be comprehensive comparative analyses of wing color across broad geographic regions and phylogenetic groups. Percher vs. flier flight-style, a trait with thermoregulatory and signaling consequences, has not yet been studied with regard to color. We used a new color clustering approach to quantify color across a dataset of over 8,000 odonate wing images representing 343 Nearctic species. We then utilized phylogenetically informed Bayesian zero-inflated mixture models to test how color varies with mean ambient temperature, body size, sex and flight-style. We found that wing coloration clustered into two groups across all specimens - light brown-yellow and black-dark brown - with black-dark brown being a much more cohesive grouping. Male perchers have a greater proportion of black-dark brown color on their wings as do species with longer wings. In colder climates, odonates were more likely to have black-dark brown color present, but we found no relationship between the proportion of black and temperature. Light brown-yellow showed similar scaling with wing length, but no relationship with temperature. Our results suggest that black-dark brown coloration may have a limited role in thermoregulation, while light brown-yellow does not have such a role. We also find that the odonate sexes are divergent in wing color in percher species only, suggesting a strong role for color in signaling in more territorial males. Our research contributes to an understanding of complex interactions driving ecological and evolutionary dynamics of color in animals.

Modeling saccade reaction time in marmosets: the contribution of earlier visual response and variable inhibition.

Marmosets are expected to serve as a valuable model for studying the primate visuomotor system due to their similar oculomotor behaviors to humans and macaques. Despite these similarities, differences exist; challenges in training marmosets on tasks requiring suppression of unwanted saccades, having consistently shorter, yet more variable saccade reaction times (SRT) compared to humans and macaques. This study investigates whether the short and variable SRT in marmosets is related to differences in visual signal transduction and variability in inhibitory control. We refined a computational SRT model, adjusting parameters to better capture the marmoset SRT distribution in a gap saccade task. Our findings indicate that visual information processing is faster in marmosets, and that saccadic inhibition is more variable compared to other species.

Temporal dynamics of scent mark composition in field‐experimental lizard populations

Abstract Animal signals are dynamic traits that can undergo considerable spatial and temporal changes and that are influenced by factors such as age, health condition and interactions with both the abiotic and biotic environment. However, much of our understanding of signal changes throughout an individual's lifetime stems from cross‐sectional, often laboratory‐based, studies focused on visual and auditory signals. Longitudinal field investigations of temporal variation in chemical signals, especially in vertebrates, remain rare despite chemical communication being the most ubiquitous form of information exchange in the natural world. To remedy this, we conducted a unique, replicated field experiment to study the temporal signal dynamics in free‐living lizard populations on natural islands. Specifically, we collected scent marks from individually marked lizards across five populations during the spring of two consecutive years and analysed the lipophilic chemical composition of these scent marks. Our findings demonstrate that the overall scent mark composition of individual lizards changed over time, shifting consistently in both direction and magnitude from year to year among individuals and across replicate populations. Similar patterns were observed for the chemical richness and diversity of scent marks. Temporal variation in the relative proportions of three potentially socially relevant signalling compounds in lizard scent marks revealed a more complex pattern: α‐tocopherol remained stable over time, oleic acid decreased and the change in octadecanoic acid proportion was body size‐dependent. Together, our results provide novel insights into how individual vertebrate chemical signals may fluctuate across space and time. We discuss the potential causes of the observed temporal variability and its consequences for chemical signal evolution. Read the free Plain Language Summary for this article on the Journal blog.

Visual signal transduction for environmental stewardship: A novel biosensing approach to identify and quantify chlorpyrifos-related residues in aquatic environments

The pervasive presence of organophosphate pesticides (OPs), such as chlorpyrifos (CPF), in aquatic ecosystems underscores the urgent need for sensitive and reliable detection methods to safeguard environmental and public health. This study addressed the critical need for a novel biosensor capable of detecting CPF and its toxic metabolite, 3,5,6-trichloro-2-pyridinol (TCP), with high sensitivity and selectivity, suitable for field applications in environmental monitoring. The study engineered a whole-cell biosensor based on E. coli strains that utilize the ChpR transcriptional regulator and the vioABCE gene cluster, providing a distinct visual and colorimetric response to CPF and TCP. The biosensor's performance was optimized and evaluated across various water matrices, including freshwater, seawater, and soil leachate. The biosensor demonstrated high sensitivity with a broad linear detection range, achieving limits of detection (LODs) at 0.8 μM for CPF and 7.813 nM for TCP. The linear regression concentration ranges were 1.6–12.5 μM for CPF and 15.6–125 nM for TCP, aligning with environmental standard limits and ensuring the biosensor's effectiveness in real-world scenarios. This innovative biosensing approach offers a robust, user-friendly tool for on-site environmental monitoring, significantly mitigating OPs contamination and advancing current detection technologies to meet environmental protection standards.

Substance P and dopamine form a “push-pull” system that diurnally regulates retinal gain

The operation of the retina, like other brain circuits, is under modulatory control. One coordinator of changes in retinal function is dopamine, a neuromodulator released in a light-dependent way to adjust vision on a diurnal cycle. Here, we demonstrate that substance P is a similarly powerful retinal modulator that interacts with the dopamine system. By imaging glutamatergic synaptic transmission in larval zebrafish, we find that substance P decreases the contrast sensitivity of ON and OFF visual channels up to 8-fold, with suppression of visual signals being strongest through the "transient" pathway responding to higher frequencies. These actions are exerted in the morning, in large part by suppressing the amplification of visual signals by dopamine, but substance P is almost completely inactive in the afternoon. Modulation of retinal gain is accompanied by changes in patterns of vesicle release at the synapses of bipolar cells: increased gain shifts coding of stimulus strength from the rate of release events to their amplitude generated by a process of multivesicular release (MVR). Together, these actions of substance P reduce the flow of visual information, measured in bits, ∼3-fold. Thus, whereas dopamine "pushes" the retina to transmit information at higher rates in the afternoon, substance P acts in antiphase to suppress dopamine signaling and "pull down" information transmission in the morning.

Inspiration from Visual Ecology for Advancing Multifunctional Robotic Vision Systems: Bio-inspired Electronic Eyes and Neuromorphic Image Sensors.

In robotics, particularly for autonomous navigation and human-robot collaboration, the significance of unconventional imaging techniques and efficient data processing capabilities is paramount. The unstructured environments encountered by robots, coupled with complex missions assigned to them, present numerous challenges necessitating diverse visual functionalities, and consequently, the development of multifunctional robotic vision systems has become indispensable. Meanwhile, rich diversity inherent in animal vision systems, honed over evolutionary epochs to meet their survival demands across varied habitats, serves as a profound source of inspirations. Here, recent advancements in multifunctional robotic vision systems drawing inspiration from natural ocular structures and their visual perception mechanisms are delineated. First, unique imaging functionalities of natural eyes across terrestrial, aerial, and aquatic habitats and visual signal processing mechanism of humans are explored. Then, designs and functionalities of bio-inspired electronic eyes are explored, engineered to mimic key components and underlying optical principles of natural eyes. Furthermore, neuromorphic image sensors are discussed, emulating functional properties of synapses, neurons, and retinas and thereby enhancing accuracy and efficiency of robotic vision tasks. Next, integration examples of electronic eyes with mobile robotic/biological systems are introduced. Finally, a forward-looking outlook on the development of bio-inspired electronic eyes and neuromorphic image sensors is provided.

Investigating premotor corticospinal excitability in fast and slow voluntary contractions of the elbow flexors.

Corticospinal excitability (CSE) increases prior to a voluntary contraction; however, the relative contributions of premotor cortical and spinal mechanisms are poorly understood. It is unknown whether the intended voluntary contractile rate affects CSE. Eighteen young, healthy participants (nine females) completed isometric elbow flexion contractions targeting 50% maximal voluntary contraction (MVC) torque, at either fast (fast as possible) or slow (25% MVC/s) contractile rates. Participants were cued to contract with warning (red) and "GO" (green) visual signals. Magnetic and electric stimulations were applied to elicit motor evoked potentials (MEPs), cervicomedullary evoked potentials (CMEPs), and M-waves, in the surface electromyogram (EMG) recorded over the biceps brachii. MEPs and CMEPs were collected at 0, 25, 50 and 75% premotor reaction time (RT - defined as the time between the "GO" cue and onset of biceps brachii EMG) and compared to a resting baseline. MEP amplitude was greater than baseline at 75% RT (p=0.009), and CMEP amplitude was significantly increased at all RT points relative to baseline (p≤0.001). However, there were no differences in MEP and CMEP amplitudes when compared between fast and slow conditions (p≥0.097). Normalized to the CMEP, there was no difference in MEP amplitude from baseline in either contractile condition (p≥0.264). These results indicate that increased premotor CSE is a spinally-mediated response. Furthermore, premotor CSE is not influenced by the intended voluntary contractile rate. CMEP amplitudes were larger for females than males within the premotor RT period (p=0.038), demonstrating that premotor spinal excitability responses may be influenced by sex.

A highly sensitive Photothermal Immunochromatographic test strip for detection of aflatoxin B1 based on CoS@TA Nanospheres

A novel photothermal immunochromatographic test strip (PITS) with tannic acid (TA) modified cobalt sulfide (CoS) nanospheres (CoS@TA) as immuno-probe element was developed for the detection of aflatoxin B1 (AFB1). CoS nanospheres (CoS NPs) with excellent photothermal conversion efficiency (η = 47.5 %) was synthesized and skillfully chelated with TA to improve its dispersion, biocompatibility, and chromatographic properties. After modification, the CoS@TA coupled with monoclonal antibody (mAb) against AFB1 (CoS@TA-mAb) by simple physical adsorption. The CoS@TA based PITS achieved highly sensitive detection of AFB1 with the limit of detection in photothermal signal (photothermal-LOD) of 0.00503 μg/L, which was 19.88-fold higher than the LOD in visual signal (visual-LOD, 0.1 μg/L). The application of TA in the modification of CoS provided ideas to improve the properties of functional nanomaterials such as dispersion and biocompatibility, and the application of CoS@TA in PITS construction laid a methodological foundation for further improving the detection sensitivity of trace targets.

Subjective and objective quality evaluation for industrial images

Recently, the demand for ever-better image processing technique continues to grow in field of industrial scenario monitoring and industrial process inspection. The subjective and objective quality evaluation of industrial images are vital for advancing the development of industrial visual perception and enhancing the quality of industrial image/video processing applications. However, the scarcity of publicly available industrial image databases with reliable subjective scores restricts the development of industrial image quality evaluation (IIQE). In preparation for a vacancy, this article first establishes two industrial image databases (i.e., industrial scenario image dataset (ISID) and industrial process image dataset (IPID)) for assessing IIQE metrics. Furthermore, in order to avoid overwhelming industrial image nuances due to the wavelet subband summation, we then present a novel industrial application subband information fidelity standard (SIFS) evaluation method using the channel capacity of visual signals in wavelet domain. Specifically, we first build a visual signals channel model based on perception process from human eyes to brain. Second, we compute and compare the channel capacity for reference and distorted images to measure the information fidelity in each wavelet subband. Third, we sum over the subbands for information fidelity ratio to obtain the overall quality score. Finally, we fairly compare some up-to-date and our proposed image quality evaluation (IQE) methods in two novelty industrial datasets respectively. Our ISID and IPID datasets are capable of evaluating most IQE metrics comprehensively and paves the way for further research on IIQE. Our SIFS model show a remarkable performance comparing with other up-to-date IQE methods.

CalTrig: A GUI-Based Machine Learning Approach for Decoding Neuronal Calcium Transients in Freely Moving Rodents.

Advances in in vivo Ca2+ imaging using miniatured microscopes have enabled researchers to study single-neuron activity in freely moving animals. Tools such as MiniAN and CalmAn have been developed to convert Ca2+ visual signals to numerical information, collectively referred to as CalV2N. However, substantial challenges remain in analyzing the large datasets generated by CalV2N, particularly in integrating data streams, evaluating CalV2N output quality, and reliably and efficiently identifying Ca2+ transients. In this study, we introduce CalTrig, an open-source graphical user interface (GUI) tool designed to address these challenges at the post-CalV2N stage of data processing. CalTrig integrates multiple data streams, including Ca2+ imaging, neuronal footprints, Ca2+ traces, and behavioral tracking, and offers capabilities for evaluating the quality of CalV2N outputs. It enables synchronized visualization and efficient Ca2+ transient identification. We evaluated four machine learning models (i.e., GRU, LSTM, Transformer, and Local Transformer) for Ca2+ transient detection. Our results indicate that the GRU model offers the highest predictability and computational efficiency, achieving stable performance across training sessions, different animals and even among different brain regions. The integration of manual, parameter-based, and machine learning-based detection methods in CalTrig provides flexibility and accuracy for various research applications. The user-friendly interface and low computing demands of CalTrig make it accessible to neuroscientists without programming expertise. We further conclude that CalTrig enables deeper exploration of brain function, supports hypothesis generation about neuronal mechanisms, and opens new avenues for understanding neurological disorders and developing treatments.

OODNet: A deep blind JPEG image compression deblocking network using out-of-distribution detection

JPEG is one of the most popular image compression techniques, with numerous applications ranging from medical imaging to surveillance systems. Since JPEG introduces the blocking artifacts to the decompressed visual signals, enhancing the quality of these images is of paramount importance. Recently, various deep neural networks have been proposed for JPEG image deblocking that can effectively reduce the blocking artifacts produced by the JPEG compression technique. However, most of these schemes could only handle decompressed images generated by a set of specific JPEG quality factor (QF) values employed in the network training process. Therefore, when the images are obtained by the JPEG QF values other than those used in the network training process, the performance of deep learning-based JPEG image deblocking schemes drops significantly. To address this, in this paper, we propose a novel deep learning-based blind JPEG image deblocking method, which employs out-of-distribution detection to perform deblocking efficiently for various quality factor (QF) values. The proposed scheme can distinguish between the decompressed images using the QF values used in the training set and those using the QF values not used in the training set, and then, a suitable deblocking strategy for generating high-quality images is developed. The proposed scheme is shown to outperform the state-of-the-art JPEG image deblocking methods for various QF values.

Concentration Techniques in the Classroom for Children with ADHD, in an Educational Unit in the Province of Santa Elena

This study analyzes the effectiveness of various concentration techniques applied in classrooms for children with Attention Deficit Hyperactivity Disorder (ADHD) in an educational unit in Santa Elena. Employing a mixed-methods approach, surveys and interviews with teachers were examined, and the behavior of students with ADHD was observed. The findings suggest that strategies like environmental adaptation, use of visual and auditory signals, and positive reinforcement are effective in enhancing concentration and academic performance of these students. It is concluded that the implementation of specific concentration techniques, tailored to the size and resources of each institution, is crucial to support students with ADHD, thereby improving their academic performance and satisfaction.

Exploring the categorical nature of colour perception: Insights from artificial networks

The electromagnetic spectrum of light from a rainbow is a continuous signal, yet we perceive it vividly in several distinct colour categories. The origins and underlying mechanisms of this phenomenon remain partly unexplained. We investigate categorical colour perception in artificial neural networks (ANNs) using the odd-one-out paradigm. In the first experiment, we compared unimodal vision networks (e.g., ImageNet object recognition) to multimodal vision-language models (e.g., CLIP text-image matching). Our results show that vision networks predict a significant portion of human data (approximately 80%), while vision-language models account for the remaining unexplained data, even in non-linguistic experiments. These findings suggest that categorical colour perception is a language-independent representation, though it is partly shaped by linguistic colour terms during its development. In the second experiment, we explored how the visual task influences the colour categories of an ANN by examining twenty-four Taskonomy networks. Our results indicate that human-like colour categories are task-dependent, predominantly emerging in semantic and 3D tasks, with a notable absence in low-level tasks. To explain this difference, we analysed kernel responses before the winner-takes-all stage, observing that networks with mismatching colour categories may still align in underlying continuous representations. Our findings quantify the dual influence of visual signals and linguistic factors in categorical colour perception and demonstrate the task-dependent nature of this phenomenon, suggesting that categorical colour perception emerges to facilitate certain visual tasks.

Channel randomisation: Self-supervised representation learning for reliable visual anomaly detection in speciality crops

Modern, automated quality control systems for speciality crops utilise computer vision together with a machine learning paradigm exploiting large datasets for learning efficient crop assessment components. To model anomalous visuals, data augmentation methods are often developed as a simple yet powerful tool for manipulating readily available normal samples. State-of-the-art augmentation methods embed arbitrary “structural” peculiarities in normal images to build a classifier of these artefacts (i.e., pretext task), enabling self-supervised representation learning of visual signals for anomaly detection (i.e., downstream task). In this paper, however, we argue that learning such structure-sensitive representations may be suboptimal for agricultural anomalies (e.g., unhealthy crops) that could be better recognised by a different type of visual element like “colour”.To be specific, we propose Channel Randomisation (CH-Rand)—a novel data augmentation method that forces deep neural networks to learn effective encoding of “colour irregularities” under self-supervision whilst performing a pretext task to discriminate channel-randomised images. Extensive experiments are performed across various types of speciality crops (apples, strawberries, oranges, and bananas) to validate the informativeness of learnt representations in detecting anomalous instances. Our results demonstrate that CH-Rand’s representations are significantly more reliable and robust, outperforming state-of-the-art methods (e.g., CutPaste) that learn structural representations by over 43% in Area Under the Precision–Recall Curve (AUC–PR), particularly for strawberries. Additional experiments suggest that adopting the L∗a∗b∗ colour space and “curriculum” learning in the pretext task — gradually disregarding channel combinations for unrealistic outcomes — further improves downstream-task performance by 16% in AUC–PR. In particular, our experiments employ Riseholme-2021, a novel speciality crop dataset consisting of 3.5K real strawberry images gathered in situ from the real farm, along with the Fresh & Stale public dataset. All our code and datasets are made publicly available online to ensure reproducibility and encourage further research in agricultural technologies.

Cryo-EM structure of human class C orphan GPCR GPR179 involved in visual processing

GPR179, an orphan class C GPCR, is expressed at the dendritic tips of ON-bipolar cells in the retina. It plays a pivotal role in the initial synaptic transmission of visual signals from photoreceptors, and its deficiency is known to be the cause of complete congenital stationary night blindness. Here, we present the cryo-electron microscopy structure of human GPR179. Notably, the transmembrane domain (TMD) of GPR179 forms a homodimer through the TM1/7 interface with a single inter-protomer disulfide bond, adopting a noncanonical dimerization mode. Furthermore, the TMD dimer exhibits architecture well-suited for the highly curved membrane of the dendritic tip and distinct from the flat membrane arrangement observed in other class C GPCR dimers. Our structure reveals unique structural features of GPR179 TMD, setting it apart from other class C GPCRs. These findings provide a foundation for understanding signal transduction through GPR179 in visual processing and offers insights into the underlying causes of ocular diseases.

Potential Biomarkers of Dysmenorrhea Relief: A MEG Study of Hinoki Aromatherapy and Working Memory.

Background/Objectives: This study explored the potential of Hinoki aromatherapy to induce biomarkers of dysmenorrhea relief through working memory. Structural magnetic resonance imaging and magnetoencephalography (MEG) were used to examine their effects on neurophysiological responses to a visual working memory (VWM) test. Behavioral performance was measured to understand its effects on the overall working memory. Methods: Twenty-four healthy participants completed the VWM task during nonmenstruation and menstruation. Behavioral (accuracy and reaction time) and neurophysiological (event-related fields, source estimation, and permutation t-test on source data) measures were assessed without and with Hinoki aromatherapy. Results: A significant difference in the ratio of accuracy to reaction time was found without and with aromatherapy in participants with dysmenorrhea, suggesting that aromatherapy may improve working memory performance in this population. MEG analysis revealed high temporal resolution of evoked latency and intensity during the VWM task. Source localization of the activation aimed to identify brain areas involved in dysmenorrhea. Aromatherapy reduced signals in these areas, which may also contribute to reducing dysmenorrhea-related visual signals. Conclusions: Based on these findings, Hinoki aromatherapy may be a promising treatment option for alleviating dysmenorrhea and improving related symptoms by reducing activity in brain pain processing regions. These regions include the left entorhinal cortex, inferior temporal gyrus, primary visual cortex, retrosplenial cortex, and presubiculum. Furthermore, decreased activity in these areas with aromatherapy suggests that they could be used as biomarkers of dysmenorrhea relief.

Barrier Polarity Reversal Based on Interfacial Modification of Au Nanoparticles for Nonvolatile Multilevel Memory and Optoelectronic Synapses.

Optoelectronic synaptic devices, integrating light sensing and information processing capabilities, have emerged as advantageous tools for the implementation of visual neuromorphic computing. However, the transient light-triggered response characteristic typically results in unstable memory retention times and restricted current response ranges, posing significant challenges to the development and practical application of neural network systems. In response to these limitations, this study developed a nonvolatile optoelectronic memory based on the indium tin oxide (ITO)/Au nanoparticles (NPs)/amorphous Ga2O3 (a-Ga2O3)/Pt structure. Unlike conventional optoelectronic memories, this device features a modification with Au NPs that markedly enhances the Schottky barrier height at the interface. Au NPs function as a charge-trapping layer for sensitive and large-scale modulation of the barrier by the light field, thereby enabling the nonvolatile reversal of the device's barrier polarity. This innovative approach enables controllable multilevel data storage with an ultra large on/off ratio (∼104) and excellent retention capability exceeding 12,000 s. Additionally, the device emulates essential synaptic functions and demonstrates potential application values in visual weak signal perception and image memory. This study introduces a mechanism for Schottky barrier polarity control and presents a promising strategy for the development of future high-performance integrated devices and optoelectronic synaptic elements.

Exploring Journey Experiences of Disabled Passengers in Using Public Transport

This study aims to enhance the understanding of disabled passengers' experiences with public transport by addressing the challenges in measuring their satisfaction and service quality. The central problem is the difficulty in accurately assessing and improving the service quality for disabled passengers, given the diverse needs and barriers they face. Utilizing the SERVQUAL model, which focuses on Tangibility, Reliability, Responsiveness, Assurance, and Empathy, the study identifies and evaluates these key dimensions in the context of public transport services. Findings reveal both unique needs and commonalities among different disability groups: hearing-impaired passengers require visual signals and nonverbal communication, visually impaired passengers need clear auditory announcements and staff assistance, and physically disabled passengers require accessible infrastructure and supportive services. Commonalities include the appreciation for reliable train schedules, cleanliness, and the need for empathetic and responsive staff. Based on these insights, recommendations are provided to enhance service quality by improving physical accessibility features, enhancing communication methods, and providing targeted staff training. Implementing these recommendations will address specific needs and improve the overall satisfaction of disabled passengers, contributing to a more inclusive and effective public transport system. Future work should focus on integrating additional models and exploring other key variables to uncover further insights into service quality and passenger satisfaction. This approach will offer a more comprehensive understanding of how various factors interact and contribute to a more inclusive and effective public transport system.

Multiplex graph aggregation and feature refinement for unsupervised incomplete multimodal emotion recognition

Multimodal Emotion Recognition (MER) involves integrating information of various modalities, including audio, visual, text and physiological signals, to comprehensively grasp human sentiments, which has emerged as a vibrant area within human–computer interaction. Researchers have developed many methods for this task, but many of these methods rely on labeled supervised learning and struggle to address the issue of missing some modalities of data. To address these issues, we propose a Multiplex Graph Aggregation and Feature Refinement framework for unsupervised incomplete MER, comprising four modules: Completion, Aggregation, Refinement, and Embedding. Specifically, we first capture the correlation information between samples using the graph structures, which aids in the completion of missing data and the multiplex aggregation of multimodal data. Then, we perform refinement operations on the aggregated features as well as alignment and enhancement operations on the embedding features to obtain the fused feature representations, which are consistent, highly separable and conducive to emotion recognition. Experimental results on multimodal emotion recognition datasets demonstrate that our method achieves state-of-the-art performance among unsupervised methods, validating its effectiveness.

The Effect of Congruent versus Incongruent Distractor Positioning on Electrophysiological Signals during Perceptual Decision-Making.

Key event-related potentials (ERPs) of perceptual decision-making such as centroparietal positivity (CPP) elucidate how evidence is accumulated toward a given choice. Furthermore, this accumulation can be impacted by visual target selection signals such as the N2 contralateral (N2c). How these underlying neural mechanisms of perceptual decision-making are influenced by the spatial congruence of distractors relative to target stimuli remains unclear. Here, we used electroencephalography (EEG) in humans of both sexes to investigate the effect of distractor spatial congruency (same vs different hemifield relative to targets) on perceptual decision-making. We confirmed that responses for perceptual decisions were slower for spatially incongruent versus congruent distractors of high salience. Similarly, markers of target selection (N2c peak amplitude) and evidence accumulation (CPP slope) were found to be lower when distractors were spatially incongruent versus congruent. To evaluate the effects of congruency further, we applied drift diffusion modeling to participant responses, which showed that larger amplitudes of both ERPs were correlated with shorter nondecision times when considering the effect of congruency. The modeling also suggested that congruency's effect on behavior occurred prior to and during evidence accumulation when considering the effects of the N2c peak and CPP slope. These findings point to spatially incongruent distractors, relative to congruent distractors, influencing decisions as early as the initial sensory processing phase and then continuing to exert an effect as evidence is accumulated throughout the decision-making process. Overall, our findings highlight how key electrophysiological signals of perceptual decision-making are influenced by the spatial congruence of target and distractor.