Top-down Attentional Mechanisms Research Articles

Interaction of spatial attention and the associated reward value of audiovisual objects

Reward value and selective attention both enhance the representation of sensory stimuli at the earliest stages of processing. It is still debated whether and how reward-driven and attentional mechanisms interact to influence perception. Here we ask whether the interaction between reward value and selective attention depends on the sensory modality through which the reward information is conveyed. Human participants first learned the reward value of uni-modal visual and auditory stimuli during a conditioning phase. Subsequently, they performed a target detection task on bimodal stimuli containing a previously rewarded stimulus in one, both, or neither of the modalities. Additionally, participants were required to focus their attention on one side and only report targets on the attended side. Our results showed a strong modulation of visual and auditory event-related potentials (ERPs) by spatial attention. We found no main effect of reward value but importantly we found an interaction effect as the strength of attentional modulation of the ERPs was significantly affected by the reward value. When reward effects were examined separately with respect to each modality, auditory value-driven modulation of attention was found to dominate the ERP effects whereas visual reward value on its own led to no effect, likely due to its interference with the target processing. These results inspire a two-stage model where first the salience of a high reward stimulus is enhanced on a local priority map specific to each sensory modality, and at a second stage reward value and top-down attentional mechanisms are integrated across sensory modalities to affect perception.

Overt and Covert Effects of Mental Fatigue on Attention Networks: Evidence from Event-Related Potentials during the Attention Network Test.

Mental fatigue is a variation in the psychophysiological state that subjects encounter during or after prolonged cognitive activity periods, affecting top-down attention and cognitive control. The present study aimed to investigate the effects of mental fatigue on attention in the context of the three attention networks according to the Posnerian model (alerting, orienting, and executive networks) by combining the Attentional Network Test (ANT) and event-related potentials technique. Thirty healthy subjects were enrolled in the study. A continuous arithmetic task lasting one hour induced mental fatigue, and EEG recordings were conducted before and after the task while subjects were performing the ANT. The efficiencies of three networks were comparable between groups, while RTs shortened only in the control group and the accuracy related to the alerting and conflict networks declined only after mental effort. Mental fatigue reduced N1 amplitude during alerting network engagement and p3 amplitude during orienting. It also reduced N2 and P3 amplitude during the conflict, particularly the incongruent target-locked response. These findings underscore the covert effects of mental fatigue on attention, suggesting that even in healthy young subjects, compensatory mechanisms may maintain adequate overt performances, but fatigue still has a detrimental effect on top-down attentional mechanisms.

Comparing Neural Correlates of Memory Encoding and Maintenance for Foveal and Peripheral Stimuli.

Visual working memory is believed to rely on top-down attentional mechanisms that sustain active sensory representations in early visual cortex, a mechanism referred to as sensory recruitment. However, both bottom-up sensory input and top-down attentional modulations thereof appear to prioritize the fovea over the periphery, such that initially peripheral percepts may even be assimilated by foveal processes. This raises the question whether and how visual working memory differs for central and peripheral input. To address this, we conducted a delayed orientation recall task in which an orientation was presented either at the center of the screen or at 15° eccentricity to the left or right. Response accuracy, EEG activity, and gaze position were recorded from 30 participants. Accuracy was slightly but significantly higher for foveal versus peripheral memories. Decoding of EEG recordings revealed a clear dissociation between early sensory and later maintenance signals. Although sensory signals were clearly decodable for foveal stimuli, they were not for peripheral input. In contrast, maintenance signals were equally decodable for both foveal and peripheral memories, suggesting comparable top-down components regardless of eccentricity. Moreover, although memory representations were initially spatially specific and reflected in voltage fluctuations, later during the maintenance period, they generalized across locations, as emerged in alpha oscillations, thus revealing a dynamic transformation within memory from separate sensory traces to what we propose are common output-related codes. Furthermore, the combined absence of reliable decoding of sensory signals and robust presence of maintenance decoding indicates that storage activity patterns as measured by EEG reflect signals beyond primary visual cortex. We discuss the implications for the sensory recruitment hypothesis.

Omnidirectional image quality assessment with local–global vision transformers

With the rising popularity of omnidirectional images (ODIs) in virtual reality applications, the need for specialized image quality assessment (IQA) methods becomes increasingly critical. Traditional IQA approaches, designed for rectilinear images, often fail to evaluate ODIs accurately due to their 360-degree scene representation. Addressing this, we introduce the Local–Global Transformer for 360-degree Image Quality Assessment (LGT360IQ). This novel framework features dual branches tailored to mimic top-down and bottom-up visual attention mechanisms, adapted for the spherical characteristics of ODIs. The local branch processes tangent viewports from salient regions within the equirectangular projection image, extracting detailed features for granular quality assessment. In parallel, the global branch utilizes a task-dependent token sampling strategy for holistic image feature processing and quality score prediction. This integrated approach combines local and global information, offering an effective IQA method for ODIs. Our extensive evaluation across three benchmark ODI datasets, CVIQ, OIQA, and ODI, demonstrates LGT360IQ superior performance and establishes its role in advancing the field of IQA for omnidirectional images.

The impact of internal-generated contextual clues on EFL vocabulary learning: insights from EEG.

With the popularity of learning vocabulary online among English as a Foreign Language (EFL) learners today, educators and researchers have been considering ways to enhance the effectiveness of this approach. Prior research has underscored the significance of contextual clues in vocabulary acquisition. However, few studies have compared the context provided by instructional materials and that generated by learners themselves. Hence, this present study sought to explore the impact of internal-generated contextual clues in comparison to those provided by instructional materials on EFL learners' online vocabulary acquisition. A total of 26 university students were enrolled and underwent electroencephalography (EEG). Based on a within-subjects design, all participants learned two groups of vocabulary words through a series of video clips under two conditions: one where the contexts were externally provided and the other where participants themselves generated the contexts. In this regard, participants were tasked with either viewing contextual clues presented on the screen or creating their own contextual clues for word comprehension. EEG signals were recorded during the learning process to explore neural activities, and post-tests were conducted to assess learning performance after each vocabulary learning session. Our behavioral results indicated that comprehending words with internal-generated contextual clues resulted in superior learning performance compared to using context provided by instructional materials. Furthermore, EEG data revealed that learners expended greater cognitive resources and mental effort in semantically integrating the meaning of words when they self-created contextual clues, as evidenced by stronger alpha and beta-band oscillations. Moreover, the stronger alpha-band oscillations and lower inter-subject correlation (ISC) among learners suggested that the generative task of creating context enhanced their top-down attentional control mechanisms and selective visual processing when learning vocabulary from videos. These findings underscored the positive effects of internal-generated contextual clues, indicating that instructors should encourage learners to construct their own contexts in online EFL vocabulary instruction rather than providing pre-defined contexts. Future research should aim to explore the limits and conditions of employing these two types of contextual clues in online EFL vocabulary learning. This could be achieved by manipulating the quality and understandability of contexts and considering learners' language proficiency levels.

Task-Based Visual Attention for Continually Improving the Performance of Autonomous Game Agents

Deep Reinforcement Learning (DRL) has been effectively performed in various complex environments, such as playing video games. In many game environments, DeepMind’s baseline Deep Q-Network (DQN) game agents performed at a level comparable to that of humans. However, these DRL models require many experience samples to learn and lack the adaptability to changes in the environment and handling complexity. In this study, we propose Attention-Augmented Deep Q-Network (AADQN) by incorporating a combined top-down and bottom-up attention mechanism into the DQN game agent to highlight task-relevant features of input. Our AADQN model uses a particle-filter -based top-down attention that dynamically teaches an agent how to play a game by focusing on the most task-related information. In the evaluation of our agent’s performance across eight games in the Atari 2600 domain, which vary in complexity, we demonstrate that our model surpasses the baseline DQN agent. Notably, our model can achieve greater flexibility and higher scores at a reduced number of time steps.Across eight game environments, AADQN achieved an average relative improvement of 134.93%. Pong and Breakout games both experienced improvements of 9.32% and 56.06%, respectively. Meanwhile, SpaceInvaders and Seaquest, which are more intricate games, demonstrated even higher percentage improvements, with 130.84% and 149.95%, respectively. This study reveals that AADQN is productive for complex environments and produces slightly better results in elementary contexts.

Inferring Attention Shifts for Salient Instance Ranking

AbstractThe human visual system has limited capacity in simultaneously processing multiple visual inputs. Consequently, humans rely on shifting their attention from one location to another. When viewing an image of complex scenes, psychology studies and behavioural observations show that humans prioritise and sequentially shift attention among multiple visual stimuli. In this paper, we propose to predict the saliency rank of multiple objects by inferring human attention shift. We first construct a new large-scale salient object ranking dataset, with the saliency rank of objects defined by the order that an observer attends to these objects via attention shift. We then propose a new deep learning-based model to leverage both bottom-up and top-down attention mechanisms for saliency rank prediction. Our model includes three novel modules: Spatial Mask Module (SMM), Selective Attention Module (SAM) and Salient Instance Edge Module (SIEM). SMM integrates bottom-up and semantic object properties to enhance contextual object features, from which SAM learns the dependencies between object features and image features for saliency reasoning. SIEM is designed to improve segmentation of salient objects, which helps further improve their rank predictions. Experimental results show that our proposed network achieves state-of-the-art performances on the salient object ranking task across multiple datasets. Code and data are available at https://github.com/SirisAvishek/Attention_Shift_Ranks.

Spatio-Temporal Feature Pyramid Interactive Attention Network for Egocentric Gaze Prediction

Over the past few years, gaze prediction technology has rapidly developed in human visual attention mechanism research. However, there is still a large gap between the prediction models and the human visual system (HVS). This work makes four contributions to video egocentric gaze prediction research. First, we introduce a new benchmark named ECVG (egocentric video gaze), which consists of 3K high-quality, elaborately selected video sequences spanning a large range of scenes. Existing eye-tracking datasets limit human activities. In contrast, ECVG achieves the collection of eye movement data under real human activity. Second, we establish a visual attention-selection and gaze-tracking model, which summarizes the human visual attention process. Third, we propose a novel feature pyramid interactive attention 3D network (FPIANet) for egocentric gaze prediction, which directly captures the long-term relationship between spatiotemporal features at different time steps and achieves feature nonlocal interactions across temporal, spatial, and scales. In addition, we propose a multiscale interactive attention (MSIA) module, which first explicitly integrates the human top-down and bottom-up visual attention mechanisms to transform any spatiotemporal feature into another feature of the same size but with richer contexts. Furthermore, we propose a new gaze transfer path consistency evaluation and thoroughly examine the performance of our model on two large datasets (ECVG and LEDOV). The experimental results show that our model outperforms the existing state-of-the-art methods.

Driver's visual fixation attention prediction in dynamic scenes using hybrid neural networks

The traffic driving scenario is dynamically varying and experienced drivers can allocate visual attention effectively in brief time intervals, focusing on prominent targets and areas in advance, thus ensuring safe driving. Therefore, it is significant to research the behavior of driver's visual attention allocation in the development of driver assistance systems and autonomous vehicles. Recently, inspired by top-down and bottom-up attention mechanisms, most visual attention models based on neural networks have been proposed. However, these models tend to yield highly distributed predictions for the specific driving scenario, and have relatively low prediction accuracy, making them unfeasible for being used in practice. Hence, to overcome these challenges, we propose a driver's visual attention model (DVAM) built on the encoder-decoder architecture with the utilization of the convolutional neural network (CNN) and the recurrent neural networks (RNN). Specifically, the model leverages the temporal and spatial dimension information to better mimic realistic dynamic driving, and extract abundant feature information, thus ensuring that the final predictions are authentic and effective. Then, extensive experiments demonstrate that our suggested DVAM achieves better robustness and precision in predicting driver attention to areas or targets compared to state-of-the-art methods on the DR(eye)VE dataset. Finally, our model is deployed on the TDV dataset to validate its generalization capabilities, which prove the potential for field adaptation.

The development of the relationship between auditory and visual neural sensitivity and autonomic arousal from 6 m to 12 m

The differential sensitivity hypothesis argues that environmental sensitivity has the bivalent effect of predisposing individuals to both the risk-inducing and development-enhancing influences of early social environments. However, the hypothesis requires that this variation in environmental sensitivity be general across domains. In this study, we focused on neural sensitivity and autonomic arousal to test domain generality. Neural sensitivity can be assessed by correlating measures of perceptual sensitivity, as indexed by event-related potentials (ERP) in electrophysiology. The sensitivity of autonomic arousal can be tested via heart rate changes. Domain generality was tested by comparing associations in perceptual sensitivity across auditory and visual domains, and associations between sensitivity in sensory domains and heart rate. We contrasted ERP components in auditory (P3) and visual (P1, N290 and P4) detection-of-difference tasks for N = 68 infants longitudinally at 6 and 12 months of age. Domain generality should produce correlated individual differences in sensitivity across the two modalities, with higher levels of autonomic arousal associating with increased perceptual sensitivity. Having controlled for multiple comparisons, at 6 months of age, the difference in amplitude of the P3 component evoked in response to standard and deviant tones correlated with the difference in amplitude of the P1 N290 and P4 face-sensitive components evoked in response to fearful and neutral faces. However, this correlation was not found at 12 months of age. Similarly, autonomic arousal correlated with neural sensitivity at 6 months but not at 12 months. The results suggest bottom-up neural perceptual sensitivity is domain-general across auditory and visual domains and is related to autonomic arousal at 6 months but not at 12 months of age. We interpret the development of the association of these markers of ES within a neuroconstructivist framework and with respect to the concept of interactive specialisation. By 12 months of age, more experience of visual processing may have led to top-down endogenous attention mechanisms that process visual information in a way that no longer associates with automatic auditory perceptual sensitivity.

Top-down control of exogenous attentional selection is mediated by beta coherence in prefrontal cortex

Salience-driven exogenous and goal-driven endogenous attentional selection are two distinct forms of attention that guide selection of task-irrelevant and task-relevant targets in primates. Top-down attentional control mechanisms enable selection of the task-relevant target by limiting the influence of sensory information. Although the lateral prefrontal cortex (LPFC) is known to mediate top-down control, the neuronal mechanisms of top-down control of attentional selection are poorly understood. Here, we trained two rhesus monkeys on a two-target, free-choice luminance-reward selection task. We demonstrate that visual-movement (VM) neurons and nonvisual neurons or movement neurons encode exogenous and endogenous selection. We then show that coherent beta activity selectively modulates mechanisms of exogenous selection specifically during conflict and consequently may support top-down control. These results reveal the VM-neuron-specific network mechanisms of attentional selection and suggest a functional role for beta-frequency coherent neural dynamics in the modulation of sensory communication channels for the top-down control of attentional selection.

Infant-directed speech facilitates word learning through attentional mechanisms: An fNIRS study of toddlers.

The speech register that adults especially caregivers use when interacting with infants and toddlers, that is, infant-directed speech (IDS) or baby talk, has been reported to facilitate language development throughout the early years. However, the neural mechanisms as well as why IDS results in such a developmental faciliatory effect remain to be investigated. The current study uses functional near-infrared spectroscopy (fNIRS) to evaluate two alternative hypotheses of such a facilitative effect, that IDS serves to enhance linguistic contrastiveness or to attract the child's attention. Behavioral and fNIRS data were acquired from twenty-seven Cantonese-learning toddlers 15-20 months of age when their parents spoke to them in either an IDS or adult-directed speech (ADS) register in a naturalistic task in which the child learned four disyllabic pseudowords. fNIRS results showed significantly greater neural responses to IDS than ADS register in the left dorsolateral prefrontal cortex (L-dlPFC), but opposite response patterns in the bilateral inferior frontal gyrus (IFG). The differences in fNIRS responses to IDS and to ADS in the L-dlPFC and the left parietal cortex (L-PC) showed significantly positive correlations with the differences in the behavioral word-learning performance of toddlers. The same fNIRS measures in the L-dlPFC and right PC (R-PC) of toddlers were significantly correlated with pitch range differences of parents between the two speech conditions. Together, our results suggest that the dynamic prosody in IDS increased toddlers' attention through greater involvement of the left frontoparietal network that facilitated word learning, compared to ADS. RESEARCH HIGHLIGHTS: This study for the first time examined the neural mechanisms of how infant-directed speech (IDS) facilitates word learning in toddlers. Using fNIRS, we identified the cortical regions that were directly involved in IDS processing. Our results suggest that IDS facilitates word learning by engaging a right-lateralized prosody processing and top-down attentional mechanisms in the left frontoparietal networks. The language network including the inferior frontal gyrus and temporal cortex was not directly involved in IDS processing to support word learning.

Neural mechanisms of top-down divided and selective spatial attention in visual and auditory perception

Top-down attention mechanisms require the selection of specific objects or locations; however, the brain mechanism involved when attention is allocated across different modalities is not well understood. The aim of this study was to use functional magnetic resonance imaging to define the neural mechanisms underlying divided and selective spatial attention. A concurrent audiovisual stimulus was used, and subjects were prompted to focus on a visual, auditory and audiovisual stimulus in a Posner paradigm. Our behavioral results confirmed the better performance of selective attention compared to devided attention. We found differences in the activation level of the frontoparietal network, visual/auditory cortex, the putamen and the salience network under different attention conditions. We further used Granger causality (GC) to explore effective connectivity differences between tasks. Differences in GC connectivity between visual and auditory selective tasks reflected the visual dominance effect under spatial attention. In addition, our results supported the role of the putamen in redistributing attention and the functional separation of the salience network. In summary, we explored the audiovisual top-down allocation of attention and observed the differences in neural mechanisms under endogenous attention modes, which revealed the differences in cross-modal expression in visual and auditory attention under attentional modulation.

Artificial Intelligence and Sensor Technologies the Future of Education Students with and without Intellectual and Developmental Disabilities

Abstract Learning environments such as classrooms and online systems for students with developmental or intellectual disabilities are typically dynamic, multisensory, and make use of top-down attention and working memory mechanisms to promote sense making by the student. However, the last five years have ushered a revolution in computational power, brain mapping, wearable sensors use, large scale data collection, generative artificial intelligence, and physiological signal processing techniques e.g., the 4th industrial revolution. Owing to the advent of inexpensive and highly accurate sensor technologies, generative artificial intelligence, and neurotechnologies, educators now have a new way to assess every student’s learning status, cognitive states, and promote adaption in a multi-modal and multi-dimensional way in real-time. Process data from sensors and neurotechnologies can be available for use by educators within milliseconds as opposed to minutes, hours, or days, as is the case for traditional educational data. Data from artificially intelligent systems can tracks students’ learning progressions using sensor-based data so that content adjustments and differentiation of instruction can meet a student’s needs in real-time. Keywords: Artificial Intelligence, Online Learning, Wearable Sensors, Special Education, Adaptive Learning.

Artificial Intelligence and Sensor Technologies the Future of Special Education for Students with Intellectual and Developmental Disabilities

Learning environments such as classrooms and online systems for students with developmental or intellectual disabilities are typically dynamic, multisensory, and make use of top-down attention and working memory mechanisms to promote sense making by the student. This is specifically true when the student with intellectual disabilities is mainstreamed into a general education classroom. The complex interactions between the classroom, the student, and the content creates serious difficulty in assessing a student’s understanding, particularly for students who are non-verbal or have deficits in communication. However, the last five years have ushered a revolution in computational power, brain mapping, wearable sensors use, large scale data collection, generative artificial intelligence, and physiological signal processing techniques e.g., the 4th industrial revolution

Listening to two speakers: Capacity and tradeoffs in neural speech tracking during Selective and Distributed Attention

Speech comprehension is severely compromised when several people talk at once, due to limited perceptual and cognitive resources. In such circumstances, top-down attention mechanisms can actively prioritize processing of task-relevant speech. However, behavioral and neural evidence suggest that this selection is not exclusive, and the system may have sufficient capacity to process additional speech input as well.Here we used a data-driven approach to contrast two opposing hypotheses regarding the system's capacity to co-represent competing speech: Can the brain represent two speakers equally or is the system fundamentally limited, resulting in tradeoffs between them?Neural activity was measured using magnetoencephalography (MEG) as human participants heard concurrent speech narratives and engaged in two tasks: Selective Attention, where only one speaker was task-relevant and Distributed Attention, where both speakers were equally relevant.Analysis of neural speech-tracking revealed that both tasks engaged a similar network of brain regions involved in auditory processing, attentional control and speech processing. Interestingly, during both Selective and Distributed Attention the neural representation of competing speech showed a bias towards one speaker. This is in line with proposed ‘bottlenecks’ for co-representation of concurrent speech and suggests that good performance on distributed attention tasks may be achieved by toggling attention between speakers over time.

Bottom-up and Top-down Object Inference Networks for Image Captioning

A bottom-up and top-down attention mechanism has led to the revolutionizing of image captioning techniques, which enables object-level attention for multi-step reasoning over all the detected objects. However, when humans describe an image, they often apply their own subjective experience to focus on only a few salient objects that are worthy of mention, rather than all objects in this image. The focused objects are further allocated in linguistic order, yielding the “object sequence of interest” to compose an enriched description. In this work, we present the Bottom-up and Top-down Object inference Network (BTO-Net), which novelly exploits the object sequence of interest as top-down signals to guide image captioning. Technically, conditioned on the bottom-up signals (all detected objects), an LSTM-based object inference module is first learned to produce the object sequence of interest, which acts as the top-down prior to mimic the subjective experience of humans. Next, both of the bottom-up and top-down signals are dynamically integrated via an attention mechanism for sentence generation. Furthermore, to prevent the cacophony of intermixed cross-modal signals, a contrastive learning-based objective is involved to restrict the interaction between bottom-up and top-down signals, and thus leads to reliable and explainable cross-modal reasoning. Our BTO-Net obtains competitive performances on the COCO benchmark, in particular, 134.1% CIDEr on the COCO Karpathy test split. Source code is available at https://github.com/YehLi/BTO-Net .

Towards Parallel Selective Attention Using Psychophysiological States as the Basis for Functional Cognition.

Attention is a complex cognitive process with innate resource management and information selection capabilities for maintaining a certain level of functional awareness in socio-cognitive service agents. The human-machine society depends on creating illusionary believable behaviors. These behaviors include processing sensory information based on contextual adaptation and focusing on specific aspects. The cognitive processes based on selective attention help the agent to efficiently utilize its computational resources by scheduling its intellectual tasks, which are not limited to decision-making, goal planning, action selection, and execution of actions. This study reports ongoing work on developing a cognitive architectural framework, a Nature-inspired Humanoid Cognitive Computing Platform for Self-aware and Conscious Agents (NiHA). The NiHA comprises cognitive theories, frameworks, and applications within machine consciousness (MC) and artificial general intelligence (AGI). The paper is focused on top-down and bottom-up attention mechanisms for service agents as a step towards machine consciousness. This study evaluates the behavioral impact of psychophysical states on attention. The proposed agent attains almost 90% accuracy in attention generation. In social interaction, contextual-based working is important, and the agent attains 89% accuracy in its attention by adding and checking the effect of psychophysical states on parallel selective attention. The addition of the emotions to attention process produced more contextual-based responses.

Enhancing spiking neural networks with hybrid top-down attention.

As the representatives of brain-inspired models at the neuronal level, spiking neural networks (SNNs) have shown great promise in processing spatiotemporal information with intrinsic temporal dynamics. SNNs are expected to further improve their robustness and computing efficiency by introducing top-down attention at the architectural level, which is crucial for the human brain to support advanced intelligence. However, this attempt encounters difficulties in optimizing the attention in SNNs largely due to the lack of annotations. Here, we develop a hybrid network model with a top-down attention mechanism (HTDA) by incorporating an artificial neural network (ANN) to generate attention maps based on the features extracted by a feedforward SNN. The attention map is then used to modulate the encoding layer of the SNN so that it focuses on the most informative sensory input. To facilitate direct learning of attention maps and avoid labor-intensive annotations, we propose a general principle and a corresponding weakly-supervised objective, which promotes the HTDA model to utilize an integral and small subset of the input to give accurate predictions. On this basis, the ANN and the SNN can be jointly optimized by surrogate gradient descent in an end-to-end manner. We comprehensively evaluated the HTDA model on object recognition tasks, which demonstrates strong robustness to adversarial noise, high computing efficiency, and good interpretability. On the widely-adopted CIFAR-10, CIFAR-100, and MNIST benchmarks, the HTDA model reduces firing rates by up to 50% and improves adversarial robustness by up to 10% with comparable or better accuracy compared with the state-of-the-art SNNs. The HTDA model is also verified on dynamic neuromorphic datasets and achieves consistent improvements. This study provides a new way to boost the performance of SNNs by employing a hybrid top-down attention mechanism.

"Emotional distractor images disrupt target processing in a graded manner": Correction.

Reports an error in "Emotional distractor images disrupt target processing in a graded manner" by Jonathan M. Keefe and David H. Zald (Emotion, Advanced Online Publication, Aug 27, 2020, np). In the article "Emotional Distractor Images Disrupt Target Processing in a Graded Manner" by Jonathan M. Keefe and David H. Zald (Emotion, advance online publication, August 27, 2020, https://doi.org/10.1037/emo0000893), there were errors in the reporting of nonresponse rate and accuracy data. Nonresponse rate was underreported for the data in the Lag 2 condition, resulting in incorrect analysis of variance values for this portion of the Results section as well as incorrect Lag 2 t-test values in Table 1 and incorrect Lag 2 values in Figure 2A. Additionally, error bars for the accuracy data in Figure 2B were mistakenly calculated with data including excluded trials, resulting in larger estimates of standard error of the mean. These corrections do not affect the interpretation of any inferential statistics and in fact increased the effect of lag and distractor valence upon both of these measures. Therefore, no conclusions of the study are altered. All versions of this article have been corrected. (The following abstract of the original article appeared in record 2020-63434-001). The emotional attentional blink (EAB), also referred to as emotion-induced blindness, refers to a transient impairment in the ability to discriminate a single target when it is presented closely in time to an emotional distractor. Although the EAB has typically been characterized as representing a complete loss of target information due to attentional capture by the emotional distractors, it is unclear whether the impact of the emotional distractor is in fact discrete or graded. Here, we tested whether the emotional distractor of the EAB interfered with target processing in a continuous or all-or-none manner by measuring changes in both reaction time (RT) and target-vividness ratings in addition to target-discrimination accuracy. Rapid sequences of landscape images were presented centrally, and participants reported the orientation of a ± 90° rotated target as quickly and accurately as possible. Replicating the classic EAB phenomenon, we found a strong impairment in target discrimination when an emotional distractor shortly preceded the target, and we also found a moderate impairment when the target preceded an emotional distractor. This decrement in accuracy at short lags was accompanied by increases in RT to the target as well as lower ratings of subjective target vividness even when the target was detected, indicating that emotional distractors impacted target processing in a lag-dependent, graded manner. We argue that these results are consistent with an interactive race model of the competition between stimulus representations in the conflict between top-down and bottom-up attentional mechanisms. (PsycInfo Database Record (c) 2022 APA, all rights reserved).