Search

In previous studies, it was established that individuals can implicitly learn spatiotemporal regularities related to how the distribution of target locations unfolds across the time course of a single trial. However, these regularities were tied to the appearance of salient targets that are known to capture attention in a bottom-up way. The current study investigated whether the saliency of target is necessary for this type of learning to occur. In a visual search task, participants were instructed to search for a unique circle with a gap (Landolt C) among two other circles and indicate the location of the gap. Unbeknownst to them, the onset timing of search displays predicted the target location. Specifically, the target appeared more frequently at one peripheral location with an early onset of the search display and at the opposite peripheral location with a late onset of the search display. Additionally, we manipulated the gap size of the Landolt C to create either nonsalient (small gap) or salient (big gap) target events. Results showed that, regardless of prior exposure to salient targets (Experiment 1) or nonsalient targets (Experiment 2), visual search efficiency increased when the target appeared at the temporally valid location compared with the temporally invalid location. In conclusion, the saliency of targets and the associated bottom-up capture is not a prerequisite for learning dynamic distributional regularities of target locations during visual search. [ABSTRACT FROM AUTHOR]

Abstract Background Several cognitive functions are related to sex. However, the relationship between auditory attention and sex remains unclear. The present study aimed to explore sex differences in auditory saliency judgments, with a particular focus on bottom-up type auditory attention. Methods Forty-five typical adults (mean age: 21.5 ± 0.64 years) with no known hearing deficits, intelligence abnormalities, or attention deficits were enrolled in this study. They were tasked with annotating attention capturing sounds from five audio clips played in a soundproof room. Each stimulus contained ten salient sounds randomly placed within a 1-min natural soundscape. We conducted a generalized linear mixed model (GLMM) analysis using the number of responses to salient sounds as the dependent variable, sex as the between-subjects factor, duration, maximum loudness, and maximum spectrum of each sound as the within-subjects factor, and each sound event and participant as the variable effect. Results No significant differences were found between male and female groups in age, hearing threshold, intellectual function, and attention function (all p > 0.05). Analysis confirmed 77 distinct sound events, with individual response rates of 4.0–100%. In a GLMM analysis, the main effect of sex was not statistically significant (p = 0.458). Duration and spectrum had a significant effect on response rate (p = 0.006 and p

Background: Several cognitive functions are related to sex. However, the relationship between auditory attention and sex remains unclear. The present study aimed to explore sex differences in auditory saliency judgments, with a particular focus on bottom-up type auditory attention. Methods: Forty-five typical adults (mean age: 21.5 ± 0.64 years) with no known hearing deficits, intelligence abnormalities, or attention deficits were enrolled in this study. They were tasked with annotating attention capturing sounds from five audio clips played in a soundproof room. Each stimulus contained ten salient sounds randomly placed within a 1-min natural soundscape. We conducted a generalized linear mixed model (GLMM) analysis using the number of responses to salient sounds as the dependent variable, sex as the between-subjects factor, duration, maximum loudness, and maximum spectrum of each sound as the within-subjects factor, and each sound event and participant as the variable effect. Results: No significant differences were found between male and female groups in age, hearing threshold, intellectual function, and attention function (all p > 0.05). Analysis confirmed 77 distinct sound events, with individual response rates of 4.0–100%. In a GLMM analysis, the main effect of sex was not statistically significant (p = 0.458). Duration and spectrum had a significant effect on response rate (p = 0.006 and p < 0.001). The effect of loudness was not statistically significant (p = 0.13). Conclusions: The results suggest that male and female listeners do not differ significantly in their auditory saliency judgments based on the acoustic characteristics studied. This finding challenges the notion of inherent sex differences in bottom-up auditory attention and highlights the need for further research to explore other potential factors or conditions under which such differences might emerge. [ABSTRACT FROM AUTHOR]

Abstract Understanding the interplay between top–down and bottom–up attention in visual working memory (VWM) is crucial, although the specific challenges arising from this interaction remain ambiguous. In this study, we address this complexity by examining how cue informativeness and probe status of the salient items influence this interaction. Through three experiments, we manipulated top–down attention by varying probe frequencies using pre-cues and bottom–up attention by varying the visual salience of memory items. Experiment 1 explored cue informativeness at 100% and 50%, while Experiments 2 and 3 maintained cue informativeness at 80% and 50%. Additionally, Experiment 1 tested a few of the salient items, Experiment 2 excluded them, and Experiment 3 tested half of them in each cue condition. Across all experiments, we consistently observed cueing benefits for cue-directed items, albeit with costs to non-cued items. Furthermore, cue informativeness and the probe status of salient items emerged as critical factors influencing the interaction between top–down and bottom–up attention in VWM. These findings underscore the pivotal roles of cue informativeness and salient item relevance in shaping the dynamics of top–down and bottom–up attention within VWM.

Understanding the interplay between top–down and bottom–up attention in visual working memory (VWM) is crucial, although the specific challenges arising from this interaction remain ambiguous. In this study, we address this complexity by examining how cue informativeness and probe status of the salient items influence this interaction. Through three experiments, we manipulated top–down attention by varying probe frequencies using pre-cues and bottom–up attention by varying the visual salience of memory items. Experiment 1 explored cue informativeness at 100% and 50%, while Experiments 2 and 3 maintained cue informativeness at 80% and 50%. Additionally, Experiment 1 tested a few of the salient items, Experiment 2 excluded them, and Experiment 3 tested half of them in each cue condition. Across all experiments, we consistently observed cueing benefits for cue-directed items, albeit with costs to non-cued items. Furthermore, cue informativeness and the probe status of salient items emerged as critical factors influencing the interaction between top–down and bottom–up attention in VWM. These findings underscore the pivotal roles of cue informativeness and salient item relevance in shaping the dynamics of top–down and bottom–up attention within VWM. [ABSTRACT FROM AUTHOR]

This study investigated how voluntary and involuntary retrospective attention prioritize working memory (WM) representations of low-level visual features (perceptual dimension) and high-level categories (semantic dimension) using real-world objects. Reaction time and accuracy from two retro-cueing experiments were analyzed with a hierarchical drift-diffusion model to assess impacts on representation quality (drift rate) and retrieval time (non-decision time). Voluntariness of attention did not differentially affect perceptual and semantic WM contents. Drift rates showed stronger retro-cueing effects on perceptual compared to semantic contents, while non-decision times revealed retro-cueing effects only for voluntary attention. These findings suggest: (1) voluntariness does not differentiate between perceptual and semantic contents competing in WM; (2) attention prioritizes perceptual over semantic contents; and (3) voluntariness is critical for retrieving WM contents in advance of decision-making. This work highlights how WM content type and attentional voluntariness independently shape the effects of retrospective attention. [ABSTRACT FROM AUTHOR]

Visual perception of stimuli appearing at the front of the head is facilitated during eccentric gaze when the directions of the eyes and the head differ substantially. This indicates that visual attention tends to move toward the front of the head. This study examined whether top-down control for attending to a specific location influences attention during eccentric gaze. We conducted an experiment in which participants identified the target stimulus presented in the peripheral visual field, manipulating the direction of the head (main-task trials). In this experiment, we added some filler task trials where the target stimulus appeared at the location of fixation. To manipulate top-down control of attention, we varied the stimulus size (i.e., filler-task difficulty), assuming that observers would focus more attention on the location of fixation during the difficult filler-task condition. Head direction influenced visual perception in the main-task trials for both levels of filler-task difficulty. Importantly, the effect of head direction was smaller for the difficult filler-task than for the easy filler-task. Therefore, the effect of head direction on visual attention during eccentric gaze is modulated by top-down control of attention. [ABSTRACT FROM AUTHOR]

Auditory salience is a fundamental property of a sound that allows it to grab a listener's attention regardless of their attentional state or behavioral goals. While previous research has shed light on acoustic factors influencing auditory salience, the semantic dimensions of this phenomenon have remained relatively unexplored owing both to the complexity of measuring salience in audition as well as limited focus on complex natural scenes. In this study, we examine the relationship between acoustic, contextual, and semantic attributes and their impact on the auditory salience of natural audio scenes using a dichotic listening paradigm. The experiments present acoustic scenes in forward and backward directions; the latter allows to diminish semantic effects, providing a counterpoint to the effects observed in forward scenes. The behavioral data collected from a crowd-sourced platform reveal a striking convergence in temporal salience maps for certain sound events, while marked disparities emerge in others. Our main hypothesis posits that differences in the perceptual salience of events are predominantly driven by semantic and contextual cues, particularly evident in those cases displaying substantial disparities between forward and backward presentations. Conversely, events exhibiting a high degree of alignment can largely be attributed to low-level acoustic attributes. To evaluate this hypothesis, we employ analytical techniques that combine rich low-level-mappings from acoustic profiles with high-level embeddings extracted from a deep neural network. This integrated approach captures both acoustic and semantic attributes of acoustic scenes along with their temporal trajectories. The results demonstrate that perceptual salience is a careful interplay between low-level and high- level attributes that shapes which moments stand out in a natural soundscape. Furthermore, our findings underscore the important role of longer-term context as a critical component of auditory salience, enabling us to discern and adapt to temporal regularities within an acoustic scene. The experimental and model- based validation of semantic factors of salience paves the way for a complete understanding of auditory salience. Ultimately, the empirical and computational analyses have implications for developing large-scale models for auditory salience and audio analytics. [ABSTRACT FROM AUTHOR]

Within the framework of the theory of visual attention (TVA), the visual attention span (VAS) deficit among individuals with developmental dyslexia has been ascribed to the problems entailed by bottom–up (BotU) and top–down (TopD) attentional processes. The former involves two VAS subcomponents: the visual short-term memory storage and perceptual processing speed; the latter consists of the spatial bias of attentional weight and the inhibitory control. Then, what about the influences of the BotU and TopD components on reading? Are there differences in the roles of the two types of attentional processes in reading? This study addresses these issues by using two types of training tasks separately, corresponding to the BotU and TopD attentional components. Three groups of Chinese children with dyslexia—15 children each in the BotU training, TopD training, and non-trained active control groups were recruited here. Participants completed reading measures and a CombiTVA task which was used to estimate VAS subcomponents, before and after the training procedure. Results showed that BotU training improved both the within-category and between-category VAS subcomponents and sentence reading performance; meanwhile, TopD training enhanced character reading fluency through improving spatial attention capacity. Moreover, benefits on attentional capacities and reading skills in the two training groups were generally maintained three months after the intervention. The present findings revealed diverse patterns in the influences of VAS on reading within the TVA framework, which contributes to enriching the understanding of VAS-reading relation. [ABSTRACT FROM AUTHOR]

Auditory salience is a fundamental property of a sound that allows it to grab a listener's attention regardless of their attentional state or behavioral goals. While previous research has shed light on acoustic factors influencing auditory salience, the semantic dimensions of this phenomenon have remained relatively unexplored owing both to the complexity of measuring salience in audition as well as limited focus on complex natural scenes. In this study, we examine the relationship between acoustic, contextual, and semantic attributes and their impact on the auditory salience of natural audio scenes using a dichotic listening paradigm. The experiments present acoustic scenes in forward and backward directions; the latter allows to diminish semantic effects, providing a counterpoint to the effects observed in forward scenes. The behavioral data collected from a crowd-sourced platform reveal a striking convergence in temporal salience maps for certain sound events, while marked disparities emerge in others. Our main hypothesis posits that differences in the perceptual salience of events are predominantly driven by semantic and contextual cues, particularly evident in those cases displaying substantial disparities between forward and backward presentations. Conversely, events exhibiting a high degree of alignment can largely be attributed to low-level acoustic attributes. To evaluate this hypothesis, we employ analytical techniques that combine rich low-level mappings from acoustic profiles with high-level embeddings extracted from a deep neural network. This integrated approach captures both acoustic and semantic attributes of acoustic scenes along with their temporal trajectories. The results demonstrate that perceptual salience is a careful interplay between low-level and high-level attributes that shapes which moments stand out in a natural soundscape. Furthermore, our findings underscore the important role of longer-term context as a critical component of auditory salience, enabling us to discern and adapt to temporal regularities within an acoustic scene. The experimental and model-based validation of semantic factors of salience paves the way for a complete understanding of auditory salience. Ultimately, the empirical and computational analyses have implications for developing large-scale models for auditory salience and audio analytics.

Visual question answering (VQA) requires a deep understanding of images and their corresponding textual questions to answer questions about images more accurately. However, existing models tend to ignore the implicit knowledge in the images and focus only on the visual information in the images, which limits the understanding depth of the image content. The images contain more than just visual objects, some images contain textual information about the scene, and slightly more complex images contain relationships between individual visual objects. Firstly, this paper proposes a model using image description for feature enhancement. This model encodes images and their descriptions separately based on the question-guided coattention mechanism. This mechanism increases the feature representation of the model, enhancing the model’s ability for reasoning. In addition, this paper improves the bottom-up attention model by obtaining two image region features. After obtaining the two visual features and the spatial position information corresponding to each feature, concatenating the two features as the final image feature can better represent an image. Finally, the obtained spatial position information is processed to enable the model to perceive the size and relative position of each object in the image. Our best single model delivers a 74.16% overall accuracy on the VQA 2.0 dataset, our model even outperforms some multi-modal pre-training models with fewer images and a shorter time. [ABSTRACT FROM AUTHOR]

Introduction: The present study aimed to develop a model of the relationship between trait anxiety and the components of attentional styles in athlete students.Methods: The current research strategy was descriptive and correlational, with the approach of developing a correlational model. In three stages, the attentional styles questionnaire was translated into Persian and was evaluated psychometrically. Its process consisted of the French version translation into Persian, re-translation into French, and the final translation. In this study, 245 athletes were selected conveniently from the student population of Shahid Beheshti University. Participants completed attentional styles and trait anxiety questionnaires. Data were analyzed using Confirmatory Factor Analysis, Cronbach's alpha coefficient, and Structural Equation Modeling (SEM).Results: Cronbach's alpha coefficient for two factors of top-down and bottom-up attention was 0.70 and 0.72, respectively. The confirmatory factor analysis results showed that the factor loadings of five items (items 2 and 4 of the top-down attention factor, and items 3, 8, and 9 of the bottom-up attention factor) were less than 0.4, so they were removed. SEM results showed that trait anxiety has an inverse and significant relationship with top-down attention and a direct and significant relationship with bottom-up attention. Conclusion: The results of the present study highlighted the importance and necessity of paying attention to the relationship between trait anxiety and the components of attentional styles in athletes. Therefore, in planning training programs and improving sports performance, taking this relationship into account can be an effective step in preventing adverse outcomes in sports competitions.

Background and Objectives: Visual attention is a high order cognitive process of human brain which defines where a human observer attends. Dynamic computational visual attention models are modeled on the behavior of the human brain and can predict what areas a human will pay attention to when viewing a scene such as a video. However, several types of computational models have been proposed to provide a better understanding of saliency maps in static and dynamic environments, most of these models are used for specific scenes. In this paper, we propose a model that can generate saliency maps in a variety of dynamic environments with complex scenes.Methods: We used a deep learner as a mediating network to combine basic saliency maps with appropriate weighting. Each of these basic saliency maps covers an important feature of human visual attention, and ultimately the final saliency map is very similar to human visual behavior.Results: The proposed model is run on two datasets and the generated saliency maps are evaluated by different criteria such as ROC, CC, NSS, SIM and KLdiv. The results show that the proposed model has a good performance compared to other similar models.Conclusion: The proposed model consists of three main parts, including basic saliency maps, gating network, and combinator. This model was implemented on the ETMD dataset and the resulting saliency maps (visual attention areas) were compared with some other models in this field by evaluation criteria and their results were evaluated. The results obtained from the proposed model are acceptable and based on the accepted evaluation criteria in this area, it performs better than similar models.

With the popularity of the Internet, a large number of pictures are transmitted to the Internet every day. In order to better utilize the value of these images, image caption generation technology emerges as the times require. An image caption generation model that fused top-down and bottom-up attention was proposed. At work, the pre-trained ResNet101 and Faster regions with convolutional neural network (R-CNN) in the proposed model were used to extract the global and local features of the input image, and top-down and bottom-up attention were used to calculate the weights of the two features respectively. gate recurrent unit (GRU) was used to extract contextual semantic information between words in a sentence; long short-term memory (LSTM) was used to parse image features and semantic information and generate caption sentences. During training, firstly, a basic model based on the encoder-decoder framework was trained by supervised learning. Then, the policy function and the reward function continuously were improved by the method of combined generative adversarial network and reinforcement learning. So that the generated sentences were more accurate and natural. The proposed model was trained and tested on the COCO dataset, and finally reaches 0.675 on the evaluation index bilingual evaluation understudy (BLEU@1), reaches 0.24 on BLEU@4, reaches 0.734 on consensus-based image description evaluation (CIDEr), and reaches 0.51 on recall-oriented understudy for gisting evaluation (ROUGE). The feasibility and advancement of the proposed model studied are shown in the experimental results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Vehicle instance retrieval (IR) often requires one to recognize the fine-grained visual differences between vehicles. Besides the holistic appearance of vehicles which is easily affected by the viewpoint variation and distortion, vehicle parts also provide crucial cues to differentiate near-identical vehicles. Motivated by these observations, we introduce a Part-Guided Attention Network (PGAN) to pinpoint the prominent part regions and effectively combine the global and local information for discriminative feature learning. PGAN first detects the locations of different part components and salient regions regardless of the vehicle identity, which serves as the bottom-up attention to narrow down the possible searching regions. To estimate the importance of detected parts, we propose a Part Attention Module (PAM) to adaptively locate the most discriminative regions with high-attention weights and suppress the distraction of irrelevant parts with relatively low weights. The PAM is guided by the identification loss and therefore provides top-down attention that enables attention to be calculated at the level of car parts and other salient regions. Finally, we aggregate the global appearance and local features together to improve the feature performance further. The PGAN combines part-guided bottom-up and top-down attention, global and local visual features in an end-to-end framework. Extensive experiments demonstrate that the proposed method achieves new state-of-the-art vehicle IR performance on four large-scale benchmark datasets. [ABSTRACT FROM AUTHOR]

Background and Objectives: Visual attention is a high-order cognitive process of the human brain that defines where a human observer attends. Dynamic computational visual attention models are modeled on the behavior of the human brain. They can predict what areas a human will pay attention to when viewing a scene such as a video. However, several types of computational models have been proposed to provide a better understanding of saliency maps in static and dynamic environments; most of these models are used for specific scenes. In this paper, we propose a model that can generate saliency maps in various dynamic environments with complex scenes. Methods: We used a deep learner as a mediating network to combine basic saliency maps with appropriate weighting. Each of these basic saliency maps covers an essential feature of human visual attention, and ultimately the final saliency map is very similar to human visual behavior. Results: The proposed model is run on two datasets, and the generated saliency maps are evaluated by different criteria such as ROC, CC, NSS, SIM, and KLdiv. The results show that the proposed model has a good performance compared to other similar models. Conclusion: The proposed model consists of three main parts, including basic saliency maps, gating network, and combinator. This model was implemented on the ETMD dataset, and the resulting saliency maps (visual attention areas) were compared with some other models in this field by evaluation criteria, and their results were evaluated. The results obtained from the proposed model are acceptable, and based on the accepted evaluation criteria in this area; it performs better than similar models. [ABSTRACT FROM AUTHOR]

While abundant in biology, foveated vision is nearly absent from computational models and especially deep learning architectures. Despite considerable hardware improvements, training deep neural networks still presents a challenge and constraints complexity of models. Here we propose an end-to-end neural model for foveal-peripheral vision, inspired by retino-cortical mapping in primates and humans. Our model has an efficient sampling technique for compressing the visual signal such that a small portion of the scene is perceived in high resolution while a large field of view is maintained in low resolution. An attention mechanism for performing “eye-movements” assists the agent in collecting detailed information incrementally from the observed scene. Our model achieves comparable results to a similar neural architecture trained on full-resolution data for image classification and outperforms it at video classification tasks. At the same time, because of the smaller size of its input, it can reduce computational effort tenfold and uses several times less memory. Moreover, we present an easy to implement bottom-up and top-down attention mechanism which relies on task-relevant features and is therefore a convenient byproduct of the main architecture. Apart from its computational efficiency, the presented work provides means for exploring active vision for agent training in simulated environments and anthropomorphic robotics.

While abundant in biology, foveated vision is nearly absent from computational models and especially deep learning architectures. Despite considerable hardware improvements, training deep neural networks still presents a challenge and constraints complexity of models. Here we propose an end-to-end neural model for foveal-peripheral vision, inspired by retino-cortical mapping in primates and humans. Our model has an efficient sampling technique for compressing the visual signal such that a small portion of the scene is perceived in high resolution while a large field of view is maintained in low resolution. An attention mechanism for performing "eye-movements" assists the agent in collecting detailed information incrementally from the observed scene. Our model achieves comparable results to a similar neural architecture trained on full-resolution data for image classification and outperforms it at video classification tasks. At the same time, because of the smaller size of its input, it can reduce computational effort tenfold and uses several times less memory. Moreover, we present an easy to implement bottom-up and top-down attention mechanism which relies on task-relevant features and is therefore a convenient byproduct of the main architecture. Apart from its computational efficiency, the presented work provides means for exploring active vision for agent training in simulated environments and anthropomorphic robotics. [ABSTRACT FROM AUTHOR]

The philosophy of human visual attention is scientifically explained in the field of cognitive psychology and neuroscience then computationally modeled in the field of computer science and engineering. Visual attention models have been applied in computer vision systems such as object detection, object recognition, image segmentation, image and video compression, action recognition, visual tracking, and so on. This work studies bottom-up visual attention, namely human fixation prediction and salient object detection models. The preliminary study briefly covers from the biological perspective of visual attention, including visual pathway, the theory of visual attention, to the computational model of bottom-up visual attention that generates saliency map. The study compares some models at each stage and observes whether the stage is inspired by biological architecture, concept, or behavior of human visual attention. From the study, the use of low-level features, center-surround mechanism, sparse representation, and higher-level guidance with intrinsic cues dominate the bottom-up visual attention approaches. The study also highlights the correlation between bottom-up visual attention and curiosity.

Does the strength of representations in long-term memory (LTM) depend on which type of attention is engaged? We tested participants' memory for objects seen during visual search. We compared implicit memory for two types of objects—related-context nontargets that grabbed attention because they matched the target defining feature (i.e., color; top-down attention) and salient distractors that captured attention only because they were perceptually distracting (bottom-up attention). In Experiment 1, the salient distractor flickered, while in Experiment 2, the luminance of the salient distractor was alternated. Critically, salient and related-context nontargets produced equivalent attentional capture, yet related-context nontargets were remembered far better than salient distractors (and salient distractors were not remembered better than unrelated distractors). These results suggest that LTM depends not only on the amount of attention but also on the type of attention. Specifically, top-down attention is more effective in promoting the formation of memory traces than bottom-up attention. [ABSTRACT FROM AUTHOR]

Visual search is a psychological function integral to most people's daily lives. The extent to which visual search efficiency, and in particular the ability to use top-down attention in visual search, changes across the lifespan has been the focus of ongoing research. Here we sought to understand how the ability to frequently and dynamically change the target in a conjunction search task was affected by ageing. To do this, we compared visual search performance of a group of younger and older adults under conditions in which the target type was determined by a cue and could change on trial-to-trial basis (Intermixed), versus when the target type was fixed for a block of trials (Blocked). Although older adults were overall slower at the conjunction visual search task, and both groups were slower in the Intermixed compared with the Blocked Condition, older adults were not disproportionately affected by the Intermixed relative to the Blocked conditions. These results indicate that the ability to frequently change the target of visual search is preserved in older adults. This conclusion is consistent with an emerging consensus that many aspects of visual search and top-down contributions to it are preserved across the lifespan. It is also consistent with a growing body of work which challenges the neurocognitive theories of ageing that predict sweeping deficits in complex top-down components of cognition. [ABSTRACT FROM AUTHOR]

Since our environment typically contains more information than can be processed at any one time due to the limited capacity of our visual system, we are bound to differentiate between relevant and irrelevant information. This process, termed attentional selection, is usually categorized into bottom-up and top-down processes. However, recent research suggests reward might also be an important factor in guiding attention. Monetary reward can bias attentional selection in favor of task-relevant targets and reduce the efficiency of visual search when a reward-associated, but task-irrelevant distractor is present. This study is the first to investigate reward-related target and distractor processing in an additional singleton task using neurophysiological measures and source space analysis. Based on previous studies, we hypothesized that source space analysis would find enhanced neural activity in regions of the value-based attention network, such as the visual cortex and the anterior cingulate. Additionally, we went further and explored the time courses of the underlying attentional mechanisms. Our neurophysiological results showed that rewarding distractors led to a stronger attentional capture. In line with this, we found that reward-associated distractors (compared with reward-associated targets) enhanced activation in frontal regions, indicating the involvement of top-down control processes. As hypothesized, source space analysis demonstrated that reward-related targets and reward-related distractors elicited activation in regions of the value-based attention network. However, these activations showed time-dependent differences, indicating that the neural mechanisms underlying reward biasing might be different for task-relevant and task-irrelevant stimuli. [ABSTRACT FROM AUTHOR]

Contagious yawning differs from spontaneous yawning because it occurs when an individual yawns in response to someone else’s yawn. In Homo sapiens and some non-human primates contagious yawning is higher between strongly than weakly bonded individuals. Up to date, it is still unclear whether this social asymmetry underlies emotional contagion (a basic form of empathy preferentially involving familiar individuals) as predicted by the Emotional Bias Hypothesis (EBH) or is linked to a top-down, selective visual attention bias (with selective attention being preferentially directed toward familiar faces) as predicted by the Attentional Bias Hypothesis (ABH). To verify whether the visual attentional bias explained the yawn contagion bias or not, in this study, we considered only yawns that could be heard but not seen by potential responders (auditory yawns). Around 294 of auditory yawning occurrences were extrapolated from over 2000 yawning bouts collected in free ranging humans for over nine years. Via GLMM, we tested the effect of intrinsic features (i.e., gender and age) and social bond (from strangers to family members) on yawn. The individual identity of the subjects (trigger and potential responder) was included as random factor. The social bond significantly predicted the occurrence of auditory yawn contagion, which was highest between friends and family members. A gender bias was also observed, with women responding most frequently to others’ yawns and men being responded to most frequently by others. These results confirm that social bond is per se one of the main drivers of the differences in yawn contagion rates between individuals in support of the EBH of yawn contagion.

Tourism, by definition, involves travel outside one's usual environment. This means that a tourist's attention cannot rely only on previously experienced known symbols and markers and instead derives meaning from paying attention to their surroundings. In this context, management of a visitor's attention processes is a key issue in the design of memorable tourist experiences and business success. Psychological research highlights two types of attention processes: bottom-up and top-down. This research is the first to identify factors influencing bottom-up attention during on-site experience and to examine the moderating impact of top-down factors of attention. The empirical research used questionnaires obtained from tourists participating in two animal-based experiences at a theme park. The results show that of the bottom-up factors analyzed, only two influenced attention, Rarity and Environment stimulation. Concerning the moderating influence of top-down factors, the research found that Interpersonal interaction and Task performance/Goal achievement affect the way external factors impact on attention, and that the intensity of this relation varies according to the relevance of this moderation. [ABSTRACT FROM AUTHOR]

• Migraineurs performed as well as healthy participants in an attention task. • However, EEG markers of both bottom-up and top-down attention are increased. • Migraine is also associated with a facilitated recruitment of the right temporo-parietal junction. To evaluate alterations of top-down and/or bottom-up attention in migraine and their cortical underpinnings. 19 migraineurs between attacks and 19 matched control participants performed a task evaluating jointly top-down and bottom-up attention, using visually-cued target sounds and unexpected task-irrelevant distracting sounds. Behavioral responses and magneto- and electro-encephalography signals were recorded. Event-related potentials and fields were processed and source reconstruction was applied to event-related fields. At the behavioral level, neither top-down nor bottom-up attentional processes appeared to be altered in migraine. However, migraineurs presented heightened evoked responses following distracting sounds (orienting component of the N1 and Re-Orienting Negativity, RON) and following target sounds (orienting component of the N1), concomitant to an increased recruitment of the right temporo-parietal junction. They also displayed an increased effect of the cue informational value on target processing resulting in the elicitation of a negative difference (Nd). Migraineurs appear to display increased bottom-up orienting response to all incoming sounds, and an enhanced recruitment of top-down attention. The interictal state in migraine is characterized by an exacerbation of the orienting response to attended and unattended sounds. These attentional alterations might participate to the peculiar vulnerability of the migraine brain to all incoming stimuli. [ABSTRACT FROM AUTHOR]

Contagious yawning differs from spontaneous yawning because it occurs when an individual yawns in response to someone else's yawn. In Homo sapiens and some non-human primates contagious yawning is higher between strongly than weakly bonded individuals. Up to date, it is still unclear whether this social asymmetry underlies emotional contagion (a basic form of empathy preferentially involving familiar individuals) as predicted by the Emotional Bias Hypothesis (EBH) or is linked to a top-down, selective visual attention bias (with selective attention being preferentially directed toward familiar faces) as predicted by the Attentional Bias Hypothesis (ABH). To verify whether the visual attentional bias explained the yawn contagion bias or not, in this study, we considered only yawns that could be heard but not seen by potential responders (auditory yawns). Around 294 of auditory yawning occurrences were extrapolated from over 2000 yawning bouts collected in free ranging humans for over nine years. Via GLMM, we tested the effect of intrinsic features (i.e., gender and age) and social bond (from strangers to family members) on yawn. The individual identity of the subjects (trigger and potential responder) was included as random factor. The social bond significantly predicted the occurrence of auditory yawn contagion, which was highest between friends and family members. A gender bias was also observed, with women responding most frequently to others' yawns and men being responded to most frequently by others. These results confirm that social bond is per se one of the main drivers of the differences in yawn contagion rates between individuals in support of the EBH of yawn contagion. [ABSTRACT FROM AUTHOR]

The philosophy of human visual attention is scientifically explained in the field of cognitive psychology and neuroscience then computationally modeled in the field of computer science and engineering. Visual attention models have been applied in computer vision systems such as object detection, object recognition, image segmentation, image and video compression, action recognition, visual tracking, and so on. This work studies bottom-up visual attention, namely human fixation prediction and salient object detection models. The preliminary study briefly covers from the biological perspective of visual attention, including visual pathway, the theory of visual attention, to the computational model of bottom-up visual attention that generates saliency map. The study compares some models at each stage and observes whether the stage is inspired by biological architecture, concept, or behavior of human visual attention. From the study, the use of low-level features, center-surround mechanism, sparse representation, and higher-level guidance with intrinsic cues dominate the bottom-up visual attention approaches. The study also highlights the correlation between bottom-up visual attention and curiosity. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: The ability to resist distraction and focus on‐task‐relevant information while being responsive to changes in the environment is fundamental to goal‐directed behavior. Such attentional control abilities are regulated by a constant interplay between previously characterized bottom‐up and top‐down attentional networks. Here we ask about the neural changes within these two attentional networks that may mediate enhanced attentional control. Materials and Methods: To address this question, we contrasted action video game players (AVGPs) and nonvideo game players (NVGPs) in a Posner‐cueing paradigm, building on studies documenting enhanced attentional control in AVGPs. Results: Behavioral results indicated a trend for more efficient target processing in AVGPs, and better suppression in rare catch trials for which responses had to be withheld. During the cue period, AVGPs recruited the top‐down network less than NVGPs, despite showing comparable validity effects, in line with a greater efficiency of that network in AVGPs. During target processing, as previously shown, recruitment of top‐down areas correlated with greater processing difficulties, but only in NVGPs. AVGPs showed no such effect, but rather greater activation across the two networks. In particular, the right temporoparietal junction, middle frontal gyrus, and superior parietal cortex predicted better task performance in catch trials. A functional connectivity analysis revealed enhanced correlated activity in AVGPs compared to NVGPs between parietal and visual areas. Conclusions: These results point to dynamic functional reconfigurations of top‐down and bottom‐up attentional networks in AVGPs as attentional demands vary. Aspects of this functional reconfiguration that may act as key signatures of high attentional control are discussed. [ABSTRACT FROM AUTHOR]

The current study proposes that top-down attentional prioritization of salient items may produce the so-called stimulus-driven capture. To test this proposal, the “expectation-based paradigm” was designed on the basis of a visual search task. In Experiment 1, a task-irrelevant singleton frame was presented at the same location in 70% of the trials. The target was either presented at chance level within the singleton location, or away from it. In line with the singleton capture phenomenon, participants were faster in identifying the target when it appeared in the singleton location compared to non-singleton locations. However, leaving out the singleton frame in 30% of the trials led to a similar effect; participants were faster in identifying the target when it appeared in the expected singleton location compared to expected non-singletons locations (a “quasi-capture” effect). These results suggest that the participants allocated their attention to the expected singleton location, rather than that the singleton itself captured attention. In Experiment 2, the same task-irrelevant color singleton was presented in a random position in 70% of the trials. This color frame was shown as a non-singleton in all of the 30% singleton-absent multicolored trials. A similar facilitation effect was obtained when the target appeared in the expected singleton color frame compared to other frames, in singleton-absent trials as in singleton-present trials. These results further support the idea that instances of singleton capture can be explained by top-down attentional shifts toward singleton items. Theoretical implications of these results are discussed. Mostly, the study calls to consider the possibility that all sources of attentional control may be represented by a continuous variable of top-down control, including the category of “physical salience.”

Attention can be involuntarily captured by physically salient stimuli, a phenomenon known as bottom-up attention. Typically, these salient stimuli occur unpredictably in time and space. Therefore, in a series of three behavioural experiments, we investigated the extent to which such bottom-up attentional capture is a function of one's prior expectations. In the context of an exogenous cueing task, we systematically manipulated participants' spatial (Experiment 1) or temporal (Experiments 2 and 3) expectations about an uninformative cue and examined the amount of attentional capture by the cue. We anticipated larger attentional capture for unexpected compared to expected cues. However, while we observed attentional capture, we did not find any evidence for a modulation of attentional capture by prior expectation. This suggests that bottom-up attentional capture does not appear modulated by the degree to which the cue is expected or surprising.

The current study proposes that top-down attentional prioritization of salient items may produce the so-called stimulus-driven capture. To test this proposal, the " expectation-based paradigm " was designed on the basis of a visual search task. In Experiment 1, a task-irrelevant singleton frame was presented at the same location in 70% of the trials. The target was either presented at chance level within the singleton location, or away from it. In line with the singleton capture phenomenon, participants were faster in identifying the target when it appeared in the singleton location compared to non-singleton locations. However, leaving out the singleton frame in 30% of the trials led to a similar effect; participants were faster in identifying the target when it appeared in the expected singleton location compared to expected non-singletons locations (a "quasi-capture" effect). These results suggest that the participants allocated their attention to the expected singleton location, rather than that the singleton itself captured attention. In Experiment 2, the same task-irrelevant color singleton was presented in a random position in 70% of the trials. This color frame was shown as a non-singleton in all of the 30% singleton-absent multicolored trials. A similar facilitation effect was obtained when the target appeared in the expected singleton color frame compared to other frames, in singleton-absent trials as in singleton-present trials. These results further support the idea that instances of singleton capture can be explained by top-down attentional shifts toward singleton items. Theoretical implications of these results are discussed. Mostly, the study calls to consider the possibility that all sources of attentional control may be represented by a continuous variable of top-down control, including the category of "physical salience." [ABSTRACT FROM AUTHOR]

Several models of selection in search predict that saccades are biased toward conspicuous objects (also referred to as salient objects). Indeed, it has been demonstrated that initial saccades are biased toward the most conspicuous candidate. However, in a recent study, no such bias was found for the second saccade, and it was concluded that the attraction of conspicuous elements is limited to only short-latency initial saccades. This conclusion is based on only a single feature manipulation (orientation contrast) and conflicts with the prediction of influential salience models. Here, we investigate whether this result can be generalized beyond the domain of orientation. In displays containing three luminance annuli (Experiment 1), we find a considerable bias toward the most conspicuous candidate for the second saccade. In Experiment 1, the target could not be discriminated peripherally. When we made the target peripherally discriminable, the second saccade was no longer biased toward the more conspicuous candidate (Experiment 2). Thus, conspicuity plays a role in saccadic selection beyond the initial saccade. Whether second saccades are biased toward conspicuous objects appears to depend on the type of feature contrast underlying the conspicuity and the peripheral discriminability of target properties. [ABSTRACT FROM AUTHOR]

In this study, we investigate whether emotionally engaged bottom-up processes of attention can be a source of 'interference' in situations where top-down control of attention is necessary. Participants were asked to monitor and report on a video of a war scenario showing a developing battle in two conditions: emotionally positive and emotionally negative. Half of the participants ( n = 15) were exposed to task-irrelevant pictures of positive emotional valence embedded within the scenario; the other half were exposed to task-irrelevant pictures of negative emotional valence. Sensitivity and Bias scores were calculated using signal detection theory. Overall, task accuracy scores were dependent upon the valence; negative pictures had an adverse effect on performance, whereas positive pictures improved performance. We concluded that negative emotional pictures interfered with top-down control of attention by attracting competing bottom-up processes of attention. We found the opposite effect for positive emotional stimuli. [ABSTRACT FROM AUTHOR]

The increase of applications that use autonomous robots has endowed them with a high number of sensors and actuators that are sometimes redundant. This new highly complex systems and the type of environment where they are expected to operate require them to deal with data overload and data fusion. In humans that face the same problem when sounds, images, and smells are presented to their sensors in a daily scene, a natural filter is applied: attention. Although there are many computational models that apply attentive systems to robotics, they usually are restricted to two classes of systems: 1) those that have complex biologically based attentional visual systems and 2) those that have simpler attentional mechanisms with a larger variety of sensors. This work proposes an attentional model inspired from biological systems and that supports a variety of robotics sensors. Furthermore, it discusses the possibility of using multiple sensors to define multiple features, with feature extraction modules that can handle exogenous and endogenous attentional processes. The experiments were performed in a simulated high-fidelity environment, and results have shown that the model proposed can account for detecting salient (bottom-up) and desired (top-down) stimuli according to the modeled features. [ABSTRACT FROM PUBLISHER]

Both theoretical proposals and empirical studies suggest that the brain interprets sensory input based on prior expectations to mitigate computational burden. However, as social beings, much of sensory input is affectively loaded – e.g., the smile of a partner, the critical voice of a boss, or the welcoming gesture of a friend. Given that affective information is highly complex and often ambiguous, building up prior expectations of upcoming affective sensory input may greatly contribute to its rapid and efficient processing. This review points to the role of affective information in the context of the ‘predictive brain’. It particularly focuses on repetition suppression (RS) effects that have recently been linked to prediction processes. We interpret the findings as evidence for more pronounced prediction processes with affective material. Importantly, we argue that possible influences from bottom-up attention might inflate the neural RS effect, and thereby particularly overshadow the magnitude of RS for affective information. Finally, anxiety disorders, such as social phobia, are briefly discussed as manifestations of modulations in affective prediction.

Attention is an important factor that is able to strongly modulate the experience of pain. In order to differentiate cortical mechanisms underlying subject-driven (i.e. top-down) and stimulus-driven (bottom-up) modes of attentional pain modulation, we recorded electric brain activity in healthy volunteers during painful laser stimulation while spatial attention and stimulus intensity were systematically varied. The subjects’ task was to evaluate the pain intensity at the attended finger, while ignoring laser stimuli delivered to the other finger. Different time-frequency signatures of the EEG data were modulated by top-down (attention) and bottom up (intensity) processes. Attention towards pain induced a decrease in alpha and an increase in gamma band power, localized in the insula, whereas pain intensity modulated delta, alpha, beta and gamma power. Source localization revealed stimulus driven modulation in the cingulate gyrus and somatosensory areas for gamma power changes. Our results indicate that bottom-up and top-down modes of attention exert different effects on pain-induced slow and fast oscillatory activities. Future studies may examine oscillations using this paradigm to test for altered attentional pain control in patients with chronic pain.

Background: An emerging trend in visual information processing is toward incorporating some interesting properties of the ventral stream in order to account for some limitations of machine learning algorithms. Selective attention and cortical magnification are two such important phenomena that have been the subject of a large body of research in recent years. In this paper, we focus on designing a new model for visual acquisition that takes these important properties into account. Methods: We propose a new framework for visual information acquisition and representation that emulates the architecture of the primate visual system by integrating features such as retinal sampling and cortical magnification while avoiding spatial deformations and other side effects produced by models that tried to implement these two features. It also explicitly integrates the notion of visual angle, which is rarely taken into account by vision models. We argue that this framework can provide the infrastructure for implementing vision tasks such as object recognition and computational visual attention algorithms. Results: To demonstrate the utility of the proposed vision framework, we propose an algorithm for bottom-up saliency prediction implemented using the proposed architecture. We evaluate the performance of the proposed model on the MIT saliency benchmark and show that it attains state-of-the-art performance, while providing some advantages over other models. Conclusion: Here is a summary of the main contributions of this paper: (1) Introducing a new bio-inspired framework for visual information acquisition and representation that offers the following properties: (a) Providing a method for taking the distance between an image and the viewer into account. This is done by incorporating a visual angle parameter which is ignored by most visual acquisition models. (b) Reducing the amount of visual information acquired by introducing a new scheme for emulating retinal sampling and cortical magnification effects observed in the ventral stream. (2) Providing a concrete application of the proposed framework by using it as a substrate for building a new saliency-based visual attention model, which is shown to attain state-of-the-art performance on the MIT saliency benchmark. (3) Providing an online Git repository that implements the introduced framework that is meant to be developed as a scalable, collaborative project. [ABSTRACT FROM AUTHOR]