Selective Attention Model Research Articles

A Semantic and Motion-Aware Spatiotemporal Transformer Network for Action Detection.

This paper presents a novel spatiotemporal transformer network that introduces several original components to detect actions in untrimmed videos. First, the multi-feature selective semantic attention model calculates the correlations between spatial and motion features to model spatiotemporal interactions between different action semantics properly. Second, the motion-aware network encodes the locations of action semantics in video frames utilizing the motion-aware 2D positional encoding algorithm. Such a motion-aware mechanism memorizes the dynamic spatiotemporal variations in action frames that current methods cannot exploit. Third, the sequence-based temporal attention model captures the heterogeneous temporal dependencies in action frames. In contrast to standard temporal attention used in natural language processing, primarily aimed at finding similarities between linguistic words, the proposed sequence-based temporal attention is designed to determine both the differences and similarities between video frames that jointly define the meaning of actions. The proposed approach outperforms the state-of-the-art solutions on four spatiotemporal action datasets: AVA 2.2, AVA 2.1, UCF101-24, and EPIC-Kitchens.

Putting the Spotlight Back Onto the Flanker Task in Autism: Autistic Adults Show Increased Interference from Foils Compared with Non-autistic Adults.

Autistic people may have a less focused spotlight of spatial selective attention than non-autistic people, meaning that distracting stimuli are less effectively suppressed. Previous studies using the flanker task have supported this suggestion with observations of increased congruency effects in autistic participants. However, findings across studies have been mixed, mainly based on research in children and on response time measures, which may be influenced by differences in response strategy between autistic and non-autistic people rather than differences in selective attention. In this pre-registered study, 153 autistic and 147 non-autistic adults completed an online flanker task. The aims of this study were to test whether increased congruency effects replicate in autistic adults and to extend previous work by fitting a computational model of spatial selective attention on the flanker task to the data. Congruency effects were increased in the autistic group. The modelling revealed that the interference time from the foils was increased in the autistic group. This suggests that the activation of the foils was increased, meaning suppression was less effective for autistic participants. There were also differences in non-interference parameters between the groups. The estimate of response caution was increased in the autistic group and the estimate of perceptual efficiency was decreased. Together these findings suggest inefficient suppression, response strategy and perceptual processing all contribute to differences in performance on the flanker task between autistic and non-autistic people.

The influence of stereopsis on visual saliency in a proto-object based model of selective attention

Some animals including humans use stereoscopic vision which reconstructs spatial information about the environment from the disparity between images captured by eyes in two separate adjacent locations. Like other sensory information, such stereoscopic information is expected to influence attentional selection. We develop a biologically plausible model of binocular vision to study its effect on bottom-up visual attention, i.e., visual saliency. In our model, the scene is organized in terms of proto-objects on which attention acts, rather than on unbound sets of elementary features. We show that taking into account the stereoscopic information improves the performance of the model in the prediction of human eye movements with statistically significant differences.

A Multiresolution Details Enhanced Attentive Dual-UNet for Hyperspectral and Multispectral Image Fusion

The fusion-based super-resolution of hyperspectral images (HSIs) draws more and more attention in order to surpass the hardware constraints intrinsic to hyperspectral imaging systems in terms of spatial resolution. Low-resolution (LR)-HSI is combined with a high-resolution multispectral image (HR-MSI) to achieve HR-HSI. In this article, we propose multiresolution details enhanced attentive dual-UNet to improve the spatial resolution of HSI. The entire network contains two branches. The first branch is the wavelet detail extraction module, which performs discrete wavelet transform on MSI to extract spatial detail features and then passes through the encoding–decoding. Its main purpose is to extract the spatial features of MSI at different scales. The latter branch is the spatio-spectral fusion module, which aims to inject the detail features of the wavelet detail extraction network into the HSI to reconstruct the HSI better. Moreover, this network uses an asymmetric feature selective attention model to focus on important features at different scales. Extensive experimental results on both simulated and real data show that the proposed network architecture achieves the best performance compared with several leading HSI super-resolution methods in terms of qualitative and quantitative aspects.

“Unattended, distracting or irrelevant”: Theoretical implications of terminological choices in auditory selective attention research

For seventy years, auditory selective attention research has focused on studying the cognitive mechanisms of prioritizing the processing a ‘main’ task-relevant stimulus, in the presence of ‘other’ stimuli. However, a closer look at this body of literature reveals deep empirical inconsistencies and theoretical confusion regarding the extent to which this ‘other’ stimulus is processed. We argue that many key debates regarding attention arise, at least in part, from inappropriate terminological choices for experimental variables that may not accurately map onto the cognitive constructs they are meant to describe. Here we critically review the more common or disruptive terminological ambiguities, differentiate between methodology-based and theory-derived terms, and unpack the theoretical assumptions underlying different terminological choices. Particularly, we offer an in-depth analysis of the terms ‘unattended’ and ‘distractor’ and demonstrate how their use can lead to conflicting theoretical inferences. We also offer a framework for thinking about terminology in a more productive and precise way, in hope of fostering more productive debates and promoting more nuanced and accurate cognitive models of selective attention.

A neurocognitive psychometrics account of individual differences in attentional control.

Attention control processes play an important role in many substantial psychological theories but are hard to reliably and validly measure on the subject-level. Therefore, associations between individual differences in attentional control and other variables are often inconsistent. Here we propose a novel neurocognitive psychometrics account of attentional control that integrates model parameters from the dual-stage two-phase model (Hübner et al., 2010), a mathematical model of selective attention, with neural correlates of conflict processing (i.e., latencies of the stimulus-locked lateralized readiness potential) in a multilayer structural equation model framework. We analyzed data from 150 participants who completed the Eriksen Flanker task while their EEG was recorded and used the neurocognitive psychometric approach to distinguish between two sequential stages of information-processing-target selection and response selection. Model parameters and neural correlates showed convergent validity and could be meaningfully related to each other. Together, these neurocognitive process parameters jointly explained 37 % of the variance in individual differences in higher-order cognitive abilities that were assessed with a battery of intelligence tests and working memory tasks. Individuals with greater cognitive abilities were not only better at focusing their attention on the target stimulus but also at subsequent response-selection. All in all, our results support the idea that individual differences in attentional control processes are related to individual differences in cognitive abilities. Moreover, they provide hope that the measurement crisis of individual differences in attentional control can be overcome by integrating measurement approaches from related disciplines such as mathematical psychology and cognitive neuroscience. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Feature similarity is non-linearly related to attentional selection: Evidence from visual search and sustained attention tasks.

Although many theories of attention highlight the importance of similarity between target and distractor items for selection, few studies have directly quantified the function underlying this relationship. Across two commonly used tasks—visual search and sustained attention—we investigated how target-distractor similarity impacts feature-based attentional selection. Importantly, we found comparable patterns of performance in both visual search and sustained feature-based attention tasks, with performance (response times and dʹ, respectively) plateauing at medium target-distractor distances (40°–50° around a luminance-matched color wheel). In contrast, visual search efficiency, as measured by search slopes, was affected by a much more narrow range of similarity levels (10°–20°). We assessed the relationship between target-distractor similarity and attentional performance using both a stimulus-based and psychologically-based measure of similarity and found this nonlinear relationship in both cases. However, psychological similarity accounted for some of the nonlinearities observed in the data, suggesting that measures of psychological similarity are more appropriate when studying effects of target-distractor similarities. These findings place novel constraints on models of selective attention and emphasize the importance of considering the similarity structure of the feature space over which attention operates. Broadly, the nonlinear effects of similarity on attention are consistent with accounts that propose attention exaggerates the distance between competing representations, possibly through enhancement of off-tuned neurons.

About time: modelling dynamic voluntary attention.

Goal-directed behaviour requires flexible prioritisation of relevant sensations in space and time. A recent paper by Denison et al. brings time into a canonical model of voluntary selective attention. We reflect on this breakthrough and consider the opportunities it offers for future research on the dynamic fabric of mind.

Fear of Missing Out Predicts Distraction by Social Reward Signals Displayed on a Smartphone in Difficult Driving Situations.

Smartphones are particularly likely to elicit driver distraction with obvious negative repercussions on road safety. Recent selective attention models lead to expect that smartphones might be very effective in capturing attention due to their social reward history. Hence, individual differences in terms of Fear of Missing Out (FoMO) – i.e., of the apprehension of missing out on socially rewarding experiences – should play an important role in driver distraction. This factor has already been associated with self-reported estimations of greater attention paid to smartphones while driving, but the potential link between FoMO and smartphone-induced distraction has never been tested empirically. Therefore, we conducted a preliminary study to investigate whether FoMO would modulate attentional capture by reward distractors displayed on a smartphone. First, participants performed a classical visual search task in which neutral stimuli (colored circles) were associated with high or low social reward outcomes. Then, they had to detect a pedestrian or a roe deer in driving scenes with various levels of fog density. The social reward stimuli were displayed as distractors on the screen of a smartphone embedded in the pictures. The results showed a significant three-way interaction between FoMO, social reward distraction, and task difficulty. More precisely, under attention-demanding conditions (i.e., high-fog density), individual FoMO scores predicted attentional capture by social reward distractors, with longer reaction times (RTs) for high rather than low social reward distractors. These results highlight the importance to consider reward history and FoMO when investigating smartphone-based distraction. Limitations are discussed, notably regarding our sample characteristics (i.e., mainly young females) that might hamper the generalization of our findings to the overall population. Future research directions are provided.

Self-selective attention using correlation between instances for distant supervision relation extraction

Distant supervision relation extraction methods are widely used to extract relational facts in text. The traditional selective attention model regards instances in the bag as independent of each other, which makes insufficient use of correlation information between instances and supervision information of all correctly labeled instances, affecting the performance of relation extractor. Aiming at this problem, a distant supervision relation extraction method with self-selective attention is proposed. The method uses a layer of convolution and self-attention mechanism to encode instances to learn the better semantic vector representation of instances. The correlation between instances in the bag is used to assign a higher weight to all correctly labeled instances, and the weighted summation of instances in the bag is used to obtain a bag vector representation. Experiments on the NYT dataset show that the method can make full use of the information of all correctly labeled instances in the bag. The method can achieve better results as compared with baselines.

Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones

Ignoring background sounds while focusing on a visual task is a necessary ability in everyday life. If attentional resources are shared between modalities, processing of task-irrelevant auditory information should become attenuated when attentional capacity is expended by visual demands. According to the early-filter model, top-down attenuation of auditory responses is possible at various stages of the auditory pathway through multiple recurrent loops. Furthermore, the adaptive filtering model of selective attention suggests that filtering occurs early when concurrent visual tasks are demanding (e.g., high load) and late when tasks are easy (e.g., low load). To test these models, this study examined the effects of three levels of visual load on auditory steady-state responses (ASSRs) at three modulation frequencies. Subjects performed a visual task with no, low, and high visual load while ignoring task-irrelevant sounds. The auditory stimuli were 500-Hz tones amplitude-modulated at 20, 40, or 80 Hz to target different processing stages of the auditory pathway. Results from bayesian analyses suggest that ASSRs are unaffected by visual load. These findings imply that attentional resources are modality specific and that the attentional filter of auditory processing does not vary with visual task demands.

Eriksen's two-phase model of spatial selective attention: Physiological evidence of trial-mixing-dependent response inhibition.

The early work of Charles W. Eriksen and colleagues provided us with both the flanker task and the concepts of response competition and continuous flow. The model of the flanker task that Eriksen and colleagues developed also includes the idea that processing occurs in two phases and the specific claim that pro-active response inhibition is employed to prevent errors under certain conditions. We first replicated and extended the behavioral evidence that motivated this specific claim and then tested it using a variety of physiological measures. We verified the prediction of Eriksen's Two-Phase Model of Spatial Selective Attention using the lateralized readiness potential and contingent negative variation. We also clarified a detail of the model using electromyographic activity and responseforce. We note that this contribution of Charles W. Eriksen has not received the attention that it deserves and that several recent models might need to be revised in light of Eriksen's work.

Resolución de Problemas Previo a Instrucción, aplicado al aprendizaje online de Modelos de Atención Selectiva

Se aplicó el enfoque de Resolución de Problemas Previo a la Instrucción (RP-I) a la enseñanza de modelos atencionales en la asignatura de Psicología de la Atención del grado de Psicología. El enfoque respondió a la necesidad de adaptación online durante el confinamiento generado por la crisis sanitaria. Este abordaje pedagógico hace al alumnado partícipe del proceso de aprendizaje, especialmente complicado dentro de la enseñanza de la atención selectiva desde el enfoque experimental. Desde el campus virtual de la asignatura y antes de la explicación de modelos atencionales, se planteó al alumnado la resolución de un problema en que debían confeccionar sus propios modelos atencionales. Al finalizar esta actividad, se pidió a los alumnos que informaran de las emociones que habían experimentado durante la misma. También se evaluó su nivel de participación y su desempeño en la actividad. Los alumnos mostraron una muy alta participación en el problema planteado, así como una riqueza y variedad de soluciones del problema. La mayoría mostraron un empleo maduro de la terminología específica. El enfoque RP-I contribuyó al aprendizaje de la asignatura y promovió una alta participación e implicación en el aprendizaje, enriqueciendo el formato de docencia no presencial.

Chordiogram image descriptor based on visual attention model for image retrieval

Abstract A novel shape-based image retrieval is presented in this study. The foreground and background contents of images are strongly concealed, so they are represented individually to reduce their influence on each other in the proposed approach. The Otsu method is employed for segmenting the foreground from the background, and the saliency map and edge map are then clearly identified. Saliency reduces the time cost for feature computation, so salient edges are computed for the foreground and background images based on the selective visual attention model. Autocorrelation-based chordiogram image descriptors are computed separately for the foreground and background images, which are then combined in a hierarchical manner to form the proposed new descriptor. This approach avoids the concealment of foreground and background information, and the new descriptor is rich in geometric and its underlying texture, structure and spatial information. The proposed novel shape-based descriptor performs considerably better than conventional descriptors at content-based image retrieval. The proposed shape descriptor were extensively tested at image retrieval based on the Gardens Point Walking, St Lucia, University of Alberta Campus, Corel 10 ​k, and self-photographed image data sets. The precision and recall values were compared for the proposed and state-of-the-art-approaches when applied for shape-based image retrieval from these databases. The proposed shape descriptor provided satisfactory retrieval results in the experiments.

CONCEPT OF INFORMATION PROCESSES ORGANIZATION IN HUMAN VISUAL SYSTEM

Recent studies have shown that both traditional and modern (electronic) visual media significantly affect the processes occurring in the visual system of children and adolescents, iin the development of which the concept of controlled and uncontrolled elements is used. When studying the adaptation of the visual system to a visual load, ensuring the uptake, transmission and processing of visual information, its structural and functional organization is taken into account. The proposed summarized scheme of information processes in the visual system takes into account and significantly supplements the main provisions of the object−oriented model of selective visual attention, based on current methods of intelligent data processing. The results of the study indicate the complexity of the visual information transformation path from a visual stimulus to the creation and awareness of the image, occurring in the higher parts of the brain. The whole apparatus of encoding, transmitting, processing and perceiving visual information is useless if the guidance and focusing unit does not provide a clear and undistorted image of the objects of the outside world on the retina. In case of any malfunctions of the first unit or in the presence of defects in the visual system, the processes of finding compensation for this condition and modes of operation are started, which allows to obtain the most complete and high−quality perception of visual objects. These processes can lead to the formation of a visual system with sufficiently high visual functions or monocular one, if the compensation of existing problems will have a high "price". With the help of the proposed concept of organization of information processes in the visual system it is possible to assess the role of each of its considered blocks not only in the perception of visual information, but also the formation of this system in children and adolescents. Key words: visual system, information processes, visual information.

Endogenous cueing effects for detection can be accounted for by a decision model of selective attention

Spatial cues help participants detect a visual target when it appears at the cued location. One hypothesis for this cueing effect, called selective perception, is that cueing a location enhances perceptual encoding at that location. Another hypothesis, called selective decision, is that the cue has no effect on perception, but instead provides prior information that facilitates decision-making. We distinguished these hypotheses by comparing a simultaneous display with two spatial locations to sequential displays with two temporal intervals. The simultaneous condition had a partially valid spatial cue, and the sequential condition had a partially valid temporal cue. Selective perception predicts no cueing effect for sequential displays given there is enough time to switch attention. In contrast, selective decision predicts cueing effects for sequential displays regardless of time. We used endogenous cueing of a detection-like coarse orientation discrimination task with clear displays (no external noise or postmasks). Results showed cueing effects for the sequential condition, supporting a decision account of selective attention for endogenous cueing of detection-like tasks.

Selective and Coverage Multi-head Attention for Abstractive Summarization

Although the Transformer model has outperformed traditional sequence-to-sequence model in a variety of natural language processing (NLP) tasks, it still suffers from semantic irrelevance and repetition for abstractive text summarization. The main reason is that the long text to be summarized is usually composed of multi-sentences and has much redundant information. To tackle this problem, we propose a selective and coverage multi-head attention framework based on the original Transformer. It contains a Convolutional Neural Network (CNN) selective gate, which combines n-gram features with whole semantic representation to obtain core information from the long input sentence. Besides, we use a coverage mechanism in the multi-head attention to keep track of the words which have been summarized. The evaluations on Chinese and English text summarization datasets both demonstrate that the proposed selective and coverage multi-head attention model outperforms the baseline models by 4.6 and 0.3 ROUGE-2 points respectively. And the analysis shows that the proposed model generates the summary with higher quality and less repetition.

Introducing a Bayesian model of selective attention based on active inference

Information gathering comprises actions whose (sensory) consequences resolve uncertainty (i.e., are salient). In other words, actions that solicit salient information cause the greatest shift in beliefs (i.e., information gain) about the causes of our sensations. However, not all information is relevant to the task at hand: this is especially the case in complex, naturalistic scenes. This paper introduces a formal model of selective attention based on active inference and contextual epistemic foraging. We consider a visual search task with a special emphasis on goal-directed and task-relevant exploration. In this scheme, attention modulates the expected fidelity (precision) of the mapping between observations and hidden states in a state-dependent or context-sensitive manner. This ensures task-irrelevant observations have little expected information gain, and so the agent – driven to reduce expected surprise (i.e., uncertainty) – does not actively seek them out. Instead, it selectively samples task-relevant observations, which inform (task-relevant) hidden states. We further show, through simulations, that the atypical exploratory behaviours in conditions such as autism and anxiety may be due to a failure to appropriately modulate sensory precision in a context-specific way.

Excitatory versus inhibitory feedback in Bayesian formulations of scene construction.

The selective attention for identification model (SAIM) is an established model of selective visual attention. SAIM implements translation-invariant object recognition, in scenes with multiple objects, using the parallel distributed processing (PDP) paradigm. Here, we show that SAIM can be formulated as Bayesian inference. Crucially, SAIM uses excitatory feedback to combine top-down information (i.e. object knowledge) with bottom-up sensory information. By contrast, predictive coding implementations of Bayesian inference use inhibitory feedback. By formulating SAIM as a predictive coding scheme, we created a new version of SAIM that uses inhibitory feedback. Simulation studies showed that both types of architectures can reproduce the response time costs induced by multiple objects—as found in visual search experiments. However, due to the different nature of the feedback, the two SAIM schemes make distinct predictions about the motifs of microcircuits mediating the effects of top-down afferents. We discuss empirical (neuroimaging) methods to test the predictions of the two inference architectures.

Expert attention: Attentional allocation depends on the differential development of multisensory number representations

Traditional models developed within cognitive psychology suggest that attention is deployed flexibly and irrespective of differences in expertise with to-be-attended stimuli. However, everyday environments are inherently multisensory and observers differ in familiarity with particular unisensory representations (e.g., number words, in contrast with digits). To test whether the predictions of the traditional models extend to such naturalistic settings, six-year-olds, 11-year-olds and young adults (N = 83) searched for predefined numerals amongst a small or large number of distractor digits, while distractor number words, digits or their combination were presented peripherally. Concurrently presented number words and audiovisual stimuli that were compatible with the target digit facilitated young children’s selective attention. In contrast, for older children and young adults number words and audiovisual stimuli that were incompatible with their visual targets resulted in a cost on reaction time. These findings suggest that multisensory and familiarity-based influences interact dynamically as they shape selective attention. Therefore, models of selective attention should include multisensory and familiarity-dependent constraints: more or less familiar object representations across modalities will be attended to differently, with their effects visible as predominant benefits for attention at one level but costs at another.