Human errors in performing security-critical tasks are typically blamed on the complexity of those tasks. However, such errors can also occur because of (possibly unexpected) sensory distractions. A sensory distraction that produces negative effects can be abused by the adversary that controls the environment. Meanwhile, a distraction with positive effects can be artificially introduced to improve user performance. The goal of this work is to explore the effects of visual stimuli on the performance of security-critical tasks. To this end, we experimented with a large number of subjects who were exposed to a range of unexpected visual stimuli while attempting to perform Bluetooth Pairing. Our results clearly demonstrate substantially increased task completion times and markedly lower task success rates. These negative effects are noteworthy, especially, when contrasted with prior results on audio distractions which had positive effects on performance of similar tasks. Experiments were conducted in a novel (fully automated and completely unattended) experimental environment. This yielded more uniform experiments, better scalability and significantly lower financial and logistical burdens. We discuss this experience, including benefits and limitations of the unattended automated experiment paradigm.
Moreover, visual stimuli were found to be dominating in multi-sensory contexts. Eimer @cite_18 showed that in experiments with tactile, visual, and audio stimuli, subjects overwhelmingly utilized visual queues to localize tactile and auditory events.
@cite_18
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The localisation of auditory and tactile events is strongly affected by visual information, reflecting the dominant role of vision in spatial perception. New research suggests that early visual experience is critical for the establishment of such multisensory links. A partial report procedure was used to test the ability of observers to split attention over noncontiguous locations. Observers reported the identity of 2 targets that appeared within a 5 X 5 stimulus array, and cues (validity = 80 ) informed them of the 2 most likely target locations. On invalid trials, 1 of the targets appeared directly in between the cued locations. Experiments 1, la, and 2 showed a strong accuracy advantage at cued locations compared with intervening ones. This effect was larger when the cues were arranged horizontally rather than vertically. Experiment 3 suggests that this effect of cue orientation reflects an advantage for processing targets that appear in different hemifields. Experiments 4 and 4a suggest that the primary mechanism supporting the flexible deployment of spatial attention is the suppression of interference from stimuli at unattended locations. There is a substantial body of research showing that when observers direct attention to specific parts of the visual field, information processing is facilitated at attended locations relative to unattended locations. A basic question is how flexibly attention can be deployed over space. Most models of spatial attention have assumed that attention is allocated over contiguous regions of space (e.g., Eriksen & Yeh, 1985; Posner, Snyder, & Davidson, 1980), and various empirical tests have supported this claim (e.g., Eriksen & Yeh, 1985; Heinze, Luck, Muente, Goes, Mangun, & Hillyard, 1994; McCormick & Klein, 1990; Pan & Eriksen, 1993; Posner, Snyder, & Davidson, 1980). However, more recent evidence has suggested that allocation of attention to noncontiguous regions is possible. Kramer and Hahn (1995) and Hahn and Kramer (1998) studied the influence of distractor compatibility at locations directly in between two cued locations and found that under certain conditions performance was unaffected by the distractors. In particular, they noted that when the distractors were presented by removing selected parts of a premask (the nononset condition), observers were able to suppress their influence. However, when the intervening distractors were presented as sudden-onset stimuli, distractor compatibility effects emerged. The investigators concluded that observers could divide attention effectively between noncontiguous locations but that this split focus was disrupted when new perceptual objects appeared in the intervening locations. They used a very strict criterion for evidence of split attention, however—namely, that distractors have absolutely no effect on target processing. The fact In this paper, we introduce a novel problem of audio-visual event localization in unconstrained videos. We define an audio-visual event as an event that is both visible and audible in a video segment. We collect an Audio-Visual Event(AVE) dataset to systemically investigate three temporal localization tasks: supervised and weakly-supervised audio-visual event localization, and cross-modality localization. We develop an audio-guided visual attention mechanism to explore audio-visual correlations, propose a dual multimodal residual network (DMRN) to fuse information over the two modalities, and introduce an audio-visual distance learning network to handle the cross-modality localization. Our experiments support the following findings: joint modeling of auditory and visual modalities outperforms independent modeling, the learned attention can capture semantics of sounding objects, temporal alignment is important for audio-visual fusion, the proposed DMRN is effective in fusing audio-visual features, and strong correlations between the two modalities enable cross-modality localization. Sounds in everyday life seldom appear in isolation. Both humans and machines are constantly flooded with a cacophony of sounds that need to be sorted through and scoured for relevant information—a phenomenon referred to as the ‘cocktail party problem’. A key component in parsing acoustic scenes is the role of attention, which mediates perception and behaviour by focusing both sensory and cognitive resources on pertinent information in the stimulus space. The current article provides a review of modelling studies of auditory attention. The review highlights how the term attention refers to a multitude of behavioural and cognitive processes that can shape sensory processing. Attention can be modulated by ‘bottom-up’ sensory-driven factors, as well as ‘top-down’ task-specific goals, expectations and learned schemas. Essentially, it acts as a selection process or processes that focus both sensory and cognitive resources on the most relevant events in the soundscape; with relevance being dictated by the stimulus itself (e.g. a loud explosion) or by a task at hand (e.g. listen to announcements in a busy airport). Recent computational models of auditory attention provide key insights into its role in facilitating perception in cluttered auditory scenes. This article is part of the themed issue ‘Auditory and visual scene analysis’. Multimodal streams of sensory information are naturally parsed and integrated by humans using signal-level feature extraction and higher level cognitive processes. Detection of attention-invoking audiovisual segments is formulated in this work on the basis of saliency models for the audio, visual, and textual information conveyed in a video stream. Aural or auditory saliency is assessed by cues that quantify multifrequency waveform modulations, extracted through nonlinear operators and energy tracking. Visual saliency is measured through a spatiotemporal attention model driven by intensity, color, and orientation. Textual or linguistic saliency is extracted from part-of-speech tagging on the subtitles information available with most movie distributions. The individual saliency streams, obtained from modality-depended cues, are integrated in a multimodal saliency curve, modeling the time-varying perceptual importance of the composite video stream and signifying prevailing sensory events. The multimodal saliency representation forms the basis of a generic, bottom-up video summarization algorithm. Different fusion schemes are evaluated on a movie database of multimodal saliency annotations with comparative results provided across modalities. The produced summaries, based on low-level features and content-independent fusion and selection, are of subjectively high aesthetic and informative quality. Abstract In 2 experiments, the development of auditory selective attention in children was assessed. The participants, aged 5, 7, and 9 years, responded to target stimuli in the left ear for 1 series (ignoring the standard stimuli) and in the right ear for the other series. In Experiment 1, event-related potentials (ERPs) evoked by the auditory stimuli were recorded from frontal, central, and pariet al sites. The 9-year-olds showed a greater processing negativity (Nd) to the attended channel compared with the 5-year-olds. Both 7-and 9-year-olds showed significantly larger amplitudes for the P3 component of ERPs in the attended vs. ignored condition. Behaviorally, the 5-year-olds made fewer hits and more false alarms than did older children, and the 9-year-olds made significantly more false alarms than did the 7-year-olds. The results of Experiment 2 showed that the detriment in the performance of the 9-year-olds was a result of task parameters. The inability of 5-year-olds to attend selectively appears to ... A single visual stimulus activates neurons in many different cortical areas. A major challenge in cortical physiology is to understand how the neural activity in these numerous active zones leads to a unified percept of the visual scene. The anatomical basis for these interactions is the dense network of connections that link the visual areas. Within this network, feedforward connections transmit signals from lower-order areas such as V1 or V2 to higher-order areas. In addition, there is a dense web of feedback connections which, despite their anatomical prominence1,2,3,4, remain functionally mysterious5,6,7,8. Here we show, using reversible inactivation of a higher-order area (monkey area V5 MT), that feedback connections serve to amplify and focus activity of neurons in lower-order areas, and that they are important in the differentiation of figure from ground, particularly in the case of stimuli of low visibility. More specifically, we show that feedback connections facilitate responses to objects moving within the classical receptive field; enhance suppression evoked by background stimuli in the surrounding region; and have the strongest effects for stimuli of low salience. The two basic phenomena that define the problem of visual attention can be illustrated in a simple example. Consider the arrays shown in each panel of Figure 1. In a typical experiment, before the arrays were presented, subjects would be asked to report letters appearing in one color (targets, here black letters), and to disregard letters in the other color (nontargets, here white letters). The array would then be briefly flashed, and the subjects, without any opportunity for eye movements, would give their report. The display mimics our. usual cluttered visual environment: It contains one or more objects that are relevant to current behavior, along with others that are irrelevant. The first basic phenomenon is limited capacity for processing information. At any given time, only a small amount of the information available on the retina can be processed and used in the control of behavior. Subjectively, giving attention to any one target leaves less available for others. In Figure 1, the probability of reporting the target letter N is much lower with two accompa­ nying targets (Figure la) than with none (Figure Ib). The second basic phenomenon is selectivity-the ability to filter out un­ wanted information. Subjectively, one is aware of attended stimuli and largely unaware of unattended ones. Correspondingly, accuracy in identifying an attended stimulus may be independent of the number of nontargets in a display (Figure la vs Ie) (see Bundesen 1990, Duncan 1980). Psychophysical and physiological evidence shows that sound localization of acoustic signals is strongly influenced by their synchrony with visual signals. This effect, known as ventriloquism, is at work when sound coming from the side of a TV set feels as if it were coming from the mouth of the actors. The ventriloquism effect suggests that there is important information about sound location encoded in the synchrony between the audio and video signals. In spite of this evidence, audiovisual synchrony is rarely used as a source of information in computer vision tasks. In this paper we explore the use of audio visual synchrony to locate sound sources. We developed a system that searches for regions of the visual landscape that correlate highly with the acoustic signals and tags them as likely to contain an acoustic source. We discuss our experience implementing the system, present results on a speaker localization task and discuss potential applications of the approach. This study examined the effect of attention engagement to compound auditory-visual stimuli on the modification of the startle blink reflex in infants. Infants at 8, 14, 20, or 26 weeks of age were presented with interesting audiovisual stimuli. After stimulus onset, at delays defined by heart rate changes known to be associated with sustained attention or attention disengagement, blink reflexes were elicited by visual or auditory stimuli. Blink amplitude to either visual or auditory stimuli was enhanced when the infants were engaged in attention to the foreground auditory-visual stimuli relative to control trials with no foreground patterns. This enhancement of the blink amplitude increased from 8 to 26 weeks of age. In contrast to selective modality enhancement for single-modality foreground stimuli, these results show that these multimodal stimuli engage both visual and auditory attention systems in this age range. Descriptors: Infants, Attention, Heart rate, Blink reflex, Multimodal stimuli Attention can be focused volitionally by “top-down” signals derived from task demands and automatically by “bottom-up” signals from salient stimuli. The frontal and pariet al cortices are involved, but their neural activity has not been directly compared. Therefore, we recorded from them simultaneously in monkeys. Prefrontal neurons reflected the target location first during top-down attention, whereas pariet al neurons signaled it earlier during bottom-up attention. Synchrony between frontal and pariet al areas was stronger in lower frequencies during top-down attention and in higher frequencies during bottom-up attention. This result indicates that top-down and bottom-up signals arise from the frontal and sensory cortex, respectively, and different modes of attention may emphasize synchrony at different frequencies. Neural responses in visual cortex are influenced by visual stimuli and by ongoing spiking activity in local circuits. An important challenge in computational neuroscience is to develop models that can account for both of these features in large multi-neuron recordings and to reveal how stimulus representations interact with and depend on cortical dynamics. Here we introduce a statistical model of neural population activity that integrates a nonlinear receptive field model with a latent dynamical model of ongoing cortical activity. This model captures temporal dynamics and correlations due to shared stimulus drive as well as common noise. Moreover, because the nonlinear stimulus inputs are mixed by the ongoing dynamics, the model can account for a multiple idiosyncratic receptive field shapes with a small number of nonlinear inputs to a low-dimensional dynamical model. We introduce a fast estimation method using online expectation maximization with Laplace approximations, for which inference scales linearly in both population size and recording duration. We test this model to multi-channel recordings from primary visual cortex and show that it accounts for neural tuning properties as well as cross-neural correlations. Summary Our nervous system is confronted with a barrage of sensory stimuli, but neural resources are limited and not all stimuli can be processed to the same extent. Mechanisms exist to bias attention toward the particularly salient events, thereby providing a weighted representation of our environment [1]. Our understanding of these mechanisms is still limited, but theoretical models can replicate such a weighting of sensory inputs and provide a basis for understanding the underlying principles [2, 3]. Here, we describe such a model for the auditory system—an auditory saliency map. We experimentally validate the model on natural acoustical scenarios, demonstrating that it reproduces human judgments of auditory saliency and predicts the detectability of salient sounds embedded in noisy backgrounds. In addition, it also predicts the natural orienting behavior of naive macaque monkeys to the same salient stimuli. The structure of the suggested model is identical to that of successfully used visual saliency maps. Hence, we conclude that saliency is determined either by implementing similar mechanisms in different unisensory pathways or by the same mechanism in multisensory areas. In any case, our results demonstrate that different primate sensory systems rely on common principles for extracting relevant sensory events. State-of-the-art deep convolutional networks (DCNs) such as squeeze-and- excitation (SE) residual networks implement a form of attention, also known as contextual guidance, which is derived from global image features. Here, we explore a complementary form of attention, known as visual saliency, which is derived from local image features. We extend the SE module with a novel global-and-local attention (GALA) module which combines both forms of attention -- resulting in state-of-the-art accuracy on ILSVRC. We further describe ClickMe.ai, a large-scale online experiment designed for human participants to identify diagnostic image regions to co-train a GALA network. Adding humans-in-the-loop is shown to significantly improve network accuracy, while also yielding visual features that are more interpretable and more similar to those used by human observers. According to conventional neurobiological accounts of visual attention, attention serves to enhance extrastriate neuronal responses to a stimulus at one spatial location in the visual field. However, recent results from recordings in extrastriate cortex of monkeys suggest that any enhancing effect of attention is best understood in the context of competitive interactions among neurons representing all of the stimuli present in the visual field. These interactions can be biased in favour of behaviourally relevant stimuli as a result of many different processes, both spatial and non–spatial, and both bottom–up and top–down. The resolution of this competition results in the suppression of the neuronal representations of behaviourally irrelevant stimuli in extrastriate cortex. A main source of top–down influence may derive from neuronal systems underlying working memory. Visual attention poses a mechanism for the selection of behaviourally relevant information from natural scenes which usually contain multiple objects. The aim of the present work is to formulate a neurodynamical model of selective visual attention based on the "biased competition hypothesis" and structured in several network modules which can be related with the different areas of the dorsal and ventral path of the visual cortex. Spatial and object attention are accomplished by a multiplicative gain control that emerges dynamically through intercortical mutual biased coupling. We also include in our computational model the "resolution hypothesis" in order to explain the role of the neurodynamics control of spatial resolution evidenced in psychophysical experiments. We propose that V1 neurons have different latencies depending on the spatial frequency to which they respond more sensitively. In concrete, we pose that V1 neurons sensitive to low spatial frequency are faster than V1 neurons sensitive to high spatial frequency. In this sense, a scene is first predominantly analysed at a coarse resolution level and the dynamics enhances subsequently the resolution at the location of an object until the object is identified. Cross-modal audio-visual perception has been a long-lasting topic in psychology and neurology, and various studies have discovered strong correlations in human perception of auditory and visual stimuli. Despite works in computational multimodal modeling, the problem of cross-modal audio-visual generation has not been systematically studied in the literature. In this paper, we make the first attempt to solve this cross-modal generation problem leveraging the power of deep generative adversarial training. Specifically, we use conditional generative adversarial networks to achieve cross-modal audio-visual generation of musical performances. We explore different encoding methods for audio and visual signals, and work on two scenarios: instrument-oriented generation and pose-oriented generation. Being the first to explore this new problem, we compose two new datasets with pairs of images and sounds of musical performances of different instruments. Our experiments using both classification and human evaluations demonstrate that our model has the ability to generate one modality, i.e., audio visual, from the other modality, i.e., visual audio, to a good extent. Our experiments on various design choices along with the datasets will facilitate future research in this new problem space. By adding the same component (e.g., glasses) to two stimuli (e.g., schematic faces) or to one stimulus only, it is possible to assess the impact of that component as a common or as a distinctive feature. A formal procedure, based on the contrast model (A. Tversky, 1977, Psychological Review, 84, 327–352), for estimating the relative weight of common to distinctive features from similarity judgments between separable stimuli with independent components, was developed. The results show that in verbal stimuli (e.g., descriptions of persons, meals, trips) common features loom larger than distinctive features. On the other hand, in pictorial stimuli (e.g., schematic faces, landscapes) distinctive features loom larger than common features. Verbal descriptions of pictorial stimuli were evaluated like other verbal stimuli and unlike their pictorial counterparts. In conceptual comparisons, the relative weight of common to distinctive features was higher in judgments of similarity than in judgments of dissimilarity. Most models of visual search, whether involving overt eye movements or covert shifts of attention, are based on the concept of a saliency map, that is, an explicit two-dimensional map that encodes the saliency or conspicuity of objects in the visual environment. Competition among neurons in this map gives rise to a single winning location that corresponds to the next attended target. Inhibiting this location automatically allows the system to attend to the next most salient location. We describe a detailed computer implementation of such a scheme, focusing on the problem of combining information across modalities, here orientation, intensity and color information, in a purely stimulus-driven manner. The model is applied to common psychophysical stimuli as well as to a very demanding visual search task. Its successful performance is used to address the extent to which the primate visual system carries out visual search via one or more such saliency maps and how this can be tested. Five important trends have emerged from recent work on computational models of focal visual attention that emphasize the bottom-up, image-based control of attentional deployment. First, the perceptual saliency of stimuli critically depends on the surrounding context. Second, a unique 'saliency map' that topographically encodes for stimulus conspicuity over the visual scene has proved to be an efficient and plausible bottom-up control strategy. Third, inhibition of return, the process by which the currently attended location is prevented from being attended again, is a crucial element of attentional deployment. Fourth, attention and eye movements tightly interplay, posing computational challenges with respect to the coordinate system used to control attention. And last, scene understanding and object recognition strongly constrain the selection of attended locations. Insights from these five key areas provide a framework for a computational and neurobiological understanding of visual attention.