Mechanisms Of Selective Attention Research Articles

Cognitive Impact of Social Robots: How Anthropomorphism Boosts Performances

Evidence shows that a robotic agent in the presence of a human can affect selective attention mechanisms in that human in the same way the presence of a fellow human can. However, it's uncertain whether this process stems from anthropomorphism attribution. We investigated this issue using a selective attention task in a social-presence paradigm. One group of participants performed the so-called Eriksen Flanker task (EFT) in the presence of a robot after a verbal social in teraction (i.e., socialrobot condition), while the other group did the same with a robot that the participants only described (i.e., nonsocialrobot condition). Results showed that, after social interaction, the robot was perceived as having human traits (according to the humanization and anthropomorphism scale). Furthermore, we found a socialpresence effect (i.e., an improvement in selective attention performance) only in the presence of the social robot but not in the presence of the nonsocial one. Finally, this latter effect was mediated by anthropomorphism attributions. Our results suggest that the influence of the robot?s presence is sociocognitive in nature and that anthropomorphism has a role in the robot-presence effect.

A flocking model base on selective attention mechanism

Motivated by the capability to compress information efficiently of selective attention mechanism, we proposed a selective attention based flocking model. In this model, individuals sense other members by fixed number of neighbors guided by line of sight method and calculate saliency of these neighbors according to their contribution to local order. Then individuals decide whether to activate themselves to respond to stimuli determined by threshold model. We found a well-defined hierarchical network concerning stimulus transfer among the flock. Individuals switch between hierarchical and egalitarian strategies according to their status of activation. Results reveal that the selective attention can enhance the transfer of social cues and make flock members able to respond to social cues rapidly. The decision to turn caused by external stimuli can propagate across the group quickly using this model, which is consistent with natural flocks.

Delayed effects of attention on pain sensitivity and conditioned pain modulation.

Efficacy of pain modulation is assessed as the difference in pain sensitivity during a painful conditioning, compared to before (conditioning pain modulation, CPM). Attention can be assessed with the Stroop task, in which participants report the number of words on a screen; either congruent or incongruent with the value of the words. Attention away from painful stimuli during CPM enhances the CPM effect. However, it is unknown if attention influences CPM effects when the two are done in sequence. Healthy men (n=25) underwent cuff algometry CPM-assessment where the pressure-pain detection and tolerance thresholds (PTT) were recorded on one leg with and without contralateral conditioning. Two identical sessions of four test stimuli equal to PTT (5s, 1-min interval, scored on a visual analogue scale, VAS) with a painful conditioning from the second to the last test-stimulus were performed. Stroop sessions were followed by test stimuli with or without painful conditioning. The VAS scores in the first two sessions showed excellent reliability (ICC=0.92). VAS scores were lower in sessions with Stroop compared to sessions without Stroop (p=.05) indicating an analgesic effect of Stroop. Participants were subgrouped into CPM responders and CPM non-responders according to CPM effects in the first two sessions. CPM non-responders (n=13) showed facilitation to repeated noxious stimuli in all sessions with no effect of conditioning or Stroop (p=.02). Attention and CPM both modulate pain in healthy men. Attention-induced analgesia works in CPM non-responders. Results indicate that attention and CPM are not the same and that they do not demonstrate additive effects when applied in sequence. Pain sensitivity is reduced after an attention task in healthy men. The delayed effects from attention only have minor effects on Conditioned Pain Modulation (CPM), and results support that attention-driven analgesia works independently of CPM. Results indicate that individual strategies for pain inhibition exist and that an overlap between the mechanisms of CPM and selective attention is limited. Moreover, painful phasic stimuli may increase the number of healthy volunteers with negative CPM effects.

Learned feature variance is encoded in the target template and drives visual search

ABSTRACTReal-world visual search targets are frequently imperfect perceptual matches to our internal templates. For example, a friend on different occasions will have different clothes, hairstyles, and accessories, but some of these may vary more than others. The ability to deal with template-to-target variability is important to visual search in natural environments, but we know relatively little about how this is handled by the attentional system. Here, we test the hypothesis that top-down attentional biases are sensitive to the variance of target features and prioritize less-variable dimensions. Subjects were shown target cues composed of coloured dots moving in a specific direction followed by a working memory probe (30%) or visual search display (70%). Critically, the target features in the visual search display differed from the cue, with one feature drawn from a narrow distribution (low-variance dimension), and the other sampled from a broader distribution (high-variance dimension). The results demonstrate that subjects used knowledge of the likely cue-to-target variance to set template precision and bias attentional selection. Our results suggest that observers are sensitive to the variance of feature dimensions within a target and use this information to weight mechanisms of attentional selection.

Cross-Relation Cross-Bag Attention for Distantly-Supervised Relation Extraction

Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C2SA), which leads to noise-robust training for distant supervised relation extractor. Specifically, we employ the sentence-level selective attention to reduce the effect of noisy or mismatched sentences, while the correlation among relations were captured to improve the quality of attention weights. Moreover, instead of treating all entity-pairs equally, we try to pay more attention to entity-pairs with a higher quality. Similarly, we adopt the selective attention mechanism to achieve this goal. Experiments with two types of relation extractor demonstrate the superiority of the proposed approach over the state-of-the-art, while further ablation studies verify our intuitions and demonstrate the effectiveness of our proposed two techniques.

Autonomous driving: cognitive construction and situation understanding

Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency, poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes, the efficiency of event-related mechanism is more significant than single-attribute scenario perception data. Therefore, an effective self-driving method should not be confined to the traditional computing framework of `perception, planning, decision-making, and control. It is necessary to explore a basic computing framework that conforms to human drivers attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence.In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It further describes how to use multi-sensor and graph data with semantic information (such as traffic maps and a spatial correlation of events) to realize the associative representations of objects and drivable areas, as well as the intuitive reasoning method applied to understanding the situations in different traffic scenarios.The computing framework of autonomous driving based on a selective attention mechanism and intuitive reasoning discussed in this study can adapt to a more complex, open, and dynamic traffic environment.

Cross-frequency coupling of alpha oscillatory power to the entrainment rhythm of a spatially attended input stream

ABSTRACTNeural entrainment and alpha oscillatory power (8–14 Hz) are mechanisms of selective attention. The extent to which these two mechanisms interact, especially in the context of visuospatial attention, is unclear. Here, we show that spatial attention to a delta-frequency, rhythmic visual stimulus in one hemifield results in phase-amplitude coupling between the delta-phase of an entrained frontal source and alpha power generated by ipsilateral visuocortical regions. The driving of ipsilateral alpha power by frontal delta also correlates with task performance. Our analyses suggest that neural entrainment may serve a previously underappreciated role in coordinating macroscale brain networks and that inhibition of processing by alpha power can be coupled to an attended temporal structure. Finally, we note that the observed coupling bolsters one dominant hypothesis of modern cognitive neuroscience, that macroscale brain networks and distributed neural computation are coordinated by oscillatory synchrony and cross-frequency interactions.

EEG model stability and online decoding of attentional demand during gait using gamma band features

Rehabilitation therapies are evolving oriented to improve their performances in terms of functional recovery. To achieve such recovery, the patients’ involvement is an important factor that correlates with the plastic properties of the brain. By evaluating electroencephalographic signals, it is possible to modify, in real time, the parameters of the rehabilitation according to the patients’ cognitive state. In this paper, an online brain–machine interface to measure the attention level during gait is presented. The system is based on the measurement of selective attention mechanisms manifested as power synchronization and desynchronization in the gamma band. A Linear Discriminant Analysis classifier is used to provide an attention index between 0 and 1 in real time. Robust techniques for artifact rejection and signal standardization are used in order to deal with the problems associated to the measurement of cortical signals during walking. The final interface is validated with 4 incomplete Spinal Cord Injury patients and 4 healthy participants. The system shows an average success rate of 68.1% in the classification of 3 attention levels and a stable behavior of these results during time.

Bilingualism and language similarity modify the neural mechanisms of selective attention

Learning and using multiple languages places major demands on our neurocognitive system, which can impact the way the brain processes information. Here we investigated how early bilingualism influences the neural mechanisms of auditory selective attention, and whether this is further affected by the typological similarity between languages. We tested the neural encoding of continuous attended speech in early balanced bilinguals of typologically similar (Dutch-English) and dissimilar languages (Spanish-English) and compared them to results from English monolinguals we reported earlier. In a dichotic listening paradigm, participants attended to a narrative in their native language while ignoring different types of interference in the other ear. The results revealed that bilingualism modulates the neural mechanisms of selective attention even in the absence of consistent behavioural differences between monolinguals and bilinguals. They also suggested that typological similarity between languages helps fine-tune this modulation, reflecting life-long experiences with resolving competition between more or less similar candidates. The effects were consistent over the time-course of the narrative and suggest that learning a second language at an early age triggers neuroplastic adaptation of the attentional processing system.

Tired and lack focus? Insomnia increases distractibility.

Chronic insomnia is associated with subjective daytime cognitive dysfunction, but objective corroborative data are often lacking. In this study, we use Perceptual Load Theory to objectively assess distractibility in participants with insomnia (N = 23) compared with age- and sex-matched controls (N = 23). Following overnight supervised sleep observation, all participants completed a selective attention task which varied in the level of perceptual load and distractor congruency. The insomnia group was found to be more distracted than controls, whereas their selective attention mechanism appeared to be intact, with reduced distractor processing under high load for both groups. Insomnia symptom severity was positively correlated with participant distractibility. These findings suggest that there are insomnia-related daytime cognitive impairments that are likely to arise from compromised cognitive control rather than an ineffective selective attention mechanism. This task may be clinically useful in assessing daytime impairments, and potentially treatment response, in those with insomnia.

Internet of Things Meets Brain–Computer Interface: A Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity

A Brain-Computer Interface (BCI) acquires brain signals, analyzes and translates them into commands that are relayed to actuation devices for carrying out desired actions. With the widespread connectivity of everyday devices realized by the advent of the Internet of Things (IoT), BCI can empower individuals to directly control objects such as smart home appliances or assistive robots, directly via their thoughts. However, realization of this vision is faced with a number of challenges, most importantly being the issue of accurately interpreting the intent of the individual from the raw brain signals that are often of low fidelity and subject to noise. Moreover, pre-processing brain signals and the subsequent feature engineering are both time-consuming and highly reliant on human domain expertise. To address the aforementioned issues, in this paper, we propose a unified deep learning based framework that enables effective human-thing cognitive interactivity in order to bridge individuals and IoT objects. We design a reinforcement learning based Selective Attention Mechanism (SAM) to discover the distinctive features from the input brain signals. In addition, we propose a modified Long Short-Term Memory (LSTM) to distinguish the inter-dimensional information forwarded from the SAM. To evaluate the efficiency of the proposed framework, we conduct extensive real-world experiments and demonstrate that our model outperforms a number of competitive state-of-the-art baselines. Two practical real-time human-thing cognitive interaction applications are presented to validate the feasibility of our approach.

Does hypnotic susceptibility influence information processing speed and motor cortical preparatory activity?

Responsiveness to suggestions while hypnotized is termed hypnotic susceptibility. An association between reaction time and hypnotic susceptibility has been demonstrated, but whether distinct changes in brain activity accompany this relationship remains unclear. We investigated the effect of hypnotic susceptibility on the speed of information processing and motor cortical preparatory activity. Twenty-one “low” (LowHS) and fifteen “high” (HighHS) hypnotically susceptible right-handed participants performed precued simple (SRT) and choice (CRT) reaction time key-press tasks under hypnotized and non-hypnotized conditions. Force and surface electromyography data were recorded from left and right index fingers. The contingent negative variation (CNV) was derived from electroencephalography data. Mean reaction time and premotor time was shorter in HighHS participants than LowHS participants for both simple and choice reaction time tasks. HighHS participants in the hypnotized state performed fewer errors than HighHS participants in the non-hypnotized state and LowHS participants in either state for the SRT task. HighHS participants made fewer errors overall than LowHS participants for the CRT task. Mean C3/C4 CNV amplitude was larger in HighHS than in LowHS participants. Furthermore, larger CNV amplitude was associated with shorter premotor time. Our findings indicate that shorter reaction time in the high hypnotically susceptible group is associated with a greater change in brain activity during motor preparation. One interpretation is that hypnotic susceptibility and neural mechanisms of arousal and selective attention are linked.

The effects of distractor set-size on neural tracking of attended speech

Attention is crucial to speech comprehension in real-world, noisy environments. Selective phase-tracking between low-frequency brain dynamics and the envelope of target speech is a proposed mechanism to reject competing distractors. Studies have supported this theory in the case of a single distractor, but have not considered how tracking is systematically affected by varying distractor set sizes. We recorded electroencephalography (EEG) during selective listening to both natural and vocoded speech as distractor set-size varied from two to six voices. Increasing set-size reduced performance and attenuated EEG tracking of target speech. Further, we found that intrusions of distractor speech into perception were not accompanied by sustained tracking of the distractor stream. Our results support the theory that tracking of speech dynamics is a mechanism for selective attention, and that the mechanism of distraction is not simple stimulus-driven capture of sustained entrainment of auditory mechanisms by the acoustics of distracting speech.

Natural Answer Generation With Attention Over Instances

Natural answer generation (NAG) is more and more popular in real-world knowledge base question answering (KBQA) systems for being able to automatically generate natural language answers with structured KB. Large-scale community QA-pairs crawled from the Internet could be directly used to train NAG models. However, it is pervasive in these datasets that one question may contain multiple answers of varied quality, and NAG models suffer from the simple principle of equal treatment of these answers. To address this problem, we propose two kinds of attention-based algorithms to handle all answers to a question at a time. Selective attention and self-attention mechanisms are used to dynamically weight the answers to one question during the training process. Specifically, selective attention methods weight the answers using the relationships between all the KB objects the question needs and the generated answers, and self-attention methods weight them according to the generating difficulty. The experiments on the public open-domain community QA dataset demonstrate that Selective-ATT outperforms the state-of-the-art by 10.53% in the entity accuracy, 9.34% in the BLEU score, and 1.19% in the Rouge score.

New intelligent interface study based on K-means gaze tracking

User interface (UI) is an interaction and information exchange medium between systems and users, which is convenient to communicate and enable users to operate hardware easily and effectively to achieve the bidirectional interaction. Traditional UI has difficulty in satisfying requirements of users. Therefore, this paper proposes a new intelligence interface scheme based on K-means gaze tracking. First, it uniformly describes the user, interface and system in intelligent interface interaction framework based on visual attention selection mechanism. Second, it uses K-means method to calculate attention degree value of interface on the basis of mapping relation of user, interface and system. Third, it adopts visual attention allocation strategy to predict users' interest degree on interface. Finally, experiments are conducted to verify the performance of our new scheme. The results show that the accuracy of intelligent interface predicting user decision intention is very high. This new method is a kind of selectable solution for marking intelligent interface of user interest goal automatically. What's more, intelligence interface scheme based on K-means gaze tracking can effectively improve the quality of gaze tracking.

New intelligent interface study based on K-means gaze tracking

User interface (UI) is an interaction and information exchange medium between systems and users, which is convenient to communicate and enable users to operate hardware easily and effectively to achieve the bidirectional interaction. Traditional UI has difficulty in satisfying requirements of users. Therefore, this paper proposes a new intelligence interface scheme based on K-means gaze tracking. First, it uniformly describes the user, interface and system in intelligent interface interaction framework based on visual attention selection mechanism. Second, it uses K-means method to calculate attention degree value of interface on the basis of mapping relation of user, interface and system. Third, it adopts visual attention allocation strategy to predict users' interest degree on interface. Finally, experiments are conducted to verify the performance of our new scheme. The results show that the accuracy of intelligent interface predicting user decision intention is very high. This new method is a kind of selectable solution for marking intelligent interface of user interest goal automatically. What's more, intelligence interface scheme based on K-means gaze tracking can effectively improve the quality of gaze tracking.

Visual Search Without Selective Attention: A Cognitive Architecture Account.

A key phenomenon in visual search experiments is the linear relation of reaction time (RT) to the number of objects to be searched (set size). The dominant theory of visual search claims that this is a result of covert selective attention operating sequentially to "bind" visual features into objects, and this mechanism operates differently depending on the nature of the search task and the visual features involved, causing the slope of the RT as a function of set size to range from zero to large values. However, a cognitive architectural model presented here shows these effects on RT in three different search task conditions can be easily obtained from basic visual mechanisms, eye movements, and simple task strategies. No selective attention mechanism is needed. In addition, there are little-explored effects of visual crowding, which is typically confounded with set size in visual search experiments. Including a simple mechanism for crowding in the model also allows it to account for significant effects on error rate (ER). The resulting model shows the interaction between visual mechanisms and task strategy, and thus it represents a more comprehensive and fruitful approach to visual search than the dominant theory.

Cognitive control in the cocktail party: Preparing selective attention to dichotically presented voices supports distractor suppression.

The goal of the present study was to investigate preparatory mechanisms of auditory selective attention. In two experiments, participants performed a classification task on one of two dichotically presented spoken number words, one spoken by a female, one spoken by a male. A cue indicated which gender participants had to attend to in the upcoming trial, so that attention switches and repetitions occurred randomly. The cue-target interval (CTI) was either 400 ms or 1,200 ms. Stimulus onset asynchrony (SOA) between target and distractor word varied; hence, the distractor could be presented before or after the target. In two experiments, we found robust performance costs of attention switches. Like in previous studies using versions of this paradigm, these switch costs were not significantly reduced by prolonged CTI, even though we found substantial general cue-based preparation effects. The most important finding refers to the influence of SOA, showing that the general preparation effect was greater in the condition with the distractor presented first than in the condition with the target presented first. Thus, increased time to prepare for the attention focus of the upcoming trials seems to benefit distractor suppression more than target enhancement. This occurred in switch trials and repetition trials alike, suggesting that it is a general feature of auditory attention.

Pupil size and search performance in low and high perceptual load

The ability to focus on a task while disregarding irrelevant information is an example of selective attention. The perceptual-load hypothesis argues that the occurrence of early or late selection mechanisms is determined by task-relevant perceptual load. Additionally, evidence shows that pupil size serves as proxy of locus coeruleus-norepinephrine (LC-NE) activity, a system associated with cognitive and attentional mediation. Here, we assessed pupil baseline (and pupil dilation) as predictors of load-related early and late selection performance. Participants were asked to search for a target in conditions of high and low perceptual load, while ignoring irrelevant stimuli. The results showed that pupil baseline size, measured prior trial onset, significantly predicted the upcoming search efficiency only in low perceptual load, when—according to the perceptual-load hypothesis—all perceptual information receives attentional resources. In addition, pupil dilation was linked to the time course of perceptual processing and predicted response times in both perceptual load conditions, an association that was enhanced in high load. Thus, this study relates attentional selection mechanisms, as defined by the perceptual-load theory, with pupil-related LC-NE activity. Because pupil baseline predicted attentional performance in low load but not in high load, this suggests that different attentional mechanisms are involved, one in which the LC-NE system plays a key role (low load) and one in which it is less relevant (high load). This suggests that the degree with which LC-NE influences behavioral performance is related to the perceptual load of the task at hand.

Laminar profile of task-related plasticity in ferret primary auditory cortex

Rapid task-related plasticity is a neural correlate of selective attention in primary auditory cortex (A1). Top-down feedback from higher-order cortex may drive task-related plasticity in A1, characterized by enhanced neural representation of behaviorally meaningful sounds during auditory task performance. Since intracortical connectivity is greater within A1 layers 2/3 (L2/3) than in layers 4–6 (L4–6), we hypothesized that enhanced representation of behaviorally meaningful sounds might be greater in A1 L2/3 than L4–6. To test this hypothesis and study the laminar profile of task-related plasticity, we trained 2 ferrets to detect pure tones while we recorded laminar activity across a 1.8 mm depth in A1. In each experiment we analyzed high-gamma local field potentials (LFPs) and multi-unit spiking in response to identical acoustic stimuli during both passive listening and active task performance. We found that neural responses to auditory targets were enhanced during task performance, and target enhancement was greater in L2/3 than in L4–6. Spectrotemporal receptive fields (STRFs) computed from both high-gamma LFPs and multi-unit spiking showed similar increases in auditory target selectivity, also greatest in L2/3. Our results suggest that activity within intracortical networks plays a key role in the underlying neural mechanisms of selective attention.