Distinct Brain Mechanisms for Conflict Adaptation within and across Conflict Types

Cognitive conflict, like other cognitive processes, shows the characteristic of adaptation, that is, conflict effects are attenuated when immediately following a conflicting event, a phenomenon known as the conflict adaptation effect (CAE). One important aspect of CAE is its sensitivity to the intertrial coherence of conflict type, that is, behavioral CAE occurs only if consecutive trials are of the same conflict type. Although reliably observed behaviorally, the neural mechanisms underlying such a phenomenon remains elusive. With a paradigm combining the classic Simon task and Stroop task, this fMRI study examined neural correlates of conflict adaptation both within and across conflict types. The results revealed that when the conflict type repeated (but not when it alternated), the CAE-like neural activations were observed in dorsal ACC, inferior frontal gyrus (IFG), superior parietal lobe, and so forth (i.e., regions within typical task-positive networks). In contrast, when the conflict type alternated (but not when it repeated), we found CAE-like neural deactivations in the left superior frontal gyri (i.e., a region within the typical task-negative network). Network analyses suggested that the regions of ACC, IFG, superior parietal lobe, and superior frontal gyrus can be clustered into two antagonistic networks, and the ACC–IFG connection was associated with the within-type CAE. This evidence suggests that our adaptation to cognitive conflicts within a conflict type and across different types may rely on these two distinct neural mechanisms.

On the reliability of behavioral measures of cognitive control: retest reliability of task-inhibition effect, task-preparation effect, Stroop-like interference, and conflict adaptation effect

This study examined the reliability (retest and split-half) of four common behavioral measures of cognitive control. In Experiment 1 (N = 96), we examined N – 2 task repetition costs as a marker of task-level inhibition, and the cue-stimulus interval (CSI) effect as a marker of time-based task preparation. In Experiment 2 (N = 48), we examined a Stroop-like face-name interference effect as a measure of distractor interference control, and the sequential congruency effect (“conflict adaptation effect”) as a measure of conflict-triggered adaptation of cognitive control. In both experiments, the measures were assessed in two sessions on the same day, separated by a 10 min-long unrelated filler task. We observed substantial experimental effects with medium to large effect sizes. At the same time, split-half reliabilities were moderate, and retest reliabilities were poor, for most measures, except for the CSI effect. Retest reliability of the Stroop-like effect was improved when considering only trials preceded by congruent trials. Together, the data suggest that these cognitive control measures are well suited for assessing group-level effects of cognitive control. Yet, except for the CSI effect, these measures do not seem suitable for reliably assessing interindividual differences in the strength of cognitive control, and therefore are not suited for correlational approaches. We discuss possible reasons for the discrepancy between robustness at the group level and reliability at the level of interindividual differences.

Integrating generalized domain adaptation and Fisher discriminative learning: A unified framework for face recognition with single sample per person

In this paper, an enhanced discriminative feature learning (EDFL) method is proposed to address single sample per person (SSPP) face recognition. With a separate auxiliary dataset, EDFL integrates Fisher discriminative learning and domain adaptation into a unified framework. The separate auxiliary dataset and the gallery/probe dataset are from two different domains (named source and target domains respectively) and have different data distributions. EDFL is modeled to transfer the discriminative knowledge learned from the source domain to the target domain for classification. Since the gallery set with SSPP contains scarce number of samples, it is hard to accurately represent the data distribution of the target domain, which hinders the adaptation effect. To overcome this problem, the generalized domain adaption (GDA) method is proposed to realize good overall domain adaptation when one domain contains limited samples. GDA considers the both global and local domain adaptation effect at the same time. Further, to guarantee that the learned domain adaptation components are optimal for discriminative learning, the domain adaptation and Fisher discriminant model learning are unified into a single framework and an efficient algorithm is designed to optimize them. The effectiveness of the proposed approach is demonstrated by extensive evaluation and comparison with some state-of-the-art methods.

Adaptation and serial choice bias are unaltered in autism

Autism Spectrum Disorder (ASD) or autism is characterized by social and non-social symptoms, including sensory hyper- and hyposensitivities. A suggestion has been put forward that some of these symptoms could be explained by differences in how sensory information is integrated with its context, including a lower tendency to leverage the past in the processing of new perceptual input. At least two history-dependent effects of opposite directions have been described in the visual perception literature: a repulsive adaptation effect, where perception of a stimulus is biased away from an adaptor stimulus, and an attractive serial choice bias, where perceptual choices are biased towards the previous choice. In this study, we investigated whether autistic participants differed in either bias from typically developing controls (TD). Sixty-four adolescent participants (31 with ASD, 33 TD) were asked to categorize oriented line stimuli in two tasks which were designed so that we would induce either adaptation or serial choice bias. Although our tasks successfully induced both biases, in comparing the two groups, we found no differences in the magnitude of adaptation nor in the modulation of perceptual choices by the previous choice. In conclusion, we find no evidence of a decreased integration of the past in visual perception of autistic individuals.

Decision-Level Fusion with a Pluginable Importance Factor Generator for Remote Sensing Image Scene Classification

Remote sensing image scene classification acts as an important task in remote sensing image applications, which benefits from the pleasing performance brought by deep convolution neural networks (CNNs). When applying deep models in this task, the challenges are, on one hand, that the targets with highly different scales may exist in the image simultaneously and the small targets could be lost in the deep feature maps of CNNs; and on the other hand, the remote sensing image data exhibits the properties of high inter-class similarity and high intra-class variance. Both factors could limit the performance of the deep models, which motivates us to develop an adaptive decision-level information fusion framework that can incorporate with any CNN backbones. Specifically, given a CNN backbone that predicts multiple classification scores based on the feature maps of different layers, we develop a pluginable importance factor generator that aims at predicting a factor for each score. The factors measure how confident the scores in different layers are with respect to the final output. Formally, the final score is obtained by a class-wise and weighted summation based on the scores and the corresponding factors. To reduce the co-adaptation effect among the scores of different layers, we propose a stochastic decision-level fusion training strategy that enables each classification score to randomly participate in the decision-level fusion. Experiments on four popular datasets including the UC Merced Land-Use dataset, the RSSCN 7 dataset, the AID dataset, and the NWPU-RESISC 45 dataset demonstrate the superiority of the proposed method over other state-of-the-art methods.

The First Evidence of the Beneficial Effects of Se-Supplementation on In Vitro Cultivated Olive Tree Explants

Selenium is an essential micronutrient that provides important benefits to plants and humans. At proper concentrations, selenium increases plant growth, pollen vitality, the shelf life of fresh products, and seems to improve stress resistance; these effects can certainly be attributed to its direct and indirect antioxidant capacity. For these reasons, in the present work, the effects of selenium at different dosages on in vitro cultivated olive explants were investigated to observe possible positive effects (in terms of growth and vigor) on the proliferation phase. The work was carried out on four different olive cultivars: “San Felice”, “Canino”, “Frantoio”, and “Moraiolo”. The explants were cultured in aseptic conditions on olive medium (OM), with the addition of 4 mg·L−1 of zeatin, 30 g·L−1 of sucrose, and 7 g·L−1 of agar. The experimental scheme included a comparison between explants grown with five different concentrations of Na2SeO4 (0, 10, 20, 40, and 80 mg L−1) added to the medium during three successive subcultures. Interesting information has emerged from the results and all varieties responded to different concentrations of Selenium. The optimal Se dosages varied for each cultivar, but in general, Se concentration between 10 and 40 mg L−1 increased fresh and dry weight of the explants and shoot lengths. Se treatment induced in all cultivars and for all dosages used an increase in total Se content in proliferated explants. Furthermore, as the subcultures proceeded, the ability of the explants to absorb Se did not diminish. The Se content ranged from 8.55 to 114.21 µg kg−1 plant DW in ‘Frantoio’, from 9.83 to 94.85 µg kg−1 plant DW in ‘Moraiolo’, from 19.84 to 114.21 µg kg−1 plant DW in ‘Canino’, and from 20.97 to 95.54 µg kg−1 plant DW in ‘San Felice’. In general, the effect of selenium tends to decrease with the progress of subcultures and this suggests a sort of “adaptation” effect of the explants to its presence. The present study highlights for the first time the possibility of using in vitro cultures as biotechnological support to study supplementation with selenium and its effects on in vitro olive plant growth.

Evidence For Selective Adaptation and Recalibration in the Perception of Lexical Stress

Individuals vary in how they produce speech. This variability affects both the segments (vowels and consonants) and the suprasegmental properties of their speech (prosody). Previous literature has demonstrated that listeners can adapt to variability in how different talkers pronounce the segments of speech. This study shows that listeners can also adapt to variability in how talkers produce lexical stress. Experiment 1 demonstrates a selective adaptation effect in lexical stress perception: repeatedly hearing Dutch trochaic words biased perception of a subsequent lexical stress continuum towards more iamb responses. Experiment 2 demonstrates a recalibration effect in lexical stress perception: when ambiguous suprasegmental cues to lexical stress were disambiguated by lexical orthographic context as signaling a trochaic word in an exposure phase, Dutch participants categorized a subsequent test continuum as more trochee-like. Moreover, the selective adaptation and recalibration effects generalized to novel words, not encountered during exposure. Together, the experiments demonstrate that listeners also flexibly adapt to variability in the suprasegmental properties of speech, thus expanding our understanding of the utility of listener adaptation in speech perception. Moreover, the combined outcomes speak for an architecture of spoken word recognition involving abstract prosodic representations at a prelexical level of analysis.

Clinical predictors of cybersickness in virtual reality (VR) among highly stressed people

The use of virtual reality (VR) in the treatment of psychiatric disorders is increasing, and cybersickness has emerged as an important obstacle to overcome. However, the clinical factors affecting cybersickness are still not well understood. In this study, we investigated clinical predictors and adaptation effect of cybersickness during VR application in highly stressed people. Eighty-three healthy adult participants with high stress level were recruited. At baseline, we conducted psychiatric, ophthalmologic, and otologic evaluations and extracted physiological parameters. We divided the participants into two groups according to the order of exposure to VR videos with different degrees of shaking and repetitively administered the Simulator Sickness Questionnaire (SSQ) and the Fast Motion sickness Scale (FMS). There was no significant difference in changes in the SSQ or the FMS between groups. The 40–59 years age group showed a greater increase in FMS compared to the 19–39 years age group. Smoking was negatively associated with cybersickness, and a high Positive Affect and Negative Affect Schedule score was positively associated with cybersickness. In conclusion, changing the intensity of shaking in VR did not affect cybersickness. While smoking was a protective factor, more expression of affect was a risk factor for cybersickness.

Distinct Brain Mechanisms for Conflict Adaptation Within and Across Conflict Types

Cognitive conflict, like other cognitive processes, shows the characteristic of adaptation, i.e., conflict effects are attenuated when immediately following a conflicting event, a phenomenon known as the conflict adaptation effect (CAE). One important aspect of CAE is its sensitivity to the intertrial coherence of conflict type, i.e., behavioral CAE occurs only if consecutive trials are of the same conflict type. Although reliably observed behaviorally, the neural mechanisms underlying such a phenomenon remains elusive. With a paradigm combining the classic Simon task and Stroop task, this fMRI study examined neural correlates of conflict adaptation both within and across conflict types. The results revealed that when the conflict type repeated (but not when it alternated), the CAE-like neural activations were observed in dorsal anterior cingulate cortex, inferior frontal gyrus, superior parietal lobe, etc. (i.e., regions within typical task-positive networks). In contrast, when the conflict type alternated (but not when it repeated), we found CAE-like neural deactivations in a range of regions including bilateral superior and medial frontal gyri, bilateral angular cortex, bilateral temporal cortices, etc. (i.e., regions within the typical task-negative network). Moreover, this CAE-like neural deactivation predicts behavior performance. Network analyses suggested that these regions (for CAE-like neural activities within and across conflict type[s] respectively) can be clustered into two antagonistic networks. This evidence suggests that our adaptation to cognitive conflicts within a conflict type and across different types may rely on these two distinct neural mechanisms.

Adaptive gearshift control of wet dual clutch transmission based on extended state observer and H∞ robust control

The clutch characteristics of dual clutch transmission (DCT) will change as the service time increases, which will lead to the deterioration of gearshift performance. To reduce the influence of the change in clutch characteristics on the gearshift performance, an adaptive gearshift control method based on the extended state observer and H∞ robust control is proposed. First, the gearshift problem of the DCT is transformed into the reference trajectory tracking problem, and the gearshift reference trajectory is designed using the minimum principle. The uncertain term related to the change in clutch characteristics in the DCT gearshift dynamic model is defined, and an extended state observer is designed to estimate the uncertain term. On this basis, the gearshift controller is designed using the backstepping method, and H∞ robust control is introduced to further improve the adaptation effect of the controller, then the adaptive control laws of the clutch pressure and engine torque are obtained. Finally, the adaptation effect of the proposed method was verified by both simulation and experiment. The results show that the proposed adaptive gearshift control method can effectively avoid the gearshift delay caused by the change in clutch characteristics, and the gearshift jerk in the simulation and experiment is reduced by 55.01% and 34.8%, respectively.