Evidence of a reduced role for circumscribed interests in the social attention patterns of children with Autism Spectrum Disorder

Reduced social attention is characteristic of Autism Spectrum Disorder (ASD). It has been suggested to result from an early onset and excessive influence of circumscribed interests (CIs) on gaze behaviour, compared to typically developing (TYP) individuals. To date, these findings have been mixed. The current eye-tracking study utilised a visual preference paradigm to investigate the influence of CI versus non-CI objects on attention patterns in children with ASD (aged 3-12 years, n = 37) and their age-matched TYP peers (n = 30). Compared to TYP, social and object attention was reduced in the ASD group irrespective of the presence of CIs. Results suggest a reduced role for CIs and extend recent evidence of atypical attention patterns across social and non-social domains in ASD.

LOANet: a lightweight network using object attention for extracting buildings and roads from UAV aerial remote sensing images

Semantic segmentation for extracting buildings and roads from uncrewed aerial vehicle (UAV) remote sensing images by deep learning becomes a more efficient and convenient method than traditional manual segmentation in surveying and mapping fields. In order to make the model lightweight and improve the model accuracy, a lightweight network using object attention (LOANet) for buildings and roads from UAV aerial remote sensing images is proposed. The proposed network adopts an encoder-decoder architecture in which a lightweight densely connected network (LDCNet) is developed as the encoder. In the decoder part, the dual multi-scale context modules which consist of the atrous spatial pyramid pooling module (ASPP) and the object attention module (OAM) are designed to capture more context information from feature maps of UAV remote sensing images. Between ASPP and OAM, a feature pyramid network (FPN) module is used to fuse multi-scale features extracted from ASPP. A private dataset of remote sensing images taken by UAV which contains 2431 training sets, 945 validation sets, and 475 test sets is constructed. The proposed basic model performs well on this dataset, with only 1.4M parameters and 5.48G floating point operations (FLOPs), achieving excellent mean Intersection-over-Union (mIoU). Further experiments on the publicly available LoveDA and CITY-OSM datasets have been conducted to further validate the effectiveness of the proposed basic and large model, and outstanding mIoU results have been achieved. All codes are available on https://github.com/GtLinyer/LOANet.

The resonant brain: How attentive conscious seeing regulates action sequences that interact with attentive cognitive learning, recognition, and prediction

This article describes mechanistic links that exist in advanced brains between processes that regulate conscious attention, seeing, and knowing, and those that regulate looking and reaching. These mechanistic links arise from basic properties of brain design principles such as complementary computing, hierarchical resolution of uncertainty, and adaptive resonance. These principles require conscious states to mark perceptual and cognitive representations that are complete, context sensitive, and stable enough to control effective actions. Surface-shroud resonances support conscious seeing and action, whereas feature-category resonances support learning, recognition, and prediction of invariant object categories. Feedback interactions between cortical areas such as peristriate visual cortical areas V2, V3A, and V4, and the lateral intraparietal area (LIP) and inferior parietal sulcus (IPS) of the posterior parietal cortex (PPC) control sequences of saccadic eye movements that foveate salient features of attended objects and thereby drive invariant object category learning. Learned categories can, in turn, prime the objects and features that are attended and searched. These interactions coordinate processes of spatial and object attention, figure-ground separation, predictive remapping, invariant object category learning, and visual search. They create a foundation for learning to control motor-equivalent arm movement sequences, and for storing these sequences in cognitive working memories that can trigger the learning of cognitive plans with which to read out skilled movement sequences. Cognitive-emotional interactions that are regulated by reinforcement learning can then help to select the plans that control actions most likely to acquire valued goal objects in different situations. Many interdisciplinary psychological and neurobiological data about conscious and unconscious behaviors in normal individuals and clinical patients have been explained in terms of these concepts and mechanisms.

Selecting category specific visual information: Top-down and bottom-up control of object based attention

The ability to select, within the complexity of sensory input, the information most relevant for our purposes is influenced by both internal settings (i.e., top-down control) and salient features of external stimuli (i.e., bottom-up control). We here investigated using fMRI the neural underpinning of the interaction of top-down and bottom-up processes, as well as their effects on extrastriate areas processing visual stimuli in a category-selective fashion. We presented photos of bodies or buildings embedded into frequency-matched visual noise to the subjects. Stimulus saliency changed gradually due to an altered degree to which photos stood-out in relation to the surrounding noise (hence generating stronger bottom-up control signals). Top-down settings were manipulated via instruction: participants were asked to attend one stimulus category (i.e., "is there a body?" or "is there a building?"). Highly salient stimuli that were inconsistent with participants' attentional top-down template activated the inferior frontal junction and dorsal parietal regions bilaterally. Stimuli consistent with participants' current attentional set additionally activated insular cortex and the parietal operculum. Furthermore, the extrastriate body area (EBA) exhibited increased neural activity when attention was directed to bodies. However, the latter effect was found only when stimuli were presented at intermediate saliency levels, thus suggesting a top-down modulation of this region only in the presence of weak bottom-up signals. Taken together, our results highlight the role of the inferior frontal junction and posterior parietal regions in integrating bottom-up and top-down attentional control signals.