Task-set switching with natural scenes: Measuring the cost of deploying top-down attention

In many everyday situations, we bias our perception from the top down, based on a task or an agenda. Frequently, this entails shifting attention to a specific attribute of a particular object or scene. To explore the cost of shifting top-down attention to a different stimulus attribute, we adopt the task-set switching paradigm, in which switch trials are contrasted with repeat trials in mixed-task blocks and with single-task blocks. Using two tasks that relate to the content of a natural scene in a gray-level photograph and two tasks that relate to the color of the frame around the image, we were able to distinguish switch costs with and without shifts of attention. We found a significant cost in reaction time of 23–31 ms for switches that require shifting attention to other stimulus attributes, but no significant switch cost for switching the task set within an attribute. We conclude that deploying top-down attention to a different attribute incurs a significant cost in reaction time, but that biasing to a different feature value within the same stimulus attribute is effortless

The use of joint attention in the naturalistic setting in children with Autism Spectrum Disorder

This study investigates the deficits in the quantity and quality of joint attention in children with Autism Spectrum Disorder (ASD). To obtain a holistic measure of joint attention, the following four aspects were considered: a) the quantity of Initiation Joint Attention (IJA) and Response Joint Attention (RJA), b) non-verbal behaviours which were atypically used during joint attention, c) the quality of joint attention and d) the association between quality and quantity of joint attention in children with ASD. These aspects were measured in three children with ASD and three typically-developing children (TDC). Measures were derived from 30-minute video recordings of a play session between each child and his/her caregiver and compared. This study established that there was a statistically significant difference in the quantity of joint attention in both IJA and RJA. The difference in the quality of joint attention was not statistically significant. However, when analysing children with ASD individually, a deficit in the quality of joint attention was identified in two of the three subjects. Compared to TDC, children with ASD engaged significantly less in IJA through manipulation of objects and eye-gaze and significantly more in IJA and RJA through challenging behaviour. In addition, there was no association between the deficits in quality and quantity of joint attention within individuals with ASD, as the three subjects portrayed diverse profiles. Children with ASD exhibited atypical joint attention skills when compared to the control group. Moreover, the frequency of initiations of joint attention bids was the most negatively affected aspect in children with ASD. Quality of joint attention is rarely researched and to the researchers’ knowledge, no other study has measured both quality and quantity of joint attention in children with ASDpeer-reviewe

Efficient Attention: Attention with Linear Complexities

Dot-product attention has wide applications in computer vision and natural language processing. However, its memory and computational costs grow quadratically with the input size. Such growth prohibits its application on high-resolution inputs. To remedy this drawback, this paper proposes a novel efficient attention mechanism equivalent to dot-product attention but with substantially less memory and computational costs. Its resource efficiency allows more widespread and flexible integration of attention modules into a network, which leads to better accuracies. Empirical evaluations demonstrated the effectiveness of its advantages. Efficient attention modules brought significant performance boosts to object detectors and instance segmenters on MS-COCO 2017. Further, the resource efficiency democratizes attention to complex models, where high costs prohibit the use of dot-product attention. As an exemplar, a model with efficient attention achieved state-of-the-art accuracies for stereo depth estimation on the Scene Flow dataset. Code is available at https://github.com/cmsflash/efficient-attention.Comment: To appear at WACV 202