EXPRESS: Lending a Hand to Storytelling: Gesture’s Effects on Narrative Comprehension Moderated by Task Difficulty and Cognitive Ability

Compelling evidence suggests observing iconic gestures benefits learning. While emerging evidence suggests typical iconic gestures benefit comprehension to a greater extent than atypical iconic gestures, it is unclear precisely when and for whom these gestures will be most helpful. The current study investigated factors that may moderate when and for whom gesture benefits narrative comprehension most, including the type of gesture, task difficulty, and individual differences in cognitive ability. Participants were shown a video narrative in which they observed either typical gestures (commonly produced gestures, highly semantically related to accompanying speech), atypical gestures (gestures that are seldom produced), or no gestures. The video narrative was either viewed with interference (background noise to increase task difficulty) or no interference (no background noise). To determine whether the effects of gesture observation and externally imposed task difficulty on narrative comprehension further depend on an individual’s cognitive abilities, participants completed four measures of cognitive abilities (immediate and delayed non-verbal memory, attention, and intellectual ability). Observing typical gestures significantly benefited narrative comprehension compared to atypical and no gestures combined, which did not differ significantly. Participants with below average and average levels of delayed non-verbal memory benefited more from typical gestures than atypical or no gestures compared to those with an above average level of delayed non-verbal memory. However, this interaction was only significant when the task was difficult (i.e., with interference) but not when the task was simple (i.e., no interference). This finding suggests that the type of iconic gesture observed may impact gesture’s beneficial effect on narrative comprehension, and that such gestures may be more beneficial in difficult tasks, but only for certain individuals.

Research on Data Acquisition Algorithms Based on Image Processing and Artificial Intelligence

At present, image recognition processing technology has been playing a decisive role in the field of pattern recognition, of which automatic recognition of bank notes is an important research topic. Due to the limitation of the size of bill layout and printing method, many invoice layouts are not clear, skewed or distorted, and even there are irregular handwritten signature contents, which lead to the problem of recognition of digital characters on bill surface. In this regard, this paper proposes a data acquisition and recognition algorithm based on improved BP neural network for ticket number identification, which is based on the theory of image processing and recognition, combined with improved bill information recognition technology. First, in the pre-processing stage of bill image, denoising and graying of bill image are processed. After binarization of bill image, the tilt detection method based on Bresenham integer algorithm is used to correct the tilted bill image. Secondly, character localization and feature extraction are carried out for par characters, and the target background is separated from the interference background in order to extract the desired target characters. Finally, the improved BP neural network-based bill digit data acquisition and recognition algorithm is used to realize the classification and recognition of bill characters. The experimental results show that the improved method has better classification and recognition effect than other data acquisition and recognition algorithms.

Experimental Study of Extracting Weak Infrared Signals of Rock Induced by Cyclic Loading under the Strong Interference Background

To understand the possibility of monitoring the crustal stress and tectonic activities via satellite remote sensing technology, an experimental study focused on the thermal infrared variation was performed for cyclic loaded rock in the outdoor condition with two types of strong interference background. The stress-induced infrared radiation was extracted using wavelet analysis. The results showed that due to the significant effect of the ambient temperature, the weak stress-induced infrared signal was indistinguishable from the original infrared radiation. However, after wavelet decomposition, the infrared radiation concurrent with the change in stress became clear, and the correlation coefficient with the stress increased significantly with the value of 0.91 after decomposition. Additionally, the amplitude of the extracted stress-induced infrared signal was close to the theoretical result, indicating that the wavelet analysis method can extract the weak infrared signals induced by cyclic loading in the background of strong interference to some degree. The results provide an experimental basis and ideas for monitoring crustal stress and tectonic activities using thermal infrared remote sensing.

Adaptive Radar Sensitivity Time Control Based on Linear Prediction Sea Clutter

Sea clutter is interference background in radar target processing. It is difficult to detect long-range weak target in sea clutter. Sea clutter signal changes complex, with high intensity. For close signal, radar receiver will overload and reach saturation point. We are interested in the most dynamic range for displaying target echo and fast changing component of clutter. By analyzing spatial and temporal correlation of sea clutter, slow changing component can be minus from sea clutter. The innovation of this paper is to propose a linear prediction error method for sea clutter to increase dynamic range of radar receiver. It prevents overloading and reaching saturation point at close range.