Using A Deep Learning-based Visual Computational Model to Identify Cognitive Strategies in Matrix Reasoning

Two cognitive strategies (i.e., constructive matching and response elimination strategies) have been found in responding to items in Raven's Advanced Progressive Matrices (APM), a valid measurement instrument of general intelligence. Identifying strategies is necessary for conducting studies on the relationship between cognitive strategy and other cognitive factors and for cognitive strategy training. This study trained a convolutional neural network-based visual computational model (CVC) for cognitive strategy identification based on eye movement images. Focusing on the APM, the trained CVC can be used for strategy identification by learning and mining the pattern information in the eye movement images with predefined training labels from a psychometric model. An empirical study was conducted to illustrate the training and application of the CVC. Based on the trained CVC, the study's main finding is that (a) the strategy identification results of the CVC and the psychometric model have a high agreement, and (b) the agreement of the former with the identification results of experts was higher than that of the latter with the identification results of experts. Overall, the proposed deep learning-based model follows the data-driven perspective and provides a new way of studying cognitive strategy in the APM by presenting objective and quantitative identification results

Using A Multi-Strategy Eye-Tracking Psychometric Model to Measure Intelligence and Identify Cognitive Strategy in Raven’s Advanced Progressive Matrices

Raven’s Advanced Progressive Matrices (APM) is a valid measurement instrument of general intelligence, and previous studies have found that participants use two cognitive strategies—constructive matching and response elimination—in responding to items in the APM. This study proposed a multi-strategy psychometric model incorporating eye-tracking measures, including but not limited to the proportional time on matrix area (PTM), the rate of toggling (ROT), and the rate of latency to first toggle (RLT). Our model has the ability to measure each participant’s intelligence and identify the cognitive strategy used by each participant for each item in the APM. An eye-tracking-based APM study using the model revealed several outcomes: (1) The effects of the three eye-tracking measures on the constructive matching strategy selection probability were positive and presented in the following order: PTM > RLT > ROT, and the effect of ROT was negligible. (2) The average intelligence of participants who used the constructive matching strategy was higher than that of participants who used the response elimination strategy, and participants with higher intelligence were more likely to use the constructive matching strategy. (3) High-intelligence participants increased their use of the constructive matching strategy as item difficulty increased, whereas low-intelligence participants decreased their use as item difficulty increased. (4) Participants took significantly less time to use the constructive matching strategy than to use the response elimination strategy. Overall, the proposed model follows the theory-driven modeling logic and provides a new way of studying cognitive strategy in the APM by presenting objective and quantitative results

Spatial–Temporal Evolution Patterns and Regulatory Strategies for Land Resource Carrying Capacity of China’s Major Grain-Producing Areas

Research on land resource carrying capacity (LRCC) focuses on the population that regional land resources can support as well as the grain output they can deliver. China’s major grain-producing areas consist of 13 provinces, and the grain produced in these areas makes up 75% of the country’s gross grain output. To boost the land carrying capacity of major grain-producing areas and to ensure national food security, it is crucial to examine the spatial–temporal evolution patterns of LRCC and to devise optimal regulatory strategies. From the perspective of human–grain relationships, this paper looks into the evolutionary features of the spatial–temporal patterns of the LRCC of China’s major grain-producing areas based on a land resource carrying capacity model, a land resource carrying capacity index model, and a land resource limitation model. We obtain three main results: (1) On the temporal scale, the land resource carrying capacity index (LRCCI) of China’s major grain-producing areas as a whole tapered off over a period from 1980 to 2020, whereas the overall LRCC increased in this period, indicating that the human–grain relationship in China’s major grain-producing areas is improving. (2) On a spatial scale, China’s major grain-producing areas ranked by LRCC from the greatest to the lowest, in 2020, were North China, the middle and lower reaches of the Yangtze River, Northeast China, and other regions. In terms of the carrying state of land resources, provinces with grain surpluses significantly rose during 1980–2020, the growth of LRCC of the aforementioned four major regions markedly slowed down in 2015–2020, and a large gap exists in LRCCI between the 13 provinces, revealing an unbalanced, insufficient development of LRCC in each province. (3) From 2000 to 2020, the limit of land resources on population aggregation in most major grain-producing areas was negative, and its absolute value continued to increase; this suggests that the land resources of major grain-producing provinces set small limits on population aggregation, with great potential for increasing LRCC. Taking into account the research results, this paper gives strategies for regulating the LRCC of China’s major grain-producing areas in a bid to further augment the human–grain carrying capacity of land resources in China’s major grain-producing areas and to guarantee national food security

Design of dual-diameter nanoholes for efficient solar-light harvesting

A dual-diameter nanohole (DNH) photovoltaic system is proposed, where a top (bottom) layer with large (small) nanoholes is used to improve the absorption for the short-wavelength (long-wavelength) solar incidence, leading to a broadband light absorption enhancement. Through three-dimensional finite-element simulation, the core device parameters, including the lattice constant, nanohole diameters, and nanohole depths, are engineered in order to realize the best light-matter coupling between nanostructured silicon and solar spectrum. The designed bare DNH system exhibits an outstanding absorption capability with a photocurrent density (under perfect internal quantum process) predicted to be 27.93 mA/cm(2), which is 17.39%, 26.17%, and over 100% higher than the best single-nanohole (SNH) system, SNH system with an identical Si volume, and equivalent planar configuration, respectively. Considering the fabrication feasibility, a modified DNH system with an anti-reflection coating and back silver reflector is examined by simulating both optical absorption and carrier transport in a coupled way in frequency and three-dimensional spatial domains, achieving a light-conversion efficiency of 13.72%. PACS: 85.60.-q; Optoelectronic device; 84.60.Jt; Photovoltaic conversio

Broadband and wide-angle light harvesting by ultra-thin silicon solar cells with partially embedded dielectric spheres

We propose a design of crystalline silicon thin-film solar cells (c-Si TFSCs, 2 mu m-thick) configured with partially embedded dielectric spheres on the light-injecting side. The intrinsic light trapping and photoconversion are simulated by the complete optoelectronic simulation. It shows that the embedding depth of the spheres provides an effective way to modulate and significantly enhance the optical absorption. Compared to the conventional planar and front sphere systems, the optimized partially embedded sphere design enables a broadband, wide-angle, and strong optical absorption and efficient carrier transportation. Optoelectronic simulation predicts that a 2 mu m-thick c-Si TFSC with half-embedded spheres shows an increment of more than 10 mA/cm(2) in short-circuit current density and an enhancement ratio of more than 56% in light-conversion efficiency, compared to the conventional planar counterparts. (C) 2016 Optical Society of Americ