Biasing Influence of ‘Mental Shortcuts’ on Diagnostic Decision-Making: Radiologists Can Overlook Breast Cancer in Mammograms When Prior Diagnostic Information Is Available

When making decisions under uncertainty, people in all walks of life, including highly trained medical professionals, tend to resort to using ‘mental shortcuts’, or heuristics. Anchoring-and-adjustment (AAA) is a well-known heuristic in which subjects reach a judgment by starting from an initial internal judgment (‘anchored position’) based on available external information (‘anchoring information’) and adjusting it until they are satisfied. We studied the effects of the AAA heuristic during diagnostic decision-making in mammography. We provided practicing radiologists (N = 27 across two studies) a random number that we told them was the estimate of a previous radiologist of the probability that a mammogram they were about to see was positive for breast cancer. We then showed them the actual mammogram. We found that the radiologists’ own estimates of cancer in the mammogram reflected the random information they were provided and ignored the actual evidence in the mammogram. However, when the heuristic information was not provided, the same radiologists detected breast cancer in the same set of mammograms highly accurately, indicating that the effect was solely attributable to the availability of heuristic information. Thus, the effects of the AAA heuristic can sometimes be so strong as to override the actual clinical evidence in diagnostic tasks.

Path Planning for Construction Machinery Based on Improved Potential Field Ant Colony Algorithm

The path planning algorithm of unmanned construction machinery is studied, and the potential field ant colony algorithm is improved to be applied in the field of unmanned construction machinery. Firstly, the raster map modeling was optimized to eliminate the trap grid in the map. At the beginning of algorithm iteration, the heuristic information of artificial potential field method was added and the global pheromone updating model was improve the convergence speed of the algorithm. In addition, the weight coefficient of potential field force and local pheromone updating model were introduced to enhance the development of raster map in the later iteration of ant colony algorithm and reduce the influence of heuristic information of potential field force. Finally, the selection range of parameters such as optimal pheromone heuristic factor and ant colony number is determined by simulation, and it is verified that the algorithm is better than the basic ant colony algorithm.

Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Aiming at the problems of slow convergence, easy to fall into local optimum, and poor smoothness of traditional ant colony algorithm in mobile robot path planning, an improved ant colony algorithm based on path smoothing factor was proposed. Firstly, the environment map was constructed based on the grid method, and each grid was marked to make the ant colony move from the initial grid to the target grid for path search. Then, the heuristic information is improved by referring to the direction information of the starting point and the end point and combining with the turning angle. By improving the heuristic information, the direction of the search is increased and the turning angle of the robot is reduced. Finally, the pheromone updating rules were improved, the smoothness of the two-dimensional path was considered, the turning times of the robot were reduced, and a new path evaluation function was introduced to enhance the pheromone differentiation of the effective path. At the same time, the Max-Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid being trapped in the local optimum path. The simulation results show that the improved ant colony algorithm can search the optimal path length and plan a smoother and safer path with fast convergence speed, which effectively solves the global path planning problem of mobile robot.

INFORMATION PROCESSING – HEURISTIC VS SYSTEMATIC AND SUSCEPTIBILITY OF SHARING COVID 19-RELATED FAKE NEWS ON SOCIAL MEDIA

Social media has turned into a fertile ground for COVID-19 fake news. The present study aims to provide a hypothetical and empirical background to elucidate the psychological and behavioral aspects of information processing and susceptibility of sharing the fake news, with especial reference to COVID-19 news on social media. The study explores the relation between the select variables and heuristic and systematic information processing. Grounded on prior studies, this paper presents a research model to address susceptibility of sharing the fake news on social media, and identifies characteristics that may be more susceptible than others for sharing fake news on social media including Sharing Motivation (SM), Social Media Fatigue (SMF), Feel Good Factor (FGF), Fear of Missing out (FoMO), News Characteristics (NC) and five Big Personality Traits. The data collected from 244 respondents was analyzed with the help of IBM SPSS 23, using descriptive and statistical test, including means, standard deviations, and correlation analysis conducted. Correlation exploration was utilized to study the association between the select variables and systematic and heuristic information processing and susceptibility of sharing the fake news on social media. The findings show several factors contribute to information processing in both modes. The study confirms that heuristic processing is significantly associated with susceptibility of sharing fake news. The research adds to the media studies, behavioral and psychological disciplines, as it examines the relationships between the select variables and the systematic and heuristic information processing and COVID-19 fake news on social media. The present investigation makes an innovative and original contribution to media studies by exploring the relationship between select variables and susceptibility for sharing fake news on social media. The study presents a research model to identify the influence of select variables on information processing and the susceptibility to falling prey to fake news on social media and contributes to the domain to media studies.

Compound Heuristic Information Guided Policy Improvement for Robot Motor Skill Acquisition

Discovering the implicit pattern and using it as heuristic information to guide the policy search is one of the core factors to speed up the procedure of robot motor skill acquisition. This paper proposes a compound heuristic information guided reinforcement learning algorithm PI2-CMA-KCCA for policy improvement. Its structure and workflow are similar to a double closed-loop control system. The outer loop realized by Kernel Canonical Correlation Analysis (KCCA) infers the implicit nonlinear heuristic information between the joints of the robot. In addition, the inner loop operated by Covariance Matrix Adaptation (CMA) discovers the hidden linear correlations between the basis functions within the joint of the robot. These patterns which are good for learning the new task can automatically determine the mean and variance of the exploring perturbation for Path Integral Policy Improvement (PI2). Compared with classical PI2, PI2-CMA, and PI2-KCCA, PI2-CMA-KCCA can not only endow the robot with the ability to realize transfer learning of trajectory planning from the demonstration to the new task, but also complete it more efficiently. The classical via-point experiments based on SCARA and Swayer robots have validated that the proposed method has fast learning convergence and can find a solution for the new task.