Working memory is a core cognitive function that supports goal-directed behavior and complex thought.We developed a spatial working memory and attention test on paired symbols(SWAPS)which has been proved to be a usefu...Working memory is a core cognitive function that supports goal-directed behavior and complex thought.We developed a spatial working memory and attention test on paired symbols(SWAPS)which has been proved to be a useful and valid tool for spatial working memory and attention studies in the fields of cognitive psychology,education,and psychiatry.The repeated administration of working memory capacity tests is common in clinical and research settings.Studies suggest that repeated cognitive tests may improve the performance scores also known as retest effects.The systematic investigation of retest effects in SWAPS is critical for interpreting scientific results,but it is still not fully developed.To address this,we recruited 77 college students aged 18–21 years and used SWAPS comprising 72 trials with different memory loads,learning time,and delay span.We repeated the test once a week for five weeks to investigate the retest effects of SWAPS.There were significant retest effects in the first two tests:the accuracy of the SWAPS tests significantly increased,and then stabilized.These findings provide useful information for researchers to appropriately use or interpret the repeated working memory tests.Further experiments are still needed to clarify the factors that mediate the retest effects,and find out the cognitive mechanism that influences the retest effects.展开更多
Heuristic and metaheuristic techniques are used for solving computationally hard optimization problems. Local search is a heuristic technique while Ant colony optimization (ACO), inspired by the ants' foraging beh...Heuristic and metaheuristic techniques are used for solving computationally hard optimization problems. Local search is a heuristic technique while Ant colony optimization (ACO), inspired by the ants' foraging behavior, is one of the most recent metaheuristic technique. These techniques are used for solving optimization problems. Multiple-Input Multiple-Output (MIMO) detection problem is an NP-hard combinatorial optimization problem. We present heuristic and metaheuristic approaches for symbol detection in multi-input multi-output (MIMO) system. Since symbol detection is an NP-hard problem so ACO is particularly attractive as ACO algorithms are one of the most successful strands of swarm intelligence and are suitable for applications where low complexity and fast convergence is of absolute importance. Maximum Likelihood (ML) detector gives optimal results but it uses exhaustive search technique. We show that 1-Opt and ACO based detector can give near-optimal bit error rate (BER) at much lower complexity levels. Comparison of ACO with another nature inspired technique, Particle Swarm Optimization (PSO) is also discussed. The simulation results suggest that the proposed detectors give an acceptable performance complexity trade-off in comparison with ML and VBLAST detectors.展开更多
基金the National Natural Science Foundation of China(No.91632103)the Shanghai Education Commission Research and Innovation Program(No.2019-01-07-00-02-E00037)+2 种基金the Program of Shanghai Subject Chief Scientist(No.17XD1401700)the Higher Education Disciplinary Innovation Programthe“Eastern Scholar”Project。
文摘Working memory is a core cognitive function that supports goal-directed behavior and complex thought.We developed a spatial working memory and attention test on paired symbols(SWAPS)which has been proved to be a useful and valid tool for spatial working memory and attention studies in the fields of cognitive psychology,education,and psychiatry.The repeated administration of working memory capacity tests is common in clinical and research settings.Studies suggest that repeated cognitive tests may improve the performance scores also known as retest effects.The systematic investigation of retest effects in SWAPS is critical for interpreting scientific results,but it is still not fully developed.To address this,we recruited 77 college students aged 18–21 years and used SWAPS comprising 72 trials with different memory loads,learning time,and delay span.We repeated the test once a week for five weeks to investigate the retest effects of SWAPS.There were significant retest effects in the first two tests:the accuracy of the SWAPS tests significantly increased,and then stabilized.These findings provide useful information for researchers to appropriately use or interpret the repeated working memory tests.Further experiments are still needed to clarify the factors that mediate the retest effects,and find out the cognitive mechanism that influences the retest effects.
文摘Heuristic and metaheuristic techniques are used for solving computationally hard optimization problems. Local search is a heuristic technique while Ant colony optimization (ACO), inspired by the ants' foraging behavior, is one of the most recent metaheuristic technique. These techniques are used for solving optimization problems. Multiple-Input Multiple-Output (MIMO) detection problem is an NP-hard combinatorial optimization problem. We present heuristic and metaheuristic approaches for symbol detection in multi-input multi-output (MIMO) system. Since symbol detection is an NP-hard problem so ACO is particularly attractive as ACO algorithms are one of the most successful strands of swarm intelligence and are suitable for applications where low complexity and fast convergence is of absolute importance. Maximum Likelihood (ML) detector gives optimal results but it uses exhaustive search technique. We show that 1-Opt and ACO based detector can give near-optimal bit error rate (BER) at much lower complexity levels. Comparison of ACO with another nature inspired technique, Particle Swarm Optimization (PSO) is also discussed. The simulation results suggest that the proposed detectors give an acceptable performance complexity trade-off in comparison with ML and VBLAST detectors.
