Accurate measurement of cognitive strategies is important in diverse areas of psychological research. Strategy self-reports are a common measure, but C. Thevenot, M. Fanget, and M. Fayol (2007) proposed a more objective method to distinguish different strategies in the context of mental arithmetic. In their operand recognition paradigm, speed of recognition memory for problem operands after solving a problem indexes strategy (e.g., direct memory retrieval vs. a procedural strategy). Here, in 2 experiments, operand recognition time was the same following simple addition or multiplication, but, consistent with a wide variety of previous research, strategy reports indicated much greater use of procedures (e.g., counting) for addition than multiplication. Operation, problem size (e.g., 2 + 3 vs. 8 + 9), and operand format (digits vs. words) had interactive effects on reported procedure use that were not reflected in recognition performance. Regression analyses suggested that recognition time was influenced at least as much by the relative difficulty of the preceding problem as by the strategy used. The findings indicate that the operand recognition paradigm is not a reliable substitute for strategy reports and highlight the potential impact of difficulty-related carryover effects in sequential cognitive tasks.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1037/a0022218 | DOI Listing |
Nanophotonics
May 2024
Microelectronics Research Center, The University of Texas at Austin, Austin, TX 78758, USA.
Optical neural networks (ONNs) are promising hardware platforms for next-generation neuromorphic computing due to their high parallelism, low latency, and low energy consumption. However, previous integrated photonic tensor cores (PTCs) consume numerous single-operand optical modulators for signal and weight encoding, leading to large area costs and high propagation loss to implement large tensor operations. This work proposes a scalable and efficient optical dot-product engine based on customized multi-operand photonic devices, namely multi-operand optical neuron (MOON).
View Article and Find Full Text PDFPsychol Res
November 2024
Department of Psychology, Potsdam University, Potsdam, Germany.
Mental arithmetic is widely studied, both with symbolic digits and with non-symbolic dot patterns that require operand estimation. Several studies reported surprising biases in adults' performance with both formats while their direction (over/underestimation in addition/subtraction) remains controversial (operational momentum effect or OM; Prado & Knops, Prado and Knops, Psychonomic Bulletin & Review, in Press., 2024).
View Article and Find Full Text PDFJ Colloid Interface Sci
November 2024
State Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
Ultralight graphene aerogels have gained extensive recognition in the impact protection field. However, attaining both elasticity and durability at low material density is challenging due to their intrinsic conflicts. Inspired by the mantis ootheca, we present a simultaneous improvement in the elasticity, durability, and density restrictions of ultralight graphene aerogels via constructing a multiscale honeycomb microstructure (MHM) within the graphene skeleton.
View Article and Find Full Text PDFMathematical entity recognition is essential for machines to define and illustrate mathematical substance faultlessly and to facilitate sufficient mathematical operations and reasoning. As mathematical entity recognition in the Bangla language is novel, to our best knowledge, there is no available dataset exists in any repository. In this paper, we present state of the art Bangla mathematical entity dataset containing 13,717 observations.
View Article and Find Full Text PDFHeliyon
February 2024
Department of Computer Science, Boise State University, Boise, ID, USA.
Mathematical entity recognition is indispensable for machines to accurately explain and depict mathematical content and to enable adequate mathematical operations and reasoning. It expedites automated theorem proving, speeds up the analysis and retrieval of mathematical knowledge from documents, and improves e-learning and educational platforms. It also simplifies translation, scientific research, data analysis, interpretation, and the practical application of mathematical information.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!