Comparison of cortical activation during subtraction in mental calculation and with a calculator

Several studies have shown that various types of cognitive processing exist and exert different effects on brain activity. However, when a subject performs the same task, whether the task involves processing or not, such as in mental calculation or with a calculator, the different influences on the brain remain unclear. The purpose of this study was to examine whether the influence of cortical activation when performing mental calculation and using a calculator have different effects on the brain. Fifteen healthy, right-handed participants (mean age, 26.3 ± 8.5 years; 12 men, 27.7 ± 9.0 years; 3 women, 20.6 ± 1.1 years) were recruited as subjects. We measured oxygenated hemoglobin (oxy-Hb) levels while subjects performed subtraction tasks by mental calculation or using a calculator (3 min each). Measurements were made at the frontal lobe and temporal lobe. In both lobes, oxy-Hb level was significantly increased during mental calculation. Locations showing significantly increased oxy-Hb in mental calculation were the prefrontal cortex in the frontal lobe and supramarginal gyrus in the temporal lobe. These results suggest that the brain responds differently to tasks in mental calculation and using a calculator. We hypothesized that using the electronic calculator needs fewer neural networks than performing mental calculation. In recent years, thanks to the development of machines, many tasks have been automated, making our lives easier and more convenient. Our results may provide one example that the developments of modern technology influence brain function.ArticleBiochemistry & Analytical Biochemistry.4(3):185(2015)journal articl

Recognition Technology for Four Arithmetic Operations

Numeral recognition is an important research direction in field of pattern recognition, and it has broad application prospects. Aiming at four arithmetic operations of general printed formats, this article adopts a multiple hybrid recognition method and is applied to automatically calculating. This method mainly uses BP neural network and template matching method to distinguish the numerals and operators, in order to increase the operation speed and recognition accuracy. Sample images of four arithmetic operations are extracted from printed books, and they are used for testing the performance of proposed recognition method. The experiments show that the method provides correct recognition rate of 96% and correct calculation rate of 89%

Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models

Creating learning models that can exhibit sophisticated reasoning skills is one of the greatest challenges in deep learning research, and mathematics is rapidly becoming one of the target domains for assessing scientific progress in this direction. In the past few years there has been an explosion of neural network architectures, data sets, and benchmarks specifically designed to tackle mathematical problems, reporting notable success in disparate fields such as automated theorem proving, numerical integration, and discovery of new conjectures or matrix multiplication algorithms. However, despite these impressive achievements it is still unclear whether deep learning models possess an elementary understanding of quantities and symbolic numbers. In this survey we critically examine the recent literature, concluding that even state-of-the-art architectures often fall short when probed with relatively simple tasks designed to test basic numerical and arithmetic knowledge

ENGINEERING MATHEMATICS EDUCATION WITH COMPUTER ALGEBRA: THE MATLAB ALTERNATIVE

Computer algebra systems have become an important tool for many engineering and technical professionals. There is a growing need to incorporate such tools into the education of such professionals. This paper discusses these systems and their role within engineering mathematics in higher education. Some advantages and problems associated with computer algebra are highlighted and illustrated using MATLAB

Automated Oracle Generation via Denotational Semantics

Software failure detection is typically done by comparing the running behaviors from a software under test (SUT) against its expected behaviors, called test oracles. In this paper, we present a formal approach to specifying test oracles in denotational semantics for systems with structured inputs. The approach introduces formal semantic evaluation rules, based on the denotational semantics methodology, defined on each productive grammar rule. We extend our grammar-based test generator, GENA, with automated test oracle generation. We provide three case studies of software testing: (i) a benchmark of Java programs on arithmetic calculations, (ii) an open source software on license identification, and (ii) selenium-based web testing. Experimental results demonstrate the effectiveness of our approach and illustrate the success of the application on the software testing

Bidirectional syntactic priming across cognitive domains: from arithmetic to language and back

Scheepers et al. (2011) showed that the structure of a correctly solved mathematical equation affects how people subsequently complete sentences containing high vs. low relative-clause attachment ambiguities. Here we investigated whether such effects generalise to different structures and tasks, and importantly, whether they also hold in the reverse direction (i.e., from linguistic to mathematical processing). In a questionnaire-based experiment, participants had to solve structurally left- or right-branching equations (e.g., 5 × 2 + 7 versus 5 + 2 × 7) and to provide sensicality ratings for structurally left- or right-branching adjective-noun-noun compounds (e.g., alien monster movie versus lengthy monster movie). In the first version of the experiment, the equations were used as primes and the linguistic expressions as targets (investigating structural priming from maths to language). In the second version, the order was reversed (language-to-maths priming). Both versions of the experiment showed clear structural priming effects, conceptually replicating and extending the findings from Scheepers et al. (2011). Most crucially, the observed bi-directionality of cross-domain structural priming strongly supports the notion of shared syntactic representations (or recursive procedures to generate and parse them) between arithmetic and language