Abdul-Rahman Anmar
Department of Ophthalmology, Counties Manukau DHB, Auckland, New Zealand.
Front Psychol. 2020 May 20;11:911. doi: 10.3389/fpsyg.2020.00911. eCollection 2020.
An ideal performance evaluation metric would be predictive, objective, easy to administer, estimate the variance in performance, and provide a confidence interval for the level of uncertainty. Time series forecasting may provide objective metrics for predictive performance in mental arithmetic. Addition and summation (addition combined with subtraction) using the Japanese Soroban computation system was undertaken over 60 days. The median calculation time in seconds for adding 10 sequential six digit numbers [CT) was 63 s (interquartile range (IQR) = 12, range 48-127 s], while that for summation (CT) was 70 s (IQR = 14, range 53-108 s), and the difference between these times was statistically significant < 0.0001. Using the mean absolute percentage error (MAPE) to measure forecast accuracy, the autoregressive integrated moving average (ARIMA) model predicted a further reduction in both CT to a mean of 51.51 ± 13.21 s (AIC = 5403.13) with an error of 6.32%, and CT to a mean of 54.57 ± 15.37 s (AIC = 3852.61) with an error of 8.02% over an additional 100 forecasted trials. When the testing was repeated, the actual mean performance differed by 1.35 and 4.41 s for each of the tasks, respectively, from the ARIMA point forecast value. There was no difference between the ARIMA model and actual performance values (-value CT = 1.0, CT=0.054). This is in contrast to both Wright's model and linear regression (-value < 0.0001). By accounting for both variability in performance over time and task difficulty, forecasting mental arithmetic performance may be possible using an ARIMA model, with an accuracy exceeding that of both Wright's model and univariate linear regression.
一个理想的绩效评估指标应该具有预测性、客观性、易于管理、能够估计绩效差异，并为不确定性水平提供置信区间。时间序列预测可能为心算的预测性能提供客观指标。使用日本的算盘计算系统进行加法和求和运算（加法结合减法），持续了60天。连续相加10个六位数的中位数计算时间（CT）为63秒（四分位间距（IQR）=12，范围48 - 127秒），而求和运算（CT）的中位数计算时间为70秒（IQR = 14，范围53 - 108秒），这两个时间之间的差异具有统计学意义（<0.0001）。使用平均绝对百分比误差（MAPE）来衡量预测准确性，自回归积分移动平均（ARIMA）模型预测在另外100次预测试验中，CT将进一步降至平均51.51±13.21秒（AIC = 5403.13），误差为6.32%，CT将降至平均54.57±15.37秒（AIC = 3852.61），误差为8.02%。当重复测试时，每个任务的实际平均性能与ARIMA点预测值分别相差1.35秒和4.41秒。ARIMA模型与实际性能值之间没有差异（CT的p值 = 1.0，CT的p值 = 0.054）。这与赖特模型和线性回归形成对比（p值<0.0001）。通过考虑随时间变化的性能变异性和任务难度，使用ARIMA模型预测心算性能可能是可行的，其准确性超过赖特模型和单变量线性回归。
