Despite the widespread use of graphs, little is known about how fast and how accurately we can extract information from them. Through a series of four behavioral experiments, we characterized human performance in “mental regression”, i.e. the perception of statistical trends from scatterplots. When presented with a noisy scatterplot, even as briefly as 100 ms, human adults could accurately judge if it was increasing or decreasing, fit a regression line, and extrapolate outside the original data range, for both linear and non-linear functions. Performance was highly consistent across those three tasks of trend judgment, line fitting and extrapolation. Participants’ linear trend judgments took into account the slope, the noise, and the number of data points, and were tightly correlated with the t-test classically used to evaluate the significance of a linear regression. However, they overestimated the absolute value of the regression slope. This bias was inconsistent with ordinary least squares (OLS) regression, which minimizes the sum of square deviations, but consistent with the use of Deming regression, which treats the x and y axes symmetrically and minimizes the Euclidean distance to the fitting line. We speculate that this fast but biased perception of scatterplots may be based on a “neuronal recycling” of the human visual capacity to identify the medial axis of a shape.

尽管图被广泛使用，但人们对我们从它们中提取信息的速度和准确度知之甚少。通过一系列四项行为实验，我们在“心理回归”中描述了人类的表现，即从散点图中感知统计趋势。当呈现嘈杂的散点图时，即使只有 100 毫秒，成年人也可以准确判断它是增加还是减少，拟合回归线，并在原始数据范围之外外推线性和非线性函数。在趋势判断、直线拟合和外推这三项任务中，性能高度一致。参与者的线性趋势判断考虑了斜率、噪声和数据点数，并与t密切相关-test 经典地用于评估线性回归的显着性。然而，他们高估了回归斜率的绝对值。这种偏差与普通最小二乘法 (OLS) 回归不一致，后者最小化平方偏差的总和，但与 Deming 回归的使用一致，后者对称地处理 x 和 y 轴并最小化到拟合线的欧几里德距离。我们推测，散点图的这种快速但有偏见的感知可能基于人类视觉能力的“神经元循环”，以识别形状的中轴。