Estimation bias and serial dependence in speed perception

Abstract

Abstract Studies have found that feature estimates are systematically compressed towards the distribution center, showing a central tendency. Additionally, the estimate of current features is affected by the previously seen feature, showing serial dependence or adaptation effect. However, these all remain unclear in the speed estimation. To address this question, we asked participants to estimate the speed of moving Gabor patches. In Experiment 1, speeds were selected from three uniform distributions with different lower and upper boundaries (i.e., slow, moderate, and fast ranges). In Experiment 2, speeds were arranged in an increasing, uniform, or decreasing distribution. The boundaries of three distributions were the same. The results found that speed estimates were systematically compressed towards the center of the uniform distribution center, showing a central tendency, and its size increased with the range boundaries. However, in the decreasing and increasing distributions, aside from central tendency, the speed estimates were also showed a bias away from the heavy tail of the distributions. Moreover, there was an attractive serial dependence that was not affected by the speed range. In summary, the current study, along with previous studies that reveal a slow-speed bias, comprehensively reveals various estimation biases in speed perception.

Keywords

• Speed perception
• Central tendency
• Serial dependence
• Slow-speed bias
• Efficient encoding
• Bayesian decoding