Center for Cognitive Neuroscience, Duke Institute for Brain Sciences, Duke University, Durham, North Carolina, United States of America.
Department of Economics, University of Maryland, College Park, Maryland, United States of America.
PLoS Comput Biol. 2021 Apr 1;17(4):e1008871. doi: 10.1371/journal.pcbi.1008871. eCollection 2021 Apr.
In recent studies of humans estimating non-stationary probabilities, estimates appear to be unbiased on average, across the full range of probability values to be estimated. This finding is surprising given that experiments measuring probability estimation in other contexts have often identified conservatism: individuals tend to overestimate low probability events and underestimate high probability events. In other contexts, repulsive biases have also been documented, with individuals producing judgments that tend toward extreme values instead. Using extensive data from a probability estimation task that produces unbiased performance on average, we find substantial biases at the individual level; we document the coexistence of both conservative and repulsive biases in the same experimental context. Individual biases persist despite extensive experience with the task, and are also correlated with other behavioral differences, such as individual variation in response speed and adjustment rates. We conclude that the rich computational demands of our task give rise to a variety of behavioral patterns, and that the apparent unbiasedness of the pooled data is an artifact of the aggregation of heterogeneous biases.
在最近对人类进行非平稳概率估计的研究中，在估计的所有概率值范围内，估计值平均来看是无偏的。鉴于在其他情境下测量概率估计的实验经常发现保守主义：个体倾向于高估低概率事件，低估高概率事件，这一发现令人惊讶。在其他情境下，也记录到了排斥偏见，个体的判断往往倾向于极端值。我们使用了一个平均产生无偏表现的概率估计任务的大量数据，发现个体水平上存在大量偏见；我们在同一实验情境中记录到了保守和排斥偏见的共存。尽管个体有大量的任务经验，但偏见仍然存在，并且与其他行为差异相关，例如反应速度和调整率的个体差异。我们得出结论，我们任务的复杂计算需求导致了各种行为模式，而聚合数据的无偏性是异质偏见聚合的人为产物。
