Information sampling behavior with explicit sampling costs.
Information sampling behavior with explicit sampling costs.
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DOI：
10.1037/dec0000045
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发表时间：
2016-07
期刊：
影响因子：
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通讯作者：
Maloney LT
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文献类型：
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作者：
Juni MZ;Gureckis TM;Maloney LT
收集信息的决定应考虑信息的价值及其在时间、精力和金钱方面的应计成本。在这里，我们探讨人们如何平衡在知觉运动估计任务中收集额外信息的金钱成本和收益。参与者因触摸触摸屏显示器上隐藏的圆形目标而获得奖励。目标的中心与圆形高斯分布的平均值重合，参与者可以从中重复采样。每个“提示”（一次采样一个）都在显示屏上绘制为一个点。在对每个提示进行采样后，参与者必须反复决定是停止采样并尝试触摸隐藏目标还是继续采样。每一个额外的提示都会增加参与者成功触及隐藏目标的可能性，但会减少他们的潜在奖励。两个实验条件的不同之处在于与触摸隐藏目标相关的初始奖励和每个提示的固定成本。对于每种情况，我们计算了参与者在采取行动之前应该采样的最佳线索数量，以最大化预期收益。与最近的说法相反，即人们在采取行动之前收集的信息少于客观应有的信息，我们发现参与者在一种实验条件下过度采样，而在另一种实验条件下并没有明显不足或过度采样。此外，虽然理想的观察者模型忽略了当前的样本离散度，但我们发现参与者用它来决定是否停止采样并采取行动或继续采样，这可能是对整个试验中潜在群体离散度的不完美学习的结果。
The decision to gather information should take into account both the value of information and its accrual costs in time, energy and money. Here we explore how people balance the monetary costs and benefits of gathering additional information in a perceptual-motor estimation task. Participants were rewarded for touching a hidden circular target on a touch-screen display. The target’s center coincided with the mean of a circular Gaussian distribution from which participants could sample repeatedly. Each “cue” — sampled one at a time — was plotted as a dot on the display. Participants had to repeatedly decide, after sampling each cue, whether to stop sampling and attempt to touch the hidden target or continue sampling. Each additional cue increased the participants’ probability of successfully touching the hidden target but reduced their potential reward. Two experimental conditions differed in the initial reward associated with touching the hidden target and the fixed cost per cue. For each condition we computed the optimal number of cues that participants should sample, before taking action, to maximize expected gain. Contrary to recent claims that people gather less information than they objectively should before taking action, we found that participants over-sampled in one experimental condition, and did not significantly under- or over-sample in the other. Additionally, while the ideal observer model ignores the current sample dispersion, we found that participants used it to decide whether to stop sampling and take action or continue sampling, a possible consequence of imperfect learning of the underlying population dispersion across trials.