Dynamic integration of reward and stimulus information in perceptual decision-making.
Dynamic integration of reward and stimulus information in perceptual decision-making.
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DOI：
10.1371/journal.pone.0016749
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发表时间：
2011-03-03
期刊：
影响因子：
3.7
通讯作者：
McClelland JL
中科院分区：
文献类型：
--
作者：
Gao J;Tortell R;McClelland JL
在感性决策中，理想的决策者应该将他们的选择偏向于与更大奖励相关的替代方案，并且随着刺激敏感性的增加，偏向的程度应该减小。当刺激开始后必须在不同时间做出反应时，刺激敏感性随着时间从零增长到最终的渐近水平。如果决策者在刺激开始后不久做出反应，是否能够产生更多偏见，但如果在积累更多证据后做出反应，则偏见较小？如果是这样，他们在这样做时能达到多接近最佳状态，以及如何机械地实现他们的表现？我们报告了一项实验，其中在刺激开始之前就表明了每种替代方案的回报。处理时间由刺激开始后不同时间发生的“开始”提示控制，要求在毫秒内做出响应。当处理时间很短时，奖励偏差确实会很高，并随着敏感度的增加而降低，最终稳定在非零值。然而，对于较短的处理时间，偏差程度并不是最佳的。我们在泄漏竞争累加器模型的框架内提出了参与者表现的机械解释，其中每个替代方案的累加器都会积累受泄漏和相互抑制影响的噪声信息。准确度的趋于平稳归因于累加器之间的相互抑制，从而允许累加器在试验早期收集最多证据来抑制替代方案。在这个框架中考虑了奖励可能影响决策的三种方式。奖励影响证据积累过程起点的三者之一与观察到的奖励偏差效应的定性模式一致，而另外两个则不然。将此假设纳入泄漏竞争累加器模型中，我们能够为个体参与者数据提供紧密的定量拟合。
In perceptual decision-making, ideal decision-makers should bias their choices toward alternatives associated with larger rewards, and the extent of the bias should decrease as stimulus sensitivity increases. When responses must be made at different times after stimulus onset, stimulus sensitivity grows with time from zero to a final asymptotic level. Are decision makers able to produce responses that are more biased if they are made soon after stimulus onset, but less biased if they are made after more evidence has been accumulated? If so, how close to optimal can they come in doing this, and how might their performance be achieved mechanistically? We report an experiment in which the payoff for each alternative is indicated before stimulus onset. Processing time is controlled by a “go” cue occurring at different times post stimulus onset, requiring a response within msec. Reward bias does start high when processing time is short and decreases as sensitivity increases, leveling off at a non-zero value. However, the degree of bias is sub-optimal for shorter processing times. We present a mechanistic account of participants' performance within the framework of the leaky competing accumulator model, in which accumulators for each alternative accumulate noisy information subject to leakage and mutual inhibition. The leveling off of accuracy is attributed to mutual inhibition between the accumulators, allowing the accumulator that gathers the most evidence early in a trial to suppress the alternative. Three ways reward might affect decision making in this framework are considered. One of the three, in which reward affects the starting point of the evidence accumulation process, is consistent with the qualitative pattern of the observed reward bias effect, while the other two are not. Incorporating this assumption into the leaky competing accumulator model, we are able to provide close quantitative fits to individual participant data.
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影响因子：
3.7
作者：
Rorie AE;Gao J;McClelland JL;Newsome WT
通讯作者：
Newsome WT
影响因子：
3.3
作者：
Eckhoff, P.;Holmes, P.;Gold, J. I.
通讯作者：
Gold, J. I.
DOI：
10.1037/0278-7393.11.3.538
发表时间：
1985-01-01
影响因子：
2.6
作者：
BUSEMEYER, JR
通讯作者：
BUSEMEYER, JR
DOI：
10.3758/pp.70.2.229
发表时间：
2008-02-01
期刊：
PERCEPTION & PSYCHOPHYSICS
影响因子：
--
作者：
Diederich, Adele
通讯作者：
Diederich, Adele
影响因子：
1.8
作者：
BUSEMEYER, JR;RAPOPORT, A
通讯作者：
RAPOPORT, A