The pigeon as particle filter
The pigeon as particle filter
复制标题
鸽子作为颗粒过滤器
DOI：
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复制
发表时间：
2007
期刊：
影响因子：
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通讯作者：
Aaron C. Courville
中科院分区：
文献类型：
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作者：
N. Daw;Aaron C. Courville
尽管理论家将经典条件反射解释为贝叶斯信念更新的实验室模型，但最近的重新分析表明，理论模型捕获的有关学习的关键特征是对受试者进行平均的人为因素。受试者不是平滑地学习渐近线（根据贝叶斯模型，反映了随着数据积累从先验到后验的逐渐权衡），而是突然学习并且他们的预测不断波动。我们建议，可以通过假设受试者使用少量样本（在我们的模拟中是一个）进行顺序蒙特卡罗采样进行推理来对突然且不稳定的学习进行建模。集成行为类似于精确的贝叶斯模型，因为与粒子过滤器一样，它对许多样本进行平均。此外，该模型能够表现出复杂的行为，例如在整体水平上进行回顾性重估，即使是对于在试验中不跟踪其信念的不确定性的最低限度复杂的个体也是如此。
Although theorists have interpreted classical conditioning as a laboratory model of Bayesian belief updating, a recent reanalysis showed that the key features that theoretical models capture about learning are artifacts of averaging over subjects. Rather than learning smoothly to asymptote (reflecting, according to Bayesian models, the gradual tradeoff from prior to posterior as data accumulate), subjects learn suddenly and their predictions fluctuate perpetually. We suggest that abrupt and unstable learning can be modeled by assuming subjects are conducting inference using sequential Monte Carlo sampling with a small number of samples — one, in our simulations. Ensemble behavior resembles exact Bayesian models since, as in particle filters, it averages over many samples. Further, the model is capable of exhibiting sophisticated behaviors like retrospective revaluation at the ensemble level, even given minimally sophisticated individuals that do not track uncertainty in their beliefs over trials.
DOI：
10.1073/pnas.0404965101
发表时间：
2004-09-07
影响因子：
11.1
作者：
Gallistel, CR;Fairhurst, S;Balsam, P
通讯作者：
Balsam, P
DOI：
10.1037/0097-7403.32.3.284
发表时间：
2006-07-01
期刊：
JOURNAL OF EXPERIMENTAL PSYCHOLOGY-ANIMAL BEHAVIOR PROCESSES
影响因子：
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作者：
Balsam, Peter D.;Fairhurst, Stephen;Gallistel, Charles R.
通讯作者：
Gallistel, Charles R.