诸多研究者认为,大脑对物理世界的感知是一种基于不确定性证据的统计推理。据此,研究人员提出了不同的计算模型来揭示视知觉的计算过程。研究系统总结了基于贝叶斯推理框架的经典贝叶斯观察者模型、高效编码约束贝叶斯观察者模型、 整体贝叶斯观察者模型和层级贝叶斯观察者模型,这些模型在解释不同物理特征的知觉行为表现方面表现出各自的优势和局限。在此基础上,研究提出了一个同时考虑自上而下与自下而上知觉交互过程以及运动控制系统的统一贝叶斯观察者模型,进而揭示了一个相对完整的视知觉计算机制。最后,研究提出了未来亟待解决的研究问题,这些问题的答案将为新模型提供扎实的实证支持,从而提升研究者对视知觉计算机制的理解。
Abstract
It is widely proposed that the perception of the visual world operates as a form of statistical inference based on uncertain evidence. In this context, researchers have developed various computational models to elucidate the process of inference. This study primarily reviews computational models grounded in the Bayesian inference framework, including:
(1)The Classical Bayesian Observer Model: This model optimally combines prior knowledge of a specific physical feature with its likelihood distribution given a particular value. It effectively accounts for the prior-peak-compression bias (i.e., Bayesian bias).
(2)The Efficient-Coding Constrained Bayesian Observer Model: This model efficiently encodes physical features based on prior information and subsequently decodes these features using Bayes' rule. Notably, it distinguishes between external (physical) noise and internal (sensory) noise that influences stimulus certainty. The results demonstrated that this model adequately explains both Bayesian and anti-Bayesian biases.
(3)The Hierarchical Bayesian Observer Model: This model posits that the sensory system first derives conclusions based on a specific context, which are then used to constrain subsequent Bayesian inference processes, thereby establishing a hierarchical inference framework. Additionally, the context typically generates multiple hypotheses, allowing observers to optimally integrate conclusions from all hypotheses or select the conclusion with the highest probability. The former scenario leads to a full inference hierarchical Bayesian observer model, while the latter results in a conditional inference hierarchical Bayesian observer model. Recent studies have shown that the performance of these two models in explaining behavioral data is influenced by the distance between the feature value and the conditional boundary.
(4)The Holistic Bayesian Observer Model: This model suggests that context and Bayesian inferences occur in parallel, with the sensory system weighting the integration losses from both inferences to perceive the physical feature. Furthermore, this model encompasses the motor system, including motor noise when observers adjust probes to reproduce stimuli.
Upon reviewing these models, the current study found that each exhibits distinct advantages and limitations in elucidating perceptual biases and variance associated with different physical features. In light of these findings, this study developed a comprehensive computational model (illustrated in Figure 2 of the paper) that integrates both bottom-up and top-down perceptual processes, as well as the motor control system including motor noise and the perceptual-response mapping scaling process. This new model effectively balances the strengths and weaknesses of previous Bayesian inference models.
The comprehensive Bayesian observer model proposed in this study is constructed from a theoretical perspective, based on a systematic comparison of the aforementioned Bayesian observer models, and incorporates their respective strengths and weaknesses. However, it still lacks empirical support from behavioral and physiological evidence. Future studies will need to design rigorous behavioral and physiological experiments to provide empirical data that substantiate the validity of the model. In this regard, we propose the following questions that need to be addressed:
(1)What are the computational and physiological mechanisms underlying the integration of long-term and short-term priors?
(2)Does the perception-to-motor mapping process, which is influenced by the response range, interact with the range of the short-term prior? If so, what are the underlying computational and physiological mechanisms?
(3)How do the priors for the integration of long-term and short-term priors interact with context inference?
(4)Is the context inference in hierarchical Bayesian inference also parallel to context inference in feature estimation?
Answering these questions will help validate the new proposed model and enhance researchers’ understanding of perception and decision-making processes. This, in turn, will foster advancements in theory and practical applications across psychology, neuroscience, artificial intelligence, and related fields.
关键词
视知觉 /
贝叶斯观察者模型 /
信息论 /
分类推理 /
层级推理 /
先验
Key words
visual perception /
bayesian observer model /
information theory /
hierarchical inference /
prior
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基金
* 本研究得到国家自然科学基金项目（32200842） 的资助
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