Encoding of probability distributions of 3D estimates in mind and brain

心智和大脑中 3D 估计概率分布的编码

• 批准号: 10463171
• 负责人: David Badre
• 金额: $ 23.93万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10463171
• 关键词: 3-Dimensional; Address; Affect; Agreement; Anatomy; Bayesian Analysis; Bayesian Modeling; Bayesian Prediction; Behavioral; Biological; Brain; Code; Collection; Cues; Data; Depth Perception; Diagnosis; Discrimination; Functional Magnetic Resonance Imaging; Goals; Health Professional; Human; Image Enhancement; Individual; Influentials; Interdisciplinary Study; Investigation; Just-Noticeable Differences; Knowledge; Laparoscopic Surgical Procedures; Measures; Medical Technology; Methods; Mind; Modeling; Motor; Noise; Outcome; Pattern; Perception; Performance; Population; Probability; Procedures; Process; Property; Psychophysics; Research; Research Project Grants; Research Proposals; Sensory; Short-Term Memory; Source; Stimulus; Techniques; Testing; Three-Dimensional Imaging; Uncertainty; Vision; Visual; Visual Perception; Visual Psychophysics; Visual system structure; Width; blood oxygen level dependent; design; instrument; interest; memory process; memory retention; memory retrieval; neuroimaging; novel; relating to nervous system; response; standard measure; stem; theories; time interval; vision science; visual process; visual processing

Project Summary One of the most influential theories of biological vision considers visual perception as a process of Bayesian inference. In order to make inferences about the external world a successful visual system must take into account the uncertainty of neural computations. In the particular case of depth perception, the focus of this project, Bayesian models postulate that uncertainty is explicitly represented as probability distributions defined over possible 3D interpretations of a scene. Only this knowledge allows the integration of multiple sources of 3D information to achieve Bayesian optimality. A large body of data compatible with this theory comes from studies involving depth discrimination, where it is indeed found that variability in perceptual responses becomes smaller as more depth cues are added to a stimulus. Here it is questioned whether this data is evidence that behavioral variability stems from neural noise representing uncertainty of 3D estimates. Instead, an alternate theory is proposed, which does not require this representation. Beyond being more parsimonious, this theory can also predict the same findings that seem to confirm the Bayesian predictions. This exploratory research proposal lays out two testable predictions of this new theory, termed Intrinsic Constraint (IC), for which (1) the brain does not represent probability distributions over 3D properties and (2) perceptual variability in depth discrimination tasks does not reflect uncertainty encoded in these probability distributions. In contrast to the Bayesian account, the IC theory postulates that responses to different 3D stimuli vary in magnitude instead of perceptual noise. In particular, stimuli that according to Bayesian models allegedly have different reliabilities for the IC model elicit different perceptual gains. Combining cues increases the perceptual gain and this factor, not higher precision, enhances performance in depth discriminations tasks. This prediction gives the IC model the explanatory power necessary to support its viability as a theory of 3D perception. Testing the validity of either theoretical account will be achieved through the synergetic collection of behavioral and fMRI data. First, it will be determined whether the Just Noticeable Difference (JND) of a two- interval depth discrimination task measures stimulus reliability or noise associated with memory retention. According to this second interpretation, it is the perceptual gain that determines the changes in physical depth required to overcome this task related noise, in agreement with the IC account. Second, an fMRI technique that can estimate both the magnitude and noise of probability distributions encoded in neural population activity will provide critical converging evidence of the existence (or absence) of neural encoding of 3D uncertainty. In summary, this research project will bring together the two separate fields of research of visual perception and visual short-term memory, with investigations leveraging behavioral and fMRI methods for addressing a fundamental problem in vision science.

项目摘要 生物视觉最有影响力的理论之一将视觉感知视为贝叶斯的过程 推理。为了推断外部世界，成功的视觉系统必须进入 说明神经计算的不确定性。在深度感知的特殊情况下，重点 项目，贝叶斯模型假定不确定性被明确表示为定义的概率分布 对场景的可能的3D解释。只有这些知识才能集成3D的多个来源 获得贝叶斯最优性的信息。与该理论兼容的大量数据来自研究 涉及深度歧视，确实发现感知反应的变异性变小 随着更多的深度线索添加到刺激中。在这里质疑这些数据是否是行为的证据 变异性来自于神经噪声代表3D估计的不确定性。相反，替代理论是 提议，不需要此表示。除了变得更加简约之外，这个理论也可以 预测似乎证实贝叶斯预测的相同发现。 这项探索性研究建议提出了对这一新理论的两个可检验的预测，称为内在理论 约束（IC），（1）大脑不代表3D属性上的概率分布和（2） 深度歧视任务中的感知变异性并不反映这些概率中编码的不确定性 分布。与贝叶斯的说法相反，IC理论假定对不同3D刺激的响应 在大小而不是感知噪声方面有所不同。特别是，根据贝叶斯模型的刺激 对于IC模型具有不同的可靠性会引起不同的感知收益。组合提示增加了 感知增益和这个因素而不是更高的精度可以增强深度区分任务的性能。这 预测为IC模型提供了支持其可行性作为3D理论所必需的解释能力 洞察力。通过协同收集的收集，可以实现任何一个理论说明的有效性 行为和fMRI数据。首先，将确定是否有两个差异（JND） 间隔深度歧视任务测量刺激可靠性或与记忆保留相关的噪声。 根据第二个解释，正是感知收益决定了物理深度的变化 与IC帐户一致，克服与此任务相关的噪声所需。第二，一种fMRI技术 可以估计在神经种群活动中编码的概率分布的幅度和噪声 提供有关3D不确定性神经编码的存在（或不存在）的关键融合证据。在 总而言之，该研究项目将汇集两个单独的视觉感知研究领域 视觉短期记忆，并进行调查利用行为和fMRI方法来解决 视力科学中的基本问题。