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.

项目概要 最有影响力的生物视觉理论之一将视觉感知视为贝叶斯过程 推理。为了推断外部世界，一个成功的视觉系统必须考虑到 考虑神经计算的不确定性。在深度感知的特殊情况下，这个焦点 项目中，贝叶斯模型假设不确定性明确表示为定义的概率分布 场景的可能 3D 解释。只有这些知识才能集成多个 3D 源 信息来实现贝叶斯最优。与该理论相符的大量数据来自研究 涉及深度辨别，确实发现知觉反应的变异性变得更小 随着更多深度线索被添加到刺激中。这里有人质疑这些数据是否是行为的证据 变异性源于代表 3D 估计不确定性的神经噪声。相反，另一种理论是 提议，不需要这种表示。除了更加节俭之外，这个理论还可以 预测的结果似乎证实了贝叶斯预测。 这项探索性研究提案提出了这一新理论的两个可测试的预测，称为“内在” 约束 (IC)，其中 (1) 大脑不代表 3D 属性上的概率分布，并且 (2) 深度辨别任务中的感知变化并不反映这些概率中编码的不确定性 分布。与贝叶斯理论相反，IC 理论假设对不同 3D 刺激的反应 幅度变化而不是感知噪声。特别是，根据贝叶斯模型据称 IC 模型具有不同的可靠性，会产生不同的感知增益。组合线索可增加 感知增益和这个因素（而不是更高的精度）提高了深度辨别任务的性能。这 预测为 IC 模型提供了必要的解释力，以支持其作为 3D 理论的可行性 洞察力。测试任一理论解释的有效性将通过协同收集来实现 行为和功能磁共振成像数据。首先，将确定两个变量的最小可觉差异 (JND) 是否为 间隔深度辨别任务测量与记忆保留相关的刺激可靠性或噪声。 根据第二种解释，感知增益决定了物理深度的变化 与 IC 帐户一致，需要克服与此任务相关的噪声。其次，功能磁共振成像技术 可以估计神经群体活动中编码的概率分布的大小和噪声 提供 3D 不确定性神经编码存在（或不存在）的关键聚合证据。在 总之，这个研究项目将汇集视觉感知和视觉感知两个独立的研究领域 视觉短期记忆，利用行为和功能磁共振成像方法的研究来解决 视觉科学的基本问题。