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: 10707016
• 负责人: David Badre
• 金额: $ 19.94万
• 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/10707016
• 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; Evaluation; 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; 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; Width; blood oxygen level dependent; design; instrument; interest; memory process; memory retention; memory retrieval; neural; neuroimaging; novel; 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 最有影响力的生物视觉理论之一认为视觉感知是一个贝叶斯过程 推论为了对外部世界做出推论，一个成功的视觉系统必须考虑到 神经计算的不确定性。在深度感知的特殊情况下，这一点的重点 项目，贝叶斯模型假设不确定性明确表示为定义的概率分布， 而不是对场景的3D解读只有这些知识才允许集成多个3D源 信息，以实现贝叶斯最优。与这一理论相一致的大量数据来自研究 涉及深度辨别，其中确实发现感知反应的可变性变得更小， 因为更多的深度提示被添加到刺激。在这里，有人质疑这些数据是否证明了行为 变异性源于表示3D估计的不确定性的神经噪声。相反，另一种理论是 建议，不需要这种代表性。除了更加节俭之外，这一理论还可以 预测了似乎证实贝叶斯预测的相同发现。 这个探索性的研究计划提出了这个新理论的两个可检验的预测，称为内在的 约束（IC），其中（1）大脑不表示3D属性上的概率分布，以及（2） 深度辨别任务中的知觉变异性并不反映这些概率中编码的不确定性 分布。与贝叶斯解释相反，IC理论假设对不同3D刺激的反应 而不是感知噪声的幅度变化。特别是，根据贝叶斯模型， 具有不同的IC模型的可靠性会引起不同的感知增益。结合线索增加了 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 预测给了IC模型必要的解释能力，以支持其作为三维理论的可行性。 perception.测试的有效性，无论是理论帐户将实现通过协同收集 行为和功能磁共振成像数据。首先，将确定是否有两个的JND（Just Noticeable Difference）， 间隔深度辨别任务测量与记忆保持相关的刺激可靠性或噪声。 根据这第二种解释，是感知增益决定了物理深度的变化 需要克服与该任务相关的噪声，与IC帐户一致。第二，功能磁共振成像技术， 可以估计编码在神经群体活动中的概率分布的幅度和噪声， 提供了关键的收敛证据的存在（或不存在）的神经编码的三维不确定性。在 总而言之，本研究项目将把视觉感知研究的两个独立领域结合起来， 视觉短期记忆，调查利用行为和功能磁共振成像方法来解决一个问题， 视觉科学的基本问题。