The role of inferior temporal cortex in core visual object recognition

我们的目标是了解大脑是如何完成视觉对象识别的。根据本条例获得的证据 格兰特和其他实验室的研究表明，每个视觉图像都是沿着腹侧视觉皮质过程进行处理的-- ING在其顶层-注入了一种新的神经活动模式。 Veys关于对象身份的明确信息，即使在面对实质性的视图不确定性(“不变性”)时也是如此。 IT群体的表征被认为是物体识别的因果关系。但准确地说 IT人口如何解释看似fiNite数量的对象歧视？何谓 IT所传达的行为批判性“特征”？多少?如何描述它们呢？ 在这里，我们的目标是构建和测试映像到IT到行为的模型，这些模型在整个 核心视觉对象识别行为领域。大量的前期工作表明，我们应该从测试开始- 设计和开发IT 100.1f型号系列：该系列中的所有型号都表示IT传达的~100、形象- 在约1毫米尺度上采样的其活动中可计算的“特征”。我们如何测试和开发这样的模型？ 首先，这个模型族预测，我们可以建立和提供一个单一的、低维(&lt；100)欧几里得EM- 预测所有基本和下级目标辨别任务的空间(目标1)。第二，模式 家庭预测，我们可以发现IT活动的特定方面(称为IT“功能”)， 当加权和求和时，准确地预测每个图像的行为对象混淆(目标2a)。第三， 模型家庭预测，对单个毫米尺度的IT皮质部分的暂时抑制将产生 跨所有基本级和下级对象任务的可靠、可预测的行为中断模式 (目标3)。第四，模型家族假设信息技术神经调节功能在空间尺度上的差异较小 大于~1 mm与核心对象歧视行为无关-我们将用这两个记录来测试这一预测- ING(目标2a)和神经扰动(目标3)实验。最后，模范家庭激励了我们的目标(Aim 2b)使用图像可计算函数和人来表征约100个IT特征的完整集合 形容词。 虽然大量的初步数据支持这些预测和目标，但还没有一个完整的模型 建造或测试。如果这些目标实现，这项工作将通过展示预先的 简要说明核心对象识别是如何在IT皮质级别进行因果解释的，并提供了一个模型 这将准确地预测任何图像处理或直接IT神经干预将如何改变 核心对象识别行为。

Our goal is to understand how the brain accomplishes visual object recognition. Evidence obtained under this grant and from other labs suggests that each visual image is processed along the ventral visual cortical process- ing stream into a new pattern of neural activity at its top level -- the inferior temporal cortex (IT) -- that con- veys explicit information about object identity, even in the face of substantial view uncertainty (“invariance”). That IT population representation is thought to be causally responsible for object recognition. But precisely how does the IT population account for a seemingly infinite number of object discriminations? What are the behaviorally critical “features” conveyed by IT? How many? How can they be described? Here we aim to build and test image-to-IT-to-behavior models that are predictively accurate over the entire domain of core visual object recognition behavior. Substantial prior work argues that we should start by test- ing and developing the IT 100.1f model family: all models in that family state that IT conveys ~100, image- computable “features” in its activity sampled at ~1 mm scale. How can we test and develop such models? First, this model family predicts that we can build and provide a single, low dimensional (<100) Euclidean em- bedding space to predict all basic and subordinate level object discrimination tasks (Aim 1). Second, the model family predicts that we can discover the particular aspects of IT activity (called IT “features”) as those that, when weighted and summed, exactly predict behavioral object confusion of every image (Aim 2a). Third, the model family predicts that temporary suppression of individual, mm-scale portions of IT cortex will produce reliable, predictable patterns of behavioral disruption across all basic-level and subordinate level object tasks (Aim 3). Fourth, the model family posits that differences in IT neural tuning functions at spatial scales less than ~1 mm are irrelevant for core object discrimination behavior — a prediction we will test with both record- ing (Aim 2a) and neural perturbation (Aim 3) experiments. Finally, the model family motivates our goal (Aim 2b) of characterizing the complete set of ~100 IT features with image-computable functions and with human shape adjectives. While substantial preliminary data support these predictions and goals, a complete model has not yet been built or tested. If these aims are accomplished, this work would transform our understanding by showing pre- cisely how core object recognition is causally accounted for at the level of IT cortex, and by providing a model that would accurately predict how any image manipulation or direct IT neural intervention would alter any core object recognition behavior.