Department of Neuroscience, Columbia University New York, NY, USA.
Front Hum Neurosci. 2011 Aug 15;5:77. doi: 10.3389/fnhum.2011.00077. eCollection 2011.
Understanding autism's ever-expanding array of behaviors, from sensation to cognition, is a major challenge. We posit that autistic and typically developing brains implement different algorithms that are better suited to learn, represent, and process different tasks; consequently, they develop different interests and behaviors. Computationally, a continuum of algorithms exists, from lookup table (LUT) learning, which aims to store experiences precisely, to interpolation (INT) learning, which focuses on extracting underlying statistical structure (regularities) from experiences. We hypothesize that autistic and typical brains, respectively, are biased toward LUT and INT learning, in low- and high-dimensional feature spaces, possibly because of their narrow and broad tuning functions. The LUT style is good at learning relationships that are local, precise, rigid, and contain little regularity for generalization (e.g., the name-number association in a phonebook). However, it is poor at learning relationships that are context dependent, noisy, flexible, and do contain regularities for generalization (e.g., associations between gaze direction and intention, language and meaning, sensory input and interpretation, motor-control signal and movement, and social situation and proper response). The LUT style poorly compresses information, resulting in inefficiency, sensory overload (overwhelm), restricted interests, and resistance to change. It also leads to poor prediction and anticipation, frequent surprises and over-reaction (hyper-sensitivity), impaired attentional selection and switching, concreteness, strong local focus, weak adaptation, and superior and inferior performances on simple and complex tasks. The spectrum nature of autism can be explained by different degrees of LUT learning among different individuals, and in different systems of the same individual. Our theory suggests that therapy should focus on training autistic LUT algorithm to learn regularities.
理解自闭症患者不断扩展的行为范围，从感觉到认知，是一项重大挑战。我们假设自闭症患者和典型发育者的大脑实施不同的算法，这些算法更适合学习、表示和处理不同的任务；因此，他们会发展出不同的兴趣和行为。从旨在精确存储经验的查找表（LUT）学习到专注于从经验中提取潜在统计结构（规律性）的插值（INT）学习，算法存在一个连续体。我们假设自闭症患者和典型大脑分别偏向于低维和高维特征空间中的 LUT 和 INT 学习，这可能是由于它们的窄和宽调谐函数。LUT 风格擅长学习局部、精确、严格且包含很少泛化规律的关系（例如电话簿中的姓名-数字关联）。但是，它不擅长学习依赖上下文、嘈杂、灵活且包含泛化规律的关系（例如，注视方向与意图、语言与意义、感官输入与解释、运动控制信号与运动以及社交情境与适当反应之间的关联）。LUT 风格信息压缩效率低，导致效率低下、感官超负荷（淹没）、兴趣受限和抗拒变化。它还导致预测和预期能力差、频繁出现意外和过度反应（超敏反应）、注意力选择和切换受损、具体性、强烈的局部焦点、适应性差，以及在简单和复杂任务上的表现优劣。自闭症的光谱性质可以通过不同个体之间不同程度的 LUT 学习以及同一个体不同系统之间的 LUT 学习来解释。我们的理论表明，治疗应该侧重于训练自闭症患者的 LUT 算法以学习规律性。
