The optimal template for signal detection in white additive noise is the signal itself: the ideal observer matches each stimulus against this template and selects the stimulus associated with largest match. In the noisy ideal observer, internal noise is added to the decision variable returned by the template. While the ideal observer represents an unrealistic approximation to the human visual process, the noisy ideal observer may be applicable under certain experimental conditions. For template values constrained to lie within a specified range, theory predicts that the template associated with a noisy ideal observer should be a clipped image of the signal, a result which we demonstrate analytically using variational calculus. It is currently unknown whether the human process conforms to theory. We report a targeted analysis of the theoretical prediction for an experimental protocol that maximizes template-matching on the part of human participants. We find indicative evidence to support the theoretical expectation when internal noise is compared across participants, but not within each participant. Our results indicate that implicit knowledge about internal variability in different individuals is reflected by their detection templates; no implicit knowledge is retained for internal-noise fluctuations experienced by a given participant during data collection. The results also indicate that template encoding is constrained by the dynamic range of weight specification, rather than the range of output values transduced by the template-matching process.

在白色加性噪声中进行信号检测的最佳模板是信号本身：理想的观察者将每个刺激与该模板进行匹配，并选择与最大匹配相关的刺激。在嘈杂的理想的观察者，内部噪声被添加到模板返回的决策变量中。虽然理想观察者代表了对人类视觉过程的不切实际的近似，但嘈杂的理想观察者可能适用于某些实验条件。对于限制在指定范围内的模板值，理论预测与嘈杂的理想观察者相关的模板应该是信号的剪辑图像，我们使用变分微积分分析证明了这一结果。目前尚不清楚人类过程是否符合理论。我们报告了对实验协议的理论预测的有针对性的分析，该协议使人类参与者的模板匹配最大化。当内部噪声在参与者之间进行比较时，我们发现指示性证据支持理论预期，但不是在每个参与者内部。我们的结果表明，关于不同个体内部变异性的隐含知识反映在他们的检测模板中；对于特定参与者在数据收集过程中所经历的内部噪声波动，不会保留任何隐含的知识。结果还表明，模板编码受权重规范的动态范围的约束，而不是模板匹配过程转换的输出值范围。对于特定参与者在数据收集过程中所经历的内部噪声波动，不会保留任何隐含的知识。结果还表明，模板编码受权重规范的动态范围的约束，而不是模板匹配过程转换的输出值范围。对于特定参与者在数据收集过程中所经历的内部噪声波动，不会保留任何隐含的知识。结果还表明，模板编码受权重规范的动态范围的约束，而不是模板匹配过程转换的输出值范围。