Institute of Ophthalmology, University College London (UCL), UK.
NIHR Moorfields Biomedical Research Centre, London, UK.
Dev Sci. 2018 May;21(3):e12584. doi: 10.1111/desc.12584. Epub 2017 Aug 15.
The mature visual system condenses complex scenes into simple summary statistics (e.g., average size, location, orientation, etc.). However, children, often perform poorly on perceptual averaging tasks. Children's difficulties are typically thought to represent the suboptimal implementation of an adult-like strategy. This paper examines another possibility: that children actually make decisions in a qualitatively different way to adults (optimal implementation of a non-ideal strategy). Ninety children (6-7, 8-9, 10-11 years) and 30 adults were asked to locate the middle of randomly generated dot-clouds. Nine plausible decision strategies were formulated, and each was fitted to observers' trial-by-trial response data (Reverse Correlation). When the number of visual elements was low (N < 6), children used a qualitatively different decision strategy from adults: appearing to "join up the dots" and locate the gravitational center of the enclosing shape. Given denser displays, both children and adults used an ideal strategy of arithmetically averaging individual points. Accounting for this difference in decision strategy explained 29% of children's lower precision. These findings suggest that children are not simply suboptimal at performing adult-like computations, but may at times use sensible, but qualitatively different strategies to make perceptual judgments. Learning which strategy is best in which circumstance might be an important driving factor of perceptual development.
成熟的视觉系统将复杂的场景浓缩为简单的汇总统计信息(例如,平均大小、位置、方向等)。然而,儿童在感知平均任务中的表现往往较差。通常认为,儿童的困难代表了成人策略的次优实现。本文探讨了另一种可能性:儿童实际上以与成人不同的方式做出决策(非理想策略的最优实现)。我们要求 90 名儿童(6-7 岁、8-9 岁、10-11 岁)和 30 名成年人定位随机生成的点云的中间位置。制定了九个合理的决策策略,并将每个策略拟合到观察者的逐次试验响应数据(反向相关)中。当视觉元素数量较少(N<6)时,儿童使用的决策策略与成人明显不同:似乎“连接点”并定位包围形状的重心。对于更密集的显示,儿童和成人都使用了一种理想的策略,即对单个点进行算术平均。解释这种决策策略差异可以解释儿童较低精度的 29%。这些发现表明,儿童并不是简单地在执行成人般的计算时表现不佳,而是有时可能会使用明智但定性不同的策略来进行感知判断。了解在何种情况下哪种策略最佳可能是感知发展的一个重要驱动因素。