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From Amateur to Professional: A Neuro-cognitive Model of Categories and Expert Development

机译：从业余到专业：类别和专家发展的神经认知模型

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The ability to group perceptual objects into functionally relevant categories is vital to our comprehension of the world. Such categorisation aids in how we search for objects in familiar scenes and how we identify an object and its likely uses despite never having seen that specific object before. The systems that mediate this process are only now coming to be understood through considerable research efforts combining neurological, psychological and behavioural studies. What is much less well understood are the differences between the categories, how they are formed and how they are used by experts and non-experts in a complex task that can take decades to master. In a quite different direction to previous studies, this work infers the different categorical structures that might be used by amateurs and professionals in the oriental game of Go. This is achieved by using a newly developed combination of artificial neural networks (Self-organising Maps) and perceptual inference to show that categories of strategic scenes can be learned while playing games using a model of 'conditional perceptual learning'. Applying this technique to two databases of games, one of amateurs and one of professionals, shows that a structural hierarchy of scene information develops that can be readily incorporated into traditional psychological models of decisions and readily implemented in computational systems. The results are discussed in terms of the heuristics and biases literature, emphasising where the significant similarities and differences lie between this work and previous work.
机译：将感知对象归为功能相关类别的能力对于我们对世界的理解至关重要。这种分类有助于我们在以前从未见过的特定对象的情况下，如何在熟悉的场景中搜索对象以及如何识别对象及其可能的用途。直到现在，通过将神经，心理和行为研究结合在一起的大量研究工作，才可以理解介导这一过程的系统。人们对类别之间的差异，类别的形成方式以及专家和非专家如何使用它们进行复杂的任务（可能需要数十年才能掌握）的了解还很少。与以前的研究大相径庭，这项工作推断出业余和专业人士在东方围棋中可能使用的不同分类结构。这是通过使用新开发的人工神经网络（自组织映射）和知觉推理的组合来实现的，表明在使用“条件知觉学习”模型玩游戏时可以学习战略场景的类别。将这项技术应用于两个游戏数据库（一个是业余游戏者，另一个是专业游戏者）表明，场景信息的结构层次得以发展，可以很容易地将其纳入传统的决策心理模型中，并可以轻松地在计算系统中实现。将根据启发式方法和偏见文献对结果进行讨论，并强调该工作与以前的工作之间存在明显的异同之处。

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