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Integrating conceptual knowledge within and across representational modalities

机译:在代表模式之内和之间整合概念知识

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摘要

Research suggests that concepts are distributed across brain regions specialized for processing information from different sensorimotor modalities. Multimodal semantic models fall into one of two broad classes differentiated by the assumed hierarchy of convergence zones over which information is integrated. In shallow models, communication within- and between-modality is accomplished using either direct connectivity, or a central semantic hub. In deep models, modalities are connected via cascading integration sites with successively wider receptive fields. Four experiments provide the first direct behavioral tests of these models using speeded tasks involving feature inference and concept activation. Shallow models predict no within-modal versus cross-modal difference in either task, whereas deep models predict a within-modal advantage for feature inference, but a cross-modal advantage for concept activation. Experiments 1 and 2 used relatedness judgments to tap participants' knowledge of relations for within- and cross-modal feature pairs. Experiments 3 and 4 used a dual-feature verification task. The pattern of decision latencies across Experiments 1-4 is consistent with a deep integration hierarchy.
机译:研究表明,这些概念分布在专门处理来自不同感觉运动方式信息的大脑区域。多模态语义模型属于两大类之一,这些大类由假定的集成信息层次的层次结构区分。在浅层模型中,模态内部和模态之间的通信是使用直接连接或中央语义中心来完成的。在深层模型中,模态通过级联集成站点与依次更宽的接收场连接。四个实验使用涉及特征推理和概念激活的加速任务提供了这些模型的首次直接行为测试。浅层模型预测这两种任务在模态内与跨模态之间均无差异,而深层模型则预测特征推论具有模内优势,而概念激活则具有跨模态优势。实验1和2使用相关性判断来挖掘参与者对模态内和跨模态特征对的关系知识。实验3和4使用了双重功能验证任务。实验1-4中决策延迟的模式与深度集成层次结构一致。

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