Epistemology and artificial intelligence

In this essay we advance the view that analytical epistemology and artificial intelligence are complementary disciplines. Both fields study epistemic relations, but whereas artificial intelligence approaches this subject from the perspective of understanding formal and computational properties of frameworks purporting to model some epistemic relation or other, traditional epistemology approaches the subject from the perspective of understanding the properties of epistemic relations in terms of their conceptual properties. We argue that these two practices should not be conducted in isolation. We illustrate this point by discussing how to represent a class of inference forms found in standard inferential statistics. This class of inference forms is interesting because its members share two properties that are common to epistemic relations, namely defeasibility and paraconsistency. Our modeling of standard inferential statistical arguments exploits results from both logical artificial intelligence and analytical epistemology. We remark how our approach to this modeling problem may be generalized to an interdisciplinary approach to the study of epistemic relations.     

Lexicons, contexts, events, and images: commentary on Elman (2009) from the perspective of dual coding theory

Elman (2009) proposed that the traditional role of the mental lexicon in language processing can largely be replaced by a theoretical model of schematic event knowledge founded on dynamic context-dependent variables. We evaluate Elman's approach and propose an alternative view, based on dual coding theory and evidence that modality-specific cognitive representations contribute strongly to word meaning and language performance across diverse contexts which also have effects predictable from dual coding theory.     

Iconic gestures prime words

Using a cross-modal semantic priming paradigm, both experiments of the present study investigated the link between the mental representations of iconic gestures and words. Two groups of the participants performed a primed lexical decision task where they had to discriminate between visually presented words and nonwords (e.g., flirp). Word targets (e.g., bird) were preceded by video clips depicting either semantically related (e.g., pair of hands flapping) or semantically unrelated (e.g., drawing a square with both hands) gestures. The duration of gestures was on average 3,500 ms in Experiment 1 but only 1,000 ms in Experiment 2. Significant priming effects were observed in both experiments, with faster response latencies for related gesture-word pairs than unrelated pairs. These results are consistent with the idea of interactions between the gestural and lexical representational systems, such that mere exposure to iconic gestures facilitates the recognition of semantically related words.     

路标信息在幼儿空间再定向中的作用

以往研究表明幼儿不能有效地整合利用路标等非几何信息,主要依赖几何信息进行再定向。即使能利用路标信息,也只是建立了靶物体与路标之间的直接联结,而不是用于空间再定向。本研究在矩形房间的四角设置颜色不同的档板作为路标线索,以平均年龄为3.5岁的幼儿为被试,结果发现,在几何信息与路标信息同时存在的情形下,幼儿可以整合利用空间中的几何信息与路标信息;即使只有路标信息,幼儿也能依赖路标信息结合左右方位感准确地再定向,这些结果表明幼儿能够充分利用空间中的路标信息以重新确定自己的方位。     

以实质心理学为基础的评估：认知诊断

评价除了了解学习者的学习情况外,同时还应该提供学习者学习状况的诊断信息,以利于教学与学习。评价应该以认知心理学对学习过程的研究,作为编制测验的实质理论依据,进行认知诊断评估。本文针对认知诊断的心理学基础：知识的习得、知识诱发、知识表征、知识结构等进行梳理,以便测验开发者对认知诊断评估有更清晰的认识。     

预期推理在长时记忆中的表征形式

该研究采用移动窗口技术,通过两个实验研究探讨在高低不同竞争条件下预期推理结果在长时记忆中的表征形式。实验结果表明,预期推理在长时记忆中的表征形式依赖于文本中预期推理的引发语境,在不同竞争强度的预期语境中,预期推理的结果在长时记忆会以不同的形式进行表征。在低竞争条件下,预期推理的结果以确定的形式表征;只有在高竞争条件下,预期推理的结果才会以假定的形式进行表征。     

Number versus continuous quantity in numerosity judgments by fish

A fundamental question in human cognition is how people reason about space. We use a computational model to explore cross-cultural commonalities and differences in spatial cognition. Our model is based upon two hypotheses: (1) the structure-mapping model of analogy can explain the visual comparisons used in spatial reasoning; and (2) qualitative, structural representations are computed by people’s visual systems and used in these comparisons. We apply our model to a visual oddity task, in which individuals are shown an array of two-dimensional images and asked to the pick the one that does not belong. This task was previously used to evaluate understanding of geometric concepts in two disparate populations: North Americans, and the Mundurukú, a South American indigenous group. Our model automatically generates representations of each hand-segmented image and compares them to solve the task. The model achieves human-level performance on this task, and problems that are hard for the model are also difficult for people in both cultures. Furthermore, ablation studies on the model suggest explanations for cross-cultural differences in terms of differences in spatial representations.     

The empirical case for role-governed categories

Most theories of categorization posit feature-based representations. Markman and Stilwell (2001) argued that many natural categories name roles in relational systems and therefore they are role-governed categories. There is little extant empirical evidence to support the existence of role-governed categories. Three experiments examine predictions for ways that role-governed categories should differ from feature-based categories. Experiment 1 shows that our knowledge of role-governed categories, in contrast to feature-based categories, is largely about properties extrinsic to category members. Experiment 2 shows that role-governed categories have more prominent ideals than feature-based categories. Experiment 3 demonstrates that novel role-governed categories are licensed by the instantiation of novel relational structures. We then discuss broader implications for the study of categories and concepts.     

Squeezing, striking, and vocalizing: Is number representation fundamentally spatial?

Numbers are fundamental entities in mathematics, but their cognitive bases are unclear. Abundant research points to linear space as a natural grounding for number representation. But, is number representation fundamentally spatial? We disentangle number representation from standard number-to-line reporting methods, and compare numerical estimations in educated participants using line-reporting with three nonspatial reporting conditions (squeezing, bell-striking, and vocalizing). All three cases of nonspatial-reporting consistently reproduced well-established results obtained with number-line methods. Furthermore, unlike line-reporting—and congruent with the psychophysical Weber–Fechner law—nonspatial reporting systematically produced logarithmic mappings for all nonsymbolic stimuli. Strikingly, linear mappings were obtained exclusively in conditions with culturally mediated elements (e.g., words). These results suggest that number representation is not fundamentally spatial, but builds on a deeper magnitude sense that manifests spatially and nonspatially mediated by magnitude, stimulus modality, and reporting condition. Number-to-space mappings—although ubiquitous in the modern world—do not seem to be rooted directly in brain evolution but have been culturally privileged and enhanced.     

Inferring hidden causal structure

We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observed cause, one unobserved common cause, and two unobserved independent causes, depending on the pattern of associations and interventions they saw. We replicated these findings with less-informative training (Experiments 2 and 3) and a new apparatus (Experiment 3) to show that the pattern of data leads to hidden causal inferences across a range of prior constraints on causal knowledge.