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The present study investigated the influence of encoding context on the types of recognition errors that adults and nine-year-olds commit . Encoding context was varied through the use of different orienting questions. In general, it was found that both nine-year-olds and adults were influenced by encoding context. Nine-year-olds, however, committed more contextually related false recognition errors only when they both answered orienting questions and generated related words, while adults were influenced by context only after answering orienting questions. When adults were asked to generate words in addition to answering questions, the encoding context effect disappeared. Two hypotheses were proposed to explain these results. The first suggested that children failed to process stimuli as elaborately as adults, and were thus less likely to incorrectly recognize more contextually related foils, unless they were explicitly required to generate related words (often foils) . The second hypothesis suggested that the retrieval strategies of adults and children differed. Children probably picked words on the test based on familiarity, and unless they generated foils, the context effect would not be expected to appear. Adults, however, may have employed more sophisticated test-taking strategies. After iv simply answering orienting questions, they may have selected some foils based on the context of remembered orienting questions. However, after generating related words, some subjects may have avoided choosing those words (usually foils) on the recognition memory test, resulting in the disappearance of the context effect

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[pt] O Word Mover s Distance (WMD) proposto por Kusner et al. [ICML,2015] é uma função de distância entre documentos que se aproveita das relações semânticas entre palavras extraidas por suas Word Embeddings. Essa função de distância se mostrou bastante eficaz, obtendo taxas de erro estado da arte para problemas de classificação, porém ao mesmo tempo inviável para largas coleções ou grandes documentos devido a ser necessário computar um problema de transporte em um grafo bipartido completo para cada par de documentos. Assumindo algumas hipóteses, que são respaldadas por propriedades empíricas das distâncias entre as Word Embeddings, nós simplificamos o WMD de forma a obter uma nova função de distância o qual requer a solução de um problema de fluxo máximo em um grafo esparço, que pode ser resolvido mais rapidamente do que um problema de transporte em um grafo denso. Nossos experimentos mostram que conseguimos obter ganhos de performance até três ordens de magnitude acima do WMD enquanto mantendo as mesmas taxas de erro na tarefa de classificação de documentos. / [en] The Word Mover s Distance (WMD) proposed in Kusner et. al. [ICML,2015] is a distance between documents that takes advantage of semantic relations among words that are captured by their Word Embeddings. This distance proved to be quite effective, obtaining state-of-the-art error rates for classification tasks, but also impracticable for large collections or documents because it needs to compute a transportation problem on a complete bipartite graph for each pair of documents. By using assumptions, that are supported by empirical properties of the distances between Word Embeddings, we simplify WMD so that we obtain a new distance whose computation requires the solution of a max flow problem in a sparse graph, which can be solved much faster than the transportation problem in a dense graph. Our experiments show that we can obtain a performance gain up to three orders of magnitude over WMD while maintaining the same error rates in document classification tasks.