The Experts below are selected from a list of 21132 Experts worldwide ranked by ideXlab platform
Andrew W. Ellis - One of the best experts on this subject based on the ideXlab platform.
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Evaluating a split processing model of visual word recognition: Effects of orthographic Neighborhood Size
Brain and language, 2004Co-Authors: Michal Lavidor, Adrian J. Hayes, Richard Shillcock, Andrew W. EllisAbstract:The split fovea theory proposes that visual word recognition of centrally presented words is mediated by the splitting of the foveal image, with letters to the left of fixation being projected to the right hemisphere (RH) and letters to the right of fixation being projected to the left hemisphere (LH). Two lexical decision experiments aimed to elucidate word recognition processes under the split fovea theory are described. The first experiment showed that when words were presented centrally, such that the initial letters were in the left visual field (LVF/RH), there were effects of orthographic Neighborhood, i.e., there were faster responses to words with high rather than low orthographic Neighborhoods for the initial letters ('lead neighbors'). This effect was limited to lead-neighbors but not end-neighbors (orthographic neighbors sharing the same final letters). When the same words were fully presented in the LVF/RH or right visual field (RVF/LH, Experiment 2), there was no effect of orthographic Neighborhood Size. We argue that the lack of an effect in Experiment 2 was due to exposure to all of the letters of the words, the words being matched for overall orthographic Neighborhood count and the sub-parts no longer having a unique effect. We concluded that the orthographic activation found in Experiment 1 occurred because the initial letters of centrally presented words were projected to the RH. The results support the split fovea theory, where the RH has primacy in representing lead neighbors of a written word.
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Word length and orthographic Neighborhood Size effects in the left and right cerebral hemispheres.
Brain and language, 2002Co-Authors: Michal Lavidor, Andrew W. EllisAbstract:Previous studies have reported an interaction between visual field (VF) and word length such that word recognition is affected more by length in the left VF (LVF) than in the right VF (RVF). A reanalysis showed that the previously reported effects of length were confounded with orthographic Neighborhood Size (N). In three experiments we manipulated length and N in lateralized lexical decision tasks. Results showed that length and VF interacted even with N controlled (Experiment 1); that N affected responses to words in the LVF but not the RVF (Experiment 2); and that when length and N were combined, length only affected performance in the LVF for words with few neighbors.
Anne Tolan - One of the best experts on this subject based on the ideXlab platform.
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Does Neighborhood Size really cause the word length effect
Memory & Cognition, 2017Co-Authors: Dominic Guitard, Jean Saint-aubin, Gerald Tehan, Anne TolanAbstract:In short-term serial recall, it is well-known that short words are remembered better than long words. This word length effect has been the cornerstone of the working memory model and a benchmark effect that all models of immediate memory should account for. Currently, there is no consensus as to what determines the word length effect. Jalbert and colleagues (Jalbert, Neath, Bireta, & Surprenant, 2011a; Jalbert, Neath, & Surprenant, 2011b) suggested that Neighborhood Size is one causal factor. In six experiments we systematically examined their suggestion. In Experiment 1, with an immediate serial recall task, multiple word lengths, and a large pool of words controlled for Neighborhood Size, the typical word length effect was present. In Experiments 2 and 3, with an order reconstruction task and words with either many or few neighbors, we observed the typical word length effect. In Experiment 4 we tested the hypothesis that the previous abolition of the word length effect when Neighborhood Size was controlled was due to a confounded factor: frequency of orthographic structure. As predicted, we reversed the word length effect when using short words with less frequent orthographic structures than the long words, as was done in both of Jalbert et al.’s studies. In Experiments 5 and 6, we again observed the typical word length effect, even if we controlled for Neighborhood Size and frequency of orthographic structure. Overall, the results were not consistent with the predictions of Jalbert et al. and clearly showed a large and reliable word length effect after controlling for Neighborhood Size.
Aimée M. Surprenant - One of the best experts on this subject based on the ideXlab platform.
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The Effect of Lexical Factors on Recall From Working Memory: Generalizing the Neighborhood Size Effect.
Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale, 2016Co-Authors: Lesley S. Derraugh, Ian Neath, Aimée M. Surprenant, Olivia Beaudry, Jean Saint-aubinAbstract:The word-length effect, the finding that lists of short words are better recalled than lists of long words, is 1 of the 4 benchmark phenomena that guided development of the phonological loop component of working memory. However, previous work has noted a confound in word-length studies: The short words used had more orthographic neighbors (valid words that can be made by changing a single letter in the target word) than long words. The confound is that words with more neighbors are better recalled than otherwise comparable words with fewer neighbors. Two experiments are reported that address criticisms of the Neighborhood-Size account of the word-length effect by (1) testing 2 new stimulus sets, (2) using open rather than closed pools of words, and (3) using stimuli from a language other than English. In both experiments, words from large Neighborhoods were better recalled than words from small Neighborhoods. The results add to the growing number of studies demonstrating the substantial contribution of long-term memory to what have traditionally been identified as working memory tasks. The data are more easily explained by models incorporating the concept of redintegration rather than by frameworks such as the phonological loop that posit decay offset by rehearsal. (PsycINFO Database Record
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Does length or Neighborhood Size cause the word length effect
Memory & cognition, 2011Co-Authors: Annie Jalbert, Ian Neath, Aimée M. SurprenantAbstract:Jalbert, Neath, Bireta, and Surprenant (2011) suggested that past demonstrations of the word length effect, the finding that words with fewer syllables are recalled better than words with more syllables, included a confound: The short words had more orthographic neighbors than the long words. The experiments reported here test two predictions that would follow if Neighborhood Size is a more important factor than word length. In Experiment 1, we found that concurrent articulation removed the effect of Neighborhood Size, just as it removes the effect of word length. Experiment 2 demonstrated that this pattern is also found with nonwords. For Experiment 3, we factorially manipulated length and Neighborhood Size, and found only effects of the latter. These results are problematic for any theory of memory that includes decay offset by rehearsal, but they are consistent with accounts that include a redintegrative stage that is susceptible to disruption by noise. The results also confirm the importance of lexical and linguistic factors on memory tasks thought to tap short-term memory.
Michal Lavidor - One of the best experts on this subject based on the ideXlab platform.
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Evaluating a split processing model of visual word recognition: Effects of orthographic Neighborhood Size
Brain and language, 2004Co-Authors: Michal Lavidor, Adrian J. Hayes, Richard Shillcock, Andrew W. EllisAbstract:The split fovea theory proposes that visual word recognition of centrally presented words is mediated by the splitting of the foveal image, with letters to the left of fixation being projected to the right hemisphere (RH) and letters to the right of fixation being projected to the left hemisphere (LH). Two lexical decision experiments aimed to elucidate word recognition processes under the split fovea theory are described. The first experiment showed that when words were presented centrally, such that the initial letters were in the left visual field (LVF/RH), there were effects of orthographic Neighborhood, i.e., there were faster responses to words with high rather than low orthographic Neighborhoods for the initial letters ('lead neighbors'). This effect was limited to lead-neighbors but not end-neighbors (orthographic neighbors sharing the same final letters). When the same words were fully presented in the LVF/RH or right visual field (RVF/LH, Experiment 2), there was no effect of orthographic Neighborhood Size. We argue that the lack of an effect in Experiment 2 was due to exposure to all of the letters of the words, the words being matched for overall orthographic Neighborhood count and the sub-parts no longer having a unique effect. We concluded that the orthographic activation found in Experiment 1 occurred because the initial letters of centrally presented words were projected to the RH. The results support the split fovea theory, where the RH has primacy in representing lead neighbors of a written word.
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Word length and orthographic Neighborhood Size effects in the left and right cerebral hemispheres.
Brain and language, 2002Co-Authors: Michal Lavidor, Andrew W. EllisAbstract:Previous studies have reported an interaction between visual field (VF) and word length such that word recognition is affected more by length in the left VF (LVF) than in the right VF (RVF). A reanalysis showed that the previously reported effects of length were confounded with orthographic Neighborhood Size (N). In three experiments we manipulated length and N in lateralized lexical decision tasks. Results showed that length and VF interacted even with N controlled (Experiment 1); that N affected responses to words in the LVF but not the RVF (Experiment 2); and that when length and N were combined, length only affected performance in the LVF for words with few neighbors.
Chao Shao - One of the best experts on this subject based on the ideXlab platform.
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Selection of the Suitable Parameter Value for ISOMAP
Journal of Software, 2011Co-Authors: Li Jing, Chao ShaoAbstract:As a promising dimensionality reduction and data visualization technique, ISOMAP is usually used for data preprocessing to avoid “the curse of dimensionality” and select more suitable algorithms or improve the performance of algorithms used in data mining process according to No Free Lunch (NFL) Theorem. ISOMAP has only one parameter, i.e. the Neighborhood Size, upon which the success of ISOMAP depends greatly. However, it’s an open problem how to select a suitable Neighborhood Size efficiently. Based on the unique feature of shortcut edges, introduced into the Neighborhood graph by using the unsuitable Neighborhood Size, this paper presents an efficient method to select a suitable Neighborhood Size according to the decrement of the sum of all the shortest path distances. In contrast with the straightforward method with residual variance, our method only requires running the former part of ISOMAP (shortest path computation) incrementally, which makes it less time-consuming, while yielding the same results. Finally, the feasibility and robustness of this method can be verified by experimental results well.
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Selection of the Suitable Neighborhood Size Based on Bayesian Information Criterion
2010 Third International Joint Conference on Computational Science and Optimization, 2010Co-Authors: Chao Shao, Bin ZhangAbstract:To select a suitable Neighborhood Size for manifold learning algorithms efficiently, a new method based on BIC (Bayesian Information Criterion) is used in this paper. Due to the locally Euclidean property of the manifold, the PCA (Principal Component Analysis) reconstruction errors of the Neighborhoods without shortcut edges remain small; however, those of the Neighborhoods with shortcut edges are relatively quite large. So all the PCA reconstruction errors fall into two clusters when the Neighborhood Size is unsuitable, or one cluster when the Neighborhood Size is suitable, which can be detected by BIC. Concretely speaking, if the BIC value of the two-cluster solution is larger than that of the one-cluster solution, all the PCA reconstruction errors fall into two clusters, which means that the Neighborhood Size is unsuitable, otherwise which means that the Neighborhood Size is suitable. This method only requires running PCA and computing BIC, whose time complexities are relatively small, but not running the time-consuming manifold learning algorithm as those methods based on residual variance do, so this method is much more efficient than those methods based on residual variance. The effectivity of this method can be verified by experimental results well.
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Incremental selection of the Neighborhood Size for ISOMAP
2008 International Conference on Machine Learning and Cybernetics, 2008Co-Authors: Chao ShaoAbstract:The success of ISOMAP depends greatly on selecting a suitable Neighborhood Size; however, itpsilas an open problem how to do this efficiently. When the Neighborhood Size becomes unsuitable, shortcut edges can be introduced into the Neighborhood graph and destroy the approximation ability of the involved shortest-path distances to the corresponding geodesic distances greatly. Itpsilas obvious that shortcut edge links two endpoints lying close in Euclidean space but far away on the manifold, which can be measured approximately by its order presented in this paper. Based on the observation, this paper presented an efficient method to find a suitable Neighborhood Size incrementally, which doesn't need to compute shortest-path distances or run the MDS algorithm as those methods based on residual variance do. Finally, the feasibility of this method can be verified by experimental results.
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Selection of the Suitable Neighborhood Size for the ISOMAP Algorithm
2007 International Joint Conference on Neural Networks, 2007Co-Authors: Chao Shao, Houkuan HuangAbstract:The success of ISOMAP depends greatly on selecting a suitable Neighborhood Size; however, it's an open problem how to do this efficiently. When the Neighborhood Size is unsuitable, shortcut edges can emerge in the Neighborhood graph and shorten the involved shortest path lengths greatly, which makes them not approximate the corresponding geodesic distances anymore, that is, there doesn't exist such an approximately monotonically increasing relationship between them anymore. Based on this observation, in the paper, we use costs over the minimal connected Neighborhood graph to approximate the corresponding geodesic distances, and then present an efficient method to judge whether a Neighborhood Size is suitable beforehand, by which a suitable Neighborhood Size can be selected more efficiently than the straightforward method with the residual variance. Besides, the correctness of the intrinsic dimensionality, estimated by ISOMAP, of the data can also be judged more easily by our method.
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IJCNN - Selection of the Suitable Neighborhood Size for the ISOMAP Algorithm
2007 International Joint Conference on Neural Networks, 2007Co-Authors: Chao Shao, Houkuan Huang, Chunhong WanAbstract:The success of ISOMAP depends greatly on selecting a suitable Neighborhood Size; however, it's an open problem how to do this efficiently. When the Neighborhood Size is unsuitable, shortcut edges can emerge in the Neighborhood graph and shorten the involved shortest path lengths greatly, which makes them not approximate the corresponding geodesic distances anymore, that is, there doesn't exist such an approximately monotonically increasing relationship between them anymore. Based on this observation, in the paper, we use costs over the minimal connected Neighborhood graph to approximate the corresponding geodesic distances, and then present an efficient method to judge whether a Neighborhood Size is suitable beforehand, by which a suitable Neighborhood Size can be selected more efficiently than the straightforward method with the residual variance. Besides, the correctness of the intrinsic dimensionality, estimated by ISOMAP, of the data can also be judged more easily by our method.
