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Relevant bibliographies by topics / Free sorting task

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Journal articles

Academic literature on the topic 'Free sorting task'

Author: Grafiati

Published: 10 March 2023

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Journal articles on the topic "Free sorting task"

1

Evans, A. William, Raegan M. Hoeft, Florian G. Jentsch, and Clint A. Bowers. "Is a Picture Worth a Thousand Words? Investigating Structural Knowledge with Textual and Pictorial Stimuli." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 3 (2002): 240–44. http://dx.doi.org/10.1177/154193120204600305.

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The differences between the processing of textual and pictorial information have been a topic of research for some time now. Previous research concerning the modality of information has often concentrated on the speed of processing rather than the organizational differences that may exist. This experiment utilized card sorting to evaluate the changes in knowledge organization that occur when information is presented in text and picture formats. In addition to this, the structure of the elicitation task was manipulated to evaluate its effects on sharedness. It was found that textual stimuli produced a greater sharedness among participants in a free sorting task. However, for a structured sorting task, results reversed, and pictorial stimuli created a greater level of sharedness. Overall, structured sorting tasks produced a greater level of sharedness than the free sorting condition, regardless of modality.

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2

Lahne, Jacob. "Sorting backbone analysis: A network-based method of extracting key actionable information from free-sorting task results." Food Quality and Preference 82 (June 2020): 103870. http://dx.doi.org/10.1016/j.foodqual.2020.103870.

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3

Berland, Aurore, Pascal Gaillard, Michèle Guidetti, and Pascal Barone. "Perception of Everyday Sounds: A Developmental Study of a Free Sorting Task." PLOS ONE 10, no. 2 (2015): e0115557. http://dx.doi.org/10.1371/journal.pone.0115557.

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4

Qannari, El Mostafa, Philippe Courcoux, and Pauline Faye. "Significance test of the adjusted Rand index. Application to the free sorting task." Food Quality and Preference 32 (March 2014): 93–97. http://dx.doi.org/10.1016/j.foodqual.2013.05.005.

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5

Pétel, Cécile, Philippe Courcoux, Noémie Génovesi, Jocelyn Rouillé, Bernard Onno, and Carole Prost. "Free Sorting and Association Task: A Variant of the Free Sorting Method Applied to Study the Impact of Dried Sourdough as an Ingredienton the Related Bread Odor." Journal of Food Science 82, no. 4 (2017): 985–92. http://dx.doi.org/10.1111/1750-3841.13678.

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6

Davies, Ian, Greville Corbett, Debi Roberson, and Marieta Vandervyver. "Free-Sorting of Colors Across Cultures: Are there Universal Grounds for Grouping?" Journal of Cognition and Culture 5, no. 3-4 (2005): 349–86. http://dx.doi.org/10.1163/156853705774648536.

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AbstractThese studies examined naming and free-sorting behavior by informants speaking a wide range of languages, from both industrialized and traditional cultures. Groups of informants, whose color vocabularies varied from 5 to 12 basic terms, were given an unconstrained color grouping task to investigate whether there are systematic differences between cultures in grouping behavior that mirror linguistic differences and, if there are not, what underlying principles might explain any universal tendencies. Despite large differences in color vocabulary, there were substantial similarities in grouping behavior across language groups, and substantial within-language variation across informants. It seems that all informants group stimuli based on some criterion of perceptual similarity, but those with large color vocabularies are more likely to group stimuli in line with their basic color terms. The data are best accounted for by a hybrid system that combines a universal principle of grouping by similarity with culture-specific category salience.

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7

Courcoux, Ph, P. Faye, and E. M. Qannari. "Determination of the consensus partition and cluster analysis of subjects in a free sorting task experiment." Food Quality and Preference 32 (March 2014): 107–12. http://dx.doi.org/10.1016/j.foodqual.2013.05.004.

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8

Cliceri, Danny, Caterina Dinnella, Laurence Depezay, et al. "Exploring salient dimensions in a free sorting task: A cross-country study within the elderly population." Food Quality and Preference 60 (September 2017): 19–30. http://dx.doi.org/10.1016/j.foodqual.2017.03.006.

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9

Kloosterman, Fabian, Stuart P. Layton, Zhe Chen, and Matthew A. Wilson. "Bayesian decoding using unsorted spikes in the rat hippocampus." Journal of Neurophysiology 111, no. 1 (2014): 217–27. http://dx.doi.org/10.1152/jn.01046.2012.

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A fundamental task in neuroscience is to understand how neural ensembles represent information. Population decoding is a useful tool to extract information from neuronal populations based on the ensemble spiking activity. We propose a novel Bayesian decoding paradigm to decode unsorted spikes in the rat hippocampus. Our approach uses a direct mapping between spike waveform features and covariates of interest and avoids accumulation of spike sorting errors. Our decoding paradigm is nonparametric, encoding model-free for representing stimuli, and extracts information from all available spikes and their waveform features. We apply the proposed Bayesian decoding algorithm to a position reconstruction task for freely behaving rats based on tetrode recordings of rat hippocampal neuronal activity. Our detailed decoding analyses demonstrate that our approach is efficient and better utilizes the available information in the nonsortable hash than the standard sorting-based decoding algorithm. Our approach can be adapted to an online encoding/decoding framework for applications that require real-time decoding, such as brain-machine interfaces.

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10

Stepanova, Olga, and John Coley. "The Green Eyed Monster: Linguistic Influences on Concepts of Envy and Jealousy in Russian and English." Journal of Cognition and Culture 2, no. 4 (2002): 235–62. http://dx.doi.org/10.1163/15685370260440991.

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AbstractThe present study examined linguistic relativity in the domain of emotion terms. Exp. 1 showed that American English speakers use the word "jealous" to describe both situations involving envy and those involving jealousy, whereas Russian speakers describe emotions involved in the situations using the Russian terms revnuet and zaviduet in a mutually exclusive manner. Bilinguals performed according to the language they were tested in. In Experiment 2 we sought evidence for conceptual consequences of the difference in how emotion terms mapped onto situations for English and Russian speakers. In a non-linguistic triad sorting task, all subjects clearly distinguished jealousy situations from envy situations, but monolingual English speakers and bilinguals were more likely to see envy situations and jealousy situations as similar than Russian speakers. In a free sorting task, high agreement across all groups was shown in sorting jealousy, envy and control situations. However, native Russian speakers, in contrast to native English speakers, labeled the groups in a mutually-exclusive way. While providing some evidence for the weak view of linguistic relativity, overall the study shows that despite the difference in labeling the emotions of jealousy and envy, Russian speakers, English speakers and bilinguals are very similar in how they conceptualize emotionally-laden situations.

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