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Varying variation: the effects of within- versus across-feature differences on relational category learning.

Livins KA, Spivey MJ, Doumas LA - Front Psychol (2015)

Bottom Line: As a result, the way that they interact with feature variation is unclear.Both sets of results indicated that learning a relational category from a training set with a lower amount of variation is easier, but that learning from a training set with increased within-feature variation is significantly less challenging than learning from a set with increased across-feature variation.These results support the claim that, like feature-based category-learning, relational category-learning is sensitive to the type of feature variation in the training set.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive Science, University of California, Merced, Merced, CA USA.

ABSTRACT
Learning of feature-based categories is known to interact with feature-variation in a variety of ways, depending on the type of variation (e.g., Markman and Maddox, 2003). However, relational categories are distinct from feature-based categories in that they determine membership based on structural similarities. As a result, the way that they interact with feature variation is unclear. This paper explores both experimental and computational data and argues that, despite its reliance on structural factors, relational category-learning should still be affected by the type of feature variation present during the learning process. It specifically suggests that within-feature and across-feature variation should produce different learning trajectories due to a difference in representational cost. The paper then uses the DORA model (Doumas et al., 2008) to discuss how this account might function in a cognitive system before presenting an experiment aimed at testing this account. The experiment was a relational category-learning task and was run on human participants and then simulated in DORA. Both sets of results indicated that learning a relational category from a training set with a lower amount of variation is easier, but that learning from a training set with increased within-feature variation is significantly less challenging than learning from a set with increased across-feature variation. These results support the claim that, like feature-based category-learning, relational category-learning is sensitive to the type of feature variation in the training set.

No MeSH data available.


Related in: MedlinePlus

The mean reaction times during training trails organized by condition. Error bars represent the means plus or minus the SEs.
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Figure 10: The mean reaction times during training trails organized by condition. Error bars represent the means plus or minus the SEs.

Mentions: Third, with regard to reaction times during the training trails, after reaction times more than 3 SDs from the mean were removed, another one-way ANOVA showed that there was a significant difference between conditions [F(2,84) = 4.414, p < 0.05]. A Bonferroni post hoc test showed that the base-variation condition (M = 1.78, SD = 0.65) had a significantly faster mean reaction time than the across-feature variation condition (M = 3.00, SD = 2.61; p < 0.05), however, it was not significantly faster than the within-feature variation condition (M = 2.62, SD = 1.22; p = 0.37). There was also no significant difference between the within-feature and across-feature conditions for this measure (p = 0.185; see Figure 10).


Varying variation: the effects of within- versus across-feature differences on relational category learning.

Livins KA, Spivey MJ, Doumas LA - Front Psychol (2015)

The mean reaction times during training trails organized by condition. Error bars represent the means plus or minus the SEs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4321646&req=5

Figure 10: The mean reaction times during training trails organized by condition. Error bars represent the means plus or minus the SEs.
Mentions: Third, with regard to reaction times during the training trails, after reaction times more than 3 SDs from the mean were removed, another one-way ANOVA showed that there was a significant difference between conditions [F(2,84) = 4.414, p < 0.05]. A Bonferroni post hoc test showed that the base-variation condition (M = 1.78, SD = 0.65) had a significantly faster mean reaction time than the across-feature variation condition (M = 3.00, SD = 2.61; p < 0.05), however, it was not significantly faster than the within-feature variation condition (M = 2.62, SD = 1.22; p = 0.37). There was also no significant difference between the within-feature and across-feature conditions for this measure (p = 0.185; see Figure 10).

Bottom Line: As a result, the way that they interact with feature variation is unclear.Both sets of results indicated that learning a relational category from a training set with a lower amount of variation is easier, but that learning from a training set with increased within-feature variation is significantly less challenging than learning from a set with increased across-feature variation.These results support the claim that, like feature-based category-learning, relational category-learning is sensitive to the type of feature variation in the training set.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive Science, University of California, Merced, Merced, CA USA.

ABSTRACT
Learning of feature-based categories is known to interact with feature-variation in a variety of ways, depending on the type of variation (e.g., Markman and Maddox, 2003). However, relational categories are distinct from feature-based categories in that they determine membership based on structural similarities. As a result, the way that they interact with feature variation is unclear. This paper explores both experimental and computational data and argues that, despite its reliance on structural factors, relational category-learning should still be affected by the type of feature variation present during the learning process. It specifically suggests that within-feature and across-feature variation should produce different learning trajectories due to a difference in representational cost. The paper then uses the DORA model (Doumas et al., 2008) to discuss how this account might function in a cognitive system before presenting an experiment aimed at testing this account. The experiment was a relational category-learning task and was run on human participants and then simulated in DORA. Both sets of results indicated that learning a relational category from a training set with a lower amount of variation is easier, but that learning from a training set with increased within-feature variation is significantly less challenging than learning from a set with increased across-feature variation. These results support the claim that, like feature-based category-learning, relational category-learning is sensitive to the type of feature variation in the training set.

No MeSH data available.


Related in: MedlinePlus