Limits...
Differential Impact of Visuospatial Working Memory on Rule-based and Information-integration Category Learning

View Article: PubMed Central - PubMed

ABSTRACT

Previous studies have indicated that the category learning system is a mechanism with multiple processing systems, and that working memory has different effects on category learning. But how does visuospatial working memory affect perceptual category learning? As there is no definite answer to this question, we conducted three experiments. In Experiment 1, the dual-task paradigm with sequential presentation was adopted to investigate the influence of visuospatial working memory on rule-based and information-integration category learning. The results showed that visuospatial working memory interferes with rule-based but not information-integration category learning. In Experiment 2, the dual-task paradigm with simultaneous presentation was used, in which the categorization task was integrated into the visuospatial working memory task. The results indicated that visuospatial working memory affects information-integration category learning but not rule-based category learning. In Experiment 3, the dual-task paradigm with simultaneous presentation was employed, in which visuospatial working memory was integrated into the category learning task. The results revealed that visuospatial working memory interferes with both rule-based and information-integration category learning. Through these three experiments, we found that, regarding the rule-based category learning, working memory load is the main mechanism by which visuospatial working memory influences the discovery of the category rules. In addition, regarding the information-integration category learning, visual resources mainly operates on the category representation.

No MeSH data available.


The RB category structure and II category structure. Open circles denote Category (A) and filled circles denote Category (B). The lines represent the optimal decision boundary. In a RB category structure, decisions are made based on only one dimension (in this example, frequency), whereas in an II category structure, decisions are made based on two or more dimensions (in this example, frequency and orientation).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5384164&req=5

Figure 1: The RB category structure and II category structure. Open circles denote Category (A) and filled circles denote Category (B). The lines represent the optimal decision boundary. In a RB category structure, decisions are made based on only one dimension (in this example, frequency), whereas in an II category structure, decisions are made based on two or more dimensions (in this example, frequency and orientation).

Mentions: This has led to an extensive series of studies that have compared the learning of RB and II category structures (Figure 1). Given that the categorization of the RB and II structures depends primarily on the verbal and implicit systems, respectively, it is possible to test two kinds of prediction made by the COVIS model (Dunn et al., 2012). For the RB category structure, the classification rules are easy to verbalize and a judgment rule does not require the integration of two dimensions. For example, consider a category set in which round objects belong to one group and square objects belong to another group. These categories could be learned by applying the easy to verbalize rule that “category 1 objects are round.” However, in contrast, the II category structure defines category membership according to the conjoint values on two or more dimensions using rules that are not easy to verbalize (e.g., if the size of a circle is greater than x and the orientation of a line is greater than y, then the stimulus is a member of category A). Consequently, such structures cannot be learned by the verbal system, which must eventually yield control of the response to the implicit system (Maddox et al., 2004; Worthy et al., 2013; Richler and Palmeri, 2014).


Differential Impact of Visuospatial Working Memory on Rule-based and Information-integration Category Learning
The RB category structure and II category structure. Open circles denote Category (A) and filled circles denote Category (B). The lines represent the optimal decision boundary. In a RB category structure, decisions are made based on only one dimension (in this example, frequency), whereas in an II category structure, decisions are made based on two or more dimensions (in this example, frequency and orientation).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5384164&req=5

Figure 1: The RB category structure and II category structure. Open circles denote Category (A) and filled circles denote Category (B). The lines represent the optimal decision boundary. In a RB category structure, decisions are made based on only one dimension (in this example, frequency), whereas in an II category structure, decisions are made based on two or more dimensions (in this example, frequency and orientation).
Mentions: This has led to an extensive series of studies that have compared the learning of RB and II category structures (Figure 1). Given that the categorization of the RB and II structures depends primarily on the verbal and implicit systems, respectively, it is possible to test two kinds of prediction made by the COVIS model (Dunn et al., 2012). For the RB category structure, the classification rules are easy to verbalize and a judgment rule does not require the integration of two dimensions. For example, consider a category set in which round objects belong to one group and square objects belong to another group. These categories could be learned by applying the easy to verbalize rule that “category 1 objects are round.” However, in contrast, the II category structure defines category membership according to the conjoint values on two or more dimensions using rules that are not easy to verbalize (e.g., if the size of a circle is greater than x and the orientation of a line is greater than y, then the stimulus is a member of category A). Consequently, such structures cannot be learned by the verbal system, which must eventually yield control of the response to the implicit system (Maddox et al., 2004; Worthy et al., 2013; Richler and Palmeri, 2014).

View Article: PubMed Central - PubMed

ABSTRACT

Previous studies have indicated that the category learning system is a mechanism with multiple processing systems, and that working memory has different effects on category learning. But how does visuospatial working memory affect perceptual category learning? As there is no definite answer to this question, we conducted three experiments. In Experiment 1, the dual-task paradigm with sequential presentation was adopted to investigate the influence of visuospatial working memory on rule-based and information-integration category learning. The results showed that visuospatial working memory interferes with rule-based but not information-integration category learning. In Experiment 2, the dual-task paradigm with simultaneous presentation was used, in which the categorization task was integrated into the visuospatial working memory task. The results indicated that visuospatial working memory affects information-integration category learning but not rule-based category learning. In Experiment 3, the dual-task paradigm with simultaneous presentation was employed, in which visuospatial working memory was integrated into the category learning task. The results revealed that visuospatial working memory interferes with both rule-based and information-integration category learning. Through these three experiments, we found that, regarding the rule-based category learning, working memory load is the main mechanism by which visuospatial working memory influences the discovery of the category rules. In addition, regarding the information-integration category learning, visual resources mainly operates on the category representation.

No MeSH data available.