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Concurrent Dynamics of Category Learning and Metacognitive Judgments

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ABSTRACT

In two experiments, we examined the correspondence between the dynamics of metacognitive judgments and classification accuracy when participants were asked to learn category structures of different levels of complexity, i.e., to learn tasks of types I, II, and III according to Shepard et al. (1961). The stimuli were simple geometrical figures varying in the following three dimensions: color, shape, and size. In Experiment 1, we found moderate positive correlations between confidence and accuracy in task type II and weaker correlation in task type I and III. Moreover, the trend analysis in the backward learning curves revealed that there is a non-linear trend in accuracy for all three task types, but the same trend was observed in confidence for the task type I and II but not for task type III. In Experiment 2, we found that the feeling-of-warmth judgments (FOWs) showed moderate positive correlation with accuracy in all task types. Trend analysis revealed a similar non-linear component in accuracy and metacognitive judgments in task type II and III but not in task type I. Our results suggest that FOWs are a more sensitive measure of the progress of learning than confidence because FOWs capture global knowledge about the category structure, while confidence judgments are given at the level of an individual exemplar.

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Mean accuracy (A), confidence judgments (B), and log10 transformed response times (C) in the last five learning blocks. Results are shown separately for all participants (left column) and for participants who successfully learned all three tasks (right column). Blocks are counted relative to the end of the learning session. Error bars represent 95% within-subjects confidence intervals computed following Cousineau (2005) and Morey (2008).
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Figure 1: Mean accuracy (A), confidence judgments (B), and log10 transformed response times (C) in the last five learning blocks. Results are shown separately for all participants (left column) and for participants who successfully learned all three tasks (right column). Blocks are counted relative to the end of the learning session. Error bars represent 95% within-subjects confidence intervals computed following Cousineau (2005) and Morey (2008).

Mentions: Polynomial contrasts revealed that there was a statistically significant linear component in all task types. There was no significant quadratic component, but there was a significant cubic component in all task types. These results suggest that classification accuracy showed a trend with a steep increase in Block -2 that reached a plateau over Blocks -1 and 0, as shown in Figure 1. Importantly, a similar trend was observed for all three task types. In addition, there was quartic trend in task types I and II.


Concurrent Dynamics of Category Learning and Metacognitive Judgments
Mean accuracy (A), confidence judgments (B), and log10 transformed response times (C) in the last five learning blocks. Results are shown separately for all participants (left column) and for participants who successfully learned all three tasks (right column). Blocks are counted relative to the end of the learning session. Error bars represent 95% within-subjects confidence intervals computed following Cousineau (2005) and Morey (2008).
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Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037202&req=5

Figure 1: Mean accuracy (A), confidence judgments (B), and log10 transformed response times (C) in the last five learning blocks. Results are shown separately for all participants (left column) and for participants who successfully learned all three tasks (right column). Blocks are counted relative to the end of the learning session. Error bars represent 95% within-subjects confidence intervals computed following Cousineau (2005) and Morey (2008).
Mentions: Polynomial contrasts revealed that there was a statistically significant linear component in all task types. There was no significant quadratic component, but there was a significant cubic component in all task types. These results suggest that classification accuracy showed a trend with a steep increase in Block -2 that reached a plateau over Blocks -1 and 0, as shown in Figure 1. Importantly, a similar trend was observed for all three task types. In addition, there was quartic trend in task types I and II.

View Article: PubMed Central - PubMed

ABSTRACT

In two experiments, we examined the correspondence between the dynamics of metacognitive judgments and classification accuracy when participants were asked to learn category structures of different levels of complexity, i.e., to learn tasks of types I, II, and III according to Shepard et al. (1961). The stimuli were simple geometrical figures varying in the following three dimensions: color, shape, and size. In Experiment 1, we found moderate positive correlations between confidence and accuracy in task type II and weaker correlation in task type I and III. Moreover, the trend analysis in the backward learning curves revealed that there is a non-linear trend in accuracy for all three task types, but the same trend was observed in confidence for the task type I and II but not for task type III. In Experiment 2, we found that the feeling-of-warmth judgments (FOWs) showed moderate positive correlation with accuracy in all task types. Trend analysis revealed a similar non-linear component in accuracy and metacognitive judgments in task type II and III but not in task type I. Our results suggest that FOWs are a more sensitive measure of the progress of learning than confidence because FOWs capture global knowledge about the category structure, while confidence judgments are given at the level of an individual exemplar.

No MeSH data available.


Related in: MedlinePlus