Socio-cognitive profiles for visual learning in young and older adults

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Abstract

It is common wisdom that practice makes perfect; but why do some adults learn better than others? Here, we investigate individuals' cognitive and social profiles to test which variables account for variability in learning ability across the lifespan. In particular, we focused on visual learning using tasks that test the ability to inhibit distractors and select task-relevant features. We tested the ability of young and older adults to improve through training in the discrimination of visual global forms embedded in a cluttered background. Further, we used a battery of cognitive tasks and psycho-social measures to examine which of these variables predict training-induced improvement in perceptual tasks and may account for individual variability in learning ability. Using partial least squares regression modeling, we show that visual learning is influenced by cognitive (i.e., cognitive inhibition, attention) and social (strategic and deep learning) factors rather than an individual's age alone. Further, our results show that independent of age, strong learners rely on cognitive factors such as attention, while weaker learners use more general cognitive strategies. Our findings suggest an important role for higher-cognitive circuits involving executive functions that contribute to our ability to improve in perceptual tasks after training across the lifespan.

Figures

  • FIGURE 1 | Example stimuli. Examples of Glass pattern stimuli (stimulus parameters are adjusted for demonstration purposes). The top panel shows Glass patterns stimuli with different spiral angles from radial (0◦) to concentric (90◦). The bottom panel shows radial Glass patterns at different levels of signal-to-noise-ratio (SNR) from 0.43 to 9.
  • FIGURE 2 | Behavioral improvement in visual discrimination task. (A) Normalized (z-score) thresholds (deg of spiral angle at 79.4% threshold performance) across training sessions for young (circles) and older (crosses) participants. (B) Normalized (z-score) threshold reduction (i.e., difference in thresholds between post- and pre-training) for young and older participants. Box plots show individual variability in learning
  • TABLE 1 | Performance in cognitive and individual differences measures.
  • TABLE 2 | Variance predicted by the PLS model.
  • FIGURE 3 | The role of cognitive and social profiles in learning variability. Outcome of the PLS model showing predictive utility of each variable in the PLS model when (A) age is included or (B) excluded from the model. Predictive utility values indicate the relative importance of each variable in predicting learning performance (i.e., threshold reduction). Variables included
  • TABLE 3 | Cognitive and individual differences measures for strong and weaker learners.
  • FIGURE 4 | Comparing profiles for strong and weaker learners. Results of partial correlations (r values) for “strong” and “weaker” learners. Threshold reduction is correlated with cognitive and individual differences measures, while controlling for pre-training performance in the visual discrimination task and age. Note that for graphical representation purposes, the signs of any negatively coded variables have been reversed, indicating that increased scores in
  • TABLE 4 | (A) Adjusted R-square values for PLS Regression without Age as an independent measure. (B) PLS model with threshold reduction as dependent variable and age as a moderator.

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CITATION STYLE

APA

Christian, J., Goldstone, A., Kuai, S. G., Chin, W., Abrams, D., & Kourtzi, Z. (2015). Socio-cognitive profiles for visual learning in young and older adults. Frontiers in Aging Neuroscience, 7(JUN). https://doi.org/10.3389/fnagi.2015.00105

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