Fluid intelligence and working memory support dissociable aspects of learning by physical but not observational practice

Abstract

Humans have a remarkable ability to learn by watching others, whether learning to tie an elaborate knot or play the piano. However, the mechanisms that translate visual input into motor skill execution remain unclear. It has been proposed that common cognitive and neural mechanisms underpin learning motor skills by physical and observational practice. Here we provide a novel test of the common mechanism hypothesis by testing the extent to which certain individual differences predict observational as well as physical learning. Participants (N = 92 per group) either physically practiced a five-element key-press sequence or watched videos of similar sequences before physically performing trained and untrained sequences in a test phase. We also measured cognitive abilities across participants that have previously been associated with rates of learning, including working memory and fluid intelligence. Our findings show that individual differences in working memory and fluid intelligence predict improvements in dissociable aspects of motor learning following physical practice, but not observational practice. Working memory predicts general learning gains from pre- to post-test that generalise to untrained sequences, whereas fluid intelligence predicts sequence-specific gains that are tied to trained sequences. However, neither working memory nor fluid intelligence predict training gains following observational learning. Therefore, these results suggest limits to the shared mechanism hypothesis of physical and observational learning. Indeed, models of observational learning need updating to reflect the extent to which such learning is based on shared as well as distinct processes compared to physical learning. We suggest that such differences could reflect the more intentional nature of learning during physical compared to observational practice, which relies to a greater extent on higher-order cognitive resources such as working memory and fluid intelligence.

Keywords: Fluid intelligence; Individual differences; Observational learning; Working memory.

Figure 1

Trial structures for sequence execution and sequence observation.(A) Sequence execution trial example. A cued sequence had to be memorised and then executed five times while receiving performance feedback. (B) Sequence observation trial example. A sequence cue was followed by a video showing a hand executing the sequence five times, either correctly or incorrectly. Occasionally a question was asked whether there was an error in any of the five repetitions, and a response had to be made.

Figure 2

Sequence execution time at pre-test, post-test and during practice sessions across observational and physical practice groups. As two sequences were allocated to each condition (trained and untrained), two data points are plotted per condition at pre-test and post-test. Similarly, during practice sessions, the physical practice group practised two sequences (note: the observational practice group did not physically practice, which is why no training data are reported for that group during training). Practice was divided into four sub-sessions (displayed as Run 1-4), which were performed on separate days. Each sub-session consisted of nine trials per sequence (which produced 18 trials per sub-session in total) and a single trial comprised five consecutive executions of a sequence. Sequence execution time during practice was measured as an average of correct sequence executions within the trial. As such, if all five executions were performed incorrectly, the trial was not included in the plot. Therefore, the number of participants who contribute to each trial varies slightly from trial to trial (range 88-92; from a total of 92 participants in the physical practice group). Error bars represent within-participant 95% confidence intervals. Abbreviations: Tr. = trained; Untr. = untrained.

Figure 3

Training effects on sequence-specific (A) and general skill (B) learning for the observational practice (grey) and physical practice (orange) groups. Large dots: group averages and 95% CI; small dots: individual participant values;** p < 0.01, **** p < 0.0001.

Figure 4

Scatter plots and marginal distribution densities showing sequence-specific (A) and general skill (B) learning versus baseline performance, fluid intelligence and working memory for observational practice (grey) and physical practice (orange) groups.

Figure 5

Illustration of regression analyses for physical and observational practice groups. For each group (physical practice and observational practice), the figure shows standardised beta estimates of how baseline performance, fluid intelligence and working memory predict the sequence-specific and general skill learning. In addition, the figure shows the predictor intercorrelation coefficients and outcome intercorrelation coefficients. Green: positive, red: negative, fading reflects significance.

Figure 6

Error detection accuracy and perceptual improvement predictors. (A) Group-averaged accuracy in response to the error question during observational training. Large dots: group averages and 95% CI; small dots: individual participant values. (B) Perceptual improvement predictor variables. The figure shows standardised beta estimates of how error detection accuracy in run 2, fluid intelligence, and working memory predict the perceptual improvement from run 2 to run 4. In addition, the figure shows the predictor intercorrelation coefficients. Green: positive, red: negative, fading reflects significance. (C) Scatter plots showing the raw data for changes in accuracy improvement from run 2 to run 4 as a function of fluid intelligence (left panel) as well as working memory (right panel).

Figure 7

A schematic illustration of the relationships between cognitive abilities and skill learning through physical and observational practice. Blue = untrained. Red = trained. S = Sequence.