Strategies in skill acquisition: reconciling continuous models of the learning curve with abrupt strategy shifts.

How does task completion time change with practice and what processes underlie this change? Despite over 100 years of scientific research (e.g., Bryan & Harter, 1899) no wholly satisfactory answer has yet emerged. After analysing many skill acquisition datasets Newell and Rosenbloom (1981) influentially declared that the relationship between practice and task completion time was best represented by a power function labelling the relationship the Power Law of Practice. Use of the term ‘law’ might suggest that the case was closed, yet several recent findings have challenged the Power Law’s ‘legal’ status. First, Heathcote, Brown, and Mewhort (2000) concluded that the Power Law of Practice was an artefact of aggregation. Across a large number of skill acquisition datasets they showed that when analysed at the individual-level the exponential function tended to provide superior fit. Second, several researchers have suggested that strategy shifts may even cause discontinuities in the learning curve (e.g., Delaney, Reder, Staszewski, & Ritter, 1998; Haider & Frensch, 2002; Rickard, 1997). In summary, findings suggest that the power function is an analytical artefact and that learning may in some instances involve discontinuities. In response to these challenges, this thesis had three aims. The first aim was to develop and test mathematical models of the relationship between practice, strategy use and performance. The second aim was to assess the role of individual differences, including prior experience, ability, and personality in predicting strategy use and performance. The third aim was to model the differential effects of instructed versus self-initiated strategy shift on strategy use and performance. Collectively, the aims were designed to provide a multifaceted explanation of the relationship between practice, strategy use, and performance. To achieve these aims three studies were conducted. In each study participants completed a set of trials on a text editing task. On each trial strategy sophistication and task completion time were measured. Final sample sizes in the three studies were n1 = 63, n2 = 154, and n3 = 154. Each study also measured a selection of individual difference variables including prior experience, demographics, personality, and ability. Text editing was chosen as the criterion task because strategy use is important to task performance and strategy use could readily be measured. The text editing task was developed to enable trial-level measurement of strategy use. Strategy sophistication was operationalised as the proportion of key presses used that were classified as sophisticated (e.g., using control and right cursor keys to move between words) as opposed to simple (e.g., using just the right cursor to move between characters). In Studies 1 and 2 all participants received the same instructions. In Study 3 participants were randomly assigned to one of three conditions with varying instructions. In a No Training condition practice preceded without interruption, in a Training condition additional instructions were presented halfway through practice, and in a Control condition a filler task was presented halfway through practice. Aims 1 and 2 were assessed by Study 1 and 2 and the No Training condition of Study 3. Aim 3 was assessed by comparing the conditions in Study 3. With regard to Aim 1 results from the three studies told a consistent story. Results reiterated the importance of analysing data at the individual-level. While at the group-level, a three parameter power function provided superior fit, at the individual-level a three parameter exponential function was significantly better in two out of three studies. Similarly, at the group-level, strategy sophistication was a continuously increasing, monotonically decelerating function of practice, well modelled by a three parameter Michaelis–Menten function. In contrast, at the individual-level, the functional form of the relationship varied dramatically between individuals with a variety of often discontinuous functions providing good fit. Although abrupt strategy shifts did occur, meaningful discontinuities in the relationship between practice and task completion time were rare. Findings supported a model that explained how abrupt strategy shifts can co-occur with continuous learning curves. These findings were that: (a) strategy shifts were more likely to occur early in practice when other learning was occurring; (b) trial-to-trial variance in task completion time was often large relative to the benefits of the strategy shift; and (c)strategy shifts often took several trials to be fully realised. These and other factors combined to generally smooth out the discontinuous effects of strategy shift on performance. In relation to the second aim, concerning individual differences, ability and prior experience consistently emerged as moderate to strong predictors of task performance, whereas self-reported Big 5 personality was unrelated to task performance. Similar but generally weaker relationships were found between individual differences and strategy sophistication. A model that proposed that the effect of ability and prior experience on task performance was mediated by strategy sophistication was not supported. Findings were broadly consistent with cognitive correlates and skill transfer models of individual differences. In relation to the third aim, looking at differences between instructed strategy shift and self-initiated strategy shift, hypotheses were partially supported. In summary, relative to self-initiated strategy shifts, instructed strategy shifts were more abrupt. Performance also tended to decline sharply immediately following the instructed strategy shift. After additional practice, performance was similar to groups that had not received instructed strategy shift. The study highlighted how the dynamics of instructed strategy shift differ from self-initiated strategy shift with regards to discontinuities. Taken together the results tell an interconnected story regarding the relationship between practice, strategy use, and performance. This thesis contributes to skill acquisition research through a unique combination of features including trial-level measurement of strategy use, individual-level modelling, and the use of nonlinear and discontinuous functions. It is hoped that future research will build on this approach using other samples, tasks, and contexts.