Implicit learning is order dependent

A kinematically complex multi-articular motor skill for investigating implicit motor learning

Here we present a task developed to probe implicit learning of a complex motor skill. This task addresses limitations related to task complexity noted in the literature for methods investigating implicit motor learning, namely the serial reaction time task and continuous tracking task. Specifically, the serial reaction time task is limited by the kinematic simplicity of the required movement and the continuous tracing task faces time-on-task confounds and limitations in the control of task difficulty. The task presented herein addresses these issues by employing a kinematically complex multi-articular movement that controls factors that contribute to task difficulty: stimulus animation velocity and trajectory complexity. Accordingly, our objective was to validate the use of this task in probing implicit motor learning, hypothesizing that participants would learn one of the repeating stimuli implicitly. Participants engaged in six blocks of training whereby they first observed and then reproduced a seemingly random complex trajectory. Repeated trajectories were embedded amongst random trajectories. In line with the hypothesis, error for the repeated trajectories was decreased in comparison to that observed for the random trajectories and 73% of participants were unable to identify one of the repeated trajectories, demonstrating the occurrence of implicit learning. While the task requires minor alteration to optimize learning, ultimately the findings underline the task's potential to investigate implicit learning of a complex motor skill.

Modeling test learning and dual-task dissociations

Much of cognitive psychology is premised on the distinction between automatic and intentional processes, but the distinction often remains vague in practice and alternative explanations are often not followed through. For example, Hendricks, Conway and Kellogg (Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 491–1500, 2013) found that dual tasks at training versus at test dissociated performance in two different artificial grammar learning tasks. This was taken as evidence for underlying automatic and intentional processes. In this article, a different explanation is considered based on test learning and similarity, where participants are assumed to update their knowledge at test. Contrasting formal memory models of test learning are implemented, and it is concluded that the models account for the relevant dissociations without assuming a distinction between automatic and intentional processes.

Cross-cultural differences in implicit learning of chunks versus symmetries

Three experiments explore whether knowledge of grammars defining global versus local regularities has an advantage in implicit acquisition and whether this advantage is affected by cultural differences. Participants were asked to listen to and memorize a number of strings of 10 syllables instantiating an inversion (i.e. a global pattern); after the training phase, they were required to judge whether new strings were well formed. In Experiment 1, Western people implicitly acquired the inversion rule defined over the Chinese tones in a similar way as Chinese participants when alternative structures (specifically, chunking and repetition structures) were controlled. In Experiments 2 and 3, we directly pitted knowledge of the inversion (global) against chunk (local) knowledge, and found that Chinese participants had a striking global advantage in implicit learning, which was greater than that of Western participants. Taken together, we show for the first time cross-cultural differences in the type of regularities implicitly acquired.

Motor imagery-based implicit sequence learning depends on the formation of stimulus-response associations

Implicit sequence learning (ISL) occurs without conscious awareness and is critical for skill acquisition. The extent to which ISL occurs is a function of exposure (i.e., total training time and/or sequence to noise ratio) to a repeated sequence, and thus the cognitive mechanism underlying ISL is the formation of stimulus-response associations. As the majority of ISL studies employ paradigms whereby individuals unknowingly physically practice a repeated sequence, the cognitive mechanism underlying ISL through motor imagery (MI), the mental rehearsal of movement, remains unknown. This study examined the cognitive mechanisms of MI-based ISL by probing the link between exposure and the resultant ISL. Seventy-two participants underwent MI-based practice of an ISL task following randomization to one of four conditions: 4 training blocks with a high (4-High) or low (4-Low) sequence to noise ratio, or 2 training blocks with a high (2-High) or low (2-Low) sequence to noise ratio. Reaction time differences (dRT) and effect sizes between repeated and random sequences assessed the extent of learning. All groups showed a degree of ISL, yet effect sizes indicated a greater degree of learning in groups with higher exposure (4-Low and 4-High). Findings indicate that the extent to which ISL occurs through MI is impacted by manipulations to total training time and the sequence to noise ratio. Overall, we show that the extent of ISL occurring through MI is a function of exposure, indicating that like physical practice, the cognitive mechanisms of MI-based ISL rely on the formation of stimulus response associations.

AGSuite: Software to conduct feature analysis of artificial grammar learning performance

To simplify the problem of studying how people learn natural language, researchers use the artificial grammar learning (AGL) task. In this task, participants study letter strings constructed according to the rules of an artificial grammar and subsequently attempt to discriminate grammatical from ungrammatical test strings. Although the data from these experiments are usually analyzed by comparing the mean discrimination performance between experimental conditions, this practice discards information about the individual items and participants that could otherwise help uncover the particular features of strings associated with grammaticality judgments. However, feature analysis is tedious to compute, often complicated, and ill-defined in the literature. Moreover, the data violate the assumption of independence underlying standard linear regression models, leading to Type I error inflation. To solve these problems, we present AGSuite, a free Shiny application for researchers studying AGL. The suite's intuitive Web-based user interface allows researchers to generate strings from a database of published grammars, compute feature measures (e.g., Levenshtein distance) for each letter string, and conduct a feature analysis on the strings using linear mixed effects (LME) analyses. The LME analysis solves the inflation of Type I errors that afflicts more common methods of repeated measures regression analysis. Finally, the software can generate a number of graphical representations of the data to support an accurate interpretation of results. We hope the ease and availability of these tools will encourage researchers to take full advantage of item-level variance in their datasets in the study of AGL. We moreover discuss the broader applicability of the tools for researchers looking to conduct feature analysis in any field.