How sequence learning unfolds: Insights from anticipatory eye movements

Co-actors represent each other's task regularity through social statistical learning

Numerous joint action studies have demonstrated that certain low-level aspects (e.g., stimuli and responses) of a co-actor's task can be automatically and implicitly represented by us as actors, biasing our own task performance in a joint action setup. However, it remains unclear whether individuals also represent more abstract, high-level aspects of a co-actor's task, such as regularity. In the first five experiments, participants participated alongside their co-actors and responded to a mixed shape sequence generated by randomly interleaving two fixed order sequences of shapes in both the pre- and post-test sessions. But different intermediate practice sessions were undergone by participants across experiments. When practicing their own fixed order sequences in a mixed shape sequence, either together with another person (Experiment 1) or alone but informed that their partner was performing the same practice task in a different room (Experiment 4), participants exhibited a learning effect on their co-actors' practiced sequences. This indirect learning effect was absent when one of the co-actors did not participate due to either being removed from the practice (Experiment 2) or sitting still without offering responses (Experiment 3), as well as when the two co-actors practiced together but responded to two distinct properties of stimuli (e.g., colour and shape, respectively), with one having regularity and the other not. Finally, participants exhibited comparable direct learning effects on their own practiced sequences for Experiments 1-5 as when performing the pre-test, practice, and post-test sessions alone for Experiment 6. These results demonstrate that, when practicing together, or even when believing that they are acting together with a partner, co-actors do represent the task regularity of one another through social statistical learning and transfer this learned regularity to subsequent task performances. The present study extends our understanding of co-representation in the joint action context in terms of the more abstract and high-level task features people co-represent, such as a co-actor's task regularity.

How associative thinking influences scene perception

Perception of our external environment is not isolated from the influence of our internal thoughts, and past evidence points to a possible common associative mechanism underlying both the perception of scenes and our internal thought. Here, we investigated the nature of the interaction between an associative mindset and scene perception, hypothesizing a functional advantage to an associative thought pattern in the perception of scenes. Experiments 1 and 2 showed that associative thinking facilitates scene perception, which evolved over the course of the experiments. In contrast to scene perception, Experiment 3 showed that associative thinking hinders the perception of mundane objects, in which associative information is minimized. Nevertheless, object perception was facilitated when associative thinking was reduced. This double dissociation suggests that an associative mind is more receptive of externally perceived associative information, and that a match between the orientation of internal and external processing may be key for perception.

Measuring statistical learning by eye-tracking

Statistical learning—the skill to pick up probability-based regularities of the environment—plays a crucial role in adapting to the environment and learning perceptual, motor, and language skills in healthy and clinical populations. Here, we developed a new method to measure statistical learning without any manual responses. We used the Alternating Serial Reaction Time (ASRT) task, adapted to eye-tracker, which, besides measuring reaction times (RTs), enabled us to track learning-dependent anticipatory eye movements. We found robust, interference-resistant learning on RT; moreover, learning-dependent anticipatory eye movements were even more sensitive measures of statistical learning on this task. Our method provides a way to apply the widely used ASRT task to operationalize statistical learning in clinical populations where the use of manual tasks is hindered, such as in Parkinson’s disease. Furthermore, it also enables future basic research to use a more sensitive version of this task to measure predictive processing.

Inhibition of Eye Movements Disrupts Spatial Sequence Learning

Implicit sequence learning is an integral part of human experience, yet the nature of the mechanisms underlying this type of learning remains a matter of debate. In the current study, we provide a test for two accounts of implicit sequence learning, that is, one that highlights sequence learning in the absence of any motor responses (with suppressed eye movements) and one that highlights the relative contribution of the motor processes (i.e., eye movements) to learning. To adjudicate between these accounts and determine whether a motor response is a requisite process in sequence learning, we used anticipation measures to compare performance on the standard oculomotor serial reaction time (SRT) task and on a version of the SRT task where the eye movements were restricted during the learning phase. our results demonstrated an increased proportion of correct anticipations in the standard SRT task compared to the restricted-movement task.

Oculomotor anticipation reveals a multitude of learning processes underlying the serial reaction time task

Sequence learning is the cognitive faculty enabling everyday skill acquisition. In the lab, it is typically measured in speed of response to sequential stimuli, whereby faster responses are taken to indicate improved anticipation. However, response speed is an indirect measure of anticipation, that can provide only limited information on underlying processes. As a result, little is known about what is learned during sequence learning, and how that unfolds over time. In this work, eye movements that occurred before targets appeared on screen in an ocular serial reaction time (O-SRT) task provided an online indication of where participants anticipated upcoming targets. When analyzed in the context of the stimuli preceding them, oculomotor anticipations revealed several simultaneous learning processes. These processes influenced each other, as learning the task grammar facilitated acquisition of the target sequence. However, they were dissociable, as the grammar was similarly learned whether a repeating sequence inhabited the task or not. Individual differences were found in how the different learning processes progressed, allowing for similar performance to be produced for different latent reasons. This study provides new insights into the processes subserving sequence learning, and a new method for high-resolution study of it.