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Unpublished MA thesis. Southern Illinois University, IL.
Tactile Sequential Learning: Artificial Grammar Learning by Touch
Christopher M. Conway
Abstract
Artificial grammar learning (AGL) is a popular method for investigating
complex sequential learning. Although AGL has been used extensively in the visual
and auditory domains, it has not been previously applied to touch. This paper describes
an experiment in which participants followed the standard AGL paradigm except that
sequences of vibration pulses served as the training and testing stimuli. After exposure
to a grammatical training set, participants were tested on their ability to classify novel
stimuli in terms of whether they followed the "rules" of the grammar or not.
Participants performed this classification task significantly better than a no-training
control group, indicating that the sense of touch encoded aspects of the sequential
information present in the training stimuli (Experiment 1). In addition, the results from
this tactile AGL task were compared to two visual conditions: one using spatiotemporal
sequences (Experiment 2) and the other using simultaneously presented sequences
(Experiment 3). Performances in all three experiments were nearly identical,
suggesting commonalities between tactile spatiotemporal, visual spatiotemporal, and
visual spatial artificial grammar learning.
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