Abstract
A pervasive issue in statistical learning has been to determine the parameters of regularity extraction. Our hypothesis was that the extraction of transitional probabilities can prevail over frequency if the task involves prediction. Participants were exposed to four repeated sequences of three stimuli (XYZ) with each stimulus corresponding to the position of a red dot on a touch screen that participants were required to touch sequentially. The temporal and spatial structure of the positions corresponded to a serial version of the exclusive-or (XOR) that allowed testing of the respective effect of frequency and first- and second-order transitional probabilities. The XOR allowed the first-order transitional probability to vary while being not completely related to frequency and to vary while the second-order transitional probability was fixed (p(Z|X, Y) = 1). The findings show that first-order transitional probability prevails over frequency to predict the second stimulus from the first and that it also influences the prediction of the third item despite the presence of second-order transitional probability that could have offered a certain prediction of the third item. These results are particularly informative in light of statistical learning models.
Highlights
Adapting behavior to complex environments relies on the ability of the cognitive system to capture and learn relations between stimuli linked by statistical distributions [1,2,3,4,5]
Our hypothesis was that the temporal feature of the sequences would prompt participants to favor transitional probability (TP) over frequency, because TP allows one to predict the item of a sequence whereas the frequency does not
A first set of results linked to RTTT1 confirm the hypothesis that first-order TP prevails over frequency when learning a pair of first and second items in a triplet
Summary
Adapting behavior to complex environments relies on the ability of the cognitive system to capture and learn relations between stimuli linked by statistical distributions [1,2,3,4,5]. Statistical learning can occur in different sensory modalities such as auditory [12], visual [13] and tactile [14]. This type of learning has been shown to occur in humans early in life, for example, to account for word segmentation [15,16,17,18]. Considering that the cognitive system can use transitional probabilities between adjacent elements to extract segments, statistical learning can appear to be a rudimentary learning process. A pervasive issue in statistical learning has been to determine the parameters that sustain regularity extraction, such as conditional probabilities, frequency, and their reciprocal influence [19].
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