Perceiving structure in unstructured stimuli: Implicitly acquired prior knowledge impacts the processing of unpredictable transitional probabilities

Abstract

<jats:title>Abstract</jats:title><jats:p>It is unclear how implicit prior knowledge is involved and remains persistent in the extraction of the statistical structure underlying sensory input. Therefore, this study investigated whether the implicit knowledge of 2<jats:sup>nd</jats:sup> order transitional probabilities characterizing a stream of visual stimuli impacts the processing of unpredictable transitional probabilities embedded in a similar input stream. Young adults (<jats:italic>N</jats:italic> = 50) performed a four-choice reaction time (RT) task that consisted of structured and unstructured blocks. In the structured blocks, more probable and less probable short-range nonadjacent transitional probabilities were present. In the unstructured blocks, the unique combinations of the short-range transitional probabilities occurred with equal probability; therefore, they were unpredictable. All task blocks were visually identical at the surface level. While one-half of the participants completed the structured blocks first followed by the unstructured blocks, this was reversed in the other half of them. The change in the structure was not explicitly denoted, and no feedback was provided on the correctness of each response. Participants completing the structured blocks first showed faster RTs to more probable than to less probable short-range transitional probabilities in both the structured and unstructured blocks, indicating the persistent effect of prior knowledge. However, after extended exposure to the unstructured blocks, they updated this prior knowledge. Participants completing the unstructured blocks first showed the RT difference only in the structured blocks, which was not constrained by the preceding exposure to unpredictable stimuli. The results altogether suggest that implicitly acquired prior knowledge of predictable stimuli influences the processing of subsequent unpredictable stimuli. Updating this prior knowledge seems to require a longer stretch of time than its initial acquisition.</jats:p>

Statistical learning abilities and their relation to language

“…For starters, the vast majority of previous SL studies assumes that learners come to the learning task tabula rasa. Real-world learning, however, constantly involves the updating of existing knowledge (see, e.g., Karuza et al, 2016;Kóbor, Horváth, Kardos, Nemeth, & Janacsek, 2019;Siegelman, Bogaerts, Elazar, et al, 2018;Siegelman, Bogaerts, Kronenfeld, & Frost, 2018;Weiss, Gerfen, & Mitchel, 2009). Thus, mapping individual differences in SL cannot focus only on how individuals differ in their ability to learn a novel set of regularities from scratch, but should also consider variance in individuals' capacity to learn novel information given existing assimilated regularities.…”

Implicit anticipation of probabilistic regularities: Larger CNV emerges for unpredictable events

“…However, beyond response preparation, implicit and nonconscious predictions were generated about the direction of the upcoming target (cf. Kóbor, Horváth, Kardos, Nemeth, et al, 2019; Kóbor, Horváth, Kardos, Takács, et al, 2019). Consequently, it is conceivable that the presentation of the target arrow per se served as a feedback about the “correctness” of these predictions (i.e., whether the targets were anticipated outcomes or not).…”

“…In this study, therefore, we aim to measure implicit anticipatory processes in a four-choice RT task that, unknown to participants, includes a sequential regularity between non-adjacent trials yielding a probability structure with predictable and unpredictable stimuli. By means of RTs, using a version of this task, we have already shown that the implicitly acquired prior knowledge on the probabilistic regularities influenced the processing of further stimuli lacking a predictable structure (Kóbor, Horváth, Kardos, Nemeth, & Janacsek, 2019). We assumed that this persistence of knowledge occurred through the formation of internal models.…”