Abstract
Statistical learning (SL), the process of extracting regularities from the environment, is a fundamental skill of our cognitive system to structure the world regularly and predictably. SL has been studied using mainly behavioral tasks under implicit conditions and with triplets presenting the same level of difficulty, i.e., a mean transitional probability (TP) of 1.00. Yet, the neural mechanisms underlying SL under other learning conditions remain largely unknown. Here, we investigated the neurofunctional correlates of SL using triplets (i.e., three-syllable nonsense words) with a mean TP of 1.00 (easy “words”) and 0.50 (hard “words”) in an SL task performed under incidental (implicit) and intentional (explicit) conditions, to determine whether the same core mechanisms were recruited to assist learning. Event-related potentials (ERPs) were recorded while participants listened firstly to a continuous auditory stream made of the concatenation of four easy and four hard “words” under implicit instructions, and subsequently to another auditory stream made of the concatenation of four easy and four hard “words” drawn from another artificial language under explicit instructions. The stream in each of the SL tasks was presented in two consecutive blocks of ~3.5-min each (~7-min in total) to further examine how ERP components might change over time. Behavioral measures of SL were collected after the familiarization phase of each SL task by asking participants to perform a two-alternative forced-choice (2-AFC) task. Results from the 2-AFC tasks revealed a moderate but reliable level of SL, with no differences between conditions. ERPs were, nevertheless, sensitive to the effect of TPs, showing larger amplitudes of N400 for easy “words,” as well as to the effect of instructions, with a reduced N250 for “words” presented under explicit conditions. Also, significant differences in the N100 were found as a result of the interaction between TPs, instructions, and the amount of exposure to the auditory stream. Taken together, our findings suggest that triplets’ predictability impacts the emergence of “words” representations in the brain both for statistical regularities extracted under incidental and intentional instructions, although the prior knowledge of the “words” seems to favor the recruitment of different SL mechanisms.
Highlights
The environment in which we live is characterized by a series of sounds, objects, and events that do not occur randomly
This term was coined by Saffran et al (1996a) in a article showing that 8-monthold infants were capable of computing the probability of a given segment to be followed by another segment in a continuous stream made up of the concatenation of three-syllable nonsense words generated from an artificial language (e.g., ‘‘tokibu,’’ ‘‘gikoba,’’ ‘‘gopila,’’ ‘‘tipolu’’) repeated in random order with no pauses between each other (e.g., ‘‘gikobatokibutipolugopilatokibu’’), and to use these computations, known as transitional probabilities (TPs), to discover word’s boundaries
We investigated the relationship between the cognitive mechanisms underpinning statistical learning (SL) and the higher-order evaluative processes characterizing the overt responses in post-learning tasks
Summary
The environment in which we live is characterized by a series of sounds, objects, and events that do not occur randomly. Note that in that artificial language, as in natural languages, the TPs between syllables composing a given ‘‘word’’ (e.g., ‘‘tokibu,’’ ‘‘gikoba’’) were higher than the TPs of syllables overlapping two ‘‘words’’ (e.g., ‘‘bugiko’’), making TPs a reliable cue for words’ segmentation Since this seminal study, several other studies using the same task, known as triplet embedded task, have shown that SL can be observed in younger infants (e.g., Kirkham et al, 2002; Teinonen et al, 2009; Bulf et al, 2011), older children, and adults (e.g., Saffran et al, 1996b, 1997, 1999; Fiser and Aslin, 2002; Saffran and Wilson, 2003; Turk-Browne et al, 2005; Endress and Mehler, 2009; Arciuli and Simpson, 2012), and with syllables as stimuli, and with tones, geometric shapes, and symbols. Even though these studies provide strong evidence for the view that individuals from different ages are sensitive to the statistical properties embedded in different inputs (see, Frost et al, 2015; Siegelman and Frost, 2015 for modality and stimulus specificities in SL), such findings, obtained mainly from standard SL experiments, provide little evidence about both the process of learning and the nature of the representations that arise from the SL tasks (see Batterink and Paller, 2017; Batterink et al, 2019 for recent discussions)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
