If a naïve learner has a stationarity bias, then whenever the environment has more nuanced structural components, learning will be suboptimal. Moreover, if a poor “fit” of a model of the environment is tolerated, then the criterion for subsequent learning may be overly AZD6244 “lax” and prevent further learning. In contrast, if a naïve learner has a nonstationarity bias, then variability due to sampling rather than to the presence of multiple structures will lead to “overfitting” this natural variability and prevent the model of the environment from generalizing to novel instances of what

is actually a uniform structure (i.e., the learner will acquire too much detail). Although the natural environment is clearly nonstationary, there is a surprising

paucity of research on this topic. In fact, the design of almost all statistical-learning www.selleckchem.com/products/forskolin.html studies ensures that whichever subset of the corpus is sampled, the statistics are the same. In one of the first studies of nonstationarity, Gebhart, Aslin, and Newport (2009) presented adults with a 10-min stream of nonsense syllables (as in Saffran et al., 1996) and, without informing the subjects, altered the structure half way through the exposure phase. In a posttest that contrasted words and part-words from each of the two structures, Gebhart et al. found that adults learned the syllable statistics of the first structure but not the second (i.e., what was called a statistical garden path). Thus, in the absence of any cues that signal a change of structure, adults have a primacy bias and appear to treat the second structure as a noisy version of the first. However, Gebhart et al. also showed that when there is a clear cue for a change in structure (e.g., by pausing between structures and informing the subjects that there is Ergoloid a new structure), adults learn both structures equally well. Importantly, Gebhart et al. also showed that a cue for a change in structure is not required—when subjects heard an extended version of the second structure, they learned its syllable statistics and yet maintained their

learning of the first structure’s syllable statistics. This overall pattern of results suggests that once a structure is learned, it takes extensive evidence that a second structure is present (rather than a noisy version of the first structure) or a strong cue for a change of structure to overcome an initial stationarity bias. Another interesting finding from Gebhart et al. (2009) was that all cues for a change in structure are not equally effective. When the first structure was spoken in a male voice and the second structure in a female voice, there was no benefit to learning the syllable statistics in the second structure. This is perhaps not surprising given that talker or voice differences in natural languages do not signal a different structure, unless the two talkers are speaking different languages.