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We hypothesized that an error has a perturbing effect: it should knock the system off the path of correct prediction on the trials immediately following the error (T23s and T34 trials), but the degree of distortion should diminish as the trials progress. We call this account ��Dynamical [http://www.selleckchem.com/products/Nutlin-3.html selleck chemical] Stability.�� To test the hypothesis, we first compared trial-level prediction accuracy for T23 and T34; the transition probability in Sequence 3 is 1 for both transitions. If noise is purely random, prediction accuracy should be roughly equal for both transition types. Binary trial-level prediction accuracy was modeled as a function of WordType (dummy-coded with word 2 as reference level); a by-subject random intercept, a by-subject random slope of WordType, and the correlation between them were included as random effects. The effect of WordType was significant, b = 0.535, SE = 0.098, z = 5.45, p [http://www.selleckchem.com/products/ABT-737.html ABT-737 chemical structure] nonadjacent dependency) and T12s (involving uncertainty) while T34s follow very easy-to-predict T23s21. This result is consistent with Dynamical Stability, but it does not take into account the hypothesized perturbing events: the errors on T42s. To test the claim that these errors were the primary source of errors on following trials, we took prediction accuracy on T23s right after T42s occurring in S2s, S3s, and S4s in the experimental sequence and classified the data into two groups depending on prediction accuracy on the previous T42 (PrevAcc = 0 vs. 1). We used a logit mixed model to test if prediction accuracy on T23 after T42 was a function of PrevAcc; [http://en.wikipedia.org/wiki/Thymidine_kinase Thymidine kinase] a by-subject random intercept, a by-subject random slope of PrevAcc, and their interaction were included as random factors. The effect of PrevAcc was significant, b = 3.444, SE = 0.464, z = 7.43, p