Another possibility is that perseveration occurs due to a failure to learn from the negative feedback that now follows a previously rewarded stimulus. We compare such potential perseveration mechanisms by fitting computational learning model to our data and subsequently test whether their estimated parameters are affected by genotype. Subjects (n = 810) completed a probabilistic reversal learning MEK inhibitor task (see Table S1, available online, for demographic information). On each trial, they selected one of two stimuli, which led probabilistically to either reward or punishment (Lawrence et al., 1999) (Figure 1).
During the first 40 trials, stimulus A was usually rewarded (70%), but sometimes punished (30%), and vice versa for stimulus B. For
the second 40 trials, these contingencies were reversed. Subjects were instructed to select the usually rewarded stimulus (for details see Experimental Procedures). All subjects were genotyped for SERT and DAT1 polymorphisms. Full behavioral, genetic, and demographic data were available for 685 participants, from which three subjects were excluded for failure to perform the task (for details on genotyping and exclusions see Supplemental Experimental Procedures). There was no significant difference between genotypes in gender distribution (both polymorphisms: χ2(2) < 4, p > 0.1). Our primary analysis focused on three main measures find more of interest: win-staying, lose-shifting (both as a function of the previous trial), and perseveration. Perseverative errors were defined as any sequence of two or more errors during the reversal
phase. These three measures were included as within-subject measures in a repeated-measures ANOVA, together with the between-subject factors gender and learning criterion attainment, and covariates age and level of education (for control analyses of basic learning measures and covariates, see Supplemental Experimental Procedures, Figure S1, and Table S2). Both SERT and DAT1 selectively affected these three measures (SERT: F(3.7, 1189) = 3.38, p = 0.011, η2 = 0.010; DAT1: F(3.7,1189) = 3.07, p = 0.019, η2 = 0.09). Below, we explore the nature of these main effects of measures of interest. Consistent with our hypothesis, SERT affected the likelihood of shifting responses after punishment (F(20,661) = 5.80, p = 0.003, η2 = 0.017; Figure 2A). Pairwise post hoc see more comparisons revealed that L′ homozygotes exhibited increased lose-shift rate relative to the S′ carriers, whereas there was no difference between the S′ homozygotes and the heterozygotes (L′/L′ > S′/S′, p = 0.001; L′/L′ > S′/L′, p = 0.033; S′/S′ versus S′/L′, p = 0.15). Indeed, grouping S′-carriers versus L′-homozygotes does not alter significance (F(15,666) = 9.28, p = 0.002, η2 = 0.014). Conversely, there was no effect of SERT on win-stay rates ( Figure 2B). In contrast to our hypothesis, DAT1 did not affect win-stay (or lose-shift) rates ( Figures 2A and 2B).
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