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• W4207034189 abstract "Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Appendix 1 Data availability References Decision letter Author response Article and author information Abstract As individuals learn through trial and error, some are more influenced by good outcomes, while others weight bad outcomes more heavily. Such valence biases may also influence memory for past experiences. Here, we examined whether valence asymmetries in reinforcement learning change across adolescence, and whether individual learning asymmetries bias the content of subsequent memory. Participants ages 8–27 learned the values of ‘point machines,’ after which their memory for trial-unique images presented with choice outcomes was assessed. Relative to children and adults, adolescents overweighted worse-than-expected outcomes during learning. Individuals’ valence biases modulated incidental memory, such that those who prioritized worse- (or better-) than-expected outcomes during learning were also more likely to remember images paired with these outcomes, an effect reproduced in an independent dataset. Collectively, these results highlight age-related changes in the computation of subjective value and demonstrate that a valence-asymmetric valuation process influences how information is prioritized in episodic memory. Editor's evaluation This paper will be of interest to cognitive and behavioral neuroscientists, behavioral economists, and developmental psychologists. The authors provide novel evidence that adolescents, relative to children and young adults, are prone to making risk-averse decisions because they are more attuned to negative outcomes during learning. The paper presents rigorous computational analyses that conclusively support the major claims and advance our understanding of age-related shifts in decision making. https://doi.org/10.7554/eLife.64620.sa0 Decision letter eLife's review process Introduction Throughout our lives, we encounter many new or uncertain situations in which we must learn, through trial and error, which actions are beneficial and which are best avoided. Determining which behaviors will earn praise from a teacher, which social media posts will be liked by peers, or which route to work will have the least traffic is often accomplished by exploring different actions, and learning from the good or bad outcomes that they yield. Importantly, individuals differ in the extent to which their evaluations (Daw et al., 2002; Frank et al., 2004; Gershman, 2015; Lefebvre et al., 2017; Sharot and Garrett, 2016) and their memories (Madan et al., 2014, Madan et al., 2017; Rouhani and Niv, 2019) are influenced by good versus bad experiences. For example, consider a diner who has a delicious meal on her first visit to a new sushi restaurant, but on her next visit, the meal is not very good. A tendency to place greater weight on past positive experiences might make her both more likely to remember the good dining experience and more likely to return and try the restaurant again. In contrast, if the recent negative experience exerts an outsized influence, it may be more easily called to mind and she may forego another visit to that restaurant in favor of a surer bet. In this manner, asymmetric prioritization of past positive versus negative outcomes may render these valenced experiences more persistent in our memories and systematically alter how we make future decisions about uncertain prospects. Understanding how experiential learning informs decision-making under uncertainty may be particularly important during adolescence, when teens’ burgeoning independence offers more frequent exposure to novel contexts in which the potential positive or negative outcomes of an action may be uncertain. Epidemiological data reveal an adolescent peak in the prevalence of many ‘risky’ behaviors that carry potential negative consequences (e.g., criminal behavior [Steinberg, 2013], risky sexual behavior [Satterwhite et al., 2013]). Moreover, consistent with proposals that adolescent risk taking might be driven by heightened sensitivity to rewarding outcomes (Casey et al., 2008; Galván, 2013; Silverman et al., 2015; Steinberg, 2008; van Duijvenvoorde et al., 2017), several neuroimaging studies have observed that adolescents exhibit neural responses to reward that are greater in magnitude than those of children or adults (Braams et al., 2015; Cohen et al., 2010; Galvan et al., 2006; Silverman et al., 2015; Van Leijenhorst et al., 2010). These findings suggest that as adolescents learn to evaluate novel situations through trial and error, positive experiences might exert an outsized influence on their subsequent actions and choices. Reinforcement learning (RL) models mathematically formalize the process of evaluating actions based on their resulting good and bad outcomes (Sutton and Barto, 1998). In such models, action value estimates are iteratively revised based on prediction errors or the extent to which an experienced outcome deviates from one’s current expectation. The magnitude of the resulting value update is scaled by an individual’s learning rate. Valence asymmetries in the estimation of action values can be captured by positing two distinct learning rates for positive versus negative prediction errors, leading to differential adjustment of value estimates following outcomes that are better or worse than one’s expectations. Importantly, an RL algorithm with such valence-dependent learning rates estimates subjective values in a ‘risk-sensitive’ manner (Mihatsch and Neuneier, 2002; Niv et al., 2012). A learner with a greater positive than negative learning rate will, across repeated choices, come to assign a greater value to a risky prospect (i.e., with variable outcomes) than to a safer choice with equivalent expected value (EV) that consistently yields intermediate outcomes, whereas a learner with the opposite asymmetry will estimate the risky option as being relatively less valuable. Outcomes that violate our expectations might also be particularly valuable to remember. Beyond the central role of prediction errors in the estimation of action values, these learning signals also appear to influence what information is prioritized in episodic memory (Ergo et al., 2020). Past studies have demonstrated enhanced memory for stimuli presented concurrently with outcomes that elicit positive (Davidow et al., 2016; Jang et al., 2019), negative (Kalbe and Schwabe, 2020), or high-magnitude (independent of valence) prediction errors (Rouhani et al., 2018), suggesting that prediction errors can facilitate memory encoding and consolidation processes. The common role of prediction errors in driving value-based learning and facilitating memory may reflect, in part, a tendency to allocate greater attention to stimuli that are uncertain (Dayan et al., 2000; Pearce and Hall, 1980). However, it is unclear whether idiosyncratic valence asymmetries in RL computations might give rise to corresponding asymmetries in the information that is prioritized for memory. Moreover, while few studies have explored the development of these interactive learning systems, a recent empirical study observing an effect of prediction errors on recognition memory in adolescents, but not adults (Davidow et al., 2016), suggests that the influence of RL signals on memory may be differentially tuned across development. In the present study, we examined whether valence asymmetries in RL change across adolescent development, conferring age differences in risk preferences. We additionally hypothesized that individuals’ learning asymmetries might asymmetrically bias their memory for images that coincide with positive versus negative prediction errors. Several past studies have characterized developmental changes in learning from valenced outcomes (Christakou et al., 2013; Hauser et al., 2015; Jones et al., 2014; Master et al., 2020; Moutoussis et al., 2018; van den Bos et al., 2012). However, the probabilistic reinforcement structures used in each of these studies demanded that the learner adopt specific valence asymmetries during value estimation in order to maximize reward in the task (Nussenbaum and Hartley, 2019). For instance, in one study, child, adolescent, and adult participants were rewarded on 80% of choices for one option and 20% of choices for a second option (van den Bos et al., 2012). In this task, a positive learning asymmetry yields better performance than a neutral or negative asymmetry (Nussenbaum and Hartley, 2019). Indeed, adults exhibited a more optimal pattern of learning, with higher positive than negative learning rates, while children and adolescents did not (van den Bos et al., 2012). Thus, choice behavior in these studies might reflect both potential age differences in the optimality of RL, as well as context-independent differences in the weighting of positive versus negative prediction errors (Cazé and van der Meer, 2013; Nussenbaum and Hartley, 2019). In Experiment 1 of the present study, we assessed whether valence asymmetries in RL varied from childhood to adulthood, using a risk-sensitive RL task (Niv et al., 2012) in which probabilistic and deterministic choice options have equal EV, making no particular learning asymmetry optimal. This parameterization allows any biases in the weighting of positive versus negative prediction errors to be revealed through subjects’ systematic risk-averse or risk-seeking choice behavior. Each choice outcome in the task was associated with a trial-unique image, enabling assessment of whether valenced learning asymmetries also biased subsequent memory for images that coincided with good or bad outcomes. To determine whether this hypothesized correspondence between valence biases in learning and memory generalized across experimental tasks and samples of different ages, in Experiment 2, we conducted a reanalysis of data from a previous study (Rouhani et al., 2018). In this study, a group of adults completed a task in which they reported value estimates for a series of images, and later completed a memory test for the images they encountered during learning. The original manuscript reported that subsequent memory varied as a function of PE magnitude, but not valence. Here, we tested whether a valence-dependent effect of PE on memory might be evident after accounting for idiosyncratic valence biases in learning. Results Experiment 1 Participants (N = 62) ages 8–27 (M = 17.63, SD = 5.76) completed a risk-sensitive RL task (Niv et al., 2012). In this task, participants learned, through trial and error, the values and probabilities associated with probabilistic and deterministic ‘point machines’ (Figure 1A and B). On each trial (183 trials), participants made a free (two-choice options) or forced (single-choice option) selection of a point machine. Within free-choice trials, ‘risky’ trials presented a pair consisting of one probabilistic and one deterministic option, where neither option strictly dominated the other and evidence of individuals’ subjective values was revealed by their choices. On ‘test’ trials, in which one option dominated the other, we could assess objectively the accuracy of participants’ learning. We presented feedback (number of points) from each choice on a ‘ticket’ that also displayed a trial-unique picture of an object. A subsequent memory test allowed us to explore the interaction between choice outcomes and memory encoding across age (Figure 1C). Figure 1 Download asset Open asset Task structure. (A) Schematic of the structure of a trial in the risk-sensitive reinforcement learning task. (B) The probabilities and point values associated with each of five ‘point machines’ (colors were counterbalanced). (C) Example memory trial. Test trial performance To ensure that participants learned the probabilities and outcomes associated with each machine, we first examined performance on test trials, in which one option dominated the other. Test trial accuracy significantly improved across the task (generalized linear mixed-effects model: z = 8.56, p<0.001, OR = 2.03, 95% CI [1.72, 2.38]), with accuracy improving from a mean of 0.63 in the first block to means of 0.80 and 0.84 in blocks 2 and 3, respectively. There was no main effect of age (z = 0.51, p=0.612, OR = 1.06, 95% CI [0.86, 1.30]) or interaction between age and trial number (z = 0.22, p=0.830, OR = 1.02, 95% CI [0.87, 1.19]; Appendix 1—figure 1A). These results suggest that accuracy on this coarse measure of value learning did not change with age in our task. Explicit reports Following the learning task, we probed participants’ explicit knowledge about the point machines. Consistent with participants’ high accuracy on test trials, accuracy was also high on participants’ reports of whether each point machine was probabilistic or deterministic (M = 0.85) and for the point values associated with each machine (M = 0.84). Linear regressions suggested that performance on these explicit accuracy metrics did not vary with linear age (probabilistic/deterministic response accuracy by age: b = –0.02, 95% CI [–0.06, 0.03], t(60) = –0.88, p=0.382, f2 = 0.01, 95% CI [0, 0.13]; point value response accuracy by age: b = 0.02, 95% CI [–0.04, 0.07], t(60) = 0.65, p=0.516, f2 = 0.01, 95% CI [0, 0.11]). Response time We explored whether response time (RT) varied with age during the learning task. We found a significant interaction between age and trial number (linear mixed-effects model: t(11279) = –2.10, p=0.036, b = –0.02, 95% CI [–0.04, 0]) predicting log-transformed RT. Although RT did not differ by age early in the experiment, older participants responded faster than younger participants by the end of the experiment. Decision-making Importantly, in our task, there were two pairs of machines in which both probabilistic and deterministic options yielded the same EV (i.e., 100% 20 points and 50/50% 0/40 points; 100% 40 points and 50/50% 0/80 points). A primary goal of this study was to examine participants’ tendency to choose probabilistic versus deterministic machines when EV was equivalent. On these equal-EV risk trials, participants chose the probabilistic option on 37% of trials (SD = 21%). This value was significantly lower than 50% (one-sample t-test: t(61) = 4.87, p<0.001, d = 0.62, 95% CI [0.37, 0.95]), suggesting that, despite exhibiting heterogeneity in risk preferences, participants as a group were generally risk averse. Next, we tested whether choices of the probabilistic machines, compared to choices for equal-EV deterministic machines, changed with age. The best-fitting model included both linear and quadratic age terms (F(1,59) = 4.58, p=0.036), indicating that risk taking changed nonlinearly with age. Contrary to our hypothesis that risk-seeking choices would be highest in adolescents, we observed a significant quadratic effect of age, such that adolescents chose the probabilistic options less often than children or adults (quadratic age effect in a linear regression including both linear and quadratic age terms: b = 0.06, 95% CI [0, 0.12], t(59) = 2.14, p=0.036, f2 = 0.08, 95% CI [0, 0.29]; Figure 2; see Appendix 1—figure 1 for plots depicting risk taking across the task as a function of age). The linear effect of age was not significant (b = –0.01, 95% CI [–0.06, 0.12], t(59) = –0.44, p=0.662, f2 = 0.01, 95% CI [0, 0.11]). We also conducted a regression using the two-lines approach (Simonsohn, 2018) and found a significant u-shaped pattern of risk taking with age, where the proportion of probabilistic choices decreased from age 8–16.45 (b = –0.03, z = –1.97, p=0.048) and increased from age 16.45–27 (b = 0.02, z = 1.98, p=0.048). Age patterns were qualitatively similar when considering the subset of trials in which participants faced choice options with unequal EV (i.e., the 0/80 point machine vs. the safe 20 point machine; see Appendix 1 and Appendix 1—figure 2 for full results). Figure 2 Download asset Open asset Probabilistic choices by age. Probabilistic (i.e., risky) choices by age on trials in which the risky and safe machines had equal expected value (EV). Data points depict the mean percentage of trials where each participant selected the probabilistic choice option as a function of age. The regression line is from a linear regression including linear and quadratic age terms (significant quadratic effect of age: b = 0.06, 95% CI [0, 0.12], t(59) = 2.14, p=0.036, f2 = .08, 95% CI [0, 0.29], N = 62). Shaded region represents 95% CIs for estimates. Reinforcement learning modeling To better understand the learning processes underlying individuals’ decision-making, we compared the fit of four RL models to participants’ choice behavior. The first was a temporal difference (TD) model with one learning rate (α). The second was a risk-sensitive temporal difference (RSTD) model with separate learning rates for better-than-expected (α+) and worse-than-expected (α-) outcomes, allowing us to index valence biases in learning. The third model included four learning rates (FourLR), with separate α+ and α- for free and forced choices, as past studies have found learning may differ as a function of agency (Chambon et al., 2020; Cockburn et al., 2014). Finally, the fourth model was a Utility model, which transforms outcome values into utilities with an exponential subjective utility function with a free parameter (ρ) capturing individual risk preferences (Pratt, 1964), updated value estimates using a single learning rate. For all models, machine values were transformed to range from 0 to 1, and values were initialized at 0.5 (equivalent to 40 points). A softmax function with an additional parameter β was used to convert the relative estimated values of the two machines into a probability of choosing each machine presented for maximum likelihood estimation. The RSTD (median Bayesian information criterion (BIC) = 131.93) and Utility (median BIC = 131.06) models both provided a better fit to participants’ choice data than both the TD (median BIC = 145.35) and FourLR (median BIC = 141.25) models (Appendix 1—figure 5). Assessment of whether the RSTD or Utility model provided the best fit to participants’ data was equivocal. At the group level, median ΔBIC was 0.87, while at the subject level, the median ΔBIC was 0.33. Thus, neither ΔBIC metric provides clear evidence in favor of either model (ΔBIC > 6 ; Raftery, 1995). To further arbitrate between the RSTD and Utility models, we ran posterior predictive checks and confirmed that simulations from both models generated using subjects’ fit parameter values yielded choice behavior that exhibited strong correspondence to the real participant data (see Appendix 1—figure 9). However, data simulated from the RSTD model exhibited a significantly stronger correlation with actual choices (r = 0.92) than those simulated using the Utility model (r = 0.89; t(61) = 2.58, p=0.012). Because the RSTD model fit choice data approximately as well as the Utility model, provided a significantly better qualitative fit to the choice data, and yielded an index of valence biases in learning, we focused our remaining analyses on the RSTD model (see Appendix 1 for additional model comparison analyses and for an examination of the relation between the Utility model and subsequent memory data). We computed an asymmetry index (AI) for each participant, which reflects the relative size of α+ and α-, from the RSTD model. Mean AI was –0.22 (SD = 0.50). Mirroring the age patterns observed in risk taking, a linear regression model with a quadratic age term fit better than the model with only linear age (F(1,59) = 5.88, p=0.018), and there was a significant quadratic age pattern in AI (b = 0.17, 95% CI [0.03, 0.31], t(59) = 2.43, p=0.018, f2 = 0.10, 95% CI [0, 0.33]; Figure 3). Further, the u-shaped relationship between AI and age was significant, with a decrease in AI from ages 8–17 (b = –0.08, z = –3.82, p<0.001), and an increase from ages 17–27 (b = 0.05, z = 2.17, p=0.030). This pattern was driven primarily by age-related changes in α-, which was greater in adolescents relative to children and adults (better fit for linear regression including quadratic term: F(1,59) = 9.04, p=0.004; quadratic age: b = –0.09, 95% CI [–0.16, –0.03], t(59) = –3.01, p=0.004, f2 = 0.15, 95% CI [0.02, 0.43]; Appendix 1—figure 10B). According to the two-lines approach, α- significantly increased from ages 8–18 (b = 0.04, z = 3.24, p=0.001) and decreased from ages 18–27 (b = –0.04, z = –3.57, p<0.001). Conversely, there were no linear or quadratic effects of age for α+ (all ps>0.24; Appendix 1—figure 10A). Finally, there were no significant linear or quadratic age patterns in the β parameter (ps>.15, see Appendix 1 for full results; Appendix 1—figure 10C). Figure 3 Download asset Open asset Asymmetry index (AI) by age. The regression line is from a linear regression model including linear and quadratic age terms (b = 0.17, 95% CI [0.03, 0.31], t(59) = 2.43, p=0.018, f2 = 0.10, 95% CI [0, 0.33], N = 62). Data points represent individual participants. Shaded region represents 95% CIs for estimates. Prior work has found that valence biases tend to be positive in free choices, but neutral or negative in forced choices (Chambon et al., 2020; Cockburn et al., 2014). While model comparison indicated that the FourLR model did not provide the best account of participants' learning process, we nonetheless conducted an exploratory analysis in which we used parameter estimates from the FourLR model to test whether learning asymmetries varied as a function of agency in our study. While the α+ and AI were both higher for free compared to forced trials, median AIs were negative for both free and forced choices (see Appendix 1 for full results; Appendix 1—figure 12). Memory performance Next, we examined accuracy during the surprise memory test for images that were presented with choice outcomes. Participants correctly identified 54% (SD = 14%) of images presented alongside choice feedback (i.e., Hits) and incorrectly indicated that 24% (SD = 15%) of foil images had been presented during the choice task (False Alarms). Mean d′ was 0.93 (SD = 0.48). Hit rate did not significantly change with linear or quadratic age (ps>0.14). However, false alarm rate significantly increased with linear age (linear regression: b = 0.04, 95% CI [0.00; 0.08], t(60) = 2.14, p=0.037, f2 = 0.08, 95% CI [0, 0.28]; Appendix 1—figure 3A). There was a marginal linear decrease in d′ with age (linear regression: b = –0.11, 95% CI [–0.23, 0.01], t(60) = 1.84, p=0.070, f2 = 0.06, 95% CI [0, 0.24]; Appendix 1—figure 3B), suggesting that adults performed slightly worse on the memory test than younger participants. Influence of choice context on memory We next tested whether the decision context in which images were presented influenced memory encoding. To explore this possibility, we first tested whether participants preferentially remembered images presented with outcomes of probabilistic versus deterministic machines. Participants were significantly more likely to remember pictures presented following a choice that yielded probabilistic rather than deterministic outcomes (probabilistic: M = 0.56, SD = 0.15; deterministic: M = 0.52, SD = 0.15; t(61) = 3.08, p=0.003, d = 0.39, 95% CI [0.13, 0.65]). This result suggests that pictures were better remembered when they followed the choice of a machine that consistently generated prediction errors (PEs), which may reflect preferential allocation of attention toward outcomes of uncertain choices (Dayan et al., 2000; Pearce and Hall, 1980). Next, we explored whether valence biases in learning could account for individual variability in subsequent memory. In theory, larger-magnitude PEs provide stronger learning signals. Thus, we hypothesized that participants would have better memory for items coinciding with larger PEs. We also expected that this effect might differ as a function of idiosyncratic valence biases, with participants preferentially remembering items coinciding with signed PEs where the sign was consistent with the valence bias of their AI. Of note, this model did not explicitly include a variable indicating whether outcomes followed probabilistic or deterministic choices. Rather, whether the choice was probabilistic or deterministic was reflected in the PE magnitude variable, which was typically higher for probabilistic choices. In a generalized linear mixed-effects model, we predicted memory accuracy as a function of AI, PE valence, PE magnitude, and their interaction. We also tested for effects of linear and quadratic age, false alarm rate, as a measure of participants’ tendency to generally deem items as old, and trial number in the memory task, to account for fatigue as the task progressed (Figure 4A). We had no a priori hypothesis about how any effect of valence bias on memory might interact with participants’ confidence in their ‘old’ and ‘new’ judgments. Therefore, consistent with prior research examining memory accuracy (e.g., Dunsmoor et al., 2015; Murty et al., 2016), we collapsed across ‘definitely’ and ‘maybe’ confidence ratings for our primary analysis (but see Appendix 1 for an exploratory ordinal regression analysis). Figure 4 Download asset Open asset The relation between valence biases in learning and incidental memory for pictures presented with choice outcomes (Experiment 1). (A) Results from generalized mixed-effects regression depicting fixed effects on memory accuracy. Whiskers represent 95% CI. (B) Estimated marginal means plot showing the three-way interaction between AI, PE valence, and PE magnitude (z = 3.45, p=0.001, OR = 1.12, 95% CI [1.05, 1.19], N = 62). Individuals with higher AIs were more likely to remember images associated with larger positive PEs, and those with lower AIs were more likely to remember images associated with larger negative PEs. Shaded areas represent 95% CI for estimates. ***p < .001. As expected, accuracy was significantly higher for those with a higher false alarm rate (suggesting a bias towards old responses; z = 4.86, p<0.001, OR = 1.41, 95% CI [1.23, 1.61]), and accuracy decreased as the task progressed (main effect of memory trial number: z = –5.83, p<0.001, OR = 0.82, 95% CI [0.76, 0.87]). There was a significant effect of unsigned PE magnitude on memory (z = 4.75, p<0.001, OR = 1.19, 95% CI [1.11, 1.28]), such images that coincided with largerPEs were better remembered. There was also a significant three-way interaction between PE magnitude, PE valence, and AI on memory accuracy (z = 3.45, p=0.001, OR = 1.12, 95% CI [1.05, 1.19]), such that people with more positive AIs were more likely to remember images associated with larger positive PEs (Figure 4B). The converse was also true: those with lower AIs were more likely to remember images presented concurrently with outcomes that elicited higher-magnitude negative PEs. Ordinal regression results that considered all four levels of confidence in recollection judgments (Supplementary file 1, Appendix 1—figure 4) yielded consistent results and suggested that effects were primarily driven by high-confidence responses. Notably, neither linear (z = 0.32, p=0.750, OR = 1.02, 95% CI [0.89, 1.17]) nor quadratic age (z = –0.18, p=0.856, OR = 0.99, 95% CI [0.84, 1.15]) were significant predictors of memory, suggesting that AI parsimoniously accounted for individual differences in memory. To test whether differences in memory for outcomes of deterministic versus probabilistic trials might have driven the observed AI × PE magnitude × PE valence interaction effect, we reran the regression model only within the subset of trials in which participants made probabilistic choices. Our results did not change — we observed both a main effect of PE magnitude (z = 2.22, p=0.026, OR = 1.11, 95% CI [1.01, 1.23], N = 62) and a significant PE valence × PE magnitude × AI interaction (z = 2.34, p=0.019, OR = 1.11, 95% CI [1.02, 1.21], N = 62). Finally, we tested for effects of agency — whether an image coincided with the outcome of a free or forced choice — on memory performance. We did not find a significant main effect of agency on memory, and agency did not significantly modulate the AI × PE magnitude × PE valence interaction effect (see Appendix 1 for full results; Appendix 1—figure 13). Self-reported risk taking One possible explanation for our unexpected u-shaped relationship between age and risk preferences in our choice task is that the adolescents in our sample might have been atypically risk averse. To investigate this possibility, we examined the relation between age and self-reported risk taking to the Domain-Specific Risk Taking (DOSPERT) scale (Blais and Weber, 2006). A linear regression model including quadratic age was a better fit than the model including linear age alone (F(1,59) = 9.55, p=0.003). Specifically, consistent with prior reports of increased self-reported risk taking in adolescents, we found a significant inverted u-shaped quadratic age pattern (Figure 5, b = –0.42, 95% CI [-0.69, –0.15], t(59) = –3.09, p=0.003, f2 = 0.16, 95% CI [0.02, 0.44]). There was not a significant linear age pattern in self-reported risk taking (b = 0.15, 95% CI [–0.09, 0.39], t(59) = 1.27, p=0.208, f2 = 0.04, 95% CI [0, 0.20]). A two-lines regression analysis indicated that risk taking increased until age 15.29 (b = 0.23, z = 2.20, p=0.028) and decreased thereafter (b = –0.09, z = –2.03, p=0.042). Despite the fact that both choices in our task and self-report risk taking exhibited nonlinear age-related changes, there was not a significant correlation between DOSPERT score and risk taking in the task (r = –0.12, 95% CI [–0.36, 0.13], t(60) = –0.95, p=0.347). Figur" @default.
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• W4207034189 title "Decision letter: Valence biases in reinforcement learning shift across adolescence and modulate subsequent memory" @default.
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