Reviewer #1 (Public review):
In this study, the authors aim to elucidate both how Pavlovian biases affect instrumental learning from childhood to adulthood, as well as how reward outcomes during learning influence incidental memory. While prior work has investigated both of these questions, findings have been mixed. The authors aim to contribute additional evidence to clarify the nature of developmental changes in these processes. Through a well-validated affective learning task and a large age-continuous sample of participants, the authors reveal that adolescents outperform children and adults when Pavlovian biases and instrumental learning are aligned, but that learning performance does not vary by age when they are misaligned. They also show that younger participants show greater memory sensitivity for images presented alongside rewards.
The manuscript has notable strengths. The task was carefully designed and modified with a clever, developmentally appropriate cover story, and the large sample size (N = 174) means their study was better powered than many comparable developmental learning studies. The addition of the memory measure adds a novel component to the design. The authors transparently report their somewhat confusing findings.
The manuscript also has weaknesses, which I describe in detail below.
It was not entirely clear to me what central question the researchers aimed to address. They note that prior studies using a very similar learning task design have reported inconsistent findings, but they do not propose a reason for why these inconsistent findings may emerge nor do they test a plausible cause of them (in contrast, for example, Raab et al. 2024 explicitly tested the idea that developmental changes in inferences about controllability may explain age-related change in Pavlovian influences on learning). While the authors test a sample of participants that is very large compared to many developmental studies of reinforcement learning, this sample is much smaller than two prior developmental studies that have used the same learning task (and which the authors cite - Betts et al., 2020; Moutoussis et al., 2018). Thus, the overall goal seems to be to add an additional ~170 subjects of data to the existing literature, which isn't problematic per se, but doesn't do much to advance our theoretical understanding of learning across development. They happen to find a pattern of results that differs from all three prior studies, and it is not clear how to interpret this.
Along those lines, the authors extend prior work by adding a memory manipulation to the task, in which trial-unique images were presented alongside reward outcomes. It was not clear to me whether the authors see the learning and memory questions as fundamentally connected or as two separate research questions that this paradigm allows them to address. The manuscript would potentially be more impactful if the authors integrated their discussion of these two ideas more. Did they have any a priori hypotheses about how Pavlovian biases may affect the encoding of incidentally presented images? Could heightened reward sensitivity explain both changes in learning and changes in memory? It was also not clear to me why the authors hypothesized that younger participants would demonstrate the greatest effects of reward on memory, when most of the introduction seems to suggest they might hypothesize an adolescent peak in both learning and memory.
As stated above, while the task methods seemed sound, some of the analytic decisions are potentially problematic and/or require greater justification for the results of the study to be interpretable.
Firstly, it is problematic not to include random participant slopes in the regression models. Not accounting for individual variation in the effects of interest may inflate Type I errors. I would suggest that the authors start with the maximal model, or follow the same model selection procedure they did to select the fixed effects to include for the random effects as well.
Secondly, the central learning finding - that adolescents demonstrate enhanced learning in Pavlovian-congruent conditions only - is interesting, but it is unclear why this is the case or how much should be made of this finding. The authors show that adolescents outperform others in the Pavlovian-congruent conditions but not the Pavlovian-incongruent conditions. However, this conclusion is made by analyzing the two conditions separately; they do not directly compare the strength of the adolescent peak across these conditions, which would be needed to draw this strong conclusion. Given that no prior study using the same learning design has found this, the authors should ensure that their evidence for it is strong before drawing firm conclusions.
It was also not clear to me whether any of the RL models that the authors fit could potentially explain this pattern. Presumably, they need an algorithmic mechanism in which the Pavlovian bias is enhanced when it is rewarded. This seems potentially feasible to implement and could help explain the condition-specific performance boosts.
I also have major concerns about the computational model-fitting results. While the authors seemingly follow a sound approach, the majority of the fitted lapse rates (Figure S10) are near 1. This suggests that for most participants, the best-fitting model is one in which choices are random. This may be why the authors do not observe age-related change in model parameters: for these subjects, the other parameter values are essentially meaningless since they contribute to the learned value estimate, which gets multiplied by a near-0 weight in the choice function. It is important that the authors clarify what is going on here. Is it the case that most of these subjects truly choose at random? It does seem from Figure 2A that there is extensive variability in performance. It might be helpful if the authors re-analyze their data, excluding participants who show no evidence of learning or of reward-seeking behavior. Alternatively, are there other biases that are not being accounted for (e.g., choice perseveration) that may contribute to the high lapse rates?
Parameter recovery also looks poor, particularly for gain & loss sensitivity, the lapse rate, and the Pavlovian bias - several parameters of interest. As noted above, this may be due to the fact that many of the simulations were conducted with lapse rates sampled from the empirical distribution. It would be helpful for the authors to a.) plot separately parameter recoverability for high and low lapse rates and b.) report the recoverability correlation for each parameter separately.
Finally, many of the analytic decisions made regarding the memory analyses were confusing and merit further justification.
(1) First, it seems as though the authors only analyze memory data from trials where participants "could gain a reward". Does this mean only half of the memory trials were included in the analyses? What about memory as a function of whether participants made a "correct" response? Or a correct x reward interaction effect?
(2) The RPE analysis overcomes this issue by including all trials, but the trial-wise RPEs are potentially not informative given the lapse rate issue described above.
(3) The authors exclude correct guesses but include incorrect guesses. Is this common practice in the memory literature? It seems like this could introduce some bias into the results, especially if there are age-related changes in meta-memory.
(4) Participants provided a continuum of confidence ratings, but the authors computed d' by discretizing memory into 'correct' or 'incorrect'. A more sensitive approach could compute memory ROC curves taking into account the full confidence data (e.g., Brady et al., 2020).
(5) The learning and memory tradeoff idea is interesting, but it was not clear to me what variables went into that regression model.
