Single-trial modeling separates multiple overlapping prediction errors during reward processing in human EEG.
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ABSTRACT:
SUBMITTER: Hoy CW
PROVIDER: S-EPMC8302587 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Dopamine is thought to provide reward prediction error signals to temporal lobe memory systems, but the role of these signals in episodic memory has not been fully characterized. Here we developed an incidental memory paradigm to (i) estimate the influence of reward prediction errors on the formation of episodic memories, (ii) dissociate this influence from surprise and uncertainty, (iii) characterize the role of temporal correspondence between prediction error and memoranda presentation and (iv) determine the extent to which this influence is dependent on memory consolidation. We found that people encoded incidental memoranda more strongly when they gambled for potential rewards. Moreover, the degree to which gambling strengthened encoding scaled with the reward prediction error experienced when memoranda were presented (and not before or after). This encoding enhancement was detectable within minutes and did not differ substantially after 24?h, indicating that it is not dependent on memory consolidation. These results suggest a computationally and temporally specific role for reward prediction error signalling in memory formation.
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Project description:Classical economic theory contends that the utility of a choice option should be independent of other options. This view is challenged by the attraction effect, in which the relative preference between two options is altered by the addition of a third, asymmetrically dominated option. Here, we leveraged the attraction effect in the context of intertemporal choices to test whether both decisions and reward prediction errors (RPE) in the absence of choice violate the independence of irrelevant alternatives principle. We first demonstrate that intertemporal decision making is prone to the attraction effect in humans. In an independent group of participants, we then investigated how this affects the neural and behavioral valuation of outcomes using a novel intertemporal lottery task and fMRI. Participants' behavioral responses (i.e., satisfaction ratings) were modulated systematically by the attraction effect and this modulation was correlated across participants with the respective change of the RPE signal in the nucleus accumbens. Furthermore, we show that, because exponential and hyperbolic discounting models are unable to account for the attraction effect, recently proposed sequential sampling models might be more appropriate to describe intertemporal choices. Our findings demonstrate for the first time that the attraction effect modulates subjective valuation even in the absence of choice. The findings also challenge the prospect of using neuroscientific methods to measure utility in a context-free manner and have important implications for theories of reinforcement learning and delay discounting.Significance statementMany theories of value-based decision making assume that people first assess the attractiveness of each option independently of each other and then pick the option with the highest subjective value. The attraction effect, however, shows that adding a new option to a choice set can change the relative value of the existing options, which is a violation of the independence principle. Using an intertemporal choice framework, we tested whether such violations also occur when the brain encodes the difference between expected and received rewards (i.e., the reward prediction error). Our results suggest that neither intertemporal choice nor valuation without choice adhere to the independence principle.
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Project description:To efficiently represent all of the possible rewards in the world, dopaminergic midbrain neurons dynamically adapt their coding range to the momentarily available rewards. Specifically, these neurons increase their activity for an outcome that is better than expected and decrease it for an outcome worse than expected, independent of the absolute reward magnitude. Although this adaptive coding is well documented, it remains unknown how this rescaling is implemented. To investigate the adaptive coding of prediction errors and its underlying rescaling process, we used human functional magnetic resonance imaging (fMRI) in combination with a reward prediction task that involved different reward magnitudes. We demonstrate that reward prediction errors in the human striatum are expressed according to an adaptive coding scheme. Strikingly, we show that adaptive coding is gated by changes in effective connectivity between the striatum and other reward-sensitive regions, namely the midbrain and the medial prefrontal cortex. Our results provide evidence that striatal prediction errors are normalized by a magnitude-dependent alteration in the interregional connectivity within the brain's reward system.
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