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Online evaluation of novel choices by simultaneous representation of multiple memories



Prior experience is critical for decision-making. It enables explicit representation of potential outcomes and provides training to valuation mechanisms. However, we can also make choices in the absence of prior experience by merely imagining the consequences of a new experience. Using functional magnetic resonance imaging repetition suppression in humans, we examined how neuronal representations of novel rewards can be constructed and evaluated. A likely novel experience was constructed by invoking multiple independent memories in hippocampus and medial prefrontal cortex. This construction persisted for only a short time period, during which new associations were observed between the memories for component items. Together, these findings suggest that, in the absence of direct experience, coactivation of multiple relevant memories can provide a training signal to the valuation system that allows the consequences of new experiences to be imagined and acted on.

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Figure 1: Experimental design.
Figure 2: Neural correlates of constructing and evaluating a novel good.
Figure 3: Sensory exposure to a novel good: comparison between the unfamiliar and familiar groups during the decision-making task.
Figure 4: Sensory exposure to a novel good: comparison between the unfamiliar and familiar groups during construction of a novel good.
Figure 5: In the absence of sensory exposure, there was evidence for the construction mechanism only in early trials: block 1 compared with blocks 2 and 3 for unfamiliar subjects.
Figure 6: In the absence of sensory exposure, repetition suppression between related components was maintained across the duration of the experiment only if participants assigned high value to the compound goods.


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We thank P. Dayan, E.A. Maguire, N. Burgess, S.W. Kennerley, L.T. Hunt, M.C. Klein-Flügge and E.D. Boorman for helpful comments on an earlier draft of the manuscript, and H. Blumenthal's Fat Duck Cookbook for recipe inspiration. This study was supported by the Wellcome Trust (grant WT088312AIA to T.E.J.B. and a Senior Investigator Award to R.J.D., 098362/Z/12/Z), the Medical Research Council (grant G1000411 to H.C.B.). The Wellcome Trust Centre for Neuroimaging is supported by core funding from the Wellcome Trust Strategic Award Grant 091593/Z/10/Z.

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All of the authors contributed to the design of the study and preparation of the manuscript. H.C.B. acquired the data and analyzed it with T.E.J.B.

Corresponding authors

Correspondence to Helen C Barron or Timothy E J Behrens.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Partial correlations between novel and component values and adaptation between related components in the unfamiliar group.

(a)(b) As shown in Fig. 2d and 2e, after removing signal attributable to the average value of the component items, there was a significant correlation between the average value of the novel items and the extent to which individual participants showed adaptation between related components across all blocks in both mPFC, (r = 0.51, p = 0.015, a), and hippocampus (r = 0.60, p = 0.004, b). (c)(d) There was also a negative effect of the component values on the same signal after variance associated with the novel compounds had been removed, in both mPFC (trend, r = -0.32, p = 0.096, c), and hippocampus (r = -0.42, p = 0.042, d). Notably, this effect, c and d, cannot be simply due to the valuation of the currently displayed component item, as the contrast in question contains each item positively and negatively equally often. Instead, one possible implication of this finding is that the plasticity between component items does not only depend on the constructed value of the novel good, but that it may be particularly prominent if the value of the novel good is surprisingly high due to the low values of the components. If it is assumed that an initial prediction of the value of the novel good is the average value of the components, then the plasticity effect is best correlated with the error between the constructed value and this prediction.

Supplementary Figure 2 Consistency of choices on the decision-making task.

These logistic regressions show how the difference in value between the left and right option predicted choice during the decision phase of the experiment. The consistency of choices made by the unfamiliar group (a) was comparable to that of the familiar group (b), with no significant difference between the two groups (beta values: 1.42 for unfamiliars, 1.46 for familiars; one-tailed Mann-Whitney U-test: p = 0.182).

Supplementary Figure 3 Group comparison: average value assigned to the 13 novel food items.

The average difference in value assigned to each novel good by the familiar and unfamiliar groups. The stars on each bar indicate the number of participants assigned to each of the goods in the repetition suppression experiment, and out of those assigned there was no significant difference in valuation between the groups.

Supplementary Figure 4 Adaptation effects in visual regions of the unfamiliar group.

(a) Left side: Between early and late blocks, there was not a significant reduction in the adaptation effect size of visual regions to repeated stimulus presentation (one-tailed t-test: t(18) = 0.50, p = 0.312, parameter estimates extracted from unfamiliar group within the ROI shown in (b), see Methods for details). This suggests that across the duration of the experiment sensitivity to adaptation effects was maintained. Right side: Whilst there was significant adaptation to repeated stimulus presentation in visual regions (one-tailed t-test: t(18) = 3.22, p = 0.002), there was no evidence for adaptation between either related components (one-tailed t-test: t(18) = 1.10, p = 0.144) or between compounds and their related components (one-tailed t-test: t(18) = 0.60, p = 0.278) (block 1 only, ROI shown in (b)). Thus, repetition suppression in visual brain regions was not specific to the construction of a novel good.

Supplementary Figure 5 Partial correlations between the value of food items and adaptation between related component items in blocks 2 and 3 of the unfamiliar group.

Partial correlations are shown as in Supplementary Fig. 1, but now show effects in blocks 2 and 3 only, with the positive correlations between compound value and adaptation between related component items (mPFC: r = 0.64, p = 0.002, a; and hippocampus: r = 0.63, p = 0.003, b; as shown in Fig. 6b and 6d respectively), and negative correlations between component value and adaptation between related component items (mPFC: r = -0.63, p = 0.003, c; hippocampus: r = -0.47, p = 0.024, d).

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Supplementary Figures 1–5 and Supplementary Table 1 (PDF 1090 kb)

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Barron, H., Dolan, R. & Behrens, T. Online evaluation of novel choices by simultaneous representation of multiple memories. Nat Neurosci 16, 1492–1498 (2013).

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