Graphical Abstract

graphicalAbstract.png

Abstract

Standard animal learning studies minimize individual reward magnitudes to maximize the repetitions of reinforced behaviors. We investigated how reward magnitude influences initial learning across five behavioral paradigms in naive mice. Especially large rewards could substantially improve learning efficiency through dissociable effects on within- and across-session learning and task engagement. The duration and magnitude of ventral striatal dopamine release scales with reward sizes, and prolonged optogenetic enhancement of dopamine reward responses also reproduced much, but not all, of the benefits to learning produced by outsized rewards. These findings indicate that the reinforcement learning efficiency of animals has traditionally been underestimated and that dopaminergic reward signals mediate task engagement in proportion to absolute reward magnitude.

Published version

available 21 May 2026

Authors’ version

available upon request

Publicly available materials

Hidden target task + dopamine photometry

IBL task replica

Details on construction of a replica IBL rig - this is a little challenging as some parts have been discontinued, but we have the details thanks to Frank Loesche and Carmen Morrow (who run MCN-NET at Janelia Research Campus) who helped us source replacement parts

iblrig-2.pdf

This was possible thanks to the fantastic documentation and sharing of open resources from the International Brain Laboratory

International Brain Laboratory

Useful code snippet to re-assemble JSONABLE files into readable JSON for Matlab

fn = '_iblrig_taskData.raw.jsonable';
fid = fopen(fn, 'r');
if fid>-1

        C = textscan(fid, '%s', 'Delimiter', '\\\\n', 'Whitespace', '');
        fclose(fid);
        lines = C{1};

        % Decode each line (each is a JSON object)
        objs = cellfun(@(s) jsondecode(s), lines, 'UniformOutput', false);

        % If objects share fields, combine into struct array; otherwise keep as cell array
        try
            S = vertcat(objs{:});      % struct array
        catch
            S = objs;                  % keep as cell array if heterogeneous
        end

        <<your output structure>>  = struct2table(S);

    end

Helpful information about IBL data structures

https://docs.google.com/spreadsheets/d/1ieLXRPLLSgUKcLvFkrqizfZl5HjdfE6bQ2KLBCRmjQo/edit?pli=1&gid=1097679410#gid=1097679410