This study was interested in investigating the existence of a shared psychological mechanism for the processing of expectations across domains. The literature on music and language shows that violations of expectations produce similar neural responses and violating the expectation in one domain may influence the processing of stimuli in the other domain. Like music and language, our social world is governed by a system of inherent rules or norms, such as fairness. The study therefore aimed to draw a parallel to the social domain and investigate whether a manipulation of melodic expectation can influence the processing of higher-level expectations of fairness. Specifically, we aimed to investigate whether the presence of an unexpected melody enhances or reduces participants’ sensitivity to the violations of fairness and the behavioural reactions associated with these. We embedded a manipulation of melodic expectation within a social decision-making paradigm, whereby musically expected and unexpected stimuli will be simultaneously presented with fair and unfair divisions in a third-party altruistic punishment game. Both behavioural and EEG responses were recorded. Results from the pre-planned analyses show that participants are less likely to punish when the melodic stimuli are more unexpected and that violations of fairness norms elicit MFN-life effects. However, since no significant interactions between melodic expectancy and fairness of the division were found, results fail to provide evidence of a shared mechanism for the processing of expectations. Exploratory analyses show two additional effects: i) unfair divisions elicit an early attentional component (P2), likely associated with stimulus saliency, and ii) mid-value divisions elicit a late MFN-like component, likely reflecting stimulus ambiguity.  Future studies could build on these results to further investigate the effect of the cross-domain influence of music on the processing of social stimuli on these early and late components.

Information about the approved Stage 1 protocol, data collection and analyses can be found in the Methods section of the Registered Report.

The uploaded folder contains EEG and behavioural data as well as the code used to analyse the data. 

The EEG data consists of

- Pre-processed (clean) EEGlab data (.set) following final stage of pre-processing with MARA(see Step 7 of pre-processing in the MATLAB .m script) 

- Individual raw (unprocessed) EEGlab (.set) files converted from .bdf Biosemi files. 

- Group_data_plotting_stats.zip: Group data for plotting and statistics (.mat files using the Fieldtrip cell structure)

The Behavioural data consists of:

- individual raw unprocessed data in .csv (converted from E-prime)

- group processed data (.csv)

- data used for plotting and factorial analyses (.csv) 

Code

- R code for behavioural analyses; it creates the group data from unprocessed raw individual data.

- .m (MATLAB) code for EEG analyses; this is used for pre-processing and analyses/plotting 

Information about triggers/conditions:

Notes: 1 = expected notes, 2 = unexpected notes 

Fairness: 1 = fair (0) , 2 = 25, 3 = 50 , 4 = 75 , 5 = 100.

Example: condition 21 = unexpected music presented on fair divisions (0)