In everyday life we are constantly updating our moral judgements of people and actions as we learn new information. We developed a novel paradigm to investigate how people update their moral judgements of fairness-related actions upon receiving contextual information regarding the deservingness of the action recipient. Participants (N = 313) observed a variant of the dictator game, whereby a ‘Decision-maker’ was given 10 dollars and decided how much of this amount to share with another person termed the ‘Receiver’. Participants first made an initial, context-absent judgement of the Decision-maker’s offer to the Receiver, and then a subsequent judgement of the same offer after learning contextual information regarding the Receiver’s previous offer to another person (context-present judgement). This sequence was repeated for varying combinations of Decision-maker and Receiver offers. Functional Principle Component Analyses revealed that participants showed patterns of judgements across offers that were interpretable in relation to moral norms, but that these patterns varied across individuals. Participants who endorsed equal-split (5 dollar) offers in their context-absent judgements also endorsed offers made by Decision-makers that were similar to the previous offer made by the Receiver (i.e. indirectly reciprocal offers). Participants who endorsed high (>5 dollar) offers in context-absent judgements also endorsed Decision-maker offers that were more generous than those made by the Receiver. Our findings show that most participants flexibly switched from relying on commonly studied context-independent norms (generosity, equality), to relying on related, context-dependent norms (relative generosity, indirect reciprocity), as they successfully integrate contextual information. The paradigm we have developed also provides a novel framework for investigating how moral judgements evolve in real time as people learn more information about a given situation.

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Hierarchical predictive coding networks are a general model of sensory processing in the brain. Under neural delays, these networks have been suggested to naturally generate oscillatory activity in approximately the alpha frequency range (∼8-12 Hz). This suggests that alpha oscillations, a prominent feature of EEG recordings, may be a spectral ‘fingerprint’ of predictive sensory processing. Here, we probed this possibility by investigating whether oscillations over the visual cortex predictively encode visual information. Specifically, we examined whether their power carries information about the position of a moving stimulus, in a temporally predictive fashion. In two experiments (N = 32, 18 female; N = 34, 17 female), participants viewed an apparent-motion stimulus moving along a circular path, while EEG was recorded. To investigate the encoding of stimulus-position information, we developed a method of deriving probabilistic spatial maps from oscillatory power estimates. With this method, we demonstrate that it is possible to reconstruct the trajectory of a moving stimulus from alpha/low-beta oscillations, tracking its position even across unexpected motion reversals. We also show that future position representations are activated in the absence of direct visual input, demonstrating that temporally predictive mechanisms manifest in alpha/beta-band oscillations. In a second experiment we replicate these findings and show that the encoding of information in this range is not driven by visual entrainment. By demonstrating that occipital alpha/beta oscillations carry stimulus-related information, in a temporally predictive fashion, we provide empirical evidence of these rhythms as a spectral ‘fingerprint’ of hierarchical predictive processing in the human visual system.SIGNIFICANCE STATEMENT:‘Hierarchical predictive coding’ is a general model of sensory information processing in the brain. Whenin silicopredictive coding models are constrained by neural transmission delays, their activity naturally oscillates in roughly the alpha range (∼8-12 Hz). Using time-resolved EEG decoding, we show that neural rhythms in this approximate range (alpha/low-beta) over the human visual cortex predictively encode the position of a moving stimulus. From the amplitude of these oscillations we are able to reconstruct the stimulus’ trajectory, revealing signatures of temporally-predictive processing. This provides direct neural evidence linking occipital alpha/beta rhythms to predictive visual processing, supporting the emerging view of such oscillations as a potential spectral ‘fingerprint’ of hierarchical predictive processing in the human visual system.