Young children integrate current observations, priors and agent information to predict others' actions.

From early on in life, children are able to use information from their environment to form predictions about events. For instance, they can use statistical information about a population to predict the sample drawn from that population and infer an agent's preferences from systematic violations of random sampling. We investigated whether and how young children infer an agent's sampling biases. Moreover, we examined whether pupil data of toddlers follow the predictions of a computational model based on the causal Bayesian network formalization of predictive processing. We formalized three hypotheses about how different explanatory variables (i.e., prior probabilities, current observations, and agent characteristics) are used to predict others' actions. We measured pupillary responses as a behavioral marker of 'prediction errors' (i.e., the perceived mismatch between what one's model of an agent predicts and what the agent actually does). Pupillary responses of 24-month-olds, but not 18-month-olds, showed that young children integrated information about current observations, priors and agents to make predictions about agents and their actions. These findings shed light on the mechanisms behind toddlers' inferences about agent-caused events. To our knowledge, this is the first study in which young children's pupillary responses are used as markers of prediction errors, which were qualitatively compared to the predictions by a computational model based on the causal Bayesian network formalization of predictive processing.

Processing of Prediction Errors in Mentalizing Areas.

When seeing people perform actions, we are able to quickly predict the action's outcomes. These predictions are not solely based on the observed actions themselves but utilize our prior knowledge of others. It has been suggested that observed outcomes that are not in line with these predictions result in prediction errors, which require additional processing to be integrated or updated. However, there is no consensus on whether this is indeed the case for the kind of high-level social-cognitive processes involved in action observation. In this fMRI study, we investigated whether observation of unexpected outcomes causes additional activation in line with the processing of prediction errors and, if so, whether this activation overlaps with activation in brain areas typically associated with social-cognitive processes. In the first part of the experiment, participants watched animated movies of two people playing a bowling game, one experienced and one novice player. In cases where the player's score was higher or lower than expected based on their skill level, there was increased BOLD activity in areas that were also activated during a theory of mind task that participants performed in the second part of the experiment. These findings are discussed in the light of different theoretical accounts of human social-cognitive processing.

One wouldn't expect an expert bowler to hit only two pins: Hierarchical predictive processing of agent-caused events.

Evidence is accumulating that our brains process incoming information using top-down predictions. If lower level representations are correctly predicted by higher level representations, this enhances processing. However, if they are incorrectly predicted, additional processing is required at higher levels to "explain away" prediction errors. Here, we explored the potential nature of the models generating such predictions. More specifically, we investigated whether a predictive processing model with a hierarchical structure and causal relations between its levels is able to account for the processing of agent-caused events. In Experiment 1, participants watched animated movies of "experienced" and "novice" bowlers. The results are in line with the idea that prediction errors at a lower level of the hierarchy (i.e., the outcome of how many pins fell down) slow down reporting of information at a higher level (i.e., which agent was throwing the ball). Experiments 2 and 3 suggest that this effect is specific to situations in which the predictor is causally related to the outcome. Overall, the study supports the idea that a hierarchical predictive processing model can account for the processing of observed action outcomes and that the predictions involved are specific to cases where action outcomes can be predicted based on causal knowledge.

Beta- and gamma-band activity reflect predictive coding in the processing of causal events.

In daily life, complex events are perceived in a causal manner, suggesting that the brain relies on predictive processes to model them. Within predictive coding theory, oscillatory beta-band activity has been linked to top-down predictive signals and gamma-band activity to bottom-up prediction errors. However, neurocognitive evidence for predictive coding outside lower-level sensory areas is scarce. We used magnetoencephalography to investigate neural activity during probability-dependent action perception in three areas pivotal for causal inference, superior temporal sulcus, temporoparietal junction and medial prefrontal cortex, using bowling action animations. Within this network, Granger-causal connectivity in the beta-band was found to be strongest for backward top-down connections and gamma for feed-forward bottom-up connections. Moreover, beta-band power in TPJ increased parametrically with the predictability of the action kinematics-outcome sequences. Conversely, gamma-band power in TPJ and MPFC increased with prediction error. These findings suggest that the brain utilizes predictive-coding-like computations for higher-order cognition such as perception of causal events.

Higher-level processes in the formation and application of associations during action understanding.

The associative account described in the target article provides a viable explanation for the origin of mirror neurons. We argue here that if mirror neurons develop purely by associative learning, then they cannot by themselves explain intentional action understanding. Higher-level processes seem to be involved in the formation of associations as well as in their application during action understanding.

Using motor imagery to study the neural substrates of dynamic balance.

This study examines the cerebral structures involved in dynamic balance using a motor imagery (MI) protocol. We recorded cerebral activity with functional magnetic resonance imaging while subjects imagined swaying on a balance board along the sagittal plane to point a laser at target pairs of different sizes (small, large). We used a matched visual imagery (VI) control task and recorded imagery durations during scanning. MI and VI durations were differentially influenced by the sway accuracy requirement, indicating that MI of balance is sensitive to the increased motor control necessary to point at a smaller target. Compared to VI, MI of dynamic balance recruited additional cortical and subcortical portions of the motor system, including frontal cortex, basal ganglia, cerebellum and mesencephalic locomotor region, the latter showing increased effective connectivity with the supplementary motor area. The regions involved in MI of dynamic balance were spatially distinct but contiguous to those involved in MI of gait (Bakker et al., 2008; Snijders et al., 2011; Crémers et al., 2012), in a pattern consistent with existing somatotopic maps of the trunk (for balance) and legs (for gait). These findings validate a novel, quantitative approach for studying the neural control of balance in humans. This approach extends previous reports on MI of static stance (Jahn et al., 2004, 2008), and opens the way for studying gait and balance impairments in patients with neurodegenerative disorders.