Perception cannot rely solely
on bottom-up processes, whereby patterns of receptor stimulation are
passed up the hierarchy to generate a corresponding awareness. Such
bottom-up processes would always generate experiences that are
out-of-date and saturated by noise. Predictive processes are thought to
play a key role in overcoming these problems, allowing us to generate
best guesses concerning the likely sensorium, and highlighting quickly
when the world is not as we expect. Action provides a crucial predictive
source and a mechanism for us to resolve uncertainty and surprise, but
further complicates our understanding due to further predictive cues and
continuous change of sensory input. Another agent who can also change
the world and who we seek to understand adds another layer of complexity
yet. How can we understand the predictive mechanisms supporting social
interaction and understanding, with such a multitude of moving and
interacting components? In this special issue,
Keysers et al. (2024) outline how predictive coding can be applied to understanding the actions and emotions of others, with
Mayo and Shamay-Tsoory (2024)
discussing how these mutual predictions might shape social learning.
They suggest that such social learning might be supported by interbrain
synchronization and
Antonelli et al. (2024) discuss the critical role of emotion in shaping these multibrain dynamics.
While
it is clearly crucial that we understand the nature of the mechanisms
underlying social interactions, we wish to highlight the challenges of
this complexity for scientific progress. Particularly, how to find ways
to properly test, refute, and improve our models, when the assumed
supporting mechanisms are so complex.
How
predictions shape neural processing is thought to differ across space
and time, even for processing of the simplest (non-social; static)
elements of our environment.
Keysers et al. (2024)
highlight the assumed neural interactions across cortical layers, such
that predictions are passed down the hierarchy to hypothesis units in
deep (and perhaps superficial) cortical layers, input arrives in middle
layers, and error signals are calculated and represented in superficial
layers. This idea is supported by recent 7 T MRI work from our lab
demonstrating increased decoding of predicted Gabor orientations in deep
layers of primary visual cortex, with an advantage for unpredicted
orientations in superficial layers (
Thomas et al., 2024). Recent evidence suggests opposing influences at the temporal level as well (
McDermott et al., 2024).
This electroencephalography (EEG) study found that early perceptual
processing is biased towards what we expect (< 200 ms; optimizing
veridicality) with the advantage flipping in later timeranges (>
200 ms; optimizing informativeness – in line with the opposing process
account proposed in
Press et al., 2020).
Building testable mechanistic accounts of these interactions across
time and space – even for the simple perception of deterministic
sequences of Gabor patches – represents a continued puzzle for future
work.
In the social domain, the stimuli are by their nature highly complex and dynamic (
Keysers et al., 2024).
Therefore, these above interactions across space and time must be
continuously updated. Despite this complexity, there is some evidence
cited by
Keysers et al. (2024)
inline with the above laminar conclusions in simpler environments.
Specifically, there is increased deep-layer information about observed
actions in parietal cortex when presented in a predictable order,
mediated via feedback connections (from premotor cortex). Social domains
also yield multiple sources of prediction about the self and other (
Mayo and Shamay-Tsoory, 2024)
and we must determine how we weight the precision, or reliability, of
these different sources, as well as how we render information about the
self and other separable. Is this achieved by different cell populations
coding information about the self and other (
Mayo and Shamay-Tsoory, 2024)?
Or could mechanisms similar to those proposed to distinguish products
of imagination from reality (similarly internal vs external sources),
also help in determining the information source in social situations?
Social predictions might be supported by interbrain synchronization (measured via hyperscanning), as discussed by
Mayo and Shamay-Tsoory (2024); focus on social learning) and
Antonelli et al. (2024);
focus on emotion). We propose that one key challenge for this approach
is determining the role played by different event-related inputs and
responses in the effects: Interpretation of hyperscanning data is
plagued by the problem that brains will be “in synch” if two individuals
are either perceiving the same events or producing the same behaviour.
The brain’s responses to moving our arm or looking at a face are
remarkably similar across individuals, such that if two of us perceive
or produce the same event our neural response will be matched.
Fluctuations in synchronisation according to, e.g., dominance of
individuals or levels of excitement on stage, could be determined by
fluctuations in whether we attend to, or produce, the same events. It is
crucial to understand the fascinating influence of these effects on
synchronisation.
