Predictions and errors are distinctly represented across V1 layers.

Popular accounts of mind and brain propose that the brain continuously forms predictions about future sensory inputs and combines predictions with inputs to determine what we perceive.1,2,3,4,5,6 Under "predictive processing" schemes, such integration is supported by the hierarchical organization of the cortex, whereby feedback connections communicate predictions from higher-level deep layers to agranular (superficial and deep) lower-level layers.7,8,9,10 Predictions are compared with input to compute the "prediction error," which is transmitted up the hierarchy from superficial layers of lower cortical regions to the middle layers of higher areas, to update higher-level predictions until errors are reconciled.11,12,13,14,15 In the primary visual cortex (V1), predictions have thereby been proposed to influence representations in deep layers while error signals may be computed in superficial layers. Despite the framework's popularity, there is little evidence for these functional distinctions because, to our knowledge, unexpected sensory events have not previously been presented in human laminar paradigms to contrast against expected events. To this end, this 7T fMRI study contrasted V1 responses to expected (75% likely) and unexpected (25%) Gabor orientations. Multivariate decoding analyses revealed an interaction between expectation and layer, such that expected events could be decoded with comparable accuracy across layers, while unexpected events could only be decoded in superficial laminae. Although these results are in line with these accounts that have been popular for decades, such distinctions have not previously been demonstrated in humans. We discuss how both prediction and error processes may operate together to shape our unitary perceptual experiences.

Neural Representations of Observed Interpersonal Synchrony/Asynchrony in the Social Perception Network.

The visual perception of individuals is thought to be mediated by a network of regions in the occipitotemporal cortex that supports specialized processing of faces, bodies, and actions. In comparison, we know relatively little about the neural mechanisms that support the perception of multiple individuals and the interactions between them. The present study sought to elucidate the visual processing of social interactions by identifying which regions of the social perception network represent interpersonal synchrony. In an fMRI study with 32 human participants (26 female, 6 male), we used multivoxel pattern analysis to investigate whether activity in face-selective, body-selective, and interaction-sensitive regions across the social perception network supports the decoding of synchronous versus asynchronous head-nodding and head-shaking. Several regions were found to support significant decoding of synchrony/asynchrony, including extrastriate body area (EBA), face-selective and interaction-sensitive mid/posterior right superior temporal sulcus, and occipital face area. We also saw robust cross-classification across actions in the EBA, suggestive of movement-invariant representations of synchrony/asynchrony. Exploratory whole-brain analyses also identified a region of the right fusiform cortex that responded more strongly to synchronous than to asynchronous motion. Critically, perceiving interpersonal synchrony/asynchrony requires the simultaneous extraction and integration of dynamic information from more than one person. Hence, the representation of synchrony/asynchrony cannot be attributed to augmented or additive processing of individual actors. Our findings therefore provide important new evidence that social interactions recruit dedicated visual processing within the social perception network that extends beyond that engaged by the faces and bodies of the constituent individuals.

Stubborn Predictions in Primary Visual Cortex.

Perceivers can use past experiences to make sense of ambiguous sensory signals. However, this may be inappropriate when the world changes and past experiences no longer predict what the future holds. Optimal learning models propose that observers decide whether to stick with or update their predictions by tracking the uncertainty or "precision" of their expectations. However, contrasting theories of prediction have argued that we are prone to misestimate uncertainty-leading to stubborn predictions that are difficult to dislodge. To compare these possibilities, we had participants learn novel perceptual predictions before using fMRI to record visual brain activity when predictive contingencies were disrupted-meaning that previously "expected" events became objectively improbable. Multivariate pattern analyses revealed that expected events continued to be decoded with greater fidelity from primary visual cortex, despite marked changes in the statistical structure of the environment, which rendered these expectations no longer valid. These results suggest that our perceptual systems do indeed form stubborn predictions even from short periods of learning-and more generally suggest that top-down expectations have the potential to help or hinder perceptual inference in bounded minds like ours.

Expectations about precision bias metacognition and awareness.

Bayesian models of the mind suggest that we estimate the reliability or "precision" of incoming sensory signals to guide perceptual inference and to construct feelings of confidence or uncertainty about what we are perceiving. However, accurately estimating precision is likely to be challenging for bounded systems like the brain. One way observers could overcome this challenge is to form expectations about the precision of their perceptions and use these to guide metacognition and awareness. Here we test this possibility. Participants made perceptual decisions about visual motion stimuli, while providing confidence ratings (Experiments 1 and 2) or ratings of subjective visibility (Experiment 3). In each experiment, participants acquired probabilistic expectations about the likely strength of upcoming signals. We found these expectations about precision altered metacognition and awareness-with participants feeling more confident and stimuli appearing more vivid when stronger sensory signals were expected, without concomitant changes in objective perceptual performance. Computational modeling revealed that this effect could be well explained by a predictive learning model that infers the precision (strength) of current signals as a weighted combination of incoming evidence and top-down expectation. These results support an influential but untested tenet of Bayesian models of cognition, suggesting that agents do not only "read out" the reliability of information arriving at their senses, but also take into account prior knowledge about how reliable or "precise" different sources of information are likely to be. This reveals that expectations about precision influence how the sensory world appears and how much we trust our senses. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Cancelling cancellation? Sensorimotor control, agency, and prediction.

For decades, classic theories of action control and action awareness have been built around the idea that the brain predictively 'cancels' expected action outcomes from perception. However, recent research casts doubt over this basic premise. What do these new findings mean for classic accounts of action? Should we now 'cancel' old data, theories and approaches generated under this idea? In this paper, we argue 'No'. While doubts about predictive cancellation may urge us to fundamentally rethink how predictions shape perception, the wider pyramid using these ideas to explain action control and agentic experiences can remain largely intact. Some adaptive functions assigned to predictive cancellation can be achieved through quasi-predictive processes, that influence perception without actively tracking the probabilistic structure of the environment. Other functions may rely upon truly predictive processes, but not require that these predictions cancel perception. Appreciating the role of these processes may help us to move forward in explaining how agents optimise their interactions with the external world, even if predictive cancellation is cancelled from theory.

Causal surgery under a Markov blanket.

Bruineberg et al. provide compelling clarity on the roles Markov blankets could (and perhaps should) play in the study of life and mind. However, here we draw attention to a further role blankets might play: as a hypothesis about cognition itself. People and other animals may use blanket-like representations to model the boundary between themselves and their worlds.

Building better theories.

In this My word, Press et al. tackle the 'theory crisis' in cognitive science. Using examples of good and not-so-good theoretical practice, they distinguish theories from effects, predictions, hypotheses, typologies, and frameworks in a self-help checklist of seven questions to guide theory construction, evaluation, and testing.

Action Enhances Predicted Touch.

It is widely believed that predicted tactile action outcomes are perceptually attenuated. The present experiments determined whether predictive mechanisms necessarily generate attenuation or, instead, can enhance perception-as typically observed in sensory cognition domains outside of action. We manipulated probabilistic expectations in a paradigm often used to demonstrate tactile attenuation. Adult participants produced actions and subsequently rated the intensity of forces on a static finger. Experiment 1 confirmed previous findings that action outcomes are perceived less intensely than passive stimulation but demonstrated more intense perception when active finger stimulation was removed. Experiments 2 and 3 manipulated prediction explicitly and found that expected touch during action is perceived more intensely than unexpected touch. Computational modeling suggested that expectations increase the gain afforded to expected tactile signals. These findings challenge a central tenet of prominent motor control theories and demonstrate that sensorimotor predictions do not exhibit a qualitatively distinct influence on tactile perception.

Precision and the Bayesian brain.

Scientific thinking about the minds of humans and other animals has been transformed by the idea that the brain is Bayesian. A cornerstone of this idea is that agents set the balance between prior knowledge and incoming evidence based on how reliable or 'precise' these different sources of information are - lending the most weight to that which is most reliable. This concept of precision has crept into several branches of cognitive science and is a lynchpin of emerging ideas in computational psychiatry - where unusual beliefs or experiences are explained as abnormalities in how the brain estimates precision. But what precisely is precision? In this Primer we explain how precision has found its way into classic and contemporary models of perception, learning, self-awareness, and social interaction. We also chart how ideas around precision are beginning to change in radical ways, meaning we must get more precise about how precision works.

Prediction and Learning: Understanding Uncertainty.

We build models of the world around us to guide perception and learning in the face of uncertainty. New evidence reveals a neurocomputational mechanism that links predictive processes across cognitive domains.

Action biases perceptual decisions toward expected outcomes.

We predict how our actions will influence the world around us. Prevailing models in the action control literature propose that we use these predictions to suppress or "cancel" perception of expected action outcomes, to highlight more informative surprising events. However, contrasting normative Bayesian models in sensory cognition suggest that we are more, not less, likely to perceive what we expect-given that what we expect is more likely to occur. Here we adjudicated between these models by investigating how expectations influence perceptual decisions about action outcomes in a signal detection paradigm. Across three experiments, participants performed one of two manual actions that were sometimes accompanied by brief presentation of expected or unexpected visual outcomes. Contrary to dominant cancellation models but consistent with Bayesian accounts, we found that observers were biased to report the presence of expected action outcomes. There were no effects of expectation on sensitivity. Computational modeling revealed that the action-induced bias reflected a sensory bias in how evidence was accumulated rather than a baseline shift in decision circuits. Expectation effects remained in Experiments 2 and 3 when orthogonal cues indicated which finger was more likely to be probed (i.e. task-relevant). These biases toward perceiving expected action outcomes are suggestive of a mechanism that would enable generation of largely veridical representations of our actions and their consequences in an inherently uncertain sensory world. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Illusions of control without delusions of grandeur.

We frequently experience feelings of agency over events we do not objectively influence - so-called 'illusions of control'. These illusions have prompted widespread claims that we can be insensitive to objective relationships between actions and outcomes, and instead rely on grandiose beliefs about our abilities. However, these illusory biases could instead arise if we are highly sensitive to action-outcome correlations, but attribute agency when such correlations emerge simply by chance. We motion-tracked participants while they made agency judgements about a cursor that could be yoked to their actions or follow an independent trajectory. A combination of signal detection analysis, reverse correlation methods and computational modelling indeed demonstrated that 'illusions' of control could emerge solely from sensitivity to spurious action-outcome correlations. Counterintuitively, this suggests that illusions of control could arise because agents have excellent insight into the relationships between actions and outcomes in a world where causal relationships are not perfectly deterministic.

Association between action kinematics and emotion perception across adolescence.

Research with adults suggests that we interpret the internal states of others from kinematic cues, using models calibrated to our own action experiences. Changes in action production that occur during adolescence may therefore have implications for adolescents' understanding of others. Here we examined whether, like adults, adolescents use velocity cues to determine others' emotions and whether any differences in emotion perception would be those predicted based on differences in action production. We measured preferred walking velocity in groups of early (11-12 years old), middle (13-14 years old), and late (16-18 years old) adolescents, as well as adults, and recorded their perception of happy, angry, and sad "point-light walkers." Preferred walking velocity decreased across age, and ratings of emotional stimuli with manipulated velocity demonstrated that all groups used velocity cues to determine emotion. Importantly, the relative intensity ratings of different emotions also differed across development in a manner that was predicted based on the group differences in walking velocity. Further regression analyses demonstrated that emotion perception was predicted by participants' own movement velocity, rather than age or pubertal stage per se. These results suggest that changes in action production across adolescence are indeed accompanied by corresponding changes in how emotions are perceived from velocity. These findings indicate the importance of examining differences in action production across development when interpreting differences in how individuals understand others. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The Perceptual Prediction Paradox.

From the noisy information bombarding our senses, our brains must construct percepts that are veridical - reflecting the true state of the world - and informative - conveying what we did not already know. Influential theories suggest that both challenges are met through mechanisms that use expectations about the likely state of the world to shape perception. However, current models explaining how expectations render perception either veridical or informative are mutually incompatible. While the former propose that perceptual experiences are dominated by events we expect, the latter propose that perception of expected events is suppressed. To solve this paradox we propose a two-process model in which probabilistic knowledge initially biases perception towards what is likely and subsequently upweights events that are particularly surprising.

Perceptual Prediction: Rapidly Making Sense of a Noisy World.

Prior knowledge shapes what we perceive. A new brain stimulation study suggests that this perceptual shaping is achieved by changes in sensory brain regions before the input arrives, with common mechanisms operating across different sensory areas.

Atypical emotion recognition from bodies is associated with perceptual difficulties in healthy aging.

A range of processes are required for recognizing others' affective states. It is particularly important that we process the perceptual cues providing information about these states. These experiments tested the hypothesis that difficulties with affective state identification in older adults (OAs) arise, at least partly, from deficits in perceptual processing. To this end we presented "point light display" whole body stimuli to healthy OAs and comparison younger adults (YAs) in 3 signal detection experiments. We examined the ability of OAs to recognize visual bodily information-posture and kinematics-and whether impaired recognition of affective states can be explained by deficits in processing these cues. OAs exhibited reduced sensitivity to postural cues (Experiment 1) but not to kinematic cues (Experiment 2) in affectively neutral stimuli. Importantly, they also exhibited reduced sensitivity only to affective states conveyed predominantly through posture (Experiment 3) -that is, the cue they were impaired in perceiving. These findings highlight how affective state identification difficulties in OAs may arise from problems in perceptual processing and demonstrate more widely how it is essential to consider the contribution of perceptual processes to emotion recognition. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The Predictive Brain as a Stubborn Scientist.

Bayesian theories of perception have traditionally cast the brain as an idealised scientist, refining predictions about the outside world based on evidence sampled by the senses. However, recent predictive coding models include predictions that are resistant to change, and these stubborn predictions can be usefully incorporated into cognitive models.

Action sharpens sensory representations of expected outcomes.

When we produce actions we predict their likely consequences. Dominant models of action control suggest that these predictions are used to 'cancel' perceptual processing of expected outcomes. However, normative Bayesian models of sensory cognition developed outside of action propose that rather than being cancelled, expected sensory signals are represented with greater fidelity (sharpened). Here, we distinguished between these models in an fMRI experiment where participants executed hand actions (index vs little finger movement) while observing movements of an avatar hand. Consistent with the sharpening account, visual representations of hand movements (index vs little finger) could be read out more accurately when they were congruent with action and these decoding enhancements were accompanied by suppressed activity in voxels tuned away from, not towards, the expected stimulus. Therefore, inconsistent with dominant action control models, these data show that sensorimotor prediction sharpens expected sensory representations, facilitating veridical perception of action outcomes.