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The brain has limited processing capacities. Attention selection processes are continuously shaping humans’ world perception. Understanding the mechanisms underlying such covert cognitive processes requires the combination of psychophysical and electrophysiological investigation methods. This combination allows researchers to describe how individual neurons and neuronal populations encode attentional function. Direct access to neuronal information through innovative electrophysiological approaches, additionally, allows the tracking of covert attention in real time. These converging approaches capture a comprehensive view of attentional function.

The human brain processes noisy information to help make adaptive choices under uncertainty. Accompanying these decisions about incoming evidence is a sense of confidence: a feeling about whether a decision is correct. Confidence typically covaries with the accuracy of decisions, in that higher confidence is associated with higher decisional accuracy. In the laboratory, decision confidence is typically measured by asking participants to make judgments about stimuli or information (type 1 judgments) and then to rate their confidence on a rating scale or by engaging in wagering (type 2 judgments). The correspondence between confidence and accuracy can be quantified in a number of ways, some based on probability theory and signal detection theory. But decision confidence does not always reflect only the probability that a decision is correct; confidence can also reflect many other factors, including other estimates of noise, evidence magnitude, nearby decisions, decision time, and motor movements. Confidence is thought to be computed by a number of brain regions, most notably areas in the prefrontal cortex. And, once computed, confidence can be used to drive other behaviors, such as learning rates or social interaction.

Predictive coding is a unifying framework for understanding perception, action, and neocortical organization. In predictive coding, different areas of the neocortex implement a hierarchical generative model of the world that is learned from sensory inputs. Cortical circuits are hypothesized to perform Bayesian inference based on this generative model. Specifically, the Rao–Ballard hierarchical predictive coding model assumes that the top-down feedback connections from higher to lower order cortical areas convey predictions of lower-level activities. The bottom-up, feedforward connections in turn convey the errors between top-down predictions and actual activities. These errors are used to correct current estimates of the state of the world and generate new predictions. Through the objective of minimizing prediction errors, predictive coding provides a functional explanation for a wide range of neural responses and many aspects of brain organization.

Vision is the most important sensory modality for humans, serving a range of fundamental daily behaviors from recognizing objects, people, places, and actions to navigation and visually guided interactions with objects and other individuals. One of the most prominent accounts of cortical functional specialization implies that the visual cortex is segregated into two pathways. The ventral pathway originates from the early visual cortex in the occipital lobe and projects to the inferior surface of the temporal cortex, and it mediates vision for perception. The dorsal pathway extends from the occipital lobe to the posterior portion of the parietal cortex, and it mediates vision for action. This key characterization of the visual system is supported by classic neuropsychological, behavioral, and neuroimaging evidence. Recent research offers new insights on the developmental trajectory of this dissociation as well as evidence for interactions between the two pathways. Importantly, an emerging hypothesis points to the existence of a third visual pathway located on the lateral surface of the ventral pathway and its potential roles in action recognition and social cognition.