Predictive coding models suggest that predicted sensory signals are attenuated (silencing of prediction error). These models, though influential, are challenged by the fact that prediction sometimes seems to enhance rather than reduce sensory signals, as in the case of attentional cueing experiments. One possible explanation is that in these experiments, prediction (i.e., stimulus probability) is confounded with attention (i.e., task relevance), which is known to boost rather than reduce sensory signal. However, recent theoretical work on predictive coding inspires an alternative hypothesis and suggests that attention and prediction operate synergistically to improve the precision of perceptual inference. This model posits that attention leads to heightened weighting of sensory evidence, thereby reversing the sensory silencing by prediction. Here, we factorially manipulated attention and prediction in a functional magnetic resonance imaging study and distinguished between these 2 hypotheses. Our results support a predictive coding model wherein attention reverses the sensory attenuation of predicted signals.

Although attention usually enhances perceptual sensitivity, we found that it can also lead to relatively conservative detection biases and lower visibility ratings in discrimination tasks. These results are explained by a model in which attention reduces the trial-by-trial variability of the perceptual signal, and we determined how this model led to the observed behavior. These findings may partially reflect our impression of 'seeing' the whole visual scene despite our limited processing capacity outside of the focus of attention.

Human perceptual decisions are often described as optimal. Critics of this view have argued that claims of optimality are overly flexible and lack explanatory power. Meanwhile, advocates for optimality have countered that such criticisms single out a few selected papers. To elucidate the issue of optimality in perceptual decision making, we review the extensive literature on suboptimal performance in perceptual tasks. We discuss eight different classes of suboptimal perceptual decisions, including improper placement, maintenance, and adjustment of perceptual criteria, inadequate tradeoff between speed and accuracy, inappropriate confidence ratings, misweightings in cue combination, and findings related to various perceptual illusions and biases. In addition, we discuss conceptual shortcomings of a focus on optimality, such as definitional difficulties and the limited value of optimality claims in and of themselves. We therefore advocate that the field drop its emphasis on whether observed behavior is optimal and instead concentrate on building and testing detailed observer models that explain behavior across a wide range of tasks. To facilitate this transition, we compile the proposed hypotheses regarding the origins of suboptimal perceptual decisions reviewed here. We argue that verifying, rejecting, and expanding these explanations for suboptimal behavior - rather than assessing optimality per se - should be among the major goals of the science of perceptual decision making.

When perceptual decisions are coupled to a specific effector, preparatory motor cortical activity may provide a window into the dynamics of the perceptual choice. Specifically, previous studies have observed a buildup of choice-selective activity in motor regions over time reflecting the integrated sensory evidence provided by visual cortex. Here we ask how this choice-selective motor activity is modified by prior expectation during a visual motion discrimination task. Computational models of decision making formalize decisions as the accumulation of evidence from a starting point to a decision bound. Within this framework, expectation could change the starting point, rate of accumulation, or the decision bound. Using magneto-encephalography in human observers, we specifically tested for changes in the starting point in choice-selective oscillatory activity over motor cortex. Inducing prior expectation about motion direction biased subjects' perceptual judgments as well as the choice-selective motor activity in the 8 -30 Hz frequency range before stimulus onset; the individual strength of these behavioral and neural biases were correlated across subjects. In the absence of explicit expectation cues, spontaneous biases in choice-selective activity were evident over motor cortex. These also predicted eventual perceptual choice and were, at least in part, induced by the choice on the previous trial. We conclude that both endogenous and explicitly induced perceptual expectations bias the starting point of decision-related activity, before the accumulation of sensory evidence.

Although recent research has shown that the frontal cortex has a critical role in perceptual decision making, an overarching theory of frontal functional organization for perception has yet to emerge. Perceptual decision making is temporally organized such that it requires the processes of selection, criterion setting, and evaluation. We hypothesized that exploring this temporal structure would reveal a large-scale frontal organization for perception. A causal intervention with transcranial magnetic stimulation revealed clear specialization along the rostrocaudal axis such that the control of successive stages of perceptual decision making was selectively affected by perturbation of successively rostral areas. Simulations with a dynamic model of decision making suggested distinct computational contributions of each region. Finally, the emergent frontal gradient was further corroborated by functional MRI. These causal results provide an organizational principle for the role of frontal cortex in the control of perceptual decision making and suggest specific mechanistic contributions for its different subregions.perception | frontal cortex | hierarchy | TMS | fMRI

Understanding how people rate their confidence is critical for characterizing a wide range of perceptual, memory, motor, and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations, and fields of study. The data from each study are structured in a common,

We live in a continuous environment in which the visual scene changes on a slow timescale. It has been shown that, to exploit such environmental stability, the brain creates a “continuity field” such that objects seen seconds ago influence the perception of current objects. What is unknown is whether a similar mechanism exists at the level of our metacognitive representations. In three experiments we demonstrate a robust inter-task “confidence leak” that cannot be explained by response priming or attentional fluctuations. Observers’ ability to modulate this confidence leak predicted higher capacity for metacognition as well as greater gray matter volume in the prefrontal cortex. A model based on normative principles from Bayesian inference explained the results by postulating that observers subjectively estimate the perceptual signal strength in a stable environment. These results point to the existence of a novel metacognitive mechanism mediated by regions in prefrontal cortex.

Rahnev DA, Maniscalco B, Luber B, Lau H, Lisanby SH. Direct injection of noise to the visual cortex decreases accuracy but increases decision confidence. J Neurophysiol 107: 1556 -1563, 2012. First published December 14, 2011 doi:10.1152/jn.00985.2011The relationship between accuracy and confidence in psychophysical tasks traditionally has been assumed to be mainly positive, i.e., the two typically increase or decrease together. However, recent studies have reported examples of exceptions, where confidence and accuracy dissociate from each other. Explanations for such dissociations often involve dual-channel models, in which a cortical channel contributes to both accuracy and confidence, whereas a subcortical channel only contributes to accuracy. Here, we show that a single-channel model derived from signal detection theory (SDT) can also account for such dissociations. We applied transcranial magnetic stimulation (TMS) to the occipital cortex to disrupt the internal representation of a visual stimulus. The results showed that consistent with previous research, occipital TMS decreased accuracy. However, counterintuitively, it also led to an increase in confidence ratings. The data were predicted well by a single-channel SDT model, which posits that occipital TMS increased the variance of the internal stimulus distributions. A formal model comparison analysis that used information theoretic methods confirmed that this model was preferred over single-channel models, in which occipital TMS changed the signal strength or dual-channel models, which assume two different processing routes. Thus our results show that dissociations between accuracy and confidence can, at least in some cases, be accounted for by a single-channel model.