Dobromir Rahnev

Direct injection of noise to the visual cortex decreases accuracy but increases decision confidence.

The 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.

Causal evidence for frontal cortex organization for perceptual decision making.

Significance The frontal cortex has long been understood as the seat of higher level cognition. Recent research, however, highlights its role in modulating perception. Here, we present a theoretical framework for frontal involvement in perceptual decision making and test it with the causal technique of transcranial magnetic stimulation. We find that progressively rostral regions of frontal cortex are involved in the control of progressively later stages of perceptual decision making. These causal findings are further corroborated by functional MRI and simulations of a dynamic model of decision making. Our results point to a critical role of the frontal cortex in the control of perceptual processes and reveal its intrinsic organization in support of modulating perception. 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.

Confidence Leak in Perceptual Decision Making

People 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 in which objects seen seconds ago influence the perception of current objects. What is unknown is whether a similar mechanism exists at the level of metacognitive representations. In three experiments, we demonstrated a robust intertask confidence leak—that is, confidence in one’s response on a given task or trial influencing confidence on the following task or trial. This confidence leak could not be explained by response priming or attentional fluctuations. Better ability to modulate 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 the prefrontal cortex.

Low attention impairs optimal incorporation of prior knowledge in perceptual decisions.

When visual attention is directed away from a stimulus, neural processing is weak and strength and precision of sensory data decreases. From a computational perspective, in such situations observers should give more weight to prior expectations in order to behave optimally during a discrimination task. Here we test a signal detection theoretic model that counter-intuitively predicts subjects will do just the opposite in a discrimination task with two stimuli, one attended and one unattended: when subjects are probed to discriminate the unattended stimulus, they rely less on prior information about the probed stimulus’ identity. The model is in part inspired by recent findings that attention reduces trial-by-trial variability of the neuronal population response and that they use a common criterion for attended and unattended trials. In five different visual discrimination experiments, when attention was directed away from the target stimulus, subjects did not adjust their response bias in reaction to a change in stimulus presentation frequency despite being fully informed and despite the presence of performance feedback and monetary and social incentives. This indicates that subjects did not rely more on the priors under conditions of inattention as would be predicted by a Bayes-optimal observer model. These results inform and constrain future models of Bayesian inference in the human brain.

Subliminal stimuli in the near absence of attention influence top-down cognitive control

Recent research has shown that visual stimuli can influence cognitive control functions, even if subjects are unaware of the identity of the stimuli. However, in those previous studies, subjects actively attended to the location of the subliminal stimuli. Here we assessed the role of endogenous spatial attention in such paradigms. We required subjects to quickly prepare for one of two numerical judgment tasks on the basis of the direction of motion in patches of moving dots presented in cued spatial locations. We found that irrelevant motion patches presented in the uncued spatial locations also influenced task performance. Motion in the uncued patches was weak and did not affect the perception of the cued patches. Further analyses suggested that the effect of priming by the uncued stimuli was present even for subjects who could only discriminate such stimuli at chance level. Three additional experiments confirmed that subjects paid minimal attention to the uncued locations, in that the subjects could not perform simple discriminations of conjunctions of features in those locations.

Top-Down Control of Perceptual Decision Making by the Prefrontal Cortex:

Although most work on perceptual decision making has focused on the processing within the visual, temporal, and parietal lobes, recent research points to an underappreciated but critical role of the prefrontal cortex (PFC). PFC provides high-level control of perception, but it is unclear whether this control can be subdivided into different processes and whether different PFC regions have different roles. Here I review evidence that prefrontal top-down control is organized in the processes of selection control, decision control, and evaluation. These three processes overlap and interact with each other while at the same time maintaining a temporal hierarchy. Further, these different stages are supported by dissociable regions within the PFC that control hierarchically organized cognition. The current proposal for PFC’s role in perceptual control can serve as the basis for a deeper understanding of both the functional organization of PFC and the processes underlying perceptual decision making.

Suboptimality in perception

Human perception is increasingly described as optimal. This view reflects recent successes of Bayesian approaches to perception but ignores an extensive literature documenting suboptimal performance in perceptual tasks. Here we review several classes of suboptimal perceptual decisions, including improper placement, maintenance, and adjustment of perceptual criteria, inadequate tradeoff between speed and accuracy, and inappropriate confidence ratings. We examine suboptimalities in the cue combination literature, which has often been taken as evidence for optimality in perception. We further discuss how findings regarding visual illusions, adaptation, and appearance relate to the concept of optimality. We extract a number of principles that account for suboptimal behavior across all of these studies. Finally, we outline an “Objectives-Constraints-Mechanisms” approach for guiding future investigations and discussions of optimal and suboptimal perception. In this approach, findings of optimality or suboptimality are not ends in themselves but serve to characterize the perceptual system’s objectives (i.e., what it aims to achieve), constraints (i.e., what costs it incurs), and mechanisms (i.e., what processes it uses to trade off the objectives and constraints). We suggest that this conceptual framework, more than a focus on optimality per se, can advance our understanding of perception.

Probabilistic Model of Onset Detection Explains Paradoxes in Human Time Perception

A very basic computational model is proposed to explain two puzzling findings in the time perception literature. First, spontaneous motor actions are preceded by up to 1–2 s of preparatory activity (Kornhuber and Deecke, 1965). Yet, subjects are only consciously aware of about a quarter of a second of motor preparation (Libet et al., 1983). Why are they not aware of the early part of preparation? Second, psychophysical findings (Spence et al., 2001) support the principle of attention prior entry (Titchener, 1908), which states that attended stimuli are perceived faster than unattended stimuli. However, electrophysiological studies reported no or little corresponding temporal difference between the neural signals for attended and unattended stimuli (McDonald et al., 2005; Vibell et al., 2007). We suggest that the key to understanding these puzzling findings is to think of onset detection in probabilistic terms. The two apparently paradoxical phenomena are naturally predicted by our signal detection theoretic model.

Sensory noise increases metacognitive efficiency

Visual metacognition is the ability to employ confidence ratings in order to predict the accuracy of ones decisions about visual stimuli. Despite years of research, it is still unclear how visual metacognitive efficiency can be manipulated. Here we show that a hierarchical model of confidence generation makes a counterintuitive prediction: Higher sensory noise should increase metacognitive efficiency. The reason is that sensory noise has a large negative influence on the decision (where it is the only corrupting influence) but a smaller negative influence on confidence (where it is one of two corrupting influences; the other one being metacognitive noise). To test this prediction, we used a perceptual learning paradigm to decrease the amount of sensory noise. In Experiment 1, seven days of training led to significant decrease in noise as well as a corresponding decrease in metacognitive efficiency. Experiment 2 showed the same effect in a brief 97-trial learning for each of two different tasks. Finally, in Experiment 3, we experimentally manipulated stimulus contrast to increase sensory noise and observed a corresponding increase in metacognitive efficiency. Our findings demonstrate the existence of a robust positive relationship between sensory noise and metacognitive efficiency. These results could not be captured by a standard model in which decision and confidence judgments are made based on the same underlying information. Thus, our study provides a novel way to directly manipulate metacognitive efficiency and suggests the existence of metacognitive noise that corrupts confidence but not the perceptual decision.

Uncertainty in perceptual representations

How are perceptual decisions made? The answer to this seemingly simple question necessitates that we specify the nature of perceptual representations on which decisions are based. Some traditional models postulate that the perceptual representation consists of a simple point estimate of the stimulus. Such models do not allow the estimation of sensory uncertainty. On the other hand, recent models have proposed that the perceptual representation involves a full probability distribution over the possible stimulus values. Such models allow a precise estimation of sensory uncertainty. These two possibilities – point estimates vs. full distributions – are often seen as the only alternatives but they are not. Here I present five possible perceptual representation schemes that allow the extraction of different levels of sensory uncertainty. I explain where popular models fall within the five schemes and explore the relevant empirical evidence and theoretical arguments. The overwhelming evidence is at odds with both point estimates vs. full distributions. This conclusion is in stark contrast with current popular models in computational neuroscience built on such distributions. Instead, the most likely scheme appears to be one in which the perceptual representation features a point estimate coupled with a strength-of-evidence value.How are perceptual decisions made? The answer to this seemingly simple question necessitates that we specify the nature of perceptual representations on which decisions are based. Recent work has taken for granted that the representation at the decision stage consists of a full probability distribution over all possible stimuli. However, to date, no empirical evidence has supported this assumption. Here I present five possible perceptual representation schemes that allow the extraction of different levels of sensory uncertainty. I review the empirical evidence from both continuous and discrete judgments and show that, at present, only the most primitive scheme based on a single point estimate can be rejected. In other words, at least four different representational schemes are consistent with the available data and therefore full probability distributions cannot be assumed. There is an urgent need for empirical research to adjudicate between these theoretical possibilities.