Noisy Sensory Evidence Research Articles

Individual differences in belief updating and phasic arousal are related to psychosis proneness

Many decisions entail the updating of beliefs about the state of the environment by accumulating noisy sensory evidence. This form of probabilistic reasoning may go awry in psychosis. Computational theory shows that optimal belief updating in environments subject to hidden changes in their state requires a dynamic modulation of the evidence accumulation process. Recent empirical findings implicate transient responses of pupil-linked central arousal systems to individual evidence samples in this modulation. Here, we analyzed behavior and pupil responses during evidence accumulation in a changing environment in a community sample of human participants. We also assessed their subclinical psychotic experiences (psychosis proneness). Participants most prone to psychosis showed overall less flexible belief updating profiles, with diminished behavioral impact of evidence samples occurring late during decision formation. These same individuals also exhibited overall smaller pupil responses and less reliable pupil encoding of computational variables governing the dynamic belief updating. Our findings provide insights into the cognitive and physiological bases of psychosis proneness and open paths to unraveling the pathophysiology of psychotic disorders.

The Ubiquitousness and Functional Roles of Evidence Accumulation.

Perceptual decision-making, the process of using information from our senses to guide behavior, is successfully described by evidence accumulation, where noisy sensory evidence is accumulated over time up to a threshold. Neural activity that represents evidence accumulation is typically found

Perceptual decisions and oculomotor responses rely on temporally distinct streams of evidence

Perceptual decisions often require the integration of noisy sensory evidence over time. This process is formalized with sequential sampling models, where evidence is accumulated up to a decision threshold before a choice is made. Although intuition suggests that decision formation must precede the preparation of a motor response (i.e., the action used to communicate the choice), neurophysiological findings have suggested that these two processes might be one and the same. To test this idea, we developed a reverse-correlation protocol in which the visual stimuli that influence decisions can be distinguished from those guiding motor responses. In three experiments, we found that the temporal weighting function of oculomotor responses did not overlap with the relatively early weighting function of stimulus properties having an impact on decision formation. These results support a timeline in which perceptual decisions are formed, at least in part, prior to the preparation of a motor response.

Variable Statistical Structure of Neuronal Spike Trains in Monkey Superior Colliculus.

Popular models of decision-making propose that noisy sensory evidence accumulates until reaching a bound. Behavioral evidence as well as trial-averaged ramping of neuronal activity in sensorimotor regions of the brain support this idea. However, averaging activity across trials can mask other processes, such as rapid shifts in decision commitment, calling into question the hypothesis that evidence accumulation is encoded by delay period activity of individual neurons. We mined two sets of data from experiments in four monkeys in which we recorded from superior colliculus neurons during two different decision-making tasks and a delayed saccade task. We applied second-order statistical measures and spike train simulations to determine whether spiking statistics were similar or different in the different tasks and monkeys, despite similar trial-averaged activity across tasks and monkeys. During a motion direction discrimination task, single-trial delay period activity behaved statistically consistent with accumulation. During an orientation detection task, the activity behaved superficially like accumulation, but statistically consistent with stepping. Simulations confirmed both findings. Importantly, during a simple saccade task, with similar trial-averaged activity, neither process explained spiking activity, ruling out interpretations based on differences in attention, reward, or motor planning. These results highlight the need for exploring single-trial spiking dynamics to understand cognitive processing and raise the interesting hypothesis that the superior colliculus participates in different aspects of decision-making depending on task differences.SIGNIFICANCE STATEMENT How are decisions based on sensory information transformed into actions? We report that single-trial neuronal activity dynamics in the superior colliculus of monkeys show differences in decision-making tasks depending on task idiosyncrasies and requirements and despite similar trial-averaged ramping activity. These results highlight the importance of exploring single-trial spiking dynamics to understand cognitive processing and raise the interesting hypothesis that the superior colliculus participates in different aspects of decision-making depending on task requirements.

The impact of learning on perceptual decisions and its implication for speed-accuracy tradeoffs

In standard models of perceptual decision-making, noisy sensory evidence is considered to be the primary source of choice errors and the accumulation of evidence needed to overcome this noise gives rise to speed-accuracy tradeoffs. Here, we investigated how the history of recent choices and their outcomes interact with these processes using a combination of theory and experiment. We found that the speed and accuracy of performance of rats on olfactory decision tasks could be best explained by a Bayesian model that combines reinforcement-based learning with accumulation of uncertain sensory evidence. This model predicted the specific pattern of trial history effects that were found in the data. The results suggest that learning is a critical factor contributing to speed-accuracy tradeoffs in decision-making, and that task history effects are not simply biases but rather the signatures of an optimal learning strategy.

Changes-of-mind in the absence of new post-decision evidence.

Decisions are occasionally accompanied by changes-of-mind. While considered a hallmark of cognitive flexibility, the mechanisms underlying changes-of-mind remain elusive. Previous studies on perceptual decision making have focused on changes-of-mind that are primarily driven by the accumulation of additional noisy sensory evidence after the initial decision. In a motion discrimination task, we demonstrate that changes-of-mind can occur even in the absence of additional evidence after the initial decision. Unlike previous studies of changes-of-mind, the majority of changes-of-mind in our experiment occurred in trials with prolonged initial response times. This suggests a distinct mechanism underlying such changes. Using a neural circuit model of decision uncertainty and change-of-mind behaviour, we demonstrate that this phenomenon is associated with top-down signals mediated by an uncertainty-monitoring neural population. Such a mechanism is consistent with recent neurophysiological evidence showing a link between changes-of-mind and elevated top-down neural activity. Our model explains the long response times associated with changes-of-mind through high decision uncertainty levels in such trials, and accounts for the observed motor response trajectories. Overall, our work provides a computational framework that explains changes-of-mind in the absence of new post-decision evidence.

Stochastic resonance enhances the rate of evidence accumulation during combined brain stimulation and perceptual decision-making.

Perceptual decision-making relies on the gradual accumulation of noisy sensory evidence. It is often assumed that such decisions are degraded by adding noise to a stimulus, or to the neural systems involved in the decision making process itself. But it has been suggested that adding an optimal amount of noise can, under appropriate conditions, enhance the quality of subthreshold signals in nonlinear systems, a phenomenon known as stochastic resonance. Here we asked whether perceptual decisions made by human observers obey these stochastic resonance principles, by adding noise directly to the visual cortex using transcranial random noise stimulation (tRNS) while participants judged the direction of coherent motion in random-dot kinematograms presented at the fovea. We found that adding tRNS bilaterally to visual cortex enhanced decision-making when stimuli were just below perceptual threshold, but not when they were well below or above threshold. We modelled the data under a drift diffusion framework, and showed that bilateral tRNS selectively increased the drift rate parameter, which indexes the rate of evidence accumulation. Our study is the first to provide causal evidence that perceptual decision-making is susceptible to a stochastic resonance effect induced by tRNS, and to show that this effect arises from selective enhancement of the rate of evidence accumulation for sub-threshold sensory events.

Transcranial magnetic stimulation of right inferior parietal cortex causally influences prefrontal activation for visual detection.

For effective interactions with the environment, the brain needs to form perceptual decisions based on noisy sensory evidence. Accumulating evidence suggests that perceptual decisions are formed by widespread interactions amongst sensory areas representing the noisy sensory evidence and fronto-parietal areas integrating the evidence into a decision variable that is compared to a decisional threshold. This concurrent transcranial magnetic stimulation (TMS)-fMRI study applied 10Hz bursts of four TMS (or Sham) pulses to the intraparietal sulcus (IPS) to investigate the causal influence of IPS on the neural systems involved in perceptual decision-making. Participants had to detect visual signals at threshold intensity that were presented in their left lower visual field on 50% of the trials. Critically, we adjusted the signal strength such that participants failed to detect the visual stimulus on approximately 30% of the trials allowing us to categorise trials into hits, misses and correct rejections (CR). Our results show that IPS-relative to Sham-TMS attenuated activation increases for misses relative to CR in the left middle and superior frontal gyri. Critically, while IPS-TMS did not significantly affect participants' performance accuracy, it affected how observers adjusted their response times after making an error. We therefore suggest that activation increases in superior frontal gyri for misses relative to correct responses may not be critical for signal detection performance, but rather reflect post-decisional processing such as metacognitive monitoring of choice accuracy or decisional confidence.

A Two-Stage Process Model of Sensory Discrimination: An Alternative to Drift-Diffusion.

Sensory decision-making under uncertainty is usually modeled as the slow accumulation of noisy sensory evidence until a threshold amount of evidence supporting one of the possible decision outcomes is reached. Furthermore, it has been suggested that this accumulation process is reflected in neural responses, e.g., in lateral intraparietal cortex. We derive two behavioral predictions of this model and show that neither prediction holds. We introduce a simple alternative model in which evidence is accumulated until a sufficiently precise estimate of the stimulus is achieved, and then that estimate is used to guide the discrimination decision. This model is consistent with the behavioral data.

A supramodal accumulation-to-bound signal that determines perceptual decisions in humans

In theoretical accounts of perceptual decision-making, a decision variable integrates noisy sensory evidence and determines action through a boundary-crossing criterion. Signals bearing these very properties have been characterized in single neurons in monkeys, but have yet to be directly identified in humans. Using a gradual target detection task, we isolated a freely evolving decision variable signal in human subjects that exhibited every aspect of the dynamics observed in its single-neuron counterparts. This signal could be continuously tracked in parallel with fully dissociable sensory encoding and motor preparation signals, and could be systematically perturbed mid-flight during decision formation. Furthermore, we found that the signal was completely domain general: it exhibited the same decision-predictive dynamics regardless of sensory modality and stimulus features and tracked cumulative evidence even in the absence of overt action. These findings provide a uniquely clear view on the neural determinants of simple perceptual decisions in humans.

The construction of confidence in a perceptual decision

Decision-making involves the selection of one out of many possible courses of action. A decision may bear on other decisions, as when humans seek a second medical opinion before undergoing a risky surgical intervention. These “meta-decisions” are mediated by confidence judgments—the degree to which decision-makers consider that a choice is likely to be correct. We studied how subjective confidence is constructed from noisy sensory evidence. The psychophysical kernels used to convert sensory information into choice and confidence decisions were precisely reconstructed measuring the impact of small fluctuations in sensory input. This is shown in two independent experiments in which human participants made a decision about the direction of motion of a set of randomly moving dots, or compared the brightness of a group of fluctuating bars, followed by a confidence report. The results of both experiments converged to show that: (1) confidence was influenced by evidence during a short window of time at the initial moments of the decision, and (2) confidence was influenced by evidence for the selected choice but was virtually blind to evidence for the non-selected choice. Our findings challenge classical models of subjective confidence—which posit that the difference of evidence in favor of each choice is the seed of the confidence signal.

Covert visual search: Prior beliefs are optimally combined with sensory evidence

Has evolution optimized visual selective attention to make the best possible use of all information available? If so, then Bayesian optimal performance in a localization task is achieved by optimally weighting the visual evidence with one's prior spatial expectations. In 2 psychophysical experiments, participants conducted covert target localization where both visual cues and prior expectations were available. The amount of information conveyed by the visual evidence was held constant, while the degree of belief was manipulated via peripheral cuing (Experiment 1) and spatial probabilities (Experiment 2). A number of findings result: (1) People appear to optimally combine slightly biased prior beliefs with sensory evidence. (2) These biases are directly comparable to those descriptively accounted for by the Prospect Theory. (3) Probabilistic information about a target's upcoming location is integrated identically, irrespective of whether endogenous or exogenous cuing is used. (4) In localization tasks, spatial attention can be understood and quantitatively modeled as a set of prior expectations over space that modulate incoming noisy sensory evidence.

Evidence for time‐variant decision making

Computational models based on diffusion processes have been proposed to account for human decision-making behaviour in a variety of tasks. The basic idea is that the brain keeps accumulating noisy sensory evidence until a critical level is reached. This study explores whether such models account for the speed and accuracy of perceptual decisions in a reaction-time random dot motion direction discrimination task, and whether they explain the decision-related activity of neurons recorded from the parietal cortex (area LIP) of monkeys performing the task. While a simple diffusion model can explain the psychometric function and the mean response times of correct responses, it fails to account for the longer response times observed for errors and for the response time distributions. Here I demonstrate that a time-variant version of the diffusion model can explain the psychometric function, the mean response times and the shape of the response time distributions. Such a time-variant mechanism could be implemented in different ways, but the best match between the physiological data and model predictions is provided by a diffusion process with a gain of the sensory signals, which increases over time. It can be shown that such a time-variant decision process allows the monkey to perform optimally (in the sense of maximizing reward rate) given the risk of aborting a trial by breaking fixation before a choice can be reported. The results suggest that the brain trades off speed and accuracy not only by adjusting parameters between trials but also by dynamic adjustments during an ongoing decision.