scholarly journals Internal noise in contrast discrimination propagates forwards from early visual cortex

2018 ◽  
Author(s):  
Greta Vilidaite ◽  
Emma Marsh ◽  
Daniel H. Baker
Keyword(s):  
Decision Making ◽  
Sensory Processing ◽  
Dynamic Range ◽  
Internal Noise ◽  
Discrimination Performance ◽  
Brain Regions ◽  
Neural Representation ◽  
Perceptual Decision Making ◽  
Neural Noise ◽  
Contrast Discrimination

AbstractHuman contrast discrimination performance is limited by transduction nonlinearities and variability of the neural representation (noise). Whereas the nonlinearities have been well-characterised, there is less agreement about the specifics of internal noise. Psychophysical models assume that it impacts late in sensory processing, whereas neuroimaging and intracranial electrophysiology studies suggest that the noise is much earlier. We investigated whether perceptually-relevant internal noise arises in early visual areas or later decision making areas. We recorded EEG and MEG during a two-interval-forced-choice contrast discrimination task and used multivariate pattern analysis to decode target/non-target and selected/non-selected intervals from evoked responses. We found that perceptual decisions could be decoded from both EEG and MEG signals, even when the stimuli in both intervals were physically identical. Above-chance decision classification started <100ms after stimulus onset, suggesting that neural noise affects sensory signals early in the visual pathway. Classification accuracy increased over time, peaking at >500ms. Applying multivariate analysis to separate anatomically-defined brain regions in MEG source space, we found that occipital regions were informative early on but then information spreads forwards across parietal and frontal regions. This is consistent with neural noise affecting sensory processing at multiple stages of perceptual decision making. We suggest how early sensory noise might be resolved with Birdsall’s linearisation, in which a dominant noise source obscures subsequent nonlinearities, to allow the visual system to preserve the wide dynamic range of early areas whilst still benefitting from contrast-invariance at later stages. A preprint of this work is available at: http://dx.doi.org/10.1101/364612

scholarly journals Supercritical dynamics at the edge-of-chaos underlies optimal decision-making

2021 ◽  
Author(s):  
Adrián Fernández Amil ◽  
Paul F.M.J. Verschure
Keyword(s):  
Phase Transition ◽  
Decision Making ◽  
Dynamic Range ◽  
High Order ◽  
Optimal Decision ◽  
Inhibitory Interneurons ◽  
Persistent Activity ◽  
Perceptual Decision Making ◽  
Edge Of Chaos ◽  
Cortical Hierarchy

Abstract Critical dynamics, characterized by scale-free neuronal avalanches, is thought to underlie optimal function in the sensory cortices by maximizing information transmission, capacity, and dynamic range. In contrast, deviations from criticality have not yet been considered to support any cognitive processes. Nonetheless, neocortical areas related to working memory and decision-making seem to rely on long-lasting periods of ignition-like persistent firing. Such firing patterns are reminiscent of supercritical states where runaway excitation dominates the circuit dynamics. In addition, a macroscopic gradient of the relative density of Somatostatin (SST+) and Parvalbumin (PV+) inhibitory interneurons throughout the cortical hierarchy has been suggested to determine the functional specialization of low- versus high-order cortex. These observations thus raise the question of whether persistent activity in high-order areas results from the intrinsic features of the neocortical circuitry. We used an attractor model of the canonical cortical circuit performing a perceptual decision-making task to address this question. Our model reproduces the known saddle-node bifurcation where persistent activity emerges, merely by increasing the SST+/PV+ ratio while keeping the input and recurrent excitation constant. The regime beyond such a phase transition renders the circuit increasingly sensitive to random fluctuations of the inputs -i.e., chaotic-, defining an optimal SST+/PV+ ratio around the edge-of-chaos. Further, we show that both the optimal SST+/PV+ ratio and the region of the phase transition decrease monotonically with increasing input noise. This suggests that cortical circuits regulate their intrinsic dynamics via inhibitory interneurons to attain optimal sensitivity in the face of varying uncertainty. Hence, on the one hand, we link the emergence of supercritical dynamics at the edge-of-chaos to the gradient of the SST+/PV+ ratio along the cortical hierarchy, and, on the other hand, explain the behavioral effects of the differential regulation of SST+ and PV+ interneurons by neuromodulators like acetylcholine in the presence of input uncertainty.

scholarly journals Making up your mind: Enhanced perceptual decision-making induced by stochastic resonance during non-invasive brain stimulation: Stochastic resonance in perceptual decision-making

2017 ◽  
Author(s):  
Onno van der Groen ◽  
Matthew F. Tang ◽  
Nicole Wenderoth ◽  
Jason B. Mattingley
Keyword(s):  
Decision Making ◽  
Visual Cortex ◽  
Stochastic Resonance ◽  
Decision Process ◽  
Random Noise ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Evidence Accumulation ◽  
Neural Noise ◽  
Stimulation Of

Summary:Perceptual decision-making relies on the gradual accumulation of noisy sensory evidence until a specified boundary is reached and an appropriate response is made. It might be assumed that adding noise to a stimulus, or to the neural systems involved in its processing, would interfere with the decision process. 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 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 motion in foveally presented random-dot motion arrays. Consistent with the stochastic resonance account, we found that adding tRNS bilaterally to visual cortex enhanced decision-making when stimuli were just below, but not well below or above, perceptual threshold. We modelled the data under a drift diffusion framework to isolate the specific components of the multi-stage decision process that were influenced by the addition of neural noise. This modelling showed that tRNS increased drift rate, which indexes the rate of evidence accumulation, but had no effect on bound separation or non-decision time. These results were specific to bilateral stimulation of visual cortex; control experiments involving unilateral stimulation of left and right visual areas showed no influence of random noise stimulation. Our study is the first to provide causal evidence that perceptual decision-making is susceptible to a stochastic resonance effect induced by tRNS, and that this effect arises from selective enhancement of the rate of evidence accumulation for sub-threshold sensory events.

scholarly journals Sucrose intensity coding and decision-making in rat gustatory cortices

2018 ◽  
Author(s):  
Esmeralda Fonseca ◽  
Victor de Lafuente ◽  
Sidney A. Simon ◽  
Ranier Gutierrez
Keyword(s):  
Decision Making ◽  
High Energy ◽  
Spike Timing ◽  
Neural Representation ◽  
Movement Direction ◽  
Gustatory System ◽  
Perceptual Decision Making ◽  
Related Information ◽  
Posterior Insula ◽  
Robust Response

AbstractSucrose’s sweet intensity is one attribute contributing to the overconsumption of high-energy palatable foods. However, it is not known how sucrose intensity is encoded and used to make perceptual decisions by neurons in taste-sensitive cortices. We trained rats in a sucrose intensity discrimination task and found that sucrose evoked a widespread response in neurons recorded in posterior-Insula (pIC), anterior-Insula (aIC), and Orbitofrontal cortex (OFC). Remarkably, only a few Intensity-selective neurons conveyed the most information about sucrose’s intensity, indicating that for sweetness the gustatory system used a compact and distributed code. Sucrose intensity was encoded in both firing-rates and spike-timing. The pIC, aIC, and OFC neurons tracked movement direction, with OFC neurons yielding the most robust response. aIC and OFC neurons encoded the subject’s choices, whereas all three regions tracked reward omission. Overall, these multimodal areas provide a neural representation of perceived sucrose intensity, and of task-related information underlying perceptual decision-making.

scholarly journals Brain-behaviour relationships in the perceptual decision-making process through cognitive processing stages

2020 ◽  
Author(s):  
Elaheh Imani ◽  
Ahad Harati ◽  
Hamidreza Pourreza ◽  
Morteza Moazami Goudarzi
Keyword(s):  
Decision Making ◽  
Cognitive Processing ◽  
Neural Representation ◽  
Decision Making Process ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Spatial Prioritization ◽  
Neural Signature ◽  
The Impact ◽  
Behavioral Characterization

AbstractPerceptual decision making, as a process of detecting and categorizing information, has been studied extensively over the last two decades. In this study, we investigated the neural characterization of the whole decision-making process by discovering the information processing stages. Such that, the timing and the neural signature of the processing stages were identified for individual trials. The association of stages duration with the stimulus coherency and spatial prioritization factors also revealed the importance of the evidence accumulation process on the speed of the whole decision-making process. We reported that the impact of the stimulus coherency and spatial prioritization on the neural representation of the decision-making process was consistent with the behavioral characterization as well. This study demonstrated that uncovering the cognitive processing stages provided more insights into the decision-making process.

scholarly journals Expectations Do Not Alter Early Sensory Processing during Perceptual Decision-Making

2018 ◽  
Vol 38 (24) ◽  
pp. 5632-5648 ◽  
Author(s):  
Nuttida Rungratsameetaweemana ◽  
Sirawaj Itthipuripat ◽  
Annalisa Salazar ◽  
John T. Serences
Keyword(s):  
Decision Making ◽  
Sensory Processing ◽  
Perceptual Decision Making ◽  
Perceptual Decision

scholarly journals Deep hierarchical sensory processing accounts for effects of arousal state on perceptual decision-making

2021 ◽  
Author(s):  
Lynn K. A. Sörensen ◽  
Sander M. Bohté ◽  
Heleen A Slagter ◽  
H. Steven Scholte
Keyword(s):  
Decision Making ◽  
Sensory Processing ◽  
Task Difficulty ◽  
Model Performance ◽  
Relevant Information ◽  
Perceptual Decision Making ◽  
Release Of Noradrenaline ◽  
Arousal State ◽  
Sensory Processes ◽  
And Task

An organism's level of arousal strongly affects task performance. Yet, what level of arousal is optimal for performance depends on task difficulty. For easy tasks, performance is best at higher arousal levels, whereas arousal levels show an inverted-U-shaped relationship with performance for difficult tasks, with best performance at medium arousal levels. This interaction between arousal and task difficulty is known as the Yerkes-Dodson effect (1908) and is thought to reflect sensory decision-making in the locus coeruleus and associated widespread release of noradrenaline. Yet, this account does not explain why perceptual performance decays with high levels of arousal in difficult, but not in simple tasks. Recent studies suggest that arousal may also affect performance by modulating sensory processes. Here, we augment a deep convolutional neural network (DCNN) with a global gain mechanism to mimic the effects of arousal on sensory processing. This allowed us to reproduce the Yerkes-Dodson effect in the model's performance. Investigating our network furthermore revealed that for easy tasks, early network features contained most task-relevant information during high global gain states, resulting in model performance on easy tasks being best at high global gain states. In contrast, later layers featured most information at medium global gain states and were essential for performance on challenging tasks. Our results therefore establish a novel account of the Yerkes-Dodson effect, where the interaction between arousal state and task difficulty directly results from an interaction between arousal states and hierarchical sensory processing.

scholarly journals Neural Representation of Costs and Rewards in Decision Making

2021 ◽  
Vol 11 (8) ◽  
pp. 1096
Author(s):  
Yixuan Chen
Keyword(s):  
Decision Making ◽  
Anterior Cingulate Cortex ◽  
Orbitofrontal Cortex ◽  
Internal State ◽  
Cingulate Cortex ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Neural Representation ◽  
Prediction Errors ◽  
The Brain

Decision making is crucial for animal survival because the choices they make based on their current situation could influence their future rewards and could have potential costs. This review summarises recent developments in decision making, discusses how rewards and costs could be encoded in the brain, and how different options are compared such that the most optimal one is chosen. The reward and cost are mainly encoded by the forebrain structures (e.g., anterior cingulate cortex, orbitofrontal cortex), and their value is updated through learning. The recent development on dopamine and the lateral habenula’s role in reporting prediction errors and instructing learning will be emphasised. The importance of dopamine in powering the choice and accounting for the internal state will also be discussed. While the orbitofrontal cortex is the place where the state values are stored, the anterior cingulate cortex is more important when the environment is volatile. All of these structures compare different attributes of the task simultaneously, and the local competition of different neuronal networks allows for the selection of the most appropriate one. Therefore, the total value of the task is not encoded as a scalar quantity in the brain but, instead, as an emergent phenomenon, arising from the computation at different brain regions.

scholarly journals Influence of expected reward on perceptual decision making

2018 ◽  
Author(s):  
Mohsen Rakhshan ◽  
Vivian Lee ◽  
Emily Chu ◽  
Lauren Harris ◽  
Lillian Laiks ◽  
...  
Keyword(s):  
Decision Making ◽  
Sensory Processing ◽  
Human Subjects ◽  
Target Selection ◽  
Judgment Task ◽  
Neural Mechanisms ◽  
Perceptual Bias ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Reward Effects

AbstractPerceptual decision making is influenced by reward expected from alternative options or actions, but the underlying neural mechanisms are currently unknown. More specifically, it is debated whether reward effects are mediated through changes in sensory processing and/or later stages of decision making. To address this question, we conducted two experiments in which human subjects made saccades to what they perceived to be the first or second of two visually identical but asynchronously presented targets, while we manipulated expected reward from correct and incorrect responses on each trial. We found that unequal reward caused similar shifts in target selection (reward bias) between the two experiments. Moreover, observed reward biases were independent of the individual’s sensitivity to sensory signals. These findings suggest that the observed reward effects were determined heuristically via modulation of decision-making processes instead of sensory processing and thus, are more compatible with response bias rather than perceptual bias. To further explain our findings and uncover plausible neural mechanisms, we simulated our experiments with a cortical network model and tested alternative mechanisms for how reward could exert its influence. We found that our observations are more compatible with reward-dependent input to the output layer of the decision circuit. Together, our results suggest that during a temporal judgment task, the influence of reward information on perceptual choice is more compatible with changing later stages of decision making rather than early sensory processing.

scholarly journals Trial-by-trial fluctuations in amygdala activity track motivational enhancement of desirable sensory evidence during perceptual decision-making

2021 ◽  
Author(s):  
Ren Paterson ◽  
Yizhou Lyu ◽  
Yuan Chang Leong
Keyword(s):  
Decision Making ◽  
Sensory Processing ◽  
Behavior Modeling ◽  
Perceptual Decision Making ◽  
Sports Fans ◽  
Perceptual Decision ◽  
Amygdala Activity ◽  
Sensory Evidence ◽  
Human Participants

AbstractPeople are biased towards seeing outcomes that they are motivated to see. For example, sports fans of opposing teams often perceive the same ambiguous foul in favor of the team they support. Here, we test the hypothesis that amygdala-dependent allocation of visual attention facilitates motivational biases in perceptual decision-making. Human participants were rewarded for correctly categorizing an ambiguous image into one of two categories while undergoing fMRI. On each trial, we used a financial bonus to motivate participants to see one category over another. The reward maximizing strategy was to perform the categorization task accurately, but participants were biased towards categorizing the images as the category we motivated them to see. Heightened amygdala activity preceded motivation consistent categorizations, and participants with higher amygdala activation exhibited stronger motivational biases in their perceptual reports. Trial-by-trial amygdala activity was associated with stronger enhancement of neural activity encoding desirable percepts in sensory cortices, suggesting that amygdala-dependent effects on perceptual decisions arose from biased sensory processing. Analyses using a drift diffusion model provide converging evidence that trial-by-trial amygdala activity was associated with stronger motivational biases in the accumulation of sensory evidence. Prior work examining biases in perceptual decision-making have focused on the role of frontoparietal regions. Our work highlights an important contribution of the amygdala. When people are motivated to see one outcome over another, the amygdala biases perceptual decisions towards those outcomes.Significance StatementForming accurate perceptions of the environment is essential for adaptive behavior. People however are biased towards seeing what they want to see, giving rise to inaccurate perceptions and erroneous decisions. Here, we combined behavior, modeling, and fMRI to show that the bias towards seeing desirable percepts is related to trial-by-trial fluctuations in amygdala activity. In particular, during moments with higher amygdala activity, sensory processing is biased in favor of desirable percepts, such that participants are more likely to see what they want to see. These findings highlight the role of the amygdala in biasing visual perception, and shed light on the neural mechanisms underlying the influence of motivation and reward on how people decide what they see.

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