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.

Optimal Decision Making on the Basis of Evidence Represented in Spike Trains

2010 ◽  
Vol 22 (5) ◽  
pp. 1113-1148 ◽  
Author(s):  
Jiaxiang Zhang ◽  
Rafal Bogacz
Keyword(s):  
Decision Making ◽  
Spike Trains ◽  
Decision Procedures ◽  
Optimal Decision ◽  
Ratio Test ◽  
Perceptual Decision Making ◽  
Sensory Evidence ◽  
Optimal Decision Making ◽  
Alternative Choice ◽  
Choice Tasks

Experimental data indicate that perceptual decision making involves integration of sensory evidence in certain cortical areas. Theoretical studies have proposed that the computation in neural decision circuits approximates statistically optimal decision procedures (e.g., sequential probability ratio test) that maximize the reward rate in sequential choice tasks. However, these previous studies assumed that the sensory evidence was represented by continuous values from gaussian distributions with the same variance across alternatives. In this article, we make a more realistic assumption that sensory evidence is represented in spike trains described by the Poisson processes, which naturally satisfy the mean-variance relationship observed in sensory neurons. We show that for such a representation, the neural circuits involving cortical integrators and basal ganglia can approximate the optimal decision procedures for two and multiple alternative choice tasks.

scholarly journals The interplay between multisensory integration and perceptual decision making

2019 ◽  
Author(s):  
Manuel R. Mercier ◽  
Celine Cappe
Keyword(s):  
Decision Making ◽  
Multisensory Integration ◽  
Multisensory Processing ◽  
Optimal Decision ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Human Participants ◽  
Sensory Encoding ◽  
Optimal Decision Making ◽  
The Brain

AbstractFacing perceptual uncertainty, the brain combines information from different senses to shape optimal decision making and to guide behavior. Despite overlapping neural networks underlying multisensory integration and perceptual decision making, the process chain of decision formation has been studied mostly in unimodal contexts and is thought to be supramodal. To reveal whether and how multisensory processing interplay with perceptual decision making, we devised a paradigm mimicking naturalistic situations where human participants were exposed to continuous cacophonous audiovisual inputs containing an unpredictable relevant signal cue in one or two modalities. Using multivariate pattern analysis on concurrently recorded EEG, we decoded the neural signatures of sensory encoding and decision formation stages. Generalization analyses across conditions and time revealed that multisensory signal cues were processed faster during both processing stages. We further established that acceleration of neural dynamics was directly linked to two distinct multisensory integration processes and associated with multisensory benefit. Our results, substantiated in both detection and categorization tasks, provide evidence that the brain integrates signals from different modalities at both the sensory encoding and the decision formation stages.

scholarly journals Distinct mechanisms mediate speed-accuracy adjustments in cortico-subthalamic networks

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Damian M Herz ◽  
Huiling Tan ◽  
John-Stuart Brittain ◽  
Petra Fischer ◽  
Binith Cheeran ◽  
...  
Keyword(s):  
Decision Making ◽  
Spectral Characteristics ◽  
Low Frequency ◽  
Optimal Decision ◽  
Beta Activity ◽  
Perceptual Decision Making ◽  
Beta Power ◽  
Precise Relationship ◽  
Optimal Decision Making ◽  
Speed Accuracy

Optimal decision-making requires balancing fast but error-prone and more accurate but slower decisions through adjustments of decision thresholds. Here, we demonstrate two distinct correlates of such speed-accuracy adjustments by recording subthalamic nucleus (STN) activity and electroencephalography in 11 Parkinson’s disease patients during a perceptual decision-making task; STN low-frequency oscillatory (LFO) activity (2–8 Hz), coupled to activity at prefrontal electrode Fz, and STN beta activity (13–30 Hz) coupled to electrodes C3/C4 close to motor cortex. These two correlates differed not only in their cortical topography and spectral characteristics but also in the relative timing of recruitment and in their precise relationship with decision thresholds. Increases of STN LFO power preceding the response predicted increased thresholds only after accuracy instructions, while cue-induced reductions of STN beta power decreased thresholds irrespective of instructions. These findings indicate that distinct neural mechanisms determine whether a decision will be made in haste or with caution.

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

Discarding optimality: Throwing out the baby with the bathwater?

2018 ◽  
Vol 41 ◽  
Author(s):  
Patrick Simen ◽  
Fuat Balcı
Keyword(s):  
Decision Making ◽  
Perceptual Decision Making ◽  
Reward Rate ◽  
Perceptual Decision ◽  
Standard Observer ◽  
Rate Maximization ◽  
Reward Rate Maximization ◽  
Descriptive Standard ◽  
Observer Model

AbstractRahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive “standard observer model” of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors’ claims.

LPCD framework: Analytical tool or psychological model?

2018 ◽  
Vol 41 ◽  
Author(s):  
David Danks
Keyword(s):  
Decision Making ◽  
Analytical Tool ◽  
Mathematical Framework ◽  
Bayesian Decision ◽  
Perceptual Decision Making ◽  
Psychological Model ◽  
Psychological Reality ◽  
Psychological Theories ◽  
Target Article ◽  
Bayesian Decision Making

AbstractThe target article uses a mathematical framework derived from Bayesian decision making to demonstrate suboptimal decision making but then attributes psychological reality to the framework components. Rahnev & Denison's (R&D) positive proposal thus risks ignoring plausible psychological theories that could implement complex perceptual decision making. We must be careful not to slide from success with an analytical tool to the reality of the tool components.

The nature of metacognitive inefficiency in perceptual decision making

2020 ◽  
Author(s):  
Medha Shekhar ◽  
Dobromir Rahnev
Keyword(s):  
Decision Making ◽  
Mechanistic Model ◽  
Perceptual Decision Making ◽  
Large Dataset ◽  
Substantial Body ◽  
Confidence Levels ◽  
Continuous Scale ◽  
Competing Models ◽  
Metacognitive Ability ◽  
Confidence Ratings

Humans have the metacognitive ability to judge the accuracy of their own decisions via confidence ratings. A substantial body of research has demonstrated that human metacognition is fallible but it remains unclear how metacognitive inefficiency should be incorporated into a mechanistic model of confidence generation. Here we show that, contrary to what is typically assumed, metacognitive inefficiency depends on the level of confidence. We found that, across five different datasets and four different measures of metacognition, metacognitive ability decreased with higher confidence ratings. To understand the nature of this effect, we collected a large dataset of 20 subjects completing 2,800 trials each and providing confidence ratings on a continuous scale. The results demonstrated a robustly nonlinear zROC curve with downward curvature, despite a decades-old assumption of linearity. This pattern of results was reproduced by a new mechanistic model of confidence generation, which assumes the existence of lognormally-distributed metacognitive noise. The model outperformed competing models either lacking metacognitive noise altogether or featuring Gaussian metacognitive noise. Further, the model could generate a measure of metacognitive ability which was independent of confidence levels. These findings establish an empirically-validated model of confidence generation, have significant implications about measures of metacognitive ability, and begin to reveal the underlying nature of metacognitive inefficiency.

scholarly journals GEAR: On Optimal Decision Making with Auxiliary Data

Stat ◽  
2021 ◽  
Author(s):  
Hengrui Cai ◽  
Rui Song ◽  
Wenbin Lu
Keyword(s):  
Decision Making ◽  
Optimal Decision ◽  
Auxiliary Data ◽  
Optimal Decision Making
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