scholarly journals Dynamic task-belief is an integral part of decision-making

2021 ◽  
Author(s):  
Cheng Xue ◽  
Lily E Kramer ◽  
Marlene R Cohen
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Task Demands ◽  
Perceptual Decision Making ◽  
Visual Neurons ◽  
Dynamic Task ◽  
Neuronal Populations ◽  
Health And Disease ◽  
Belief Changes ◽  
And Behavior

Unlike in laboratory settings, natural decisions are often made under uncertain beliefs about task demands. To quantify the unexplored dynamics between task-belief and decisions, we trained macaque monkeys to make perceptual discriminations under implicitly evolving task rules. By analyzing task- and perception-related signals from simultaneously recorded neuronal populations in cortical areas 7a and V1, we demonstrated that fluctuating task-belief and perceptual decision-making are inextricably linked. Stronger task-belief is correlated with better perception, and in turn, response fluctuations in visual neurons affect task-belief changes. Our results demonstrate that combining large-scale inter-area recordings with rigorously controlled complex, realistic behaviors can bring new understanding of the relationship between cognition and behavior in health and disease.

scholarly journals Attention improves information flow between neuronal populations without changing the communication subspace

2021 ◽  
Author(s):  
Ramanujan Srinath ◽  
Douglas A Ruff ◽  
Marlene R Cohen
Keyword(s):  
Decision Making ◽  
Visual Attention ◽  
Visual Information ◽  
Receptive Fields ◽  
Perceptual Decision Making ◽  
Visual Neurons ◽  
Temporal Area ◽  
Mt Neurons ◽  
Neuronal Populations ◽  
Oculomotor Neurons

Visual attention allows observers to flexibly use or ignore visual information, suggesting that information can be flexibly routed between visual cortex and neurons involved in decision-making. We investigated the neural substrate of flexible information routing by analyzing the activity of populations of visual neurons in the medial temporal area (MT) and oculomotor neurons in the superior colliculus (SC) while rhesus monkeys switched spatial attention. We demonstrated that attention increases the efficacy of visuomotor communication: trial-to-trial variability of the population of SC neurons was better predicted by the activity of MT neurons (and vice versa) when attention was directed toward their joint receptive fields. Surprisingly, this improvement in prediction was not explained or accompanied by changes in the dimensionality of the shared subspace or in local or shared pairwise noise correlations. These results suggest a mechanism by which visual attention can affect perceptual decision-making without altering local neuronal representations.

scholarly journals Animal Spirits in the Beautiful Game. Testing social pressure in professional football during the COVID-19 lockdown

2020 ◽  
Author(s):  
Carlos Cueva
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Natural Experiment ◽  
Social Pressure ◽  
Professional Football ◽  
Large Dataset ◽  
Animal Spirits ◽  
Almost All ◽  
And Behavior ◽  
The Impact

The COVID-19 pandemic forced almost all professional football matches worlwide to be played in empty stadiums. This large-scale natural experiment offers a unique opportunity to assess the impact of social pressure on decision making and behavior. In particular, I investigate the effect of the home crowd on match outcomes and referee decisions. Using a large dataset from 41 professional football leagues in 30 different countries, I find that the home advantage in match outcomes drops by around one half and that referee bias against away teams completely disappears following the lockdowns. My results therefore suggest that social pressure exerted by home crowds has an important effect on the behavior of referees and on game outcomes.

scholarly journals Large-scale network dynamics of beta-band oscillations underlie auditory perceptual decision-making

2017 ◽  
Vol 1 (2) ◽  
pp. 166-191 ◽  
Author(s):  
Mohsen Alavash ◽  
Christoph Daube ◽  
Malte Wöstmann ◽  
Alex Brandmeyer ◽  
Jonas Obleser
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Network Dynamics ◽  
Neural Oscillations ◽  
Perceptual Decision Making ◽  
Beta Band ◽  
Perceptual Decision ◽  
Large Scale Network ◽  
Scale Network ◽  
Decision Speed

Perceptual decisions vary in the speed at which we make them. Evidence suggests that translating sensory information into perceptual decisions relies on distributed interacting neural populations, with decision speed hinging on power modulations of the neural oscillations. Yet the dependence of perceptual decisions on the large-scale network organization of coupled neural oscillations has remained elusive. We measured magnetoencephalographic signals in human listeners who judged acoustic stimuli composed of carefully titrated clouds of tone sweeps. These stimuli were used in two task contexts, in which the participants judged the overall pitch or direction of the tone sweeps. We traced the large-scale network dynamics of the source-projected neural oscillations on a trial-by-trial basis using power-envelope correlations and graph-theoretical network discovery. In both tasks, faster decisions were predicted by higher segregation and lower integration of coupled beta-band (∼16–28 Hz) oscillations. We also uncovered the brain network states that promoted faster decisions in either lower-order auditory or higher-order control brain areas. Specifically, decision speed in judging the tone sweep direction critically relied on the nodal network configurations of anterior temporal, cingulate, and middle frontal cortices. Our findings suggest that global network communication during perceptual decision-making is implemented in the human brain by large-scale couplings between beta-band neural oscillations.

scholarly journals Simultaneous multi-area recordings suggest that attention improves performance by reshaping stimulus representations

2018 ◽  
Author(s):  
Douglas A. Ruff ◽  
Marlene R. Cohen
Keyword(s):  
Sensory Information ◽  
The Novel ◽  
Neuronal Responses ◽  
Visual Neurons ◽  
Neuronal Mechanisms ◽  
Oculomotor Neurons ◽  
Health And Disease ◽  
Neural Underpinnings ◽  
And Behavior ◽  
The Relationship

AbstractVisual attention dramatically improves subjects’ ability to see and also modulates the responses of neurons in every known visual and oculomotor area, but whether those modulations can account for perceptual improvements remains unclear. We measured the relationship between populations of visual neurons, oculomotor neurons, and behavior during detection and discrimination tasks. We found that neither of the two prominent hypothesized neuronal mechanisms underlying attention (which concern changes in information coding and the way sensory information is read out) provide a satisfying account of the observed behavioral improvements. Instead, our results are more consistent with the novel hypothesis that attention reshapes the representation of attended stimuli to more effectively influence behavior. Our results suggest a path toward understanding the neural underpinnings of perception and cognition in health and disease by analyzing neuronal responses in ways that are constrained by behavior and interactions between brain areas.

scholarly journals Topological insights into the neural basis of flexible behavior

2021 ◽  
Author(s):  
Tevin C. Rouse ◽  
Amy M. Ni ◽  
Chengcheng Huang ◽  
Marlene R. Cohen
Keyword(s):  
Mechanistic Model ◽  
Network Control ◽  
Topological Data Analysis ◽  
Neural Basis ◽  
Visual Neurons ◽  
Neural Computations ◽  
Flexible Behavior ◽  
Health And Disease ◽  
The Subject ◽  
And Behavior

1AbstractIt is widely accepted that there is an inextricable link between neural computations, biological mechanisms, and behavior, but there exists no framework that can simultaneously explain all three. Here, we show that topological data analysis (TDA) provides that necessary bridge. We demonstrate that cognitive processes change the topological description of the shared activity of populations of visual neurons. These topological changes provide uniquely strong constraints on a mechanistic model, explain behavior, and, via a link with network control theory, reveal a tradeoff between improving sensitivity to subtle visual stimulus changes and increasing the chance that the subject will stray off task. These discoveries provide a blueprint for using TDA to uncover the biological and computational mechanisms by which cognition affects behavior in health and disease.

scholarly journals Residual dynamics resolves recurrent contributions to neural computation

2021 ◽  
Author(s):  
Aniruddh R Galgali ◽  
Maneesh Sahani ◽  
Valerio Mante
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Neural Circuit ◽  
Neural Population ◽  
Perceptual Decision Making ◽  
Neural Recordings ◽  
Fine Grained ◽  
Neural Computations ◽  
Population Trajectory ◽  
The Mean

Relating neural activity to behavior requires an understanding of how neural computations arise from the coordinated dynamics of distributed, recurrently connected neural populations. However, inferring the nature of recurrent dynamics from partial recordings of a neural circuit presents significant challenges. Here, we show that some of these challenges can be overcome by a fine-grained analysis of the dynamics of neural residuals, i.e. trial-by-trial variability around the mean neural population trajectory for a given task condition. Residual dynamics in macaque pre-frontal cortex (PFC) in a saccade-based perceptual decision-making task reveals recurrent dynamics that is time-dependent, but consistently stable, and implies that pronounced rotational structure in PFC trajectories during saccades are driven by inputs from upstream areas. The properties of residual dynamics restrict the possible contributions of PFC to decision-making and saccade generation, and suggest a path towards fully characterizing distributed neural computations with large-scale neural recordings and targeted causal perturbations.

scholarly journals A self-initiated two-alternative forced choice paradigm for head-fixed mice

2016 ◽  
Author(s):  
Fred Marbach ◽  
Anthony M. Zador
Keyword(s):  
Decision Making ◽  
Forced Choice ◽  
Perceptual Decision Making ◽  
Neuronal Populations ◽  
Freely Moving ◽  
Forced Choice Tasks ◽  
Genetic Targeting ◽  
Choice Tasks ◽  
Decision Errors

AbstractPsychophysical tasks for non-human primates have been instrumental in studying circuits underlying perceptual decision-making. To obtain greater experimental flexibility, these tasks have subsequently been adapted for use in freely moving rodents. However, advances in functional imaging and genetic targeting of neuronal populations have made it critical to develop similar tasks for head-fixed mice. Although head-fixed mice have been trained in two-alternative forced choice tasks before, these tasks were not self-initiated, making it difficult to attribute error trials to perceptual or decision errors as opposed to mere lapses in task engagement. Here, we describe a paradigm for head-fixed mice with three lick spouts, analogous to the well-established 3-port paradigm for freely moving rodents. Mice readily learned to initiate trials on the center spout and performed around 200 self-initiated trials per session, reaching good psychometric performance within two weeks of training. We expect this paradigm will be useful to study the role of defined neural populations in sensory processing and decision-making.

scholarly journals Age-related deficits in rapid visuomotor decision-making

2021 ◽  
Author(s):  
Ana Gómez-Granados ◽  
Deborah A Barany ◽  
Margaret Schrayer ◽  
Isaac L. Kurtzer ◽  
Cédrick T Bonnet ◽  
...  
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Sensory Information ◽  
Dorsal Stream ◽  
Task Demands ◽  
Decision Task ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Age Related ◽  
Induced Changes

Many goal-directed actions that require rapid visuomotor planning and perceptual decision-making are affected in older adults, causing difficulties in execution of many functional activities of daily living. Visuomotor planning and perceptual decision-making are mediated by the dorsal and ventral visual streams, respectively, but it is unclear how age-induced changes in sensory processing in these streams contribute to declines in goal-directed actions. Previously, we have shown that in healthy adults, task demands influence movement strategies during visuomotor decision-making, reflecting differential integration of sensory information between the two streams. Here, we asked the question if older adults would exhibit larger declines in interactions between the two streams during demanding motor tasks. Older adults (n=15) and young controls (n=26) performed reaching or interception movements towards virtual objects. In some blocks of trials, participants also had to select an appropriate movement goal based on the shape of the object. Our results showed that older adults corrected fewer initial decision errors during both reaching and interception movements. During the interception decision task, older adults made more decision- and execution-related errors than young adults, which were related to early initiation of their movements. Together, these results suggest that older adults have a reduced ability to integrate new perceptual information to guide online action, which may reflect impaired ventral-dorsal stream interactions.

scholarly journals Spectral signature and behavioral consequence of spontaneous shifts of pupil-linked arousal in human

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Ella Podvalny ◽  
Leana E King ◽  
Biyu J He
Keyword(s):  
Decision Making ◽  
Resting State ◽  
Pupil Size ◽  
Large Scale ◽  
Spectral Power ◽  
Brain Activity ◽  
Spectral Signature ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Pupil Constriction

Arousal levels perpetually rise and fall spontaneously. How markers of arousal - pupil size and frequency content of brain activity - relate to each other and influence behavior in humans is poorly understood. We simultaneously monitored magnetoencephalography and pupil in healthy volunteers at rest and during a visual perceptual decision-making task. Spontaneously varying pupil size correlates with power of brain activity in most frequency bands across large-scale resting-state cortical networks. Pupil size recorded at prestimulus baseline correlates with subsequent shifts in detection bias (c) and sensitivity (d'). When dissociated from pupil-linked state, prestimulus spectral power of resting state networks still predicts perceptual behavior. Fast spontaneous pupil constriction and dilation correlate with large-scale brain activity as well but not perceptual behavior. Our results illuminate the relation between central and peripheral arousal markers and their respective roles in human perceptual decision-making.

scholarly journals Learning optimal decisions with confidence

2019 ◽  
Vol 116 (49) ◽  
pp. 24872-24880 ◽  
Author(s):  
Jan Drugowitsch ◽  
André G. Mendonça ◽  
Zachary F. Mainen ◽  
Alexandre Pouget
Keyword(s):  
Decision Making ◽  
Time Pressure ◽  
A Priori ◽  
Learning Rule ◽  
Normative Theory ◽  
Perceptual Decision Making ◽  
Neuronal Populations ◽  
Optimal Decisions ◽  
Optimal Linear ◽  
Rate Of Learning

Diffusion decision models (DDMs) are immensely successful models for decision making under uncertainty and time pressure. In the context of perceptual decision making, these models typically start with two input units, organized in a neuron–antineuron pair. In contrast, in the brain, sensory inputs are encoded through the activity of large neuronal populations. Moreover, while DDMs are wired by hand, the nervous system must learn the weights of the network through trial and error. There is currently no normative theory of learning in DDMs and therefore no theory of how decision makers could learn to make optimal decisions in this context. Here, we derive such a rule for learning a near-optimal linear combination of DDM inputs based on trial-by-trial feedback. The rule is Bayesian in the sense that it learns not only the mean of the weights but also the uncertainty around this mean in the form of a covariance matrix. In this rule, the rate of learning is proportional (respectively, inversely proportional) to confidence for incorrect (respectively, correct) decisions. Furthermore, we show that, in volatile environments, the rule predicts a bias toward repeating the same choice after correct decisions, with a bias strength that is modulated by the previous choice’s difficulty. Finally, we extend our learning rule to cases for which one of the choices is more likely a priori, which provides insights into how such biases modulate the mechanisms leading to optimal decisions in diffusion models.

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