Spontaneous Neural Fluctuations Predict Decisions to Attend

2014 ◽  
Vol 26 (11) ◽  
pp. 2578-2584 ◽  
Author(s):  
Jesse J. Bengson ◽  
Todd A. Kelley ◽  
Xiaoke Zhang ◽  
Jane-Ling Wang ◽  
George R. Mangun
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Intrinsic Property ◽  
Alternative View ◽  
Alpha Band ◽  
Band Power ◽  
Trial Basis ◽  
Human Participants ◽  
Neural Signaling ◽  
The Brain

Ongoing variability in neural signaling is an intrinsic property of the brain. Often this variability is considered to be noise and ignored. However, an alternative view is that this variability is fundamental to perception and cognition and may be particularly important in decision-making. Here, we show that a momentary measure of occipital alpha-band power (8–13 Hz) predicts choices about where human participants will focus spatial attention on a trial-by-trial basis. This finding provides evidence for a mechanistic account of decision-making by demonstrating that ongoing neural activity biases voluntary decisions about where to attend within a given moment.

The Emerging Standard Neurobiological Model of Decision Making

2018 ◽  
Author(s):  
Shih-Wei Wu ◽  
Paul W. Glimcher
Keyword(s):  
Decision Making ◽  
Economic Theory ◽  
Neural Activity ◽  
First Century ◽  
The Other ◽  
Neoclassical Economic ◽  
Internal Representations ◽  
Neurobiological Model ◽  
Neoclassical Economic Theory ◽  
The Brain

The standard neurobiological model of decision making has evolved, since the turn of the twenty-first century, from a confluence of economic, psychological, and neurosci- entific studies of how humans make choices. Two fundamental insights have guided the development of this model during this period, one drawn from economics and the other from neuroscience. The first derives from neoclassical economic theory, which unambiguously demonstrated that logically consistent choosers behave “as if” they had some internal, continuous, and monotonic representation of the values of any choice objects under consideration. The second insight derives from neurobiological studies suggesting that the brain can both represent, in patterns of local neural activity, and compare, by a process of interneuronal competition, internal representations of value associated with different choices.

scholarly journals Ventral Striatum and the Evaluation of Memory Retrieval Strategies

2014 ◽  
Vol 26 (9) ◽  
pp. 1928-1948 ◽  
Author(s):  
David Badre ◽  
Sophie Lebrecht ◽  
David Pagliaccio ◽  
Nicole M. Long ◽  
Jason M. Scimeca
Keyword(s):  
Decision Making ◽  
Memory Retrieval ◽  
Ventral Striatum ◽  
Semantic Category ◽  
Relevant Information ◽  
Decision Task ◽  
Drift Diffusion ◽  
Retrieval Strategies ◽  
Human Participants ◽  
The Brain

Adaptive memory retrieval requires mechanisms of cognitive control that facilitate the recovery of goal-relevant information. Frontoparietal systems are known to support control of memory retrieval. However, the mechanisms by which the brain acquires, evaluates, and adapts retrieval strategies remain unknown. Here, we provide evidence that ventral striatal activation tracks the success of a retrieval strategy and correlates with subsequent reliance on that strategy. Human participants were scanned with fMRI while performing a lexical decision task. A rule was provided that indicated the likely semantic category of a target word given the category of a preceding prime. Reliance on the rule improved decision-making, as estimated within a drift diffusion framework. Ventral striatal activation tracked the benefit that relying on the rule had on decision-making. Moreover, activation in ventral striatum correlated with a participant's subsequent reliance on the rule. Taken together, these results support a role for ventral striatum in learning and evaluating declarative retrieval strategies.

Decision making by urgency gating: theory and experimental support

2012 ◽  
Vol 108 (11) ◽  
pp. 2912-2930 ◽  
Author(s):  
David Thura ◽  
Julie Beauregard-Racine ◽  
Charles-William Fradet ◽  
Paul Cisek
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Time Window ◽  
Sensory Information ◽  
Natural Conditions ◽  
Accuracy Criterion ◽  
Sensory Evidence ◽  
Dot Motion ◽  
The Brain ◽  
Random Dot Motion

It is often suggested that decisions are made when accumulated sensory information reaches a fixed accuracy criterion. This is supported by many studies showing a gradual build up of neural activity to a threshold. However, the proposal that this build up is caused by sensory accumulation is challenged by findings that decisions are based on information from a time window much shorter than the build-up process. Here, we propose that in natural conditions where the environment can suddenly change, the policy that maximizes reward rate is to estimate evidence by accumulating only novel information and then compare the result to a decreasing accuracy criterion. We suggest that the brain approximates this policy by multiplying an estimate of sensory evidence with a motor-related urgency signal and that the latter is primarily responsible for neural activity build up. We support this hypothesis using human behavioral data from a modified random-dot motion task in which motion coherence changes during each trial.

Perception from Action

2019 ◽  
pp. 53-82
Author(s):  
György Buzsáki
Keyword(s):  
Decision Making ◽  
Sensory Input ◽  
Perception And Action ◽  
Physiological Mechanism ◽  
Corollary Discharge ◽  
Alternative View ◽  
The World ◽  
The Relationship ◽  
The Brain

The outside-in framework inevitably poses the question: What comes between perception and action? The homunculus with its decision-making power produces unavoidable logical consequences from the separation of perception from action. I promote the alternative view that things and events in the world can acquire meaning only through brain-initiated actions. In this process, the brain builds a simplified, customized model of the world by encoding the relationships of events to each other. I introduce the concept of “corollary discharge,” the main physiological mechanism that grounds the sensory input to make it an experience. This is a comparator mechanism that allows the brain to examine the relationship between a true change in the sensory input and a change due to self-initiated movement of the sensors.

scholarly journals A Probabilistic, Distributed, Recursive Mechanism for Decision-making in the Brain

2016 ◽  
Author(s):  
Javier A. Caballero ◽  
Mark D. Humphries ◽  
Kevin N. Gurney
Keyword(s):  
Decision Making ◽  
Basal Ganglia ◽  
Neural Activity ◽  
Large Scale ◽  
Single Equation ◽  
Brain Regions ◽  
Compulsive Gambling ◽  
Choice Behaviour ◽  
Usable Information ◽  
The Brain

AbstractDecision formation recruits many brain regions, but the procedure they jointly execute is unknown. Here we characterize its essential composition, using as a framework a novel recursive Bayesian algorithm that makes decisions based on spike-trains with the statistics of those in sensory cortex (MT). Using it to simulate the random-dot-motion task, we demonstrate it quantitatively replicates the choice behaviour of monkeys, whilst predicting losses of otherwise usable information from MT. Its architecture maps to the recurrent cortico-basal-ganglia-thalamo-cortical loops, whose components are all implicated in decision-making. We show that the dynamics of its mapped computations match those of neural activity in the sensorimotor cortex and striatum during decisions, and forecast those of basal ganglia output and thalamus. This also predicts which aspects of neural dynamics are and are not part of inference. Our single-equation algorithm is probabilistic, distributed, recursive, and parallel. Its success at capturing anatomy, behaviour, and electrophysiology suggests that the mechanism implemented by the brain has these same characteristics.Author SummaryDecision-making is central to cognition. Abnormally-formed decisions characterize disorders like over-eating, Parkinson’s and Huntington’s diseases, OCD, addiction, and compulsive gambling. Yet, a unified account of decisionmaking has, hitherto, remained elusive. Here we show the essential composition of the brain’s decision mechanism by matching experimental data from monkeys making decisions, to the knowable function of a novel statistical inference algorithm. Our algorithm maps onto the large-scale architecture of decision circuits in the primate brain, replicating the monkeys’ choice behaviour and the dynamics of the neural activity that accompany it. Validated in this way, our algorithm establishes a basic framework for understanding the mechanistic ingredients of decisionmaking in the brain, and thereby, a basic platform for understanding how pathologies arise from abnormal function.

scholarly journals Neural Circuits Trained with Standard Reinforcement Learning Can Accumulate Probabilistic Information during Decision Making

2017 ◽  
Vol 29 (2) ◽  
pp. 368-393 ◽  
Author(s):  
Nils Kurzawa ◽  
Christopher Summerfield ◽  
Rafal Bogacz
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Neural Activity ◽  
Neural Circuits ◽  
Likelihood Ratios ◽  
Sensory Inputs ◽  
Probabilistic Information ◽  
Learning Rules ◽  
Log Likelihood ◽  
The Brain

Much experimental evidence suggests that during decision making, neural circuits accumulate evidence supporting alternative options. A computational model well describing this accumulation for choices between two options assumes that the brain integrates the log ratios of the likelihoods of the sensory inputs given the two options. Several models have been proposed for how neural circuits can learn these log-likelihood ratios from experience, but all of these models introduced novel and specially dedicated synaptic plasticity rules. Here we show that for a certain wide class of tasks, the log-likelihood ratios are approximately linearly proportional to the expected rewards for selecting actions. Therefore, a simple model based on standard reinforcement learning rules is able to estimate the log-likelihood ratios from experience and on each trial accumulate the log-likelihood ratios associated with presented stimuli while selecting an action. The simulations of the model replicate experimental data on both behavior and neural activity in tasks requiring accumulation of probabilistic cues. Our results suggest that there is no need for the brain to support dedicated plasticity rules, as the standard mechanisms proposed to describe reinforcement learning can enable the neural circuits to perform efficient probabilistic inference.

There Are No Killer Apps but Connecting Neural Activity to Behavior through Computation Is Still a Good Idea

2016 ◽  
Author(s):  
P. Read Montague
Keyword(s):  
Decision Making ◽  
Cognitive Function ◽  
Simple Model ◽  
Behavioral Economics ◽  
Cognitive Science ◽  
Neural Activity ◽  
Mental States ◽  
And Behavior ◽  
The Relationship ◽  
The Brain

The quest to understand the relationship between neural activity and behavior has been ongoing for well over a hundred years. Although research based on the stimulus-and-response approach to behavior, advocated by behaviorists, flourished during the last century, this view does not, by design, account for unobservable variables (e.g., mental states). Putting aside this approach, modern cognitive science, cognitive neuroscience, neuroeconomics, and behavioral economics have sought to explain this connection computationally. One major hurdle lies in the fact that we lack even a simple model of cognitive function. This chapter sketches an application that connects neuromodulator function to decision making and the valuation that underlies it. The nature of this hypothesized connection offers a fruitful platform to understand some of the informational aspects of dopamine function in the brain and how it exposes many different ways of understanding motivated choice.

scholarly journals Dopamine modulates dynamic decision-making during foraging

2019 ◽  
Author(s):  
Campbell Le Heron ◽  
Nils Kolling ◽  
Olivia Plant ◽  
Annika Kienast ◽  
Rebecca Janska ◽  
...  
Keyword(s):  
Decision Making ◽  
Opportunity Cost ◽  
Dopamine D2 Receptor ◽  
D2 Receptor ◽  
Receptor Activity ◽  
Dynamic Decision Making ◽  
Healthy Human ◽  
Pharmacological Manipulation ◽  
Human Participants ◽  
The Brain

ABSTRACTThe mesolimbic dopaminergic system exerts a crucial influence on incentive processing. However, the contribution of dopamine in dynamic, ecological situations where reward rates vary, and decisions evolve over time, remains unclear. In such circumstances, current (foreground) reward accrual needs to be compared continuously with potential rewards that could be obtained by travelling elsewhere (background reward rate), in order to determine the opportunity cost of staying versus leaving. We hypothesised that dopamine specifically modulates the influence of background – but not foreground – reward information when making a dynamic comparison of these variables for optimal behaviour. On a novel foraging task based on an ecological account of animal behaviour (marginal value theorem), human participants were required to decide when to leave locations in situations where foreground rewards depleted at different rates, either in rich or poor environments with high or low background rates. In line with theoretical accounts, people’s decisions to move from current locations were independently modulated by both foreground and background reward rates. Pharmacological manipulation of dopamine D2 receptor activity using the agonist cabergoline significantly affected decisions to move on, specifically modulating the effect of background but not foreground rewards rates. In particular, when on cabergoline, people left patches in poor environments much earlier. These results demonstrate a role of dopamine in signalling the opportunity cost of rewards, not value per se. Using this ecologically derived framework we uncover a specific mechanism by which D2 dopamine receptor activity modulates decision-making when foreground and background reward rates are dynamically compared.Significance statementMany decisions, across economic, political and social spheres, involve choices to “leave”. Such decisions depend on a continuous comparison of a current location’s value, with that of other locations you could move on to. However, how the brain makes such decisions is poorly understood. Here, we developed a computerized task, based around theories of how animals make decisions to move on when foraging for food. Healthy human participants had to decide when to leave collecting financial rewards in a location, and travel to collect rewards elsewhere. Using a pharmacological manipulation, we show that the activity of dopamine in the brain modulates decisions to move on, with people valuing other locations differently depending on their dopaminergic state.

scholarly journals Spectral distribution dynamics across different attentional priority states

2021 ◽  
Author(s):  
Mattia Pietrelli ◽  
Jason Samaha ◽  
Bradley R. Postle
Keyword(s):  
Time Course ◽  
Physiological State ◽  
Alpha Power ◽  
Alpha Band ◽  
Distribution Dynamics ◽  
Band Power ◽  
Human Participants ◽  
And Shifts ◽  
Secondary Consequence ◽  
Rule Out

AbstractAnticipatory covert spatial attention improves performance on tests of visual detection and discrimination, and shifts are accompanied by decreases and increases of alpha-band power at EEG electrodes corresponding to the attended and unattended location, respectively. Although the increase at the unattended location is often interpreted as an active mechanism (e.g., inhibiting processing at the unattended location), most experiments cannot rule out the alternative possibility that it is a secondary consequence of selection elsewhere. To adjudicate between these accounts, we designed a Posner-style cuing task in which male and female human participants made orientation judgments of targets appearing at one of four locations: up, down, right, or left. Critically, trials were blocked such that within a block the locations along one meridian alternated in status between attended and unattended, and targets never appeared at the other two, making them irrelevant. Analyses of the concurrently measured EEG signal were carried out on traditional narrowband alpha (8-14 Hz), as well as on two components resulting from the decomposition of this signal: periodic alpha; and the slope of the aperiodic 1/f-like component. Although data from right-left blocks replicated the familiar pattern of lateralized asymmetry in narrowband alpha power, with neither alpha signal could we find evidence for any difference in the time course at unattended versus irrelevant locations, an outcome consistent with the secondary-consequence interpretation of attention-related dynamics in the alpha band. Additionally, 1/f slope was lower at attended and unattended locations, relative to irrelevant, suggesting a tonic adjustment of physiological state.

scholarly journals Regular cycling between representations of alternatives in the hippocampus

2019 ◽  
Author(s):  
Kenneth Kay ◽  
Jason E. Chung ◽  
Marielena Sosa ◽  
Jonathan S. Schor ◽  
Mattias P. Karlsson ◽  
...  
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Brain Structure ◽  
Theta Rhythm ◽  
Neural Mechanism ◽  
Heading Direction ◽  
Cognitive Faculties ◽  
Alternative Scenarios ◽  
The Brain

Cognitive faculties such as imagination, planning, and decision-making require the ability to represent alternative scenarios. In animals, split-second decision-making implies that the brain can represent alternatives at a commensurate speed. Yet despite this insight, it has remained unknown whether there exists neural activity that can consistently represent alternatives in <1 s. Here we report that neural activity in the hippocampus, a brain structure vital to cognition, can regularly cycle between representations of alternative locations (bifurcating paths in a maze) at 8 Hz. This cycling dynamic was paced by the internally generated 8 Hz theta rhythm, often occurred in the absence of overt deliberative behavior, and unexpectedly also governed an additional hippocampal representation defined by alternatives (heading direction). These findings implicate a fast, regular, and generalized neural mechanism underlying the representation of competing possibilities.

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