scholarly journals Optional Stopping in a Heteroscedastic World

2020 ◽  
Author(s):  
hannah tickle ◽  
Konstantinos Tsetsos ◽  
Maarten Speekenbrink ◽  
Christopher Summerfield
Keyword(s):  
Posterior Probability ◽  
Relevant Information ◽  
Natural Environments ◽  
Decision Threshold ◽  
Neural Signals ◽  
Adaptive Weighting ◽  
Inverse Prediction ◽  
Optional Stopping ◽  
In Series ◽  
Information Harvesting

When making decisions, animals must trade off the benefits of information harvesting against the opportunity cost of prolonged deliberation. Deciding when to stop accumulating information and commit to a choice is challenging in natural environments, where the reliability of decision-relevant information may itself vary unpredictably over time (variable variance or “heteroscedasticity”). We asked humans to perform a categorisation task in which discrete, continuously-valued samples (oriented gratings) arrived in series until the observer made a choice. Human behaviour was best described by a model that adaptively weighted sensory signals by their inverse prediction error, and integrated the resulting quantities to a collapsing decision threshold. This model approximated the output of a Bayesian model that computed the full posterior probability of a correct response, and successfully predicted adaptive weighting of decision information in neural signals. Adaptive weighting of decision information may have evolved to promote optional stopping in hetereoscedastic natural environments.

scholarly journals Rate and timing of cortical responses driven by separate sensory channels

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Hannes P Saal ◽  
Michael A Harvey ◽  
Sliman J Bensmaia
Keyword(s):  
Computational Model ◽  
Relevant Information ◽  
Nerve Fibers ◽  
The Other ◽  
Fiber Types ◽  
Neural Signals ◽  
Neuronal Populations ◽  
Cortical Responses ◽  
Behaving Monkeys ◽  
Sense Of Touch

The sense of touch comprises multiple sensory channels that each conveys characteristic signals during interactions with objects. These neural signals must then be integrated in such a way that behaviorally relevant information about the objects is preserved. To understand the process of integration, we implement a simple computational model that describes how the responses of neurons in somatosensory cortex—recorded from awake, behaving monkeys—are shaped by the peripheral input, reconstructed using simulations of neuronal populations that reproduce natural spiking responses in the nerve with millisecond precision. First, we find that the strength of cortical responses is driven by one population of nerve fibers (rapidly adapting) whereas the timing of cortical responses is shaped by the other (Pacinian). Second, we show that input from these sensory channels is integrated in an optimal fashion that exploits the disparate response behaviors of different fiber types.

scholarly journals Selective Integration during Sequential Sampling in Posterior Neural Signals

2020 ◽  
Vol 30 (8) ◽  
pp. 4454-4464 ◽  
Author(s):  
Fabrice Luyckx ◽  
Bernhard Spitzer ◽  
Annabelle Blangero ◽  
Konstantinos Tsetsos ◽  
Christopher Summerfield
Keyword(s):  
Sequential Sampling ◽  
Relevant Information ◽  
Average Height ◽  
Neural Signals ◽  
Posterior Cortex ◽  
Integration Model ◽  
Trial Analysis ◽  
Rival Model ◽  
Processing Resources ◽  
Selective Integration

Abstract Decisions are typically made after integrating information about multiple attributes of alternatives in a choice set. Where observers are obliged to consider attributes in turn, a computational framework known as “selective integration” can capture salient biases in human choices. The model proposes that successive attributes compete for processing resources and integration is biased towards the alternative with the locally preferred attribute. Quantitative analysis shows that this model, although it discards choice-relevant information, is optimal when the observers’ decisions are corrupted by noise that occurs beyond the sensory stage. Here, we used electroencephalography (EEG) to test a neural prediction of the model: that locally preferred attributes should be encoded with higher gain in neural signals over the posterior cortex. Over two sessions, human observers judged which of the two simultaneous streams of bars had the higher (or lower) average height. The selective integration model fits the data better than a rival model without bias. Single-trial analysis showed that neural signals contralateral to the preferred attribute covaried more steeply with the decision information conferred by locally preferred attributes. These findings provide neural evidence in support of selective integration, complementing existing behavioral work.

scholarly journals Perturbing Neural Representations of Working Memory with Task-irrelevant Interruption

2020 ◽  
Vol 32 (3) ◽  
pp. 558-569 ◽  
Author(s):  
Nicole Hakim ◽  
Tobias Feldmann-Wüstefeld ◽  
Edward Awh ◽  
Edward K. Vogel
Keyword(s):  
Working Memory ◽  
Memory Task ◽  
Relevant Information ◽  
Alpha Power ◽  
Neural Signals ◽  
Neural Representations ◽  
Contralateral Delay Activity ◽  
The Face ◽  
Task Irrelevant ◽  
The Impact

Working memory maintains information so that it can be used in complex cognitive tasks. A key challenge for this system is to maintain relevant information in the face of task-irrelevant perturbations. Across two experiments, we investigated the impact of task-irrelevant interruptions on neural representations of working memory. We recorded EEG activity in humans while they performed a working memory task. On a subset of trials, we interrupted participants with salient but task-irrelevant objects. To track the impact of these task-irrelevant interruptions on neural representations of working memory, we measured two well-characterized, temporally sensitive EEG markers that reflect active, prioritized working memory representations: the contralateral delay activity and lateralized alpha power (8–12 Hz). After interruption, we found that contralateral delay activity amplitude momentarily sustained but was gone by the end of the trial. Lateralized alpha power was immediately influenced by the interrupters but recovered by the end of the trial. This suggests that dissociable neural processes contribute to the maintenance of working memory information and that brief irrelevant onsets disrupt two distinct online aspects of working memory. In addition, we found that task expectancy modulated the timing and magnitude of how these two neural signals responded to task-irrelevant interruptions, suggesting that the brain's response to task-irrelevant interruption is shaped by task context.

scholarly journals Towards a better understanding of galaxy clusters

2014 ◽  
Vol 10 (S306) ◽  
pp. 273-275
Author(s):  
Pedro T. P. Viana
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Probability Distribution Function ◽  
Large Scale ◽  
Posterior Probability ◽  
Galaxy Cluster ◽  
Relevant Information ◽  
Posterior Probability Distribution ◽  
Cluster Properties ◽  
The Universe

AbstractObservational data on clusters of galaxies holds relevant information that can be used to determine the relative plausibility of different models for the large-scale evolution of the Universe, or estimate the joint posterior probability distribution function of the parameters that pertain to each model. Within the next few years, several surveys of the sky will yield large galaxy cluster catalogues. In order to make use of the vast amount of information they will contain, their selection functions will have to be properly understood. We argue this, as well as the estimation of the full joint posterior probability distribution function of the most relevant cluster properties, can be best achieved in the framework of bayesian statistics.

scholarly journals Selective integration during sequential sampling in posterior neural signals

2019 ◽  
Author(s):  
Fabrice Luyckx ◽  
Bernhard Spitzer ◽  
Annabelle Blangero ◽  
Konstantinos Tsetsos ◽  
Christopher Summerfield
Keyword(s):  
Sequential Sampling ◽  
Relevant Information ◽  
Average Height ◽  
Neural Signals ◽  
Posterior Cortex ◽  
Integration Model ◽  
Rival Model ◽  
Multiple Attributes ◽  
Eeg Recordings ◽  
Selective Integration

AbstractDecisions are typically made after integrating information about multiple attributes of alternatives in a choice set. The computational mechanisms by which this integration occurs have been a focus of extensive research in humans and other animals. Where observers are obliged to consider attributes in turn, a framework known as “selective integration” can capture salient biases in human choices. The model proposes that successive attributes compete for processing resources and integration is biased towards the alternative with the locally preferred attribute. Quantitative analysis shows that this model, although it discards choice-relevant information, is optimal when the observers’ decisions are corrupted by noise that occurs beyond the sensory stage. Here, we used scalp electroencephalographic (EEG) recordings to test a neural prediction of the model: that locally preferred attributes should be encoded with higher gain in neural signals over posterior cortex. Over two sessions, human observers (of either sex) judged which of two simultaneous streams of bars had the higher (or lower) average height. The selective integration model fit the data better than a rival model without bias. Single-trial analysis showed that neural signals contralateral to the preferred attribute covaried more steeply with the decision information conferred by locally preferred attributes. These findings provide neural evidence in support of selective integration, complementing existing behavioural work.Significance StatementWe often make choices about stimuli with multiple attributes, such as when deciding which car to buy on the basis of price, performance and fuel economy. A model of the choice process, known as selective integration, proposes that rather than taking all of the decision-relevant information equally into account when making choices, we discard or overlook a portion of it. Although information is discarded, this strategy can lead to better decisions when memory is limited. Here, we test and confirm predictions of the model about the brain signals that occur when different stimulus attributes of stimulus are being evaluated. Our work provides the first neural support for the selective integration model.

scholarly journals The Perils of Misspecified Priors and Optional Stopping in Multi-Armed Bandits

2021 ◽  
Vol 4 ◽  
Author(s):  
Markus Loecher
Keyword(s):  
Posterior Probability ◽  
Prior Probability ◽  
Strong Dependence ◽  
Stopping Times ◽  
Bayesian Posterior Probability ◽  
Thompson Sampling ◽  
Optional Stopping ◽  
The Subject ◽  
Active Research ◽  
Optimal Stopping Times

The connection between optimal stopping times of American Options and multi-armed bandits is the subject of active research. This article investigates the effects of optional stopping in a particular class of multi-armed bandit experiments, which randomly allocates observations to arms proportional to the Bayesian posterior probability that each arm is optimal (Thompson sampling). The interplay between optional stopping and prior mismatch is examined. We propose a novel partitioning of regret into peri/post testing. We further show a strong dependence of the parameters of interest on the assumed prior probability density.

scholarly journals Estimation of Parameter Probability Distributions for Lithium-Ion Battery String Models Using Bayesian Methods

2020 ◽  
Author(s):  
Luis D. Couto ◽  
Dong Zhang ◽  
Antti Aitio ◽  
Scott Moura ◽  
David Howey
Keyword(s):  
Probability Distribution ◽  
Lithium Ion Battery ◽  
Posterior Probability ◽  
Lithium Ion ◽  
Estimation Problem ◽  
Battery Pack ◽  
Model Parameters ◽  
Modeling Framework ◽  
Posterior Probability Distribution ◽  
In Series

Abstract This paper addresses the parameter estimation problem for lithium-ion battery pack models comprising cells in series. This valuable information can be exploited in fault diagnostics to estimate the number of cells that are exhibiting abnormal behaviour, e.g. large resistances or small capacities. In particular, we use a Bayesian approach to estimate the parameters of a two-cell arrangement modelled using equivalent circuits. Although our modeling framework has been extensively reported in the literature, its structural identifiability properties have not been reported yet to the best of the authors’ knowledge. Moreover, most contributions in the literature tackle the estimation problem through point-wise estimates assuming Gaussian noise using e.g. least-squares methods (maximum likelihood estimation) or Kalman filters (maximum a posteriori estimation). In contrast, we apply methods that are suitable for nonlinear and non-Gaussian estimation problems and estimate the full posterior probability distribution of the parameters. We study how the model structure, available measurements and prior knowledge of the model parameters impact the underlying posterior probability distribution that is recovered for the parameters. For two cells in series, a bimodal distribution is obtained whose modes are centered around the real values of the parameters for each cell. Therefore, bounds on the model parameters for a battery pack can be derived.

scholarly journals Control of gaze in natural environments: effects of rewards and costs, uncertainty and memory in target selection

2018 ◽  
Vol 8 (4) ◽  
pp. 20180009 ◽  
Author(s):  
Mary M. Hayhoe ◽  
Jonathan Samir Matthis
Keyword(s):  
Virtual Environments ◽  
Target Selection ◽  
Relevant Information ◽  
Natural Environments ◽  
Gaze Control ◽  
Visually Guided ◽  
Natural Vision ◽  
Body Tracking ◽  
Systematic Exploration

The development of better eye and body tracking systems, and more flexible virtual environments have allowed more systematic exploration of natural vision and contributed a number of insights. In natural visually guided behaviour, humans make continuous sequences of sensory-motor decisions to satisfy current goals, and the role of vision is to provide the relevant information in order to achieve those goals. This paper reviews the factors that control gaze in natural visually guided actions such as locomotion, including the rewards and costs associated with the immediate behavioural goals, uncertainty about the state of the world and prior knowledge of the environment. These general features of human gaze control may inform the development of artificial systems.

scholarly journals Evidence and urgency related EEG signals during dynamic decision-making in humans

2020 ◽  
Author(s):  
Y. Yau ◽  
T. Hinault ◽  
M. Taylor ◽  
P. Cisek ◽  
L.K. Fellows ◽  
...  
Keyword(s):  
Decision Making ◽  
Dynamic Decision Making ◽  
Decision Variable ◽  
Decision Threshold ◽  
Neural Signals ◽  
Addictive Disorders ◽  
Evidence Accumulation ◽  
Gating Model ◽  
Decision Variables ◽  
Sensory Evidence

AbstractA successful class of models link decision-making to brain signals by assuming that evidence accumulates to a decision threshold. These evidence accumulation models have identified neuronal activity that appears to reflect sensory evidence and decision variables that drive behavior. More recently, an additional evidence-independent and time-variant signal, named urgency, has been hypothesized to accelerate decisions in the face of insufficient evidence. However, most decision-making paradigms tested with fMRI or EEG in humans have not been designed to disentangle evidence accumulation from urgency. Here we use a face-morphing decision-making task in combination with EEG and a hierarchical Bayesian model to identify neural signals related to sensory and decision variables, and to test the urgency-gating model. We find that an evoked potential time-locked to the decision, the centroparietal positivity, reflects the decision variable from the computational model. We further show that the unfolding of this signal throughout the decision process best reflects the product of sensory evidence and an evidence-independent urgency signal. Urgency varied across subjects, suggesting that it may represent an individual trait. Our results show that it is possible to use EEG to distinguish neural signals related to sensory evidence accumulation, decision variables, and urgency. These mechanisms expose principles of cognitive function in general and may have applications to the study of pathological decision-making as in impulse control and addictive disorders.Significance StatementPerceptual decisions are often described by a class of models that assumes sensory evidence accumulates gradually over time until a decision threshold is reached. In the present study, we demonstrate that an additional urgency signal impacts how decisions are formed. This endogenous signal encourages one to respond as time elapses. We found that neural decision signals measured by EEG reflect the product of sensory evidence and an evidence-independent urgency signal. A nuanced understanding of human decisions, and the neural mechanisms that support it, can improve decision-making in many situations and potentially ameliorate dysfunction when it has gone awry.

scholarly journals Endotaxis: A Universal Algorithm for Mapping, Goal-Learning, and Navigation

2021 ◽  
Author(s):  
Tony Zhang ◽  
Matthew Rosenberg ◽  
Pietro Perona ◽  
Markus Meister
Keyword(s):  
Neural Circuit ◽  
Cognitive Tasks ◽  
General Mechanism ◽  
Natural Environments ◽  
Neural Signals ◽  
Brain Areas ◽  
Universal Algorithm ◽  
Learning Rules ◽  
Small Set ◽  
The Brain

An animal entering a new environment typically faces three challenges: explore the space for resources, memorize their locations, and navigate towards those targets as needed. Experimental work on exploration, mapping, and navigation has mostly focused on simple environments - such as an open arena, a pond [1], or a desert [2] - and much has been learned about neural signals in diverse brain areas under these conditions [3,4]. However, many natural environments are highly constrained, such as a system of burrows, or of paths through the underbrush. More generally, many cognitive tasks are equally constrained, allowing only a small set of actions at any given stage in the process. Here we propose an algorithm that learns the structure of an arbitrary environment, discovers useful targets during exploration, and navigates back to those targets by the shortest path. It makes use of a behavioral module common to all motile animals, namely the ability to follow an odor to its source [5]. We show how the brain can learn to generate internal "virtual odors'" that guide the animal to any location of interest. This endotaxis algorithm can be implemented with a simple 3-layer neural circuit using only biologically realistic structures and learning rules. Several neural components of this scheme are found in brains from insects to humans. Nature may have evolved a general mechanism for search and navigation on the ancient backbone of chemotaxis.

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