scholarly journals Neural integrators for decision making: a favorable tradeoff between robustness and sensitivity

2013 ◽  
Vol 109 (10) ◽  
pp. 2542-2559 ◽  
Author(s):  
Nicholas Cain ◽  
Andrea K. Barreiro ◽  
Michael Shadlen ◽  
Eric Shea-Brown
Keyword(s):  
Decision Making ◽  
Sequential Analysis ◽  
Basic Function ◽  
Fine Tuning ◽  
Decay Modes ◽  
Decision Performance ◽  
Recurrent Excitation ◽  
Dot Motion ◽  
Long Timescales ◽  
Evidence Integration

A key step in many perceptual decision tasks is the integration of sensory inputs over time, but a fundamental questions remain about how this is accomplished in neural circuits. One possibility is to balance decay modes of membranes and synapses with recurrent excitation. To allow integration over long timescales, however, this balance must be exceedingly precise. The need for fine tuning can be overcome via a “robust integrator” mechanism in which momentary inputs must be above a preset limit to be registered by the circuit. The degree of this limiting embodies a tradeoff between sensitivity to the input stream and robustness against parameter mistuning. Here, we analyze the consequences of this tradeoff for decision-making performance. For concreteness, we focus on the well-studied random dot motion discrimination task and constrain stimulus parameters by experimental data. We show that mistuning feedback in an integrator circuit decreases decision performance but that the robust integrator mechanism can limit this loss. Intriguingly, even for perfectly tuned circuits with no immediate need for a robustness mechanism, including one often does not impose a substantial penalty for decision-making performance. The implication is that robust integrators may be well suited to subserve the basic function of evidence integration in many cognitive tasks. We develop these ideas using simulations of coupled neural units and the mathematics of sequential analysis.

scholarly journals Decision-making through integration of sensory evidence at prolonged timescales

2018 ◽  
Author(s):  
Michael L. Waskom ◽  
Roozbeh Kiani
Keyword(s):  
Decision Making ◽  
Biological Networks ◽  
Network Models ◽  
Neuronal Responses ◽  
Neural Basis ◽  
Neural Network Models ◽  
Memory Leak ◽  
Sensory Evidence ◽  
Long Timescales ◽  
Evidence Integration

SummaryWhen multiple pieces of information bear on a decision, the best approach is to combine the evidence provided by each one. Evidence integration models formalize the computations underlying this process [1–3], explain human perceptual discrimination behavior [4–11], and correspond to neuronal responses elicited by discrimination tasks [12–17]. These findings indicate that evidence integration is key to understanding the neural basis of decision-making [18–21]. Evidence integration has most often been studied with simple tasks that limit the timescale of deliberation to hundreds of milliseconds, but many natural decisions unfold over much longer durations. Because neural network models imply acute limitations on the timescale of evidence integration [22–26], it is unknown whether current computational insights can generalize beyond rapid judgments. Here, we introduce a new psychophysical task and report model-based analyses of human behavior that demonstrate evidence integration at long timescales. Our task requires probabilistic inference using brief samples of visual evidence that are separated in time by long and unpredictable gaps. We show through several quantitative assays how decision-making can approximate a normative integration process that extends over tens of seconds without accruing significant memory leak or noise. These results support the generalization of evidence integration models to a broader class of behaviors while posing new challenges for models of how these computations are implemented in biological networks.

scholarly journals Deep Learning-Based Differentiation between Mucinous Cystic Neoplasm and Serous Cystic Neoplasm in the Pancreas Using Endoscopic Ultrasonography

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1052
Author(s):  
Leang Sim Nguon ◽  
Kangwon Seo ◽  
Jung-Hyun Lim ◽  
Tae-Jun Song ◽  
Sung-Hyun Cho ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Network Model ◽  
Endoscopic Ultrasonography ◽  
Data Augmentation ◽  
Clinical Information ◽  
Training Data ◽  
Fine Tuning ◽  
Cystic Neoplasm ◽  
Cystic Neoplasms

Mucinous cystic neoplasms (MCN) and serous cystic neoplasms (SCN) account for a large portion of solitary pancreatic cystic neoplasms (PCN). In this study we implemented a convolutional neural network (CNN) model using ResNet50 to differentiate between MCN and SCN. The training data were collected retrospectively from 59 MCN and 49 SCN patients from two different hospitals. Data augmentation was used to enhance the size and quality of training datasets. Fine-tuning training approaches were utilized by adopting the pre-trained model from transfer learning while training selected layers. Testing of the network was conducted by varying the endoscopic ultrasonography (EUS) image sizes and positions to evaluate the network performance for differentiation. The proposed network model achieved up to 82.75% accuracy and a 0.88 (95% CI: 0.817–0.930) area under curve (AUC) score. The performance of the implemented deep learning networks in decision-making using only EUS images is comparable to that of traditional manual decision-making using EUS images along with supporting clinical information. Gradient-weighted class activation mapping (Grad-CAM) confirmed that the network model learned the features from the cyst region accurately. This study proves the feasibility of diagnosing MCN and SCN using a deep learning network model. Further improvement using more datasets is needed.

scholarly journals Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

2013 ◽  
Vol 17 (12) ◽  
pp. 5013-5039 ◽  
Author(s):  
S. E. Thompson ◽  
M. Sivapalan ◽  
C. J. Harman ◽  
V. Srinivasan ◽  
M. R. Hipsey ◽  
...  
Keyword(s):  
Decision Making ◽  
Water Resources ◽  
Water Resources Management ◽  
Research Agenda ◽  
Water Systems ◽  
Coupled Systems ◽  
Model Data ◽  
Resources Management ◽  
Long Timescales ◽  
Insight Into

Abstract. Globally, many different kinds of water resources management issues call for policy- and infrastructure-based responses. Yet responsible decision-making about water resources management raises a fundamental challenge for hydrologists: making predictions about water resources on decadal- to century-long timescales. Obtaining insight into hydrologic futures over 100 yr timescales forces researchers to address internal and exogenous changes in the properties of hydrologic systems. To do this, new hydrologic research must identify, describe and model feedbacks between water and other changing, coupled environmental subsystems. These models must be constrained to yield useful insights, despite the many likely sources of uncertainty in their predictions. Chief among these uncertainties are the impacts of the increasing role of human intervention in the global water cycle – a defining challenge for hydrology in the Anthropocene. Here we present a research agenda that proposes a suite of strategies to address these challenges from the perspectives of hydrologic science research. The research agenda focuses on the development of co-evolutionary hydrologic modeling to explore coupling across systems, and to address the implications of this coupling on the long-time behavior of the coupled systems. Three research directions support the development of these models: hydrologic reconstruction, comparative hydrology and model-data learning. These strategies focus on understanding hydrologic processes and feedbacks over long timescales, across many locations, and through strategic coupling of observational and model data in specific systems. We highlight the value of use-inspired and team-based science that is motivated by real-world hydrologic problems but targets improvements in fundamental understanding to support decision-making and management. Fully realizing the potential of this approach will ultimately require detailed integration of social science and physical science understanding of water systems, and is a priority for the developing field of sociohydrology.

Competitive and Cooperative Decision-Making Groups

1980 ◽  
Vol 7 (4) ◽  
pp. 457-478 ◽  
Author(s):  
Dorothy Lenk Krueger
Keyword(s):  
Decision Making ◽  
Sequential Analysis ◽  
Group Interaction ◽  
Communication Patterns ◽  
Sample Group ◽  
Lag Sequential Analysis ◽  
Patterns Of Communication ◽  
Two Measures ◽  
Cooperative Decision Making

This study investigates differences among four decision-making groups and describes the patterns of communication unique to two groups. In the first part of the investigation, four decision-making groups are given either competitive or cooperative inducements and are compared on two measures: competition and satisfaction. The two groups given the competitive inducement (Groups I and III) were found to have significantly higher competition and lower satisfaction than the groups given cooperative inducements (Groups II and IV). In the second part of the study a lag sequential analysis is conducted on the coded communicative sequences in the highest and lowest competition groups (I and II, respectively). This analysis yields patterns to decision-making unique to each sample group. Group I's communication is characterized by highly probable (above-chance) sequences of disagreement messages and few probable agreement messages. Group II's communication patterns consist of highly probable sequences of decision development and probable agreement/support messages throughout the group interaction.

Causal evidence for a domain-specific role of left superior frontal sulcus in human perceptual decision-making

2021 ◽  
Author(s):  
Miguel Barretto Garcia ◽  
Marcus Grueschow ◽  
Marius Moisa ◽  
Rafael Polania ◽  
Christian Carl Ruff
Keyword(s):  
Decision Making ◽  
Sequential Sampling ◽  
Computational Modelling ◽  
Perceptual Decision Making ◽  
Domain Specific ◽  
Neural Processes ◽  
Sequential Sampling Model ◽  
Evidence Integration ◽  
Superior Frontal Sulcus

Humans and animals can flexibly choose their actions based on different information, ranging from objective states of the environment (e.g., apples are bigger than cherries) to subjective preferences (e.g., cherries are tastier than apples). Whether the brain instantiates these different choices by recruiting either specialized or shared neural circuitry remains debated. Specifically, domain-general theories of prefrontal cortex (PFC) function propose that prefrontal areas flexibly process either perceptual or value-based evidence depending on what is required for the present choice, whereas domain-specific theories posit that PFC sub- areas, such as the left superior frontal sulcus (SFS), selectively integrate evidence relevant for perceptual decisions. Here we comprehensively test the functional role of the left SFS for choices based on perceptual and value-based evidence, by combining fMRI with a behavioural paradigm, computational modelling, and transcranial magnetic stimulation. Confirming predictions by a sequential sampling model, we show that TMS-induced excitability reduction of the left SFS selectively changes the processing of decision-relevant perceptual information and associated neural processes. In contrast, value-based decision making and associated neural processes remain unaffected. This specificity of SFS function is evident at all levels of analysis (behavioural, computational, and neural, including functional connectivity), demonstrating that the left SFS causally contributes to evidence integration for  perceptual but not value-based decisions.

scholarly journals Robust parallel decision-making in neural circuits with nonlinear inhibition

2020 ◽  
Vol 117 (41) ◽  
pp. 25505-25516
Author(s):  
Birgit Kriener ◽  
Rishidev Chaudhuri ◽  
Ila R. Fiete
Keyword(s):  
Decision Making ◽  
Neural Circuits ◽  
Fine Tuning ◽  
Neural Computing ◽  
Hick’S Law ◽  
Noisy Input ◽  
Finite Memory ◽  
Winner Take All ◽  
The Brain ◽  
Take All

An elemental computation in the brain is to identify the best in a set of options and report its value. It is required for inference, decision-making, optimization, action selection, consensus, and foraging. Neural computing is considered powerful because of its parallelism; however, it is unclear whether neurons can perform this max-finding operation in a way that improves upon the prohibitively slow optimal serial max-finding computation (which takes∼N⁡log(N)time for N noisy candidate options) by a factor of N, the benchmark for parallel computation. Biologically plausible architectures for this task are winner-take-all (WTA) networks, where individual neurons inhibit each other so only those with the largest input remain active. We show that conventional WTA networks fail the parallelism benchmark and, worse, in the presence of noise, altogether fail to produce a winner when N is large. We introduce the nWTA network, in which neurons are equipped with a second nonlinearity that prevents weakly active neurons from contributing inhibition. Without parameter fine-tuning or rescaling as N varies, the nWTA network achieves the parallelism benchmark. The network reproduces experimentally observed phenomena like Hick’s law without needing an additional readout stage or adaptive N-dependent thresholds. Our work bridges scales by linking cellular nonlinearities to circuit-level decision-making, establishes that distributed computation saturating the parallelism benchmark is possible in networks of noisy, finite-memory neurons, and shows that Hick’s law may be a symptom of near-optimal parallel decision-making with noisy input.

Are we really better together? A bottom-up aggregation of communal orientation and its effect on interdependent decision-making

2020 ◽  
pp. 136843022093041
Author(s):  
Bret Sanner ◽  
Hassan Ziauddin ◽  
Eileen Chou
Keyword(s):  
Decision Making ◽  
Bottom Up ◽  
Decision Performance ◽  
Relationship Orientation ◽  
Interdependence Theory ◽  
Communal Orientation ◽  
Positive Effect ◽  
Prohibitive Voice

Though communal orientation impacts how people interact, and members’ interactions influence interdependent decision-making, communal orientation’s impact on interdependent decision-making has received little attention. We address this by applying interdependence theory to take a bottom-up approach across three studies. We find that individuals who are higher on communal orientation are less likely to use prohibitive voice. We also show that dyadic communal orientation harms interdependent decision performance by lowering the amount of prohibitive voice used. At the team level, we find that team communal orientation is negatively related to interdependent decision performance unless the team is also high on relationship orientation diversity, which has a positive effect on interdependent decision performance. Combined, these studies contribute to the communal orientation literature by extending it to an important context—interdependent decision-making—and helping it be more balanced by demonstrating communal orientation’s downside.

scholarly journals Sampling for IPM Decision Making: Where Should We Invest Time and Resources?

1999 ◽  
Vol 89 (11) ◽  
pp. 1104-1111 ◽  
Author(s):  
Jan P. Nyrop ◽  
Michael R. Binns ◽  
Wopke van der Werf
Keyword(s):  
Decision Making ◽  
Sample Size ◽  
Operating Characteristic ◽  
Crop Protection ◽  
Critical Density ◽  
Sampling Distribution ◽  
Fine Tuning ◽  
Sampling Distributions ◽  
Sample Plan

Guides for making crop protection decisions based on assessments of pest abundance or incidence are cornerstones of many integrated pest management systems. Much research has been devoted to developing sample plans for use in these guides. The development of sampling plans has usually focused on collecting information on the sampling distribution of the pest, describing this sampling distribution with a mathematical model, formulating a sample plan, and sometimes, but not always, evaluating the performance of the proposed sample plan. For crop protection decision making, classification of density or incidence is usually more appropriate than estimation. When classification is done, the average outcome of classification (the operating characteristic) is frequently robust to large changes in the sampling distribution, including estimates of the variance of pest counts, and to sample size. In contrast, the critical density, or critical incidence, about which classifications are made, has a large influence on the operating characteristic. We suggest that rather than investing resources in elaborate descriptions of sampling distributions, or in fine-tuning sample size to achieve desired levels of precision, greater emphasis should be placed on characterizing pest densities that signal the need for management action and on designing decision guides that will be adopted by practitioners.

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