Decision-making through evidence integration at long timescales

2018 ◽  
Author(s):  
Michael Waskom ◽  
Roozbeh Kiani
Keyword(s):  
Decision Making ◽  
Long Timescales ◽  
Evidence Integration

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 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.

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 Continuous track paths reveal additive evidence integration in multistep decision making

2017 ◽  
Vol 114 (40) ◽  
pp. 10618-10623 ◽  
Author(s):  
Cristian Buc Calderon ◽  
Myrtille Dewulf ◽  
Wim Gevers ◽  
Tom Verguts
Keyword(s):  
Decision Making ◽  
Daily Life ◽  
Second Step ◽  
Reaching Task ◽  
Decision Dynamics ◽  
Probabilistic Evidence ◽  
Underlying Mechanisms ◽  
Initial Dip ◽  
Future Outcomes ◽  
Evidence Integration

Multistep decision making pervades daily life, but its underlying mechanisms remain obscure. We distinguish four prominent models of multistep decision making, namely serial stage, hierarchical evidence integration, hierarchical leaky competing accumulation (HLCA), and probabilistic evidence integration (PEI). To empirically disentangle these models, we design a two-step reward-based decision paradigm and implement it in a reaching task experiment. In a first step, participants choose between two potential upcoming choices, each associated with two rewards. In a second step, participants choose between the two rewards selected in the first step. Strikingly, as predicted by the HLCA and PEI models, the first-step decision dynamics were initially biased toward the choice representing the highest sum/mean before being redirected toward the choice representing the maximal reward (i.e., initial dip). Only HLCA and PEI predicted this initial dip, suggesting that first-step decision dynamics depend on additive integration of competing second-step choices. Our data suggest that potential future outcomes are progressively unraveled during multistep decision making.

scholarly journals Inhibition of Pre–Supplementary Motor Area by Continuous Theta Burst Stimulation Leads to More Cautious Decision-making and More Efficient Sensory Evidence Integration

2017 ◽  
Vol 29 (8) ◽  
pp. 1433-1444 ◽  
Author(s):  
Tuğçe Tosun ◽  
Dilara Berkay ◽  
Alexander T. Sack ◽  
Yusuf Ö. Çakmak ◽  
Fuat Balcı
Keyword(s):  
Decision Making ◽  
Decision Threshold ◽  
Theta Burst Stimulation ◽  
Burst Stimulation ◽  
Sensory Evidence ◽  
Speed Accuracy ◽  
Theta Burst ◽  
Continuous Theta Burst Stimulation ◽  
Accuracy Tradeoff ◽  
Evidence Integration

Decisions are made based on the integration of available evidence. The noise in evidence accumulation leads to a particular speed–accuracy tradeoff in decision-making, which can be modulated and optimized by adaptive decision threshold setting. Given the effect of pre-SMA activity on striatal excitability, we hypothesized that the inhibition of pre-SMA would lead to higher decision thresholds and an increased accuracy bias. We used offline continuous theta burst stimulation to assess the effect of transient inhibition of the right pre-SMA on the decision processes in a free-response two-alternative forced-choice task within the drift diffusion model framework. Participants became more cautious and set higher decision thresholds following right pre-SMA inhibition compared with inhibition of the control site (vertex). Increased decision thresholds were accompanied by an accuracy bias with no effects on post-error choice behavior. Participants also exhibited higher drift rates as a result of pre-SMA inhibition compared with the vertex inhibition. These results, in line with the striatal theory of speed–accuracy tradeoff, provide evidence for the functional role of pre-SMA activity in decision threshold modulation. Our results also suggest that pre-SMA might be a part of the brain network associated with the sensory evidence integration.

scholarly journals Serial, parallel and hierarchical decision making in primates

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Ariel Zylberberg ◽  
Jeannette AM Lorteije ◽  
Brian G Ouellette ◽  
Chris I De Zeeuw ◽  
Mariano Sigman ◽  
...  
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Decision Tree ◽  
Time Course ◽  
Hierarchical Decision Making ◽  
Hierarchical Decision ◽  
Motor Actions ◽  
Evidence Integration

The study of decision-making has mainly focused on isolated decisions where choices are associated with motor actions. However, problem-solving often involves considering a hierarchy of sub-decisions. In a recent study (Lorteije et al. 2015), we reported behavioral and neuronal evidence for hierarchical decision making in a task with a small decision tree. We observed a first phase of parallel evidence integration for multiple sub-decisions, followed by a phase in which the overall strategy formed. It has been suggested that a 'flat' competition between the ultimate motor actions might also explain these results. A reanalysis of the data does not support the critical predictions of flat models. We also examined the time-course of decision making in other, related tasks and report conditions where evidence integration for successive decisions is decoupled, which excludes flat models. We conclude that the flexibility of decision-making implies that the strategies are genuinely hierarchical.

scholarly journals Rat Prefrontal Cortex Inactivations during Decision Making Are Explained by Bistable Attractor Dynamics

2017 ◽  
Vol 29 (11) ◽  
pp. 2861-2886 ◽  
Author(s):  
Alex T. Piet ◽  
Jeffrey C. Erlich ◽  
Charles D. Kopec ◽  
Carlos D. Brody
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Memory Model ◽  
Network Configuration ◽  
Perceptual Decision Making ◽  
Attractor Networks ◽  
Attractor Dynamics ◽  
Flexible Models ◽  
Evidence Integration

Two-node attractor networks are flexible models for neural activity during decision making. Depending on the network configuration, these networks can model distinct aspects of decisions including evidence integration, evidence categorization, and decision memory. Here, we use attractor networks to model recent causal perturbations of the frontal orienting fields (FOF) in rat cortex during a perceptual decision-making task (Erlich, Brunton, Duan, Hanks, & Brody, 2015 ). We focus on a striking feature of the perturbation results. Pharmacological silencing of the FOF resulted in a stimulus-independent bias. We fit several models to test whether integration, categorization, or decision memory could account for this bias and found that only the memory configuration successfully accounts for it. This memory model naturally accounts for optogenetic perturbations of FOF in the same task and correctly predicts a memory-duration-dependent deficit caused by silencing FOF in a different task. Our results provide mechanistic support for a “postcategorization” memory role of the FOF in upcoming choices.

scholarly journals Differentiating between integration and non-integration strategies in perceptual decision making

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Gabriel M Stine ◽  
Ariel Zylberberg ◽  
Jochen Ditterich ◽  
Michael N Shadlen
Keyword(s):  
Decision Making ◽  
Human Subjects ◽  
Perceptual Decision Making ◽  
Integration Model ◽  
Integration Strategy ◽  
Behavioral Observations ◽  
Integration Strategies ◽  
Multiple Samples ◽  
The Brain ◽  
Evidence Integration

Many tasks used to study decision-making encourage subjects to integrate evidence over time. Such tasks are useful to understand how the brain operates on multiple samples of information over prolonged timescales, but only if subjects actually integrate evidence to form their decisions. We explored the behavioral observations that corroborate evidence-integration in a number of task-designs. Several commonly accepted signs of integration were also predicted by non-integration strategies. Furthermore, an integration model could fit data generated by non-integration models. We identified the features of non-integration models that allowed them to mimic integration and used these insights to design a motion discrimination task that disentangled the models. In human subjects performing the task, we falsified a non-integration strategy in each and confirmed prolonged integration in all but one subject. The findings illustrate the difficulty of identifying a decision-maker’s strategy and support solutions to achieve this goal.

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