scholarly journals Latent brain state dynamics distinguish behavioral variability, impaired decision-making, and inattention

2021 ◽  
Author(s):  
Weidong Cai ◽  
Stacie L. Warren ◽  
Katherine Duberg ◽  
Bruce Pennington ◽  
Stephen P. Hinshaw ◽  
...  
Keyword(s):  
Decision Making ◽  
Functional Connectivity ◽  
Learning Algorithm ◽  
Response Variability ◽  
Brain State ◽  
Behavioral Variability ◽  
Individual Response ◽  
Decision Threshold ◽  
Default Mode ◽  
Evidence Accumulation

AbstractChildren with Attention Deficit Hyperactivity Disorder (ADHD) have prominent deficits in sustained attention that manifest as elevated intra-individual response variability and poor decision-making. Influential neurocognitive models have linked attentional fluctuations to aberrant brain dynamics, but these models have not been tested with computationally rigorous procedures. Here we use a Research Domain Criteria approach, drift-diffusion modeling of behavior, and a novel Bayesian Switching Dynamic System unsupervised learning algorithm, with ultrafast temporal resolution (490 ms) whole-brain task-fMRI data, to investigate latent brain state dynamics of salience, frontoparietal, and default mode networks and their relation to response variability, latent decision-making processes, and inattention. Our analyses revealed that occurrence of a task-optimal latent brain state predicted decreased intra-individual response variability and increased evidence accumulation related to decision-making. In contrast, occurrence and dwell time of a non-optimal latent brain state predicted inattention symptoms and furthermore, in a categorical analysis, distinguished children with ADHD from controls. Importantly, functional connectivity between salience and frontoparietal networks predicted rate of evidence accumulation to a decision threshold, whereas functional connectivity between salience and default mode networks predicted inattention. Taken together, our computational modeling reveals dissociable latent brain state features underlying response variability, impaired decision-making, and inattentional symptoms common to ADHD. Our findings provide novel insights into the neurobiology of attention deficits in children.

scholarly journals Foraging as an evidence accumulation process

2018 ◽  
Author(s):  
Jacob D. Davidson ◽  
Ahmed El Hady
Keyword(s):  
Decision Making ◽  
Diffusion Processes ◽  
Optimal Strategies ◽  
Theoretical Work ◽  
Decision Threshold ◽  
Residence Times ◽  
Marginal Value ◽  
Evidence Accumulation ◽  
Current Resource ◽  
Noisy Measurements

AbstractA canonical foraging task is the patch-leaving problem, in which a forager must decide to leave a current resource in search for another. Theoretical work has derived optimal strategies for when to leave a patch, and experiments have tested for conditions where animals do or do not follow an optimal strategy. Nevertheless, models of patch-leaving decisions do not consider the imperfect and noisy sampling process through which an animal gathers information, and how this process is constrained by neurobiological mechanisms. In this theoretical study, we formulate an evidence accumulation model of patch-leaving decisions where the animal averages over noisy measurements to estimate the state of the current patch and the overall environment. Evidence accumulation models belong to the class of drift diffusion processes and have been used to model decision making in different contexts especially in cognitive and systems neuroscience. We solve the model for conditions where foraging decisions are optimal and equivalent to the marginal value theorem, and perform simulations to analyze deviations from optimal when these conditions are not met. By adjusting the drift rate and decision threshold, the model can represent different “strategies”, for example an increment-decrement or counting strategy. These strategies yield identical decisions in the limiting case but differ in how patch residence times adapt when the foraging environment is uncertain. To account for sub-optimal decisions, we introduce an energy-dependent utility function that predicts longer than optimal patch residence times when food is plentiful. Our model provides a quantitative connection between ecological models of foraging behavior and evidence accumulation models of decision making. Moreover, it provides a theoretical framework for potential experiments which seek to identify neural circuits underlying patch leaving decisions.

scholarly journals Evidence or Confidence: What Really Accumulates During a Decision?

2021 ◽  
Author(s):  
Douglas G. Lee ◽  
Giovanni Pezzulo
Keyword(s):  
Decision Making ◽  
Empirical Evidence ◽  
Alternative Hypothesis ◽  
Decision Threshold ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Evidence Accumulation ◽  
Confidence Assessment ◽  
Standard Models ◽  
Post Hoc

Assessing one's confidence in one's choices is of paramount importance to making adaptive decisions, and it is thus no surprise that humans excel in this ability. However, standard models of decision-making, such as the drift-diffusion model (DDM), treat confidence assessment as a post-hoc or parallel process that does not directly influence the choice -- the latter depends only on accumulated evidence. Here, we pursue the alternative hypothesis that what is accumulated during a decision is confidence (that the to-be selected option is the best) rather than raw evidence. Accumulating confidence has the appealing consequence that the decision threshold corresponds to a desired level of confidence for the choice, and that confidence improvements can be traded off against the resources required to secure them. We show that most previous findings on perceptual and value-based decisions traditionally interpreted from an evidence-accumulation perspective can be explained more parsimoniously from our novel confidence-driven perspective. Furthermore, we show that our novel confidence-driven DDM (cDDM) naturally generalizes to any number of decisions -- which is notoriously extemporaneous using traditional DDM or related models. Finally, we discuss future empirical evidence that could be useful in adjudicating between these alternatives.

scholarly journals A race between Proactive and Reactive processes during perceptual decisions

2020 ◽  
Author(s):  
Lluís Hernández-Navarro ◽  
Ainhoa Hermoso-Mendizabal ◽  
Daniel Duque ◽  
Jaime de la Rocha ◽  
Alexandre Hyafil
Keyword(s):  
Decision Making ◽  
Reaction Time ◽  
Stimulus Presentation ◽  
Auditory Task ◽  
Decision Threshold ◽  
Perceptual Decision Making ◽  
Evidence Accumulation ◽  
Standard Models ◽  
Reactive Processes ◽  
Action Initiation

Standard models of perceptual decision-making postulate that a response is triggered in reaction to stimulus presentation when the accumulated stimulus evidence reaches a decision threshold. This framework excludes however the possibility that informed responses are generated proactively at a time independent of stimulus. Here, we find that, in a free reaction time auditory task in rats, reactive and proactive responses coexist, suggesting that choice selection and motor initiation, commonly viewed as serial processes, are decoupled in general. We capture this behavior by a novel model in which proactive and reactive responses are triggered whenever either of two competing processes, respectively Action Initiation or Evidence Accumulation, reaches a bound. In both types of response, the choice is ultimately informed by the Evidence Accumulation process. By including the Action Initiation process, the model readily explains premature responses, urgency effects at long reaction times and the slowing of the responses as animals get satiated and tired during sessions. Moreover, it successfully predicts reaction time distributions when the stimulus was either delayed, advanced or omitted. Overall, these results fundamentally extend standard models of evidence accumulation in decision making by showing that proactive and reactive processes compete for the generation of responses.

scholarly journals Bottom-up sensory processing can decrease activity and functional connectivity in the default mode like network in rats

2018 ◽  
Author(s):  
Rukun Hinz ◽  
Lore M. B. Peeters ◽  
Disha Shah ◽  
Stephan Missault ◽  
Michaël Belloy ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Sensory Processing ◽  
Large Scale ◽  
Visual Stimulation ◽  
Block Design ◽  
Brain State ◽  
Top Down ◽  
Similar Reduction ◽  
Bottom Up ◽  
Default Mode

AbstractThe default mode network is a large-scale brain network that is active during rest and internally focused states and deactivates as well as desynchronizes during externally oriented (top-down) attention demanding cognitive tasks. However, it is not sufficiently understood if unpredicted salient stimuli, able to trigger bottom-up attentional processes, could also result in similar reduction of activity and functional connectivity in the DMN. In this study, we investigated whether bottom-up sensory processing could influence the default mode like network (DMLN) in rats. DMLN activity was examined using block-design visual functional magnetic resonance imaging (fMRI) while its synchronization was investigated by comparing functional connectivity during a resting versus a continuously stimulated brain state by unpredicted light flashes. We demonstrated that activity in DMLN regions was decreased during visual stimulus blocks and increased during blanks. Furthermore, decreased inter-network functional connectivity between the DMLN and visual networks as well as decreased intra-network functional connectivity within the DMLN was observed during the continuous visual stimulation. These results suggest that triggering of bottom-up attention mechanisms in anesthetized rats can lead to a cascade similar to top-down orienting of attention in humans and is able to deactivate and desynchronize the DMLN.

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 Think fast! The implications of emphasizing urgency in decision-making

2020 ◽  
Author(s):  
Nathan J. Evans
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Drift Rate ◽  
Decision Time ◽  
Decision Making Process ◽  
Data Sets ◽  
Decision Threshold ◽  
Evidence Accumulation ◽  
Rate Decrease ◽  
Linear Ballistic Accumulator

Evidence accumulation models (EAMs) have become the dominant explanation of how the decision-making process operates, proposing that decisions are the result of a process of evidence accumulation. The primary use of EAMs has been as "measurement tools" of the underlying decision-making process, where researchers apply EAMs to empirical data to estimate participants' task ability (i.e., the "drift rate"), response caution (i.e., the "decision threshold"), and the time taken for other processes (i.e., the "non-decision time"), making EAMs a powerful tool for discriminating between competing psychological theories. Recent studies have brought into question the mapping between the latent parameters of EAMs and the theoretical constructs that they are thought to represent, showing that emphasizing urgent responding -- which intuitively should selectively influence decision threshold -- may also influence drift rate and/or non-decision time. However, these findings have been mixed, leading to differences in opinion between experts in the field. The current study aims to provide a more conclusive answer to the implications of emphasizing urgent responding, providing a re-analyse of 6 data sets from previous studies using two different EAMs -- the diffusion model and the linear ballistic accumulator (LBA) -- with state-of-the-art methods for model selection based inference. The findings display clear evidence for a difference in conclusions between the two models, with the diffusion model suggesting that decision threshold and non-decision time decrease when urgency is emphasized, and the LBA suggesting that decision threshold and drift rate decrease when urgency is emphasized. Furthermore, although these models disagree regarding whether non-decision time or drift rate decrease under urgency emphasis, both show clear evidence that emphasizing urgency does not selectively influence decision threshold. These findings suggest that researchers should revise their assumptions about certain experimental manipulations, the specification of certain EAMs, or perhaps both.

scholarly journals Proactive and reactive accumulation-to-bound processes compete during perceptual decisions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lluís Hernández-Navarro ◽  
Ainhoa Hermoso-Mendizabal ◽  
Daniel Duque ◽  
Jaime de la Rocha ◽  
Alexandre Hyafil
Keyword(s):  
Decision Making ◽  
Reaction Time ◽  
Reaction Times ◽  
Stimulus Presentation ◽  
Auditory Task ◽  
Decision Threshold ◽  
Perceptual Decision Making ◽  
Evidence Accumulation ◽  
Standard Models ◽  
Action Initiation

AbstractStandard models of perceptual decision-making postulate that a response is triggered in reaction to stimulus presentation when the accumulated stimulus evidence reaches a decision threshold. This framework excludes however the possibility that informed responses are generated proactively at a time independent of stimulus. Here, we find that, in a free reaction time auditory task in rats, reactive and proactive responses coexist, suggesting that choice selection and motor initiation, commonly viewed as serial processes, are decoupled in general. We capture this behavior by a novel model in which proactive and reactive responses are triggered whenever either of two competing processes, respectively Action Initiation or Evidence Accumulation, reaches a bound. In both types of response, the choice is ultimately informed by the Evidence Accumulation process. The Action Initiation process readily explains premature responses, contributes to urgency effects at long reaction times and mediates the slowing of the responses as animals get satiated and tired during sessions. Moreover, it successfully predicts reaction time distributions when the stimulus was either delayed, advanced or omitted. Overall, these results fundamentally extend standard models of evidence accumulation in decision making by showing that proactive and reactive processes compete for the generation of responses.

scholarly journals Long-term stress and trait anxiety alter brain network balance in dynamic decisions during working memory

2021 ◽  
Author(s):  
Liangying Liu ◽  
Jianhu Wu ◽  
Haiyang Geng ◽  
Chao Liu ◽  
Yuejia Luo ◽  
...  
Keyword(s):  
Working Memory ◽  
Trait Anxiety ◽  
Default Mode Network ◽  
Brain Network ◽  
Decision Threshold ◽  
Default Mode ◽  
Evidence Accumulation ◽  
Functional Brain Network ◽  
Dynamic Decisions

Long-term stress has a profound impact on the human brain and cognition, and trait anxiety influences stress-induced adaptive and maladaptive effects. However, the neurocognitive mechanisms underlying long-term stress and trait anxiety interactions remain elusive. Here we investigated how long-term stress and trait anxiety interact to affect dynamic decisions during working-memory (WM) by altering functional brain network balance. In comparison to controls, male participants under long-term stress experienced higher psychological distress and exhibited faster evidence accumulation but had a lower decision-threshold during WM. This corresponded with hyper-activation in the anterior insula, less WM-related deactivation in the default-mode network, and stronger default-mode network decoupling with the frontoparietal network. Critically, high trait anxiety under long-term stress led to slower evidence accumulation through higher WM-related frontoparietal activity, and increased decoupling between the default-mode and frontoparietal networks. Our findings provide neurocognitive evidence for long-term stress and trait anxiety interactions on executive functions with (mal)adaptive changes.

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