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 Computational modeling of the N-Back task in the ABCD study: associations of drift diffusion model parameters to polygenic scores of mental disorders and cardiometabolic diseases

2021 ◽  
Author(s):  
Mads Lund Pedersen ◽  
Dag Alnæs ◽  
Dennis van der Meer ◽  
Sara Fernandez ◽  
Pierre Berthet ◽  
...  
Keyword(s):  
Decision Making ◽  
Working Memory ◽  
Mental Disorders ◽  
Computational Modeling ◽  
Decision Time ◽  
Decision Threshold ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Cardiometabolic Diseases ◽  
Back Task

Background. Cognitive dysfunction is common in mental disorders and represents a potential risk factor in childhood. The nature and extent of associations between childhood cognitive function and polygenic risk for mental disorders is unclear. We applied computational modeling to gain insight into mechanistic processes underlying decision making and working memory in childhood and their associations with PRS for mental disorders and comorbid cardiometabolic diseases. Methods. We used the drift diffusion model to infer latent computational processes underlying decision-making and working memory during the N-back task in 3707 children aged 9-10 from the ABCD Study. SNP-based heritability was estimated for cognitive phenotypes, including computational parameters, aggregated N-back task performance and neurocognitive assessments. PRS was calculated for Alzheimer’s disease (AD), bipolar disorder, coronary artery disease (CAD), major depressive disorder, obsessive-compulsive disorder, schizophrenia and type 2 diabetes. Results. Heritability estimates of cognitive phenotypes ranged from 12 to 39%. Bayesian mixed models revealed that slower accumulation of evidence was associated with higher PRS for CAD and schizophrenia. Longer non-decision time was associated with higher PRS for AD and lower PRS for CAD. Narrower decision threshold was associated with higher PRS for CAD. Load-dependent effects on non-decision time and decision threshold were associated with PRS for AD and CAD, respectively. Aggregated neurocognitive test scores were not associated with PRS for any of the mental or cardiometabolic phenotypes.Conclusions. We identified distinct associations between computational cognitive processes to genetic risk for mental illness and cardiometabolic disease, which could represent childhood cognitive risk factors.

scholarly journals Reward-driven changes in striatal pathway competition shape evidence evaluation in decision-making

2018 ◽  
Author(s):  
Kyle Dunovan ◽  
Catalina Vich ◽  
Matthew Clapp ◽  
Timothy Verstynen ◽  
Jonathan Rubin
Keyword(s):  
Decision Making ◽  
Basal Ganglia ◽  
Diffusion Model ◽  
Process Models ◽  
Decision Task ◽  
Drift Diffusion Model ◽  
Indirect Pathway ◽  
Drift Diffusion ◽  
Pathway Activity ◽  
Evidence Accumulation

AbstractCortico-basal-ganglia-thalamic (CBGT) networks are critical for adaptive decision-making, yet how changes to circuit-level properties impact cognitive algorithms remains unclear. Here we explore how dopaminergic plasticity at corticostriatal synapses alters competition between striatal pathways, impacting the evidence accumulation process during decision-making. Spike-timing dependent plasticity simulations showed that dopaminergic feedback based on rewards modified the ratio of direct and indirect corticostriatal weights within opposing action channels. Using the learned weight ratios in a full spiking CBGT network model, we simulated neural dynamics and decision outcomes in a reward-driven decision task and fit them with a drift diffusion model. Fits revealed that the rate of evidence accumulation varied with inter-channel differences in direct pathway activity while boundary height varied with overall indirect pathway activity. This multi-level modeling approach demonstrates how complementary learning and decision computations can emerge from corticostriatal plasticity.Author summaryCognitive process models such as reinforcement learning (RL) and the drift diffusion model (DDM) have helped to elucidate the basic algorithms underlying error-corrective learning and the evaluation of accumulating decision evidence leading up to a choice. While these relatively abstract models help to guide experimental and theoretical probes into associated phenomena, they remain uninformative about the actual physical mechanics by which learning and decision algorithms are carried out in a neurobiological substrate during adaptive choice behavior. Here we present an “upwards mapping” approach to bridging neural and cognitive models of value-based decision-making, showing how dopaminergic feedback alters the network-level dynamics of cortico-basal-ganglia-thalamic (CBGT) pathways during learning to bias behavioral choice towards more rewarding actions. By mapping “up” the levels of analysis, this approach yields specific predictions about aspects of neuronal activity that map to the quantities appearing in the cognitive decision-making framework.

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 Optimizing sequential decisions in the drift-diffusion model

2018 ◽  
Author(s):  
Khanh P. Nguyen ◽  
Krešimir Josić ◽  
Zachary P. Kilpatrick
Keyword(s):  
Decision Making ◽  
Ideal Observer ◽  
Natural Environments ◽  
Drift Diffusion Model ◽  
Sequential Decisions ◽  
Drift Diffusion ◽  
Evidence Accumulation ◽  
Temporal Correlations ◽  
Finite Sequences ◽  
Decision Times

AbstractTo make decisions organisms often accumulate information across multiple timescales. However, most experimental and modeling studies of decision-making focus on sequences of independent trials. On the other hand, natural environments are characterized by long temporal correlations, and evidence used to make a present choice is often relevant to future decisions. To understand decision-making under these conditions we analyze how a model ideal observer accumulates evidence to freely make choices across a sequence of correlated trials. We use principles of probabilistic inference to show that an ideal observer incorporates information obtained on one trial as an initial bias on the next. This bias decreases the time, but not the accuracy of the next decision. Furthermore, in finite sequences of trials the rate of reward is maximized when the observer deliberates longer for early decisions, but responds more quickly towards the end of the sequence. Our model also explains experimentally observed patterns in decision times and choices, thus providing a mathematically principled foundation for evidence-accumulation models of sequential decisions.

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 The Leaky Integrating Threshold and its impact on evidence accumulation models of choice RT

2020 ◽  
Author(s):  
Stijn Verdonck ◽  
Tim Loossens ◽  
Marios G. Philiastides
Keyword(s):  
Diffusion Model ◽  
Motor Preparation ◽  
Difficulty Level ◽  
Choice Response ◽  
Decision Threshold ◽  
Actual Process ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Evidence Accumulation ◽  
Speed Accuracy

A common assumption in choice response time modelling is that after evidence accumulation reaches a certain decision threshold, the choice is categorically communicated to the motor system that then executes the response. However, neurophysiological findings suggest that motor preparation partly overlaps with evidence accumulation, and is not independent from stimulus difficulty level. We propose to model this entanglement by changing the nature of the decision criterion from a simple threshold to an actual process. More specifically, we propose a secondary, motor preparation related, leaky accumulation process that takes the accumulated evidence of the original decision process as a continuous input, and triggers the actual response when it reaches its own threshold. We analytically develop this Leaky Integrating Threshold (LIT), applying it to a simple constant drift diffusion model, and show how its parameters can be estimated with the D*M method. Reanalyzing three different datasets, the LIT extension is shown to outperform a standard drift diffusion model using multiple statistical approaches. Further, the LIT leak parameter is shown to be better at explaining the speed/accuracy trade-off manipulation than the commonly used boundary separation parameter. These improvements can also be verified using traditional diffusion model analyses, for which the LIT predicts the violation of several common selective parameter influence assumptions. These predictions are consistent with what is found in the data and with what is reported experimentally in the literature. Crucially, this work offers a new benchmark against which to compare neural data to offer neurobiological validation for the proposed processes.

scholarly journals Flexible categorization in perceptual decision making

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Network Models ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Sensory Evidence ◽  
Winner Take All

AbstractPerceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.

Methodological issues with value-based decision-making (VBDM) tasks: does trial wording matter?

2021 ◽  
Author(s):  
Amber Copeland ◽  
Tom Stafford ◽  
Matt Field
Keyword(s):  
Decision Making ◽  
Drift Diffusion Model ◽  
Choice Data ◽  
Methodological Issues ◽  
Drift Diffusion ◽  
Initial Validation ◽  
Main Effect ◽  
Value Judgements ◽  
Forced Choice Task ◽  
Positive Correlations

Objective: Most value-based decision-making (VBDM) tasks instruct people to make value judgements about stimuli using wording relating to consumption, however in some contexts this may be inappropriate. This study aims to explore whether variations of trial wording capture a common construct of value. Method: Pre-registered, within-subject design. Fifty-nine participants completed a two-alternative forced choice task where they chose between two food images. Participants completed three blocks of trials: one asked which they would rather consume (standard wording), one asked which image they like more, and one asked them to recall which image they rated higher during a previous block. We fitted a drift-diffusion model to the reaction time and choice data to estimate evidence accumulation (EA) processes during the different blocks. Results: There was a highly significant main effect of trial difficulty, but this was not modified by trial wording (F = 2.00, p = .11, np2 = .03, BF10 = .05). We also found highly significant positive correlations between EA rates across task blocks (rs > .44, ps < .001). Conclusions: Findings provide initial validation of substitute wording for VBDM tasks that can be used in contexts where it may be undesirable to ask participants to make consummatory judgements.

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 A flexible framework for simulating and fitting generalized drift-diffusion models

2020 ◽  
Author(s):  
Maxwell Shinn ◽  
Norman H. Lam ◽  
John D. Murray
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Cognitive Neuroscience ◽  
Software Package ◽  
Planck Equation ◽  
Experimental Designs ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Flexible Framework ◽  
Decision Making Model

AbstractThe drift-diffusion model (DDM) is an important decision-making model in cognitive neuroscience. However, innovations in model form have been limited by methodological challenges. Here, we introduce the generalized drift-diffusion model (GDDM) framework for building, simulating, and fitting DDM extensions, and provide a software package which implements the framework. The GDDM framework augments traditional DDM parameters through arbitrary user-defined functions. Models are simulated numerically by directly solving the Fokker-Planck equation using efficient numerical methods, yielding a 100-fold or greater speedup over standard methodology. This speed allows GDDMs to be fit to data using maximum likelihood on the full response time (RT) distribution. We show that a GDDM fit with our framework explains a classic open dataset with better accuracy and fewer parameters than several DDMs implemented using the latest methodology. Overall, our framework will allow for decision-making model innovation and novel experimental designs.

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