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.

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

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Maxwell Shinn ◽  
Norman H Lam ◽  
John D Murray
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Cognitive Neuroscience ◽  
Software Package ◽  
Planck Equation ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Flexible Framework ◽  
Decision Making Model

The 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 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 solved 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 demonstrate fitting of GDDMs within our framework to both animal and human datasets from perceptual decision-making tasks, with better accuracy and fewer parameters than several DDMs implemented using the latest methodology, to test hypothesized decision-making mechanisms. Overall, our framework will allow for decision-making model innovation and novel experimental designs.

scholarly journals The drift diffusion model as the choice rule in inter-temporal and risky choice: a case study in medial orbitofrontal cortex lesion patients and controls

2019 ◽  
Author(s):  
Jan Peters ◽  
Mark D’Esposito
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Choice Model ◽  
Sequential Sampling ◽  
Temporal Discounting ◽  
Risky Choice ◽  
Choice Rule ◽  
Model Parameters ◽  
Drift Diffusion Model ◽  
Drift Diffusion

AbstractSequential sampling models such as the drift diffusion model have a long tradition in research on perceptual decision-making, but mounting evidence suggests that these models can account for response time distributions that arise during reinforcement learning and value-based decision-making. Building on this previous work, we implemented the drift diffusion model as the choice rule in inter-temporal choice (temporal discounting) and risky choice (probability discounting) using a hierarchical Bayesian estimation scheme. We validated our approach in data from nine patients with focal lesions to the ventromedial prefrontal cortex / medial orbitofrontal cortex (vmPFC/mOFC) and nineteen age- and education-matched controls. Choice model parameters estimated via standard softmax action selection were reliably reproduced using the drift diffusion model as the choice rule, both for temporal discounting and risky choice. Model comparison revealed that, for both tasks, the data were best accounted for by a variant of the drift diffusion model including a non-linear mapping from value-differences to trial-wise drift rates. Posterior predictive checks of the winning models revealed a reasonably good fit to individual participants reaction time distributions. We then applied this modeling framework and 1) reproduced our previous results regarding temporal discounting in vmPFC/mOFC patients and 2) showed in a previously unpublished data set on risky choice that vmPFC/mOFC patients exhibit increased risk-taking relative to controls. Analyses of diffusion model parameters revealed that vmPFC/mOFC damage abolished neither value sensitivity nor asymptote of the drift rate. Rather, it substantially increased non-decision times and reduced response caution during risky choice. Our results highlight that novel insights can be gained from applying sequential sampling models in studies of inter-temporal and risky decision-making in cognitive neuroscience.

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 Motor response vigour and fixations reflect subjective preferences during intertemporal choice

2021 ◽  
Author(s):  
Elke Smith ◽  
Jan Peters
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Motor Response ◽  
Intertemporal Choice ◽  
Magnitude Effect ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Value Differences ◽  
Subjective Value ◽  
Subjective Utility

Value-based decision-making is of central interest in cognitive neuroscience and psychology, as well as in the context of neuropsychiatric disorders characterised by decision-making impairments. Studies examining (neuro-)computational mechanisms underlying choice behaviour typically focus on participants' decisions. However, there is increasing evidence that option valuation might also be reflected in motor response vigour and eye movements, implicit measures of subjective utility. To examine motor response vigour and visual fixation correlates of option valuation in intertemporal choice, we set up a task where the participants selected an option by pressing a grip force transducer, simultaneously tracking fixation shifts between options. As outlined in our preregistration (https://osf.io/k6jct), we used hierarchical Bayesian parameter estimation to model the choices assuming hyperbolic discounting, compared variants of the softmax and drift diffusion model, and assessed the relationship between response vigour and the estimated model parameters. The behavioural data were best explained by a drift diffusion model specifying a non-linear scaling of the drift rate by the subjective value differences. Replicating previous findings (Green et al., 1997; Wagner et al., 2020a), we found a magnitude effect for temporal discounting, such that higher rewards were discounted less. This magnitude effect was further reflected in response vigour, such that stronger forces were exerted in the high vs. the low magnitude condition. Bayesian hierarchical linear regression further revealed higher grip forces, faster response times and a lower number of fixation shifts for trials with higher subjective value differences. Our data suggest that subjective utility or implicit valuation is reflected in response vigour during intertemporal choice. Taking into account response vigour might thus provide deeper insight into decision-making, reward valuation and maladaptive changes in these processes, e.g. in the context of neuropsychiatric disorders.

scholarly journals Drift Diffusion Model of Animacy Categorization Task Can Detect Patients with Mild Cognitive Impairment and Mild Alzheimer's Disease

2021 ◽  
Author(s):  
Hamed Karimi ◽  
Haniye Marefat ◽  
Mahdiye Khanbagi ◽  
Alireza Karami ◽  
Zahra Vahabi
Keyword(s):  
Alzheimer’S Disease ◽  
Decision Making ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Diffusion Model ◽  
Computational Approach ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Categorization Task ◽  
Early Stages

Purpose: The process of neurodegeneration in Alzheimer's Disease (AD) is irreversible using current therapeutics. An earlier diagnosis of the disease can lead to earlier interventions, which will help patients sustain their cognitive abilities for longer. Individuals within the early stages of AD, shown to have trouble making confident and sounds decisions. Here we proposed a computational approach to quantify the decision-making ability in patients with mild cognitive impairment and mild AD. Materials and Methods: To study the quantified decision-making abilities at the early stages of the disease, we took advantage of a 2-Alternative Forced-Choice (2AFC) task. We applied the Drift Diffusion Model to determine whether the information accumulation process in a categorization task is altered in patients with mild cognitive impairment and mild AD. We implemented a classification model to detect cognitive impairment based on the Drift Diffusion Model's estimated parameters. Results: The results show a significant correlation of the classification score with the standard pen-and-paper tests, suggesting that the quantified decision-making parameters are undergoing significant change in patients with cognitive impairment. Conclusion: We confirmed that the decision-making ability deteriorates at the early stages of AD. We introduced a computational approach for measuring the decline in decision-making and used that measurement to distinguish patients from healthy individuals.

scholarly journals Decision-Making in the Human-Machine Interface

2021 ◽  
Vol 12 ◽  
Author(s):  
J. Benjamin Falandays ◽  
Samuel Spevack ◽  
Philip Pärnamets ◽  
Michael Spivey
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Human Machine Interface ◽  
Assistive Robotics ◽  
Clear Understanding ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Directional Bias ◽  
Model Simulations ◽  
Machine Interface

If our choices make us who we are, then what does that mean when these choices are made in the human-machine interface? Developing a clear understanding of how human decision making is influenced by automated systems in the environment is critical because, as human-machine interfaces and assistive robotics become even more ubiquitous in everyday life, many daily decisions will be an emergent result of the interactions between the human and the machine – not stemming solely from the human. For example, choices can be influenced by the relative locations and motor costs of the response options, as well as by the timing of the response prompts. In drift diffusion model simulations of response-prompt timing manipulations, we find that it is only relatively equibiased choices that will be successfully influenced by this kind of perturbation. However, with drift diffusion model simulations of motor cost manipulations, we find that even relatively biased choices can still show some influence of the perturbation. We report the results of a two-alternative forced-choice experiment with a computer mouse modified to have a subtle velocity bias in a pre-determined direction for each trial, inducing an increased motor cost to move the cursor away from the pre-designated target direction. With queries that have each been normed in advance to be equibiased in people’s preferences, the participant will often begin their mouse movement before their cognitive choice has been finalized, and the directional bias in the mouse velocity exerts a small but significant influence on their final choice. With queries that are not equibiased, a similar influence is observed. By exploring the synergies that are developed between humans and machines and tracking their temporal dynamics, this work aims to provide insight into our evolving decisions.

Numeral order and the operationalization of the numerical system

2021 ◽  
pp. 174702182110419
Author(s):  
David Muñez ◽  
Josetxu Orrantia ◽  
Laura Matilla ◽  
Rosario Sanchez
Keyword(s):  
Decision Making ◽  
Mathematics Achievement ◽  
Diffusion Model ◽  
Math Achievement ◽  
Core Competency ◽  
Drift Diffusion Model ◽  
Cognitive Mechanisms ◽  
Drift Diffusion ◽  
Large Numbers ◽  
Ordered Response

Recent years have witnessed an increase in research on how numeral ordering skills relate to children’s and adults’ mathematics achievement both cross-sectionally and longitudinally. Nonetheless, it remains unknown which core competency numeral ordering tasks measure, which cognitive mechanisms underlie performance on these tasks, and why numeral ordering skills relate to arithmetic and math achievement. In the current study, we focused on the processes underlying decision-making in the numeral order judgement task with triplets to investigate these questions. A drift-diffusion model for two-choice decisions was fit to data from 97 undergraduates. Findings aligned with the hypothesis that numeral ordering skills reflected the operationalization of the numerical system, where small numbers provide more evidence of an ordered response than large numbers. Furthermore, the pattern of findings suggested that arithmetic achievement was associated with the accuracy of the ordinal representations of numbers.

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