scholarly journals A Bayesian Reformulation of the Extended Drift-Diffusion Model in Perceptual Decision Making

2017 ◽  
Vol 11 ◽  
Author(s):  
Pouyan R. Fard ◽  
Hame Park ◽  
Andrej Warkentin ◽  
Stefan J. Kiebel ◽  
Sebastian Bitzer
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision

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.

scholarly journals Good Me Bad Me: Prioritization of the Good-Self During Perceptual Decision-Making

2019 ◽  
Author(s):  
Chuan-Peng Hu ◽  
Yuxuan Lan ◽  
Neil Macrae ◽  
Jie Sui
Keyword(s):  
Decision Making ◽  
The Self ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Association Task ◽  
Identity Based ◽  
Core Identity ◽  
Shape Categorization

People display systematic priorities to self-related stimuli. As the self is not a unified entity, however, it remains unclear which aspects of the self are crucial to producing this stimulus prioritization. To explore this issue, we manipulated the valence of the self-concept (good me vs. bad me) — a core identity-based facet of the self — using a standard shape-label association task in which participants initially learned the associations (e.g., circle/good-self, triangle/good-other, diamond/bad-self, square/bad-other), after which they completed shape-label matching and shape-categorization tasks, such that attention was directed to different aspects of the stimuli (i.e., self-relevance and valence). The results revealed that responses were more efficient to the good-self shape (vs. other shapes), regardless of the task that was undertaken. A hierarchical drift diffusion model (HDDM) analysis indicated that this good-self prioritization effect was underpinned by differences in the rate of information uptake. These findings demonstrate that activation of the good-self representation exclusively facilitates perceptual decision-making, thereby furthering understanding of the self-prioritization effect.

scholarly journals Good Me Bad Me: Prioritization of the Good-Self During Perceptual Decision-Making

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Chuan-Peng Hu ◽  
Yuxuan Lan ◽  
C. Neil Macrae ◽  
Jie Sui
Keyword(s):  
Decision Making ◽  
The Self ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Association Task ◽  
Identity Based ◽  
Core Identity ◽  
Shape Categorization

People display systematic priorities to self-related stimuli. As the self is not a unified entity, however, it remains unclear which aspects of the self are crucial to producing this stimulus prioritization. To explore this issue, we manipulated the valence of the self-concept (good me vs. bad me) — a core identity-based facet of the self — using a standard shape-label association task in which participants initially learned the associations (e.g., circle/good-self, triangle/good-other, diamond/bad-self, square/bad-other), after which they completed shape-label matching and shape-categorization tasks, such that attention was directed to different aspects of the stimuli (i.e., self-relevance and valence). The results revealed that responses were more efficient to the good-self shape (vs. other shapes), regardless of the task that was undertaken. A hierarchical drift diffusion model (HDDM) analysis indicated that this good-self prioritization effect was underpinned by differences in the rate of information uptake. These findings demonstrate that activation of the good-self representation exclusively facilitates perceptual decision-making, thereby furthering understanding of the self-prioritization effect.

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 Flexible categorization in perceptual decision making

2020 ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Large Body ◽  
Network Models ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Winner Take All ◽  
The Brain

AbstractPerceptual decisions require the brain to make categorical choices based on accumulated sensory evidence. The underlying computations have been studied using either phenomenological drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both classes of models can account for a large body of experimental data, it remains unclear to what extent their dynamics are qualitatively equivalent. Here we show that, unlike the drift diffusion model, the attractor model can operate in different integration regimes: an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision-states leading to a crossover between weighting mostly early evidence (primacy regime) to weighting late evidence (recency regime). Between these two limiting cases, we found a novel regime, which we name flexible categorization, in which fluctuations are strong enough to reverse initial categorizations, but only if they are incorrect. This asymmetry in the reversing probability results in a non-monotonic psychometric curve, a novel and distinctive feature of the attractor model. Finally, we show psychophysical evidence for the crossover between integration regimes predicted by the attractor model and for the relevance of this new regime. Our findings point to correcting transitions as an important yet overlooked feature of perceptual decision making.

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 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.

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