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 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 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 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 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 Paradoxical relationship between speed and accuracy in olfactory figure-background segregation

2021 ◽  
Author(s):  
Lior Lebovich ◽  
Michael Yunerman ◽  
Viviana Scaiewicz ◽  
Yonatan Loewenstein ◽  
Dan Rokni
Keyword(s):  
Decision Making ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Sensory Stimuli ◽  
Decision Accuracy ◽  
Detection And Identification ◽  
Sensory Modalities ◽  
Alternative Choice ◽  
Natural Settings ◽  
Speed And Accuracy

AbstractIn natural settings, many stimuli impinge on our sensory organs simultaneously. Parsing these sensory stimuli into perceptual objects is a fundamental task faced by all sensory systems. Similar to other sensory modalities, increased odor backgrounds decrease the detectability of target odors by the olfactory system. The mechanisms by which background odors interfere with the detection and identification of target odors are unknown. Here we utilized the framework of the Drift Diffusion Model (DDM) to consider possible interference mechanisms in an odor detection task. We consider effects of background odors on both signal and noise in the decision-making dynamics, and show that these produce different predictions about decision accuracy and speed. To test these predictions, we trained mice to detect target odors that are embedded in random background mixtures in a two-alternative choice task. We found that the behavioral data is most consistent with background odors acting by adding noise to the decision-making dynamics. The added noise decreases the correct rate, but also decreases decision times, thereby creating a paradoxical relationship between speed and accuracy of target detection, where mice make faster and less accurate decisions in the presence of background odors.

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 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.

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