Vicarious Rewards Modulate the Drift Rate of Evidence Accumulation From the Drift Diffusion Model

Background: Visual hallucinations (VH) are a common symptom in dementia with Lewy bodies (DLB); however, their cognitive underpinnings remain unclear. Hallucinations have been related to cognitive slowing in DLB and may arise due to impaired sensory input, dysregulation in top-down influences over perception, or an imbalance between the two, resulting in false visual inferences. Methods: Here we employed a drift diffusion model yielding estimates of perceptual encoding time, decision threshold, and drift rate of evidence accumulation to (i) investigate the nature of DLB-related slowing of responses and (ii) their relationship to visuospatial performance and visual hallucinations. The EZ drift diffusion model was fitted to mean reaction time (RT), accuracy and RT variance from two-choice reaction time (CRT) tasks and data were compared between groups of mild cognitive impairment (MCI-LB) LB patients (n = 49) and healthy older adults (n = 25). Results: No difference was detected in drift rate between patients and controls, but MCI-LB patients showed slower non-decision times and boundary separation values than control participants. Furthermore, non-decision time was negatively correlated with visuospatial performance in MCI-LB, and score on visual hallucinations inventory. However, only boundary separation was related to clinical incidence of visual hallucinations. Conclusions: These results suggest that a primary impairment in perceptual encoding may contribute to the visuospatial performance, however a more cautious response strategy may be related to visual hallucinations in Lewy body disease. Interestingly, MCI-LB patients showed no impairment in information processing ability, suggesting that, when perceptual encoding was successful, patients were able to normally process information, potentially explaining the variability of hallucination incidence.

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Bayesian confidence for drift diffusion observers in dynamic stimuli tasks

10.1101/2020.02.25.965384 ◽

2020 ◽

Cited By ~ 2

Author(s):

Joshua Calder-Travis ◽

Rafal Bogacz ◽

Nick Yeung

Keyword(s):

Diffusion Model ◽

Drift Rate ◽

Response Times ◽

Time Dependent ◽

Drift Diffusion Model ◽

Drift Diffusion ◽

Dynamic Stimuli ◽

Computationally Expensive ◽

Insight Into ◽

Confidence Data

AbstractMuch work has explored the possibility that the drift diffusion model, a model of response times and choices, could be extended to account for confidence reports. Many methods for making predictions from such models exist, although these methods either assume that stimuli are static over the course of a trial, or are computationally expensive, making it difficult to capitalise on trial-by-trial variability in dynamic stimuli. Using the framework of the drift diffusion model with time-dependent thresholds, and the idea of a Bayesian confidence readout, we derive expressions for the probability distribution over confidence reports. In line with current models of confidence, the derivations allow for the accumulation of “pipeline” evidence which has been received but not processed by the time of response, the effect of drift rate variability, and metacognitive noise. The expressions are valid for stimuli which change over the course of a trial with normally distributed fluctuations in the evidence they provide. A number of approximations are made to arrive at the final expressions, and we test all approximations via simulation. The derived expressions only contain a small number of standard functions, and only require evaluating once per trial, making trial-by-trial modelling of confidence data in dynamic stimuli tasks more feasible. We conclude by using the expressions to gain insight into the confidence of optimal observers, and empirically observed patterns.

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

10.1101/418756 ◽

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.

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At the Zebra Crossing: Modelling Complex Decision Processes with Variable-Drift Diffusion Models

10.31234/osf.io/cgj7r ◽

2019 ◽

Author(s):

Oscar Terence Giles ◽

Gustav Markkula ◽

Jami Pekkanen ◽

Naoki Yokota ◽

Naoto Matsunaga ◽

...

Keyword(s):

Diffusion Model ◽

Drift Rate ◽

Sensory Information ◽

Drift Diffusion Model ◽

Drift Diffusion ◽

Multimodal Distributions ◽

Complex Models ◽

Complex Decision ◽

Pedestrian Crossing ◽

Decision Tasks

Drift diffusion (or evidence accumulation) models have found widespread use in the modelling of simple decision tasks. Extensions of these models, in which the model’s instantaneous drift rate is not fixed but instead allowed to vary over time as a function of a stream of perceptual inputs, have allowed these models to account for more complex sensorimotor decision tasks. However, many real-world tasks seemingly rely on a myriad of even more complex underlying processes. One interesting example is the task of deciding whether to cross a road with an approaching vehicle. This action decision seemingly depends on sensory information both about own affordances (whether one can make it across before the vehicle) and action intention of others (whether the vehicle is yielding to oneself). Here, we compared three extensions of a standard drift diffusion model, with regards to their ability to capture timing of pedestrian crossing decisions in a virtual reality environment. We find that a single variable-drift diffusion model (S-VDDM) in which the varying drift rate is determined by visual quantities describing vehicle approach and deceleration, saturated at an upper and lower bound, can explain multimodal distributions of crossing times well across a broad range vehicle approach scenarios. More complex models, which attempt to partition the final crossing decision into constituent perceptual decisions, improve the fit to the human data but further work is needed before firm conclusions can be drawn from this finding.

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A drift diffusion model account of the semantic congruity effect in a classification paradigm

Journal of Numerical Cognition ◽

10.5964/jnc.v3i1.79 ◽

2017 ◽

Vol 3 (1) ◽

pp. 77-96 ◽

Cited By ~ 2

Author(s):

Angelo Pirrone ◽

James A. R. Marshall ◽

Tom Stafford

Keyword(s):

Diffusion Model ◽

Time Course ◽

Target Stimulus ◽

Congruity Effect ◽

Drift Diffusion Model ◽

Drift Diffusion ◽

Semantic Congruity ◽

Evidence Accumulation ◽

Model Account ◽

Semantic Congruity Effect

The semantic congruity effect refers to the facilitation of judgements (i) when the direction of the comparison of two items coincides with the relative position of the items along the dimension comparison or (ii) when the relative size of a standard and a target stimulus coincides. For example, people are faster in judging 'which is bigger?' for two large items, than judging 'which is smaller?' for two large items (selection paradigm). Also, people are faster in judging a target stimulus as smaller when compared to a small standard, than when compared to a large standard, and vice versa (classification paradigm). We use the Drift Diffusion Model (DDM) to explain the time course of a semantic congruity effect in a classification paradigm. Formal modelling of semantic congruity allows the time course of the decision process to be described, using an established model of decision making. Moreover, although there have been attempts to explain the semantic congruity effect within evidence accumulation models, two possible accounts for the congruity effect have been proposed but their specific predictions have not been compared directly, using a model that could quantitatively account for both; a shift in the starting point of evidence accumulation or a change in the rate at which evidence is accumulated. With our computational investigation we provide evidence for the latter, while controlling for other possible explanations such as a variation in non-decision time or boundary separation, that have not been taken into account in the explanation of this phenomenon.

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Face masks influence how facial expressions are perceived: A drift-diffusion model of emotion judgments

10.31234/osf.io/a8yxf ◽

2021 ◽

Author(s):

W. Craig Williams ◽

Eisha Haque ◽

Becky Mai ◽

Vinod Venkatraman

Keyword(s):

Diffusion Model ◽

Facial Expressions ◽

Drift Rate ◽

Diffusion Modeling ◽

Drift Diffusion Model ◽

Drift Diffusion ◽

Lower Face ◽

Upper Face

Face masks slow the spread of SARS-CoV-2, but it has been unknown whether masks influence how individuals communicate emotion through facial expressions. Masks could influence how accurately—or how quickly—individuals perceive expressions, and how rapidly they accumulate evidence for emotion. Over two independent pre-registered studies, conducted three and six months into the COVID-19 pandemic, participants judged expressions of 6 emotions (anger, disgust, fear, happiness, sadness, surprise) with the lower or upper face “masked” or unmasked. Participants in Study 1 (N = 228) identified expressions above chance with lower face masks. However, they were less likely—and slower—to correctly identify these expressions versus without masks, and they accumulated evidence for emotion more slowly—via decreased drift rate in drift-diffusion modeling. This pattern replicated and intensified three months later in Study 2 (N = 264). These data could inform interventions to promote mask wearing by addressing concerns with emotion communication.

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Lexical-semantic and executive deficits revealed by computational modelling: a drift diffusion model perspective

10.31234/osf.io/kdpgz ◽

2019 ◽

Author(s):

Lara Todorova ◽

David Neville ◽

Vitória Piai

Keyword(s):

Semantic Memory ◽

Diffusion Model ◽

Drift Rate ◽

Related Word ◽

Model Parameters ◽

Decision Threshold ◽

Drift Diffusion Model ◽

Drift Diffusion ◽

Lexical Semantic ◽

Two Systems

Flexible language use requires coordinated functioning of two systems: conceptual representations and control. The interaction between two systems can be observed when people are asked to match a word to a picture. Participants are slower and less accurate for related word-picture pairs (word: banana, picture: apple) relative to unrelated pairs (word: banjo, picture: apple). The mechanism underlying interference however is still unclear. We analyzed word-picture verification (WPM) performance of patients with stroke-induced lesions to the left-temporal (N = 5) or left-frontal cortex (N = 5) and matched controls (N = 12) using the drift diffusion model (DDM). In DDM the process of making a decision is described as the stochastic accumulation of evidence towards a response. The parameters of the DDM model that characterize this process are decision threshold, drift rate, starting point and non-decision time. Each of them bears cognitive interpretability and we compared the estimated model parameters from controls and patients to investigate the mechanisms of WPM interference. WPM performance in controls was explained by the amount of information needed to make a decision (decision threshold): a higher threshold was associated with related word-picture pairs relative to unrelated ones. No difference was found in the quality of the evidence (drift rate). This suggests an executive rather than semantic mechanism underlying WPM interference. Both patients with temporal and frontal lesions exhibited both increased drift rate and decision threshold for unrelated pairs relative to related ones. Left-frontal and temporal damage affected the computations required by WPM similarly, resulting in systematic deficits across lexical-semantic memory and executive functions. These results support a diverse but interactive role of lexical-semantic memory and semantic control mechanisms.

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

10.31234/osf.io/5t6yz ◽

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.

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