Modified leaky competing accumulator model of decision making with multiple alternatives: the Lie-algebraic approach

Lo, Chi‐Fai; Ip, Ho-Yan

doi:10.1038/s41598-021-90356-7

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…However, the discretization can be a major disadvantage of these methods, as some information about the observed data may be missed during discretizing procedure. One way to overcome this disadvantage is by instead considering a continuous approximation for the likelihood function that is accessible in some numerical algorithms like spectral (Shizgal, 2015;Hafez et al, 2015), mesh-less (Fasshauer, 2007;Kazem et al, 2012), or Lei-algebraic (Lo and Ip, 2021) methods.…”

Section: Discussionmentioning

confidence: 99%

Race Lévy flights: A mathematically tractable framework for studying heavy-tailed accumulation noise

Rasanan¹,

Evans²,

Padash³

et al. 2022

Preprint

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Levy flights is a particular exemplar of the generalised random walk processes, in which the jump lengths are drawn from a power-law asymptote ($\alpha$-stable) distribution. While employing this heavy-tailed distribution for accumulation noise within the diffusion decision model framework provides improved fitting performance over the standard Gaussian accumulation noise, the Levy flight model contains two key limitations. Specifically, the use of the diffusion framework limits the model to only being applicable to two alternative decision-making tasks, and the lack of an exact likelihood function can make fitting behavioral data a challenging and computationally costly task. This paper aims to provide a mathematically tractable framework for modeling $n$-alternative ($n\geq1$) decision-making tasks with an $\alpha$-stable distribution for the accumulation noise. We propose a race Levy flight model, with a racing architecture, making the model directly applicable to decision tasks with any number of alternatives. Moreover, with the corresponding space-fractional diffusion-advection equation for the accumulation process, a numerical scheme based on approximating of the joint probability distribution for each accumulator is provided. In addition, we fit our proposed model to two perceptual decision-making data sets, and show its improved performance compared to the standard racing diffusion model.

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Section: Discussionmentioning

confidence: 99%

Race Lévy flights: A mathematically tractable framework for studying heavy-tailed accumulation noise

Rasanan¹,

Evans²,

Padash³

et al. 2022

Preprint

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“…( 11 ) denotes the number of times thread alternative h has been chosen previously, and parameter models the effect of repeated choices of the same alternative approaching the asymptotic curve defined in [ 38 ]. Recent works have shown convergence to a decision for large number of choices in a modified LCA model [ 45 ], but their model is limited to a single agent. They show that it is possible to recover the model parameters by maximum likelihood approach, however, they refer to the reproduction of simulation traces while we deal in the next section with parameter estimation to approximate the user decision behavior extracted from the real OSN data.…”

Section: Methodsmentioning

confidence: 99%

“…Thirdly, we implement a genetic algorithm search for the ELCA model parameter calibration (aka training) using data from the content contribution decisions in a real life VCoP. The recovery of LCA parameters, stated as the induction of model parameters from simulation accumulator trajectories, has been acknowledged as an open difficult problem [ 49 ], which has been tackled by exploitation of Lie symmetries for a modified formulation of LCA equations [ 45 ]. Contrary to these approaches, we look for the optimal ELCA parameters that reproduce the actual user decisions after convergence of the simulation.…”

Section: Introductionmentioning

confidence: 99%

Neuro-semantic prediction of user decisions to contribute content to online social networks

Cleveland

Ríos

Aguilera

et al. 2022

Neural Comput & Applic

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Understanding at microscopic level the generation of contents in an online social network (OSN) is highly desirable for an improved management of the OSN and the prevention of undesirable phenomena, such as online harassment. Content generation, i.e., the decision to post a contributed content in the OSN, can be modeled by neurophysiological approaches on the basis of unbiased semantic analysis of the contents already published in the OSN. This paper proposes a neuro-semantic model composed of (1) an extended leaky competing accumulator (ELCA) as the neural architecture implementing the user concurrent decision process to generate content in a conversation thread of a virtual community of practice, and (2) a semantic modeling based on the topic analysis carried out by a latent Dirichlet allocation (LDA) of both users and conversation threads. We use the similarity between the user and thread semantic representations to built up the model of the interest of the user in the thread contents as the stimulus to contribute content in the thread. The semantic interest of users in discussion threads are the external inputs for the ELCA, i.e., the external value assigned to each choice.. We demonstrate the approach on a dataset extracted from a real life web forum devoted to fans of tinkering with musical instruments and related devices. The neuro-semantic model achieves high performance predicting the content posting decisions (average F score 0.61) improving greatly over well known machine learning approaches, namely random forest and support vector machines (average F scores 0.19 and 0.21).

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“…Cox & Shiffrin, 2017). A practical computational disadvantage of the AROM+ is the lack of a closed-form solution for the LCA, which makes model fitting very time consuming and stochastic (but see C.-F. Lo & Ip, 2021). Alternative decision mechanisms, such as the generalized drift-diffusion model (Shinn et al, 2020) could be implemented instead of the LCA layer to make the model fitting more efficient.…”

Section: Limitationsmentioning

confidence: 99%

How to say ‘no’ to a false memory: Leaky and noisy evidence accumulation during associative read-out

Sokolovič,

Hofmann

2024

Preprint

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The associative-read-out model (AROM) is an interactive activation model (IAM) of semantic and episodic memory, which has already predicted behavioral and neural data during recognition memory. The core AROM is a deterministic model containing the original IAM and a semantic layer, where long-term associations between words are defined by word co-occurrence statistics. Episodic traces on the other hand are reflected by the resting level activations of semantic word units. However, the AROM lacks an explicit decision layer. Here, we extend AROM with a layer encompassing linear leaky competing accumulators, where evidence accumulation is driven by the activation from the episodic-semantic layer. We fit the resulting AROM+ with individual episodic memory traces and decision parameters and show that it can account for accuracies and response times in a recognition memory task. Further it mechanistically shows that false and veridical memories are driven by the combination of the semantic and episodic memory signals. When exploring decision-related vs. mnemonic explanations of the N400, we find that the amplitude of the N400 is driven by episodic-semantic activation at frontal, central and posterior locations. This amplitude modulation is strongest over frontal electrodes supporting the view that the frontal FN400 is also driven by memory signals. In addition, the amplitude of the FN400 was influenced by the response criteria. In sum, the AROM+ provides a convenient tool for disentangling episodic, semantic, and executive processes in recognition memory.

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Modified leaky competing accumulator model of decision making with multiple alternatives: the Lie-algebraic approach

Cited by 7 publications

References 25 publications

Race Lévy flights: A mathematically tractable framework for studying heavy-tailed accumulation noise

Race Lévy flights: A mathematically tractable framework for studying heavy-tailed accumulation noise

Neuro-semantic prediction of user decisions to contribute content to online social networks

How to say ‘no’ to a false memory: Leaky and noisy evidence accumulation during associative read-out

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