A geometric framework for modeling dynamic decisions among arbitrarily many alternatives

Kvam, Peter D.

doi:10.1016/j.jmp.2019.03.001

Cited by 32 publications

(63 citation statements)

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“…Such models, despite being much more complex than those presented here, offer many potential benefits. For example, they can be used to predict the effect of condition manipulations (e.g., different sets of colors in the Stroop task) on accuracy and response times in decision tasks (Bhatia, 2017;Kvam, 2019b), which makes them well suited to identify correspondence between different behavioral tasks (e.g., Stroop, Flanker). Indeed, many generative and cognitive models are developed to jointly capture phenomena across paradigmsa process that often produces mechanistic insights that are easily obscured when using summary statistics (e.g., Kellen et al, 2016;Luckman et al, 2018;Turner et al, 2018).…”

Section: Further Improvementsmentioning

confidence: 99%

Theoretically Informed Generative Models Can Advance the Psychological and Brain Sciences: Lessons from the Reliability Paradox

Haines¹,

Pd²,

Lh³

et al. 2020

Preprint

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105

119

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Behavioral tasks (e.g., Stroop task) that produce replicable group-level effects (e.g., Stroop effect) often fail to reliably capture individual differences between participants (e.g., low test-retest reliability). This “reliability paradox” has led many researchers to conclude that most behavioral tasks cannot be used to develop and advance theories of individual differences. However, these conclusions are derived from statistical models that provide only superficial summary descriptions of behavioral data, thereby ignoring theoretically-relevant data-generating mechanisms that underly individual-level behavior. More generally, such descriptive methods lack the flexibility to test and develop increasingly complex theories of individual differences. To resolve this theory-description gap, we present generative modeling approaches, which involve using background knowledge to specify how behavior is generated at the individual level, and in turn how the distributions of individual-level mechanisms are characterized at the group level—all in a single joint model. Generative modeling shifts our focus away from estimating descriptive statistical “effects” toward estimating psychologically meaningful parameters, while simultaneously accounting for measurement error that would otherwise attenuate individual difference correlations. Using simulations and empirical data from the Implicit Association Test and Stroop, Flanker, Posner Cueing, and Delay Discounting tasks, we demonstrate how generative models yield (1) higher test-retest reliability estimates, and (2) more theoretically informative parameter estimates relative to traditional statistical approaches. Our results reclaim optimism regarding the utility of behavioral paradigms for testing and advancing theories of individual differences, and emphasize the importance of formally specifying and checking model assumptions to reduce theory-description gaps and facilitate principled theory development.

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Section: Further Improvementsmentioning

confidence: 99%

Theoretically Informed Generative Models Can Advance the Psychological and Brain Sciences: Lessons from the Reliability Paradox

Haines¹,

Pd²,

Lh³

et al. 2020

Preprint

Self Cite

105

119

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“…In many ways, the GSR model's stopping rule is similar to the rule in the CDM and SCDM in that it will halt as soon as evidence for any of the alternatives in the choice set exceeds the threshold. In fact, the distribution of response times in the GSR model will follow exactly the first-passage distribution for hitting times on the hypersphere in d dimensions derived by Smith & Corbett (2018), and the raw hitting locations will follow a von Mises-Fisher distribution (Kvam, 2019a).The key extension provided by the GSR model is that not every hitting point on the hypersphere corresponds to a unique response option. Instead, the distribution of hitting points is re-mapped onto the set of response options by interpolating the location of a response from the locations of a more limited set of responses along the hypersphere.…”

Section: Formal Specification Of the Gsr Modelmentioning

confidence: 94%

Reconciling similarity across models of continuous selections.

Kvam

Turner

2021

Psychological Review

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Recently-developed models of decision making have provided accounts of the cognitive processes underlying choice on tasks where responses can fall along a continuum, such as identifying the color or orientation of a stimulus. Even though nearly all of these models seek to extend diffusion decision processes to a continuum of response options, they vary in terms of complexity, tractability, and their ability to predict patterns of data such as multimodal distributions of responses. We suggest that these differences are almost entirely due to differences in how these models account for the similarity among response options. In this theoretical note, we reconcile these differences by characterizing the existing models under a common framework, where the assumptions about psychological representations of similarity, and their implications for behavioral data (e.g., multimodal responses), are made explicit. Furthermore, we implement a simulation-based approach to computing model likelihoods that allows for greater freedom in constructing and implementing continuous response models. The resulting geometric similarity representation (GSR) can supplement approaches like the circular / spherical diffusion models by allowing them to generate multimodal distributions of responses from a single drift, or simplify models like the spatially continuous diffusion model by condensing their representations of similarity and allowing them to generate simulations more efficiently. To illustrate its utility, we apply this approach to multimodal distributions responses, twodimensional responses (such as locations on a computer screen), and continuua of response options with nontrivial, nonlinear similarity relations between response options.

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“…There is a growing consensus that the evidence accumulates gradually and sequentially to make a decision ( 29,30 ). As a result, sequential sampling models (SSMs; Stone, 1960 ( 31 ), Ratcliff, Smith, Brown, and McKoon, 2016 ( 32 ) and Evans and Wagenmakers, 2020 ( 33 ) for the reviews), as the most well-known explanation of how the decision-making process works ( 26,30 ), have obtained very achievements in modeling the cognitive processes underlying decision making across a wide variety of paradigms, such as optimality polices ( [34][35][36][37] ), stop signal paradigms ( 38 ), go/no-go paradigms ( 39 ), multi-attribute & many alternatives choice ( [40][41][42] ), learning strategies ( [43][44][45] ), attentional choice ( [8][9][10]46 ), continuous responses ( 29,47 ), neural processes ( 1 ), and so on. In general, SSMs assume that decisions are made from a noisy process of accumulating evidence, that is to say, according to this theory, the evidence is gradually accumulated in favor of different choice alternatives over time with some rate until a sufficient amount of evidence for one of the options reaches a predetermined threshold to make a decision.…”

Section: Cognitive Modeling Of Perceptual Decisionsmentioning

confidence: 99%

How spatial attention affects the decision process: looking through the lens of Bayesian hierarchical diffusion model & EEG analysis

Ghaderi-Kangavari

Parand

Ebrahimpour

et al. 2021

Preprint

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Model-based cognitive neuroscience consolidates the cognitive processes and neurophysiological oscillations which are reflections of behavioral performance (e.g., reaction times and accuracy). Here, based on one of the well-known sequential sampling models (SSMs), named the diffusion decision model, and the nested model comparison, we explore the underlying latent process of spatial prioritization in perceptual decision processes, so that for estimating the model parameters (i.e. the drift rate, the boundary separation, and the non-decision time), a Bayesian hierarchical approach is considered, which allows inferences to be done simultaneously in the group and individual level. Moreover, well-established neural components of spatial attention which contributed to the latent process and behavioral performance in a visual face-car perceptual decision are detected based on the event-related potential (ERP) analysis. Our cognitive modeling analysis revealed that the non-decision time parameter provides a better fit to the top-down attention with the measures of two powerful weapons, i.e. the deviance information criterion called DIC score and R-square. Also, using multiple regression analysis on the contralateral minus neutral N2 sub-component (N2nc) at central electrodes and contralateral minus neutral alpha power (Anc) at posterior-occipital electrodes in the voluntary attention, it can be concluded that poststimulus N2nc can predict reaction time (RT) and non-decision time parameter relating to spatial prioritization. Whereas, the poststimulus Anc only can predict the RT and not the non-decision time relating to spatial prioritization. The result suggested that the difference of contralateral minus neutral oscillations was more important to reflect the modulation of the top-down spatial attention mechanism in comparison with the difference of ipsilateral minus neutral oscillations.

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A geometric framework for modeling dynamic decisions among arbitrarily many alternatives

Cited by 32 publications

References 100 publications

Theoretically Informed Generative Models Can Advance the Psychological and Brain Sciences: Lessons from the Reliability Paradox

Theoretically Informed Generative Models Can Advance the Psychological and Brain Sciences: Lessons from the Reliability Paradox

Reconciling similarity across models of continuous selections.

How spatial attention affects the decision process: looking through the lens of Bayesian hierarchical diffusion model & EEG analysis

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