A divisive model of evidence accumulation explains uneven weighting of evidence over time

Keung, Waitsang; Hagen, Todd A.; Wilson, Robert

doi:10.1101/598789

Cited by 4 publications

(4 citation statements)

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“…1f). Thus, context-dependent temporal dynamics of evidence accumulation differed markedly across individual rats, and even within individual rats for the two different types of evidence, a result consistent with single-context findings in humans 6,7 . What underlies this variability across individuals?…”

Section: Individual Variability Of Behavioral Kernelssupporting

confidence: 78%

A new theoretical framework jointly explains behavioral and neural variability across subjects performing flexible decision-making

Pagan

Tang

Aoi

et al. 2022

Preprint

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The ability to flexibly select and accumulate relevant information to form decisions, while ignoring irrelevant information, is a fundamental component of higher cognition. Yet its neural mechanisms remain unclear. Here we demonstrate that, under assumptions supported by both monkey and rat data, the space of possible network mechanisms to implement this ability is spanned by the combination of three different components, each with specific behavioral and anatomical implications. We further show that existing electrophysiological and modeling data are compatible with the full variety of possible combinations of these components, suggesting that different individuals could use different component combinations. To study variations across subjects, we developed a rat task requiring context-dependent evidence accumulation, and trained many subjects on it. Our task delivers sensory evidence through pulses that have random but precisely known timing, providing high statistical power to characterize each individual's neural and behavioral responses. Consistent with theoretical predictions, neural and behavioral analysis revealed remarkable heterogeneity across rats, despite uniformly good task performance. The theory further predicts a specific link between behavioral and neural signatures, which was robustly supported in the data. Our results provide a new experimentally-supported theoretical framework to analyze biological and artificial systems performing flexible decision-making tasks, and open the door to the study of individual variability in neural computations underlying higher cognition.

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Section: Individual Variability Of Behavioral Kernelssupporting

confidence: 78%

A new theoretical framework jointly explains behavioral and neural variability across subjects performing flexible decision-making

Pagan

Tang

Aoi

et al. 2022

Preprint

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“…Representing this quantity in logarithmic form allows it to be implemented as a successive summation (Eqn. 8c), which can naturally be implemented by neurons (up to normalization constraints, see (Keung et al, 2020) . Bogacz et al (2006) rearranged these terms to denote the logLR as integrated evidence ( I t ) and show that the summation is a recursion which takes the form of a discrete random walk (with stochasticity inherent in the densities given by the evidences e t ):…”

Section: Mathematical Models Of Sequential Inference: Gaussian and "Jump" Diffusionmentioning

confidence: 99%

“…Gold and Shadlen (2001) proposed that neural circuits could implement evidence accumulation by computing this product in log space. Representing this quantity in logarithmic form allows it to be implemented as a successive summation (Equation 8c), which can naturally be implemented by neurons (up to normalization constraints, see Keung et al (2020).…”

Section: Mathematical Models Of Sequential Inference: Gaussian and "J...mentioning

confidence: 99%

Mixing memory and desire: How memory reactivation supports deliberative decision‐making

Wang

Feng

Bornstein

2021

WIRES Cognitive Science

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Memories affect nearly every aspect of our mental life. They allow us to both resolve uncertainty in the present and to construct plans for the future. Recently, renewed interest in the role memory plays in adaptive behavior has led to new theoretical advances and empirical observations. We review key findings, with particular emphasis on how the retrieval of many kinds of memories affect deliberative action selection. These results are interpreted in a sequential inference framework, in which reinstatements from memory serve as "samples" of potential action outcomes. The resulting model suggests a central role for the dynamics of memory reactivation in determining the influence of different kinds of memory in decisions. We propose that representation-specific dynamics can implement a bottom-up "product of experts" rule that integrates multiple sets of action-outcome predictions weighted on the basis of their uncertainty. We close by reviewing related findings and identifying areas for further research.

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“…In the first model, based on approximate Bayesian inference, the primacy effect produced by bottom-up vs. top-down hierarchical dynamics, was modulated by the stimulus properties which could yield different PK time-courses, a prediction that was tested in a visual discrimination task 41 . The second study proposed a model that can produce different PK time-courses by adjusting the time scales of a divisive normalization mechanism, which yields primacy, and a leak mechanism, which promotes recency 42 . In addition, this model can also account for bump shaped PKs, a class of PK that was found together with primacy, recency and flat PKs, in a study carried out using a large cohort of subjects (>100) 43 .…”

Section: Discussionmentioning

confidence: 99%

Flexible categorization in perceptual decision making

Prat-Ortega

Wimmer

Roxin

et al. 2020

Preprint

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Perceptual 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. 5/23/2020 Copy of Theoretical paper_post_thesis_biorxiv_nolinks -Google Docs https://docs.google.com/document/d/1GzJnoKh80LGPHNONiRWI8FqU4SV5qtRlgl-Cr8hPWBs/edit# 2/47stimulus evidence linearly until one of the bounds is reached 1 . The DDMA and its different variations have been successfully used to fit psychometric and chronometric curves 2 , to capture the speed accuracy trade off 1,3,4 , to account for history dependent choice biases 5 , changes of mind 6 , confidence reports 7 or the Weber's law 8 . Although the absorbing bounds were originally thought of as a mechanism to terminate the integration process, the DDMA has also been applied to fixed duration tasks 9,10 . In motion discrimination tasks, for instance, it can reproduce the subjects' tendency to give more weight to early rather than late stimulus information, which is called a primacy effect 9,11-15 . However, depending on the details of the task and the stimulus, subjects can also give more weight to late rather than to early evidence (i.e. a recency effect) 16,17 or weigh the whole stimulus uniformly 18 . In order to account for these differences, the DDMA needs to be modified by using reflecting instead of absorbing bounds or by removing the bounds altogether 19 . Despite their considerable success in fitting experimental data, the DDMA and its many variants remain purely phenomenological descriptions of sensory integration. This makes it difficult to link the DDMA to the actual neural circuit mechanisms underlying perceptual decision making.These neural circuit mechanisms have been studied with biophysical attractor network models that ca...

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A divisive model of evidence accumulation explains uneven weighting of evidence over time

Cited by 4 publications

References 50 publications

A new theoretical framework jointly explains behavioral and neural variability across subjects performing flexible decision-making

A new theoretical framework jointly explains behavioral and neural variability across subjects performing flexible decision-making

Mixing memory and desire: How memory reactivation supports deliberative decision‐making

Flexible categorization in perceptual decision making

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