Paradoxical Evidence Integration in Rapid Decision Processes

Wulfram Gerstner, Michael Herzog, Nicolas Marcille, Johannes Rüter
Public Library of Science, 2012
Journal paper

Decisions about noisy stimuli require evidence integration over time. Traditionally, evidence integration and decision making are described as a one-stage process: a decision is made when evidence for the presence of a stimulus crosses a threshold. Here, we show that one-stage models cannot explain psychophysical experiments on feature fusion, where two visual stimuli are presented in rapid succession. Paradoxically, the second stimulus biases decisions more strongly than the first one, contrary to predictions of one-stage models and intuition. We present a two-stage model where sensory information is integrated and buffered before it is fed into a drift diffusion process. The model is tested in a series of psychophysical experiments and explains both accuracy and reaction time distributions.

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Wulfram Gerstner, Michael Herzog, Nicolas Marcille, Johannes Rüter
Public Library of Science, 2012
Journal paper

Abstract

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https://infoscience.epfl.ch/record/176051?ln=en

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Decision-making

In psychology, decision-making (also spelled decision making and decisionmaking) is regarded as the cognitive process resulting in the selection of a belief or a course of action among several possib

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Psychophysics quantitatively investigates the relationship between physical stimuli and the sensations and perceptions they produce. Psychophysics has been described as "the scientific study of the r

Evidence

Evidence for a proposition is what supports the proposition. It is usually understood as an indication that the supported proposition is true. What role evidence plays and how it is conceived varies f

Models of Evidence Integration in Rapid Decision Making Processes

Nicolas Marcille

Decision making is of crucial interest in many disciplines such as psychology, neuroscience, economics and machine learning. Binary perceptual decision theories relate to situations where an observer (or machine) is confronted with one of two possible noisy stimuli. For example, human readers have to decide whether a handwritten character is an n or a u; a trader has to decide whether to sell or to keep; a monkey has to decide whether dots on a screen are moving to the left or to the right. While engineering and economical decision theories focus on how to compute optimal decisions, psychology and neuroscience investigate the actual decision making process in humans and animals. Humans only need a fraction of a second to recognize objects. This astonishing speed is also evident in sports such as table tennis or soccer requiring rapid reactions to moving balls. In these tasks, the brain has to decide rapidly upon visual information available for only a hundred milliseconds or less. However, most experimental and theoretical work on decision making focuses on stationary paradigms and surprisingly few studies have taken the timing of the stimulus into account. Decisions about noisy stimuli require evidence integration over time. Traditionally, evidence integration and decision making are described as a one-stage process: a decision is made when evidence for the presence of a stimulus crosses a threshold. We will show that this model is incompatible with psychophysical experiments on feature fusion, where two visual stimuli are presented in rapid succession. Paradoxically, the second stimulus biases decisions more strongly than the first one, contrary to predictions of one-stage models and intuition. We present a two-stage model where sensory information is integrated and buffered before it starts driving the diffusion process.

EPFL2011

Learning continuous-time working memory tasks with on-policy neural reinforcement learning

Davide Zambrano

An animals' ability to learn how to make decisions based on sensory evidence is often well described by Reinforcement Learning (RL) frameworks. These frameworks, however, typically apply to event-based representations and lack the explicit and fine-grained notion of time needed to study psychophysically relevant measures like reaction times and psychometric curves. Here, we develop and use a biologically plausible continuous-time RL scheme of CT-AuGMEnT (Continuous-Time Attention-Gated MEmory Tagging) to study these behavioural quantities. We show how CT-AuGMEnT implements on-policy SARSA learning as a biologically plausible form of reinforcement learning with working memory units using 'attentional' feedback. We show that the CT-AuGMEnT model efficiently learns tasks in continuous time and can learn to accumulate relevant evidence through time. This allows the model to link task difficulty to psychophysical measurements such as accuracy and reaction-times. We further show how the implementation of a separate accessory network for feedback allows the model to learn continuously, also in case of significant transmission delays between the network's feedforward and feedback layers and even when the accessory network is randomly initialized. Our results demonstrate that CTAuGMEnT represents a fully time-continuous biologically plausible end-to-end RL model for learning to integrate evidence and make decisions. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

2021

The dynamics of perceptual decision making

Johannes Rüter

Decisions about noisy stimuli require evidence integration over time. Traditionally, evidence integration and decision making are described as a one-stage process: a decision is made when evidence for the presence of a stimulus crosses a threshold. However, this model is incompatible with psychophysical experiments on feature fusion, where two visual stimuli are presented in rapid succession. Paradoxically, the second stimulus biases decisions more strongly than the first one, contrary to predictions of one-stage models and intuition. This can only be explained using a two-stage model where sensory information is integrated and buffered before it is fed into a drift diffusion process. I will present a series of psychophysical experiments to test both, accuracy and reaction time distributions predicted by the model.

2011