Tagged: Decision Making

A multimodal encoding model applied to imaging decision-related neural cascades in the human brain

Abstract Perception and cognition in the brain are naturally characterized as spatiotemporal processes. Decision-making, for example, depends on coordinated patterns of neural activity cascading across the brain, running in time from stimulus to response and in space from primary sensory regions to the frontal lobe. Measuring this cascade is key to developing an understanding of brain function. Here we report on a novel methodology that employs multi-modal imaging for inferring this cascade in humans at unprecedented spatiotemporal resolution. Specifically, we develop an encoding model to link simultaneously measured electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) signals to infer high-resolution spatiotemporal brain dynamics during a perceptual decision. After demonstrating replication of results from the literature, we report previously unobserved sequential reactivation of a substantial fraction of the pre-response network whose magnitude correlates with a proxy for decision confidence. Our encoding model, which temporally tags BOLD activations using time localized EEG variability, identifies a coordinated and spatially distributed neural cascade that is associated with a perceptual decision. In general the methodology illuminates complex brain dynamics that would otherwise be unobservable using fMRI or EEG acquired separately.

Perceptual Decision Making Investigated via Sparse Decoding of a Spiking Neuron Model of V1

Recent empirical evidence supports the hypothesis that invariant visual object recognition might result from non-linear encoding of the visual input followed by linear decoding [1]. This hypothesis has received theoretical support through the development of neural network architectures which are based on a non-linear encoding of the input via recurrent network dynamics followed by a linear decoder [2], [3]. In this paper we consider such an architecture in which the visual input is non-linearly encoded by a biologically realistic spiking model of V1, and mapped to a perceptual decision via a sparse linear decoder. Novel is that we 1) utilize a large-scale conductance based spiking neuron model of V1 which has been well-characterized in terms of classical and extra-classical response properties, and 2) use the model to investigate decoding over a large population of neurons. We compare decoding performance of the model system to human performance by comparing neurometric and psychometric curves.

Single-trial Analysis of Neuroimaging Data: Inferring Neural Networks Underlying Perceptual, Decision Making in the Human Brain

Advances in neural signal and image acquisition as well as in multivariate signal processing and machine learning are enabling a richer and more rigorous understanding of the neural basis of human decision-making. Decision-making is essentially characterized behaviorally by the variability of the decision across individual trials—e.g., error and response time distributions. To infer the neural processes that govern decision-making requires identifying neural correlates of such trial-to-trial behavioral variability. In this paper, we review efforts that utilize signal processing and machine learning to enable single-trial analysis of neural signals acquired while subjects perform simple decision-making tasks. Our focus is on neuroimaging data collected noninvasively via electroencephalograpy (EEG) and functional magnetic resonance imaging (fMRI). We review the specific frame-work for extracting decision-relevant neural components from the neuroimaging data, the goal being to analyze the trial-to-trial variability of the neural signal along these component directions and to relate them to elements of the decision-making process. We review results for perceptual decision-making and discrimination tasks, including paradigms in which EEG variability is used to inform an fMRI analysis. We discuss how single-trial analysis reveals aspects of the underlying decision-making networks that are unobservable using traditional trial-averaging methods.

Neural representation of task difficulty and decision making during perceptual categorization: a timing diagram

When does the brain know that a decision is difficult to make? How does decision difficulty affect the allocation of neural resources and timing of constituent cortical processing? Here, we use single-trial analysis of electroencephalography (EEG) to identify neural correlates of decision difficulty and relate these to neural correlates of decision accuracy. Using a cued paradigm, we show that we can identify a component in the EEG that reflects the inherent task difficulty and not simply a correlation with the stimulus. We find that this decision difficulty component arises ≈220 ms after stimulus presentation, between two EEG components that are predictive of decision accuracy [an “early” (170 ms) and a “late” (≈300 ms) component]. We use these results to develop a timing diagram for perceptual decision making and relate the component activities to parameters of a diffusion model for decision making.