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An integration-to-bound model of decision-making that accounts for the spectral properties of neural data.

ABSTRACT: Integration-to-bound models are among the most widely used models of perceptual decision-making due to their simplicity and power in accounting for behavioral and neurophysiological data. They involve temporal integration over an input signal ("evidence") plus Gaussian white noise. However, brain data shows that noise in the brain is long-term correlated, with a spectral density of the form 1/fα (with typically 1 < α < 2), also known as pink noise or '1/f' noise. Surprisingly, the adequacy of the spectral properties of drift-diffusion models to electrophysiological data has received little attention in the literature. Here we propose a model of accumulation of evidence for decision-making that takes into consideration the spectral properties of brain signals. We develop a generalization of the leaky stochastic accumulator model using a Langevin equation whose non-linear noise term allows for varying levels of autocorrelation in the time course of the decision variable. We derive this equation directly from magnetoencephalographic data recorded while subjects performed a spontaneous movement-initiation task. We then propose a nonlinear model of accumulation of evidence that accounts for the '1/f' spectral properties of brain signals, and the observed variability in the power spectral properties of brain signals. Furthermore, our model outperforms the standard drift-diffusion model at approximating the empirical waiting time distribution.

SUBMITTER: Guevara Erra R

PROVIDER: S-EPMC6557846 | biostudies-other | 2019 Jun

REPOSITORIES: biostudies-other

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An integration-to-bound model of decision-making that accounts for the spectral properties of neural data.

Guevara Erra Ramón R Arbotto Marco M Schurger Aaron A

Scientific reports 20190610 1

Integration-to-bound models are among the most widely used models of perceptual decision-making due to their simplicity and power in accounting for behavioral and neurophysiological data. They involve temporal integration over an input signal ("evidence") plus Gaussian white noise. However, brain data shows that noise in the brain is long-term correlated, with a spectral density of the form 1/f<sup>α</sup> (with typically 1 < α < 2), also known as pink noise or '1/f' noise. Surprisingly, the ade ...[more]

PMID: 31182724

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Project description:BackgroundBest interests is a ubiquitous principle in medical policy and practice, informing the treatment of both children and adults. Yet theory underlying the concept of best interests is unclear and rarely articulated. This paper examines bioethical literature for theoretical accounts of best interests to gain a better sense of the meanings and underlying philosophy that structure understandings.MethodsA scoping review of was undertaken. Following a literature search, 57 sources were selected and analysed using the thematic method.ResultsThree themes emerged. The first placed best interests within the structure of wider theory, noting relationships with consequentialism, deontology, prudential value theory, rights and political philosophy. The second mapped a typology of processes of decision-making, among which best interests was ambiguously positioned. It further indicated factors that informed best interests decision-making, primarily preferences, dignity and quality of life. The final theme considered best interests from a relational perspective.ConclusionsCharacterisation of best interests as strictly paternalist and consequentialist is questionable: while accounts often suggested a consequentialist basis for best interests, arguments appeared philosophically weak. Deontological accounts, found in law and Kantianism, and theories of political liberalism influenced accounts of best interests, with accounts often associating best interests with negative patient preferences (i.e. individual refusals). There was much more emphasis on negative interests than positive interests. Besides preference, factors like dignity and quality of life were held to inform best interests decisions, but generally were weakly defined. To the extent that preferences were unable to inform decision making, decisions were either made by proxy authority or by an intersubjective process of diffuse authority. Differing approaches reflect bifurcations in liberal philosophy between new liberalism and neo-liberalism. Although neither account of authority appears dominant, bias to negative interests suggests that bioethical debate tends to reflect the widespread ascendancy of neo-liberalism. This attitude was underscored by the way relational accounts converged on private familial authority. The visible connections to theory suggest that best interests is underpinned by socio-political trends that may set up frictions with practice. How practice negotiates these frictions remains a key question.

Project description:According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations.

Dataset Information

An integration-to-bound model of decision-making that accounts for the spectral properties of neural data.

Publications

An integration-to-bound model of decision-making that accounts for the spectral properties of neural data.

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