How to Model the Neurocognitive Dynamics of Decision Making

Neurocognitive processes may help people navigate uncertainty. These processes are often invisible and do not manifest themselves in overt actions. might also be involved in the workings of the human memory. In this article, we will discuss three methods for modeling neurocognitive processes in decision making.

Model-free learning

In computational neuroscience, there are two main approaches to studying the decision-making process. Both involve the use of mathematical models. The Drift-Diffusion Model (DDM) is the most common of these models. It assumes that sensory information is gathered sequentially and integrated.

In this approach, the posterior probability of latent brain states is averaged across participants in a choice response task. This is similar to Model 3 except that it does not generate CPP slopes per trial, nor does it produce drift-rates, a cognitive parameter that describes the mean rate at which evidence accumulates.

One way to model the neurocognitive dynamics of the decision-making process is to measure the occupancy rate of the decision-making state S1. found that the SN is significantly associated with inattention, while the DMN is significantly related to hyperactivity and impulsivity.
Dopamine-rich striatum

The brain’s striatum is involved in value-based decision making, including evaluating the mental effort required to reach a goal. Researchers have hypothesized that dopaminergic activity in the striatum influences the stabilization of decision options when faced with uncertain outcomes. However, the precise contribution of mesencephalic dopamine pathways to decision making is still unclear. However, a multidisciplinary approach has demonstrated that the striatum is involved in the neurocognitive dynamics of decision making in rats. Specifically, activation of the VTA-nucleus accumbens pathway causes insensitivity to loss and poor processing of negative reward prediction errors, and it promotes risky decision making.

Increased dopamine levels in the striatum are associated with improved temporal precision, and striatal dopamine depletion can impair signalling of precise priors. Using an experiment in which participants were presented with foreperiods between a go and warning stimulus, Tomassini et al. found that participants responded faster when their foreperiods were drawn from conditions characterized by low variance. The low-variance conditions were associated with improved temporal precision and predictability. However, treatment with haloperidol decreased response times and reduced temporal precision.
Bayesian switching linear dynamic systems

A new method based on Bayesian switching linear dynamic systems (BSDS) can help us understand the neurocognitive dynamics of decision making. BSDS combines state-space methods and Bayesian models to model the complex, dynamic brain. The new method can help us identify latent states and their lifetimes and characterize the dynamics of brain functional interactions.

The BSDS approach uses an unsupervised learning algorithm to discover hidden brain states and dynamic switching processes. Each brain state is associated with a specific pattern of time-varying functional connectivity. Unlike traditional methods, BSDS does not impose arbitrary moving windows. It learns latent representations within a uniform framework by applying a hidden Markov model to latent space variables.

In addition to using Bayesian networks, we can also use more efficient Monte Carlo simulation methods. These methods are more effective than the full Bayesian networks because they represent local knowledge much more efficiently. However, check here work best with small amounts of data.
Evidence accumulation speed

A recent study has shown that neurocognitive processes governing decision making and evidence accumulation speed converge on a common level. This result was based on observations of the neural activity of animals in fMRI experiments and M/EEG recordings. It found that the convergent rate for a subset of neurons is highest when an animal commits to a decision.

Previous research has examined how changes in the brain affect decision-making, particularly with regard to the speed and quality of evidence. For example, researchers have found that the decision-making process is more efficient if a person combines evidence from different sources and views to form a coherent view of the situation. In the study, neuroscientists identified a neural pathway that facilitated evidence accumulation in response to the presence of high-quality and low-quality evidence.

In contrast, a traditional threshold approach relies on the assumption that the executive process is independent of evidence accumulation. Hence, the Leaky Integrating Threshold (LIT) model is a simple leaky integrator of evidence variables. The LIT signal is associated with motor preparation. The boundary of the LIT process is based on the comparison of the original evidence accumulation signal to the measured one.