Probabilistic Population Codes for Bayesian Decision Making

Abstract

When making a decision, one must first accumulate evidence, often over time, and then select the appropriate action. Here, we present a neural model of decision making that can perform both evidence accumulation and action selection optimally. More specifically, we show that, given a Poisson-like distribution of spike counts, biological neural networks can accumulate evidence without loss of information through linear integration of neural activity, and can select the most likely action through attractor dynamics. This holds for arbitrary correlations, any tuning curves, continuous and discrete variables, and sensory evidence whose reliability varies over time. Our model predicts that the neurons in the lateral intraparietal cortex involved in evidence accumulation encode, on every trial, a probability distribution which predicts the animal’s performance. We present experimental evidence consistent with this prediction, and discuss other predictions applicable to more general settings.

The Modulation of BOLD Variability between Cognitive States Varies by Age and Processing Speed

“…Some researchers (Knill and Pouget 2004;Ma et al 2006;Beck et al 2008) argue that variability is critical for the nervous system to operate in an optimal, probabilistic, Bayesian manner. Effectively, neural variability yields adaptability across levels of uncertainty in one's environment.…”

“…Interestingly, some work suggests that brain variability is the basis for the probabilistic nature of the brain (Knill and Pouget 2004;Ma et al 2006;Beck et al 2008), whereby neurons may utilize a Bayesian process that generates optimal responses in the face of external stimuli of varying reliabilities. Essentially, the authors argued that neural variability yields adaptability in the presence of stimulus uncertainty in one's environment.…”

“…Interestingly, our previous work suggests near orthogonality between mean-and variabilitybased spatial patterns Garrett et al 2011); we would thus not expect modulations between rest and task modes to occur in anticorrelated default and task positive regions. Rather, there may be a unidirectional increase in brain variability upon external cognitive demand (task) to handle increased stimulus uncertainty (Knill and Pouget 2004;Ma et al 2006;Beck et al 2008). Older and poorer performing adults may be less able to increase their brain variability levels on task to flexibly adjust to stimulus uncertainty, either because their brain variability levels are too low or too dedifferentiated to permit the occurrence of state transitions Garrett et al 2011).…”

“…Here, we expand our recent work on fMRI-based brain variability Garrett et al 2011) to examine two primary hypotheses with regard to brain state modulations: 1) brain variability will increase upon external cognitive demand (Knill and Pouget 2004;Ma et al 2006;Beck et al 2008), but not necessarily in regions that typify default and task-positive networks when mean activity changes are assessed, and; 2) older and poorer performing adults will exhibit smaller internal-to-external state transitions in brain variability, as well as less signal variability overall Garrett et al 2011). We assessed these hypotheses using a series of multivariate models assessed within a resampling framework, and subsequently provide evidence for the split-half reliability of our brain variability data.…”

The encoding of alternatives in multiple-choice decision making

“…Two theoretical studies (14,15) just recently proposed continuous models of multiple-choice decision making. Both models can account for important findings of Churchland et al (13).…”