From Poisson shot noise to the integrated Ornstein-Uhlenbeck process: Neurally principled models of information accumulation in decision-making and response time

In the diffusion model of decision-making, evidence is accumulated by a Wiener diffusion process. A neurally motivated account of diffusive evidence accumulation is given, in which diffusive accumulation arises from an interaction between neural integration processes operating on short and long time scales. The short time scale process is modeled as a Poisson shot noise process with exponential decay. Stimulus information is coded by excitatory-inhibitory shot noise pairs. The long time scale process is modeled as algebraic integration, possibly implemented as a first-order autoregressive process realized by recurrent connections within a population of neurons. At high intensities, an excitatory-inhibitory shot noise pair converges weakly to an Ornstein-Uhlenbeck (OU) velocity process. The integrated OU process, or OU displacement process, obtained by integrating the velocity process over time, is indistinguishable at long times from the Wiener process. Diffusive information accumulation may therefore be characterized as an integrated OU process whose properties mimic those of the Wiener process.     

On the statistical and theoretical basis of signal detection theory and extensions: Unequal variance, random coefficient, and mixture models

Basic results for conditional means and variances, as well as distributional results, are used to clarify the similarities and differences between various extensions of signal detection theory (SDT). It is shown that a previously presented motivation for the unequal variance SDT model (varying strength) actually leads to a related, yet distinct, model. The distinction has implications for other extensions of SDT, such as models with criteria that vary over trials. It is shown that a mixture extension of SDT is also consistent with unequal variances, but provides a different interpretation of the results; mixture SDT also offers a way to unify results found across several types of studies.     

Reaction time and established risk factors for total and cardiovascular disease mortality: Comparison of effect estimates in the follow-up of a large, UK-wide, general-population based survey

Higher cognitive function is associated with faster choice reaction time (CRT), and both are associated with a reduced risk of mortality from all-causes and cardiovascular disease (CVD). However, comparison of the predictive capacity of CRT, an emerging risk factor, with that for established ‘classic’ risk factors for mortality, such as smoking, hypertension or obesity, is lacking. The purpose of this study was to compare the relative impact of CRT with a range of established risk factors for all-cause and CVD mortality. The UK Health and Lifestyle Survey (HALS) is a national sample survey of adults in England, Scotland, and Wales. In 1984/85, data on lifestyle factors, socioeconomic status, and health were collected for 9003 individuals. CRT data were available for 7414 individuals. With different predictor variables having differing coding structures, we used the relative index of inequality (RII) to explore the relation of a range of risk factors with mortality by computing the risk in disadvantaged (high risk; e.g., smokers) relative to advantaged (low risk; e.g., non-smokers) persons. During an average of 20 years of follow-up, there were 1289 deaths (568 ascribed to CVD). In age- and sex-adjusted models in which all-cause mortality was the outcome of interest, CRT mean (RII = 2.57, 95% CI = 1.98, 3.33) was the second most important predictor of death after smoking (RII = 3.03, 95% CI = 2.45, 3.75). For death from CVD, CRT mean (RII = 2.31, 95% CI = 1.55, 3.43) was again the second most important risk factor for death, behind systolic blood pressure (RII = 4.37, 95% CI = 3.03, 6.29). These analyses suggest that CRT, a moderately high correlate of intelligence, is an important risk factor for death from all-causes and CVD.