The thermodynamics of human reaction times

I present a new approach for the interpretation of reaction time (RT) data from behavioral experiments. From a physical perspective, the entropy of the RT distribution provides a model-free estimate of the amount of processing performed by the cognitive system. In this way, the focus is shifted from the conventional interpretation of individual RTs being either long or short, into their distribution being\ud more or less complex in terms of entropy. The new approach enables the estimation of the cognitive processing load without reference to the informational content of the stimuli themselves, thus providing a more appropriate estimate of the cognitive impact of dierent sources of information that are carried by experimental stimuli or tasks. The paper introduces the formulation of the theory, followed by an empirical validation using a database of human RTs in lexical tasks (visual lexical decision and word\ud naming). The results show that this new interpretation of RTs is more powerful than the traditional one. The method provides theoretical estimates of the processing loads elicited by individual stimuli. These loads sharply distinguish the responses from different tasks. In addition, it provides upper-bound estimates for the speed at which the system processes information. Finally, I argue that the theoretical proposal, and the associated empirical evidence, provide strong arguments for an adaptive system that systematically adjusts its operational processing speed to the particular demands of each stimulus. This\ud finding is in contradiction with Hick's law, which posits a relatively constant processing speed within an experimental context

Solving reaction-diffusion equations 10 times faster

The most popular numerical method for solving systems of reaction-diffusion equations continues to be a low order finite-difference scheme coupled with low order Euler time stepping. This paper extends previous 1D work and reports experiments that show that with high--order methods one can speed up such simulations for 2D and 3D problems by factors of 10--100. A short MATLAB code (2/3D) that can serve as a template is included.\ud \ud This work was supported by the Engineering and Physical Sciences Research Council (UK) and by the MathWorks, Inc

Methodological issues in measures of imitative reaction times

Ideomotor (IM) theory suggests that observing someone else perform an action activates an internal motor representation of that behaviour within the observer. Evidence supporting the case for an ideomotor theory of imitation has come from studies that show imitative responses to be faster than the same behavioural measures performed in response to spatial cues. In an attempt to replicate these findings, we manipulated the salience of the visual cue and found that we could reverse the advantage of the imitative cue over the spatial cue. We suggest that participants utilised a simple visuomotor mechanism to perform all aspects of this task, with performance being driven by the relative visual salience of the stimuli. Imitation is a more complex motor skill that would constitute an inefficient strategy for rapid performance

How coherent structures dominate the residence time in a bubble wake: an experimental example

Mixing timescales and residence times in reactive multiphase flows can be essential for product selectivity. For instance when a gas species is consumed e.g. by a competitive consecutive reaction with moderate reaction kinetics where reaction timescales are comparable to relevant mixing timescales. To point out the importance of the details of the fluid flow, we analyze experimental velocity data from a Taylor bubble wake by means of Lagrangian methods. By adjusting the channel diameter in which the Taylor bubble rises, and thus the rise velocity, we obtain three different wake regimes. Remarkably the normalized residence times of passive particles advected in the wake velocity field show a peak for intermediate rise velocities. This fact seems unintuitive at first glance because one expects a faster removal of passive tracers for a faster overall flow rate. However, the details of the flow topology analyzed using Finite Time Lyapunov Exponent (FTLE) fields and Lagrangian Coherent Structures (LCS) reveal the existence of a very coherent vortical pattern in the bubble wake which explains the long residence times. The increased residence times within the vortical structure and the close bubble interface acting as a constant gas species source could enhance side product generation of a hypothetical competitive consecutive reaction, where the first reaction with the gas species forms the desired product and the second the side product.Comment: 13 pages, 7 figures, 1 tabl

Short relaxation times but long transient times in both simple and complex reaction networks

When relaxation towards an equilibrium or steady state is exponential at large times, one usually considers that the associated relaxation time $\tau$, i.e., the inverse of that decay rate, is the longest characteristic time in the system. However that need not be true, and in particular other times such as the lifetime of an infinitesimal perturbation can be much longer. In the present work we demonstrate that this paradoxical property can arise even in quite simple systems such as a chain of reactions obeying mass action kinetics. By mathematical analysis of simple reaction networks, we pin-point the reason why the standard relaxation time does not provide relevant information on the potentially long transient times of typical infinitesimal perturbations. Overall, we consider four characteristic times and study their behavior in both simple chains and in more complex reaction networks taken from the publicly available database "Biomodels." In all these systems involving mass action rates, Michaelis-Menten reversible kinetics, or phenomenological laws for reaction rates, we find that the characteristic times corresponding to lifetimes of tracers and of concentration perturbations can be much longer than $\tau$

Perception of categories: from coding efficiency to reaction times

Reaction-times in perceptual tasks are the subject of many experimental and theoretical studies. With the neural decision making process as main focus, most of these works concern discrete (typically binary) choice tasks, implying the identification of the stimulus as an exemplar of a category. Here we address issues specific to the perception of categories (e.g. vowels, familiar faces, ...), making a clear distinction between identifying a category (an element of a discrete set) and estimating a continuous parameter (such as a direction). We exhibit a link between optimal Bayesian decoding and coding efficiency, the latter being measured by the mutual information between the discrete category set and the neural activity. We characterize the properties of the best estimator of the likelihood of the category, when this estimator takes its inputs from a large population of stimulus-specific coding cells. Adopting the diffusion-to-bound approach to model the decisional process, this allows to relate analytically the bias and variance of the diffusion process underlying decision making to macroscopic quantities that are behaviorally measurable. A major consequence is the existence of a quantitative link between reaction times and discrimination accuracy. The resulting analytical expression of mean reaction times during an identification task accounts for empirical facts, both qualitatively (e.g. more time is needed to identify a category from a stimulus at the boundary compared to a stimulus lying within a category), and quantitatively (working on published experimental data on phoneme identification tasks)

New reaction tester accurate within 56 microseconds

Testing device measures simple and disjunctive reaction time of human subject to light stimuli. Tester consists of reaction key, logic card, panel mounted neon indicators, and interconnecting wiring. Device is used for determining reaction times of patients undergoing postoperative neurological therapy

Reaction times of monitoring schemes for ARMA time series

This paper is concerned with deriving the limit distributions of stopping times devised to sequentially uncover structural breaks in the parameters of an autoregressive moving average, ARMA, time series. The stopping rules are defined as the first time lag for which detectors, based on CUSUMs and Page's CUSUMs for residuals, exceed the value of a prescribed threshold function. It is shown that the limit distributions crucially depend on a drift term induced by the underlying ARMA parameters. The precise form of the asymptotic is determined by an interplay between the location of the break point and the size of the change implied by the drift. The theoretical results are accompanied by a simulation study and applications to electroencephalography, EEG, and IBM data. The empirical results indicate a satisfactory behavior in finite samples.Comment: Published at http://dx.doi.org/10.3150/14-BEJ604 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

A modified Next Reaction Method for simulating chemical systems with time dependent propensities and delays

Chemical reaction systems with a low to moderate number of molecules are typically modeled as discrete jump Markov processes. These systems are oftentimes simulated with methods that produce statistically exact sample paths such as the Gillespie Algorithm or the Next Reaction Method. In this paper we make explicit use of the fact that the initiation times of the reactions can be represented as the firing times of independent, unit rate Poisson processes with internal times given by integrated propensity functions. Using this representation we derive a modified Next Reaction Method and, in a way that achieves efficiency over existing approaches for exact simulation, extend it to systems with time dependent propensities as well as to systems with delays.Comment: 25 pages, 1 figure. Some minor changes made to add clarit

Facilitated diffusion of DNA-binding proteins: Simulation of large systems

The recently introduced method of excess collisions (MEC) is modified to estimate diffusion-controlled reaction times inside systems of arbitrary size. The resulting MEC-E equations contain a set of empirical parameters, which have to be calibrated in numerical simulations inside a test system of moderate size. Once this is done, reaction times of systems of arbitrary dimensions are derived by extrapolation, with an accuracy of 10 to 15 percent. The achieved speed up, when compared to explicit simulations of the reaction process, is increasing proportional to the extrapolated volume of the cell.Comment: 8 pages, 4 figures, submitted to J. Chem. Phy