No evidence for an item limit in change detection.

Change detection is a classic paradigm that has been used for decades to argue that working memory can hold no more than a fixed number of items ("item-limit models"). Recent findings force us to consider the alternative view that working memory is limited by the precision in stimulus encoding, with mean precision decreasing with increasing set size ("continuous-resource models"). Most previous studies that used the change detection paradigm have ignored effects of limited encoding precision by using highly discriminable stimuli and only large changes. We conducted two change detection experiments (orientation and color) in which change magnitudes were drawn from a wide range, including small changes. In a rigorous comparison of five models, we found no evidence of an item limit. Instead, human change detection performance was best explained by a continuous-resource model in which encoding precision is variable across items and trials even at a given set size. This model accounts for comparison errors in a principled, probabilistic manner. Our findings sharply challenge the theoretical basis for most neural studies of working memory capacity.

Probabilistic computation in human perception under variability in encoding precision.

A key function of the brain is to interpret noisy sensory information. To do so optimally, observers must, in many tasks, take into account knowledge of the precision with which stimuli are encoded. In an orientation change detection task, we find that encoding precision does not only depend on an experimentally controlled reliability parameter (shape), but also exhibits additional variability. In spite of variability in precision, human subjects seem to take into account precision near-optimally on a trial-to-trial and item-to-item basis. Our results offer a new conceptualization of the encoding of sensory information and highlight the brain's remarkable ability to incorporate knowledge of uncertainty during complex perceptual decision-making.

Gaussian process change point models

We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to locate change points in an online manner; and, unlike other Bayesian online change point detection algorithms, is applicable when temporal correlations in a regime are expected. We show three variations on how to apply Gaussian processes in the change point context, each with their own advantages. We present methods to reduce the computational burden of these models and demonstrate it on several real world data sets. Copyright 2010 by the author(s)/owner(s).

Electrical detection of trapped charge displacements using an ultra-thin SOI percolation transistor

Electrical detection of solid-state charge qubits requires ultrasensitive charge measurement, typically using a quantum point contact or single-electron-transistor, which imposes strict limits on operating temperature, voltage and current. A conventional FET offers relaxed operating conditions, but the back-action of the channel charge is a problem for such small quantum systems. Here, we discuss the use of a percolation transistor as a measurement device, with regard to charge sensing and backaction. The transistor is based on a 10nm thick SOI channel layer and is designed to measure the displacement of trapped charges in a nearby dielectric. At cryogenic temperatures, the trapped charges result in strong disorder in the channel layer, so that current is constrained to a percolation pathway in sub-threshold conditions. A microwave driven spatial Rabi oscillation of the trapped charge causes a change in the percolation pathway, which results in a measurable change in channel current. © The Electrochemical Society.

Direct detection of molecular biorecognition by dipole sensing mechanism.

This work investigates the feasibility of transducing molecular-recognition events into a measurable potentiometric signal. It is shown for the first time that biorecognition of acetylcholine (ACh) can be translated to conformational changes in the enzyme, acetylcholine-esterase (AChE), which in turn induces a measurable change in surface potential. Our results show that a highly sensitive detector for ACh can be obtained by the dilute assembly of AChE on a floating gate derived field effect transistor (FG-FET). A wide concentration range response is observed for ACh (10(-2)-10(-9)M) and for the inhibitor carbamylcholine CCh (10(-6)-10(-11)M). These enhanced sensitivities are modeled theoretically and explained by the combined response of the device to local pH changes and molecular dipole variations due to the enzyme-substrate recognition event.