Stochastic Motion Stimuli Influence Perceptual Choices in Human Participants.

In the study of perceptual decision making, it has been widely assumed that random fluctuations of motion stimuli are irrelevant for a participant's choice. Recently, evidence was presented that these random fluctuations have a measurable effect on the relationship between neuronal and behavioral variability, the so-called choice probability. Here, we test, in a behavioral experiment, whether stochastic motion stimuli influence the choices of human participants.

Reward modulates the association between sensory noise and brain activity during perceptual decision-making.

Perceptual decisions entail the accumulation of evidence until a decision criterion is reached. The amount of noise in this process is inversely related to the behavioral performance of the decision-maker. Hence, reducing the amount of perceived noise could improve performance in perceptual decisions.

Representation of Perceptual Evidence in the Human Brain Assessed by Fast, Within-Trial Dynamic Stimuli.

In perceptual decision making the brain extracts and accumulates decision evidence from a stimulus over time and eventually makes a decision based on the accumulated evidence. Several characteristics of this process have been observed in human electrophysiological experiments, especially an average build-up of motor-related signals supposedly reflecting accumulated evidence, when averaged across trials. Another recently established approach to investigate the representation of decision evidence in brain signals is to correlate the within-trial fluctuations of decision evidence with the measured signals.

A Bayesian Reformulation of the Extended Drift-Diffusion Model in Perceptual Decision Making.

Perceptual decision making can be described as a process of accumulating evidence to a bound which has been formalized within drift-diffusion models (DDMs). Recently, an equivalent Bayesian model has been proposed. In contrast to standard DDMs, this Bayesian model directly links information in the stimulus to the decision process.

Random Projection for Fast and Efficient Multivariate Correlation Analysis of High-Dimensional Data: A New Approach.

In recent years, the advent of great technological advances has produced a wealth of very high-dimensional data, and combining high-dimensional information from multiple sources is becoming increasingly important in an extending range of scientific disciplines. Partial Least Squares Correlation (PLSC) is a frequently used method for multivariate multimodal data integration. It is, however, computationally expensive in applications involving large numbers of variables, as required, for example, in genetic neuroimaging.

Spatiotemporal dynamics of random stimuli account for trial-to-trial variability in perceptual decision making.

Decisions in everyday life are prone to error. Standard models typically assume that errors during perceptual decisions are due to noise. However, it is unclear how noise in the sensory input affects the decision.

Modelling Odor Decoding in the Antennal Lobe by Combining Sequential Firing Rate Models with Bayesian Inference.

The olfactory information that is received by the insect brain is encoded in the form of spatiotemporal patterns in the projection neurons of the antennal lobe. These dense and overlapping patterns are transformed into a sparse code in Kenyon cells in the mushroom body. Although it is clear that this sparse code is the basis for rapid categorization of odors, it is yet unclear how the sparse code in Kenyon cells is computed and what information it represents.

A Bayesian Attractor Model for Perceptual Decision Making.

Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence about a decision. We propose a novel approach of how perceptual decisions are made by combining two influential formalisms into a new model.

Comparison of variants of canonical correlation analysis and partial least squares for combined analysis of MRI and genetic data.

The standard analysis approach in neuroimaging genetics studies is the mass-univariate linear modeling (MULM) approach. From a statistical view, however, this approach is disadvantageous, as it is computationally intensive, cannot account for complex multivariate relationships, and has to be corrected for multiple testing. In contrast, multivariate methods offer the opportunity to include combined information from multiple variants to discover meaningful associations between genetic and brain imaging data.

Human preferences are biased towards associative information.

There is ample evidence that the brain generates predictions that help interpret sensory input. To build such predictions the brain capitalizes upon learned statistical regularities and associations (e.g.

Perceptual decision making: drift-diffusion model is equivalent to a Bayesian model.

Behavioral data obtained with perceptual decision making experiments are typically analyzed with the drift-diffusion model. This parsimonious model accumulates noisy pieces of evidence toward a decision bound to explain the accuracy and reaction times of subjects. Recently, Bayesian models have been proposed to explain how the brain extracts information from noisy input as typically presented in perceptual decision making tasks.

Hysteresis as an implicit prior in tactile spatial decision making.

Perceptual decisions not only depend on the incoming information from sensory systems but constitute a combination of current sensory evidence and internally accumulated information from past encounters. Although recent evidence emphasizes the fundamental role of prior knowledge for perceptual decision making, only few studies have quantified the relevance of such priors on perceptual decisions and examined their interplay with other decision-relevant factors, such as the stimulus properties. In the present study we asked whether hysteresis, describing the stability of a percept despite a change in stimulus property and known to occur at perceptual thresholds, also acts as a form of an implicit prior in tactile spatial decision making, supporting the stability of a decision across successively presented random stimuli (i.

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks.

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute.