Topic: reaction times

Within the last decade, Deep Artificial Neural Networks (DNNs) have emerged as powerful computer vision systems that match or exceed human performance on many benchmark tasks such as image classification. But whether current DNNs are suitable computational models of the human visual system remains an open question: While DNNs have proven to be capable of predicting neural activations in primate visual cortex, psychophysical experiments have shown behavioral differences between DNNs and human subjects, as quantified by error consistency. Error consistency is typically measured by briefly presen

First, a new measure of MI for reaction times is proposed that takes the entire RT distribution into account. Second, we present some recent developments in TWIN modeling, including a new proposal for the sound-induced flash illusion (SIFI).

A central problem in neuroscience is to understand how areas in the brain communicate. Inputs to brain areas largely influence the intrinsic dynamics of each region, which shapes ongoing computations. Hence, characterizing how inputs drive different population responses is key to understanding how brain circuits are controlled to generate different behaviors. To this end, we developed an inter-areal patterned microstimulation (uStim) protocol that allowed us to finely manipulate the activity of a neural population in one brain area while simultaneously recording the activity of a second popula

Spontaneous behavior in animals and humans shows a striking amount of variability both in the spatial domain (which actions to choose) and temporal domain (when to act). Concatenating actions into sequences and behavioral plans reveals the existence of a hierarchy of timescales ranging from hundreds of milliseconds to minutes. How do multiple timescales emerge from neural circuit dynamics? How do circuits modulate temporal responses to flexibly adapt to changing demands? In this talk, we will present recent results from experiments and theory suggesting a new computational mechanism generating

This study explores the decision-making processes involved in trajectory extrapolation tasks. Vertical paths were disturbed in the horizontal direction by noise generated by Random Walks (RDW) or Independent and Identically Distributed (IID) noise. In the Experiment, participants (n = 28) extrapolated if a moving point would end right or left of the screen’s center. We modeled path extrapolation using a three-parameter Random Sampling Model (RSM), and a Multi-Layer Perceptron (MLP). Statistically, for RDW paths, the optimal extrapolation strategy is to use the last visible position, whereas fo