Sensory evidence

…of population activity in the sensory cortex. And second, that this relation differs from the one between sensory activity and perceptual decisions. To this end, we studied neural population activity in the primary visual cortex of macaque monkeys performing a task that reveals the animal's subjective sense of perceptual confidence. Specifical

…to predict and prepare for future (sensory) events. Bayesian predictive processing posits that these expectations play a major role in sensory processing, perception being the result of their integration with sensory evidence in this framework. Placebo hypoalgesia (when expectations of pain relief are sufficient to relieve pain) is probably the best example of expectations biassing perception. In this experiment, we manipulated pain expectations using a classic probabilistic reversal learning task (2 visual cues assoc

…this behavior guiding choice relies on sensory information. The associative part of the striatum has been proposed to be essential for such decision-making processes, in particular to select the appropriate action plan according to the sensory evidence. Parvalbumin interneurons (PVI) in the associative striatum may have a crucial role in this action plan selection despite their low numbers. Indeed, by inhibiting striatal processing, PVI may

Predicting the timing of external stimuli is a key function in sensory processing. In noisy environments, the importance of prior information increases due to reduced reliability of sensory information. At the same time, sudden changes in the environment often render prior information invalid. In such cases, observers should update beliefs and establish new priors using current sensory evidence. To study these dynamic processes, our current EEG experiment investigates the behavioral and neural patterns associated with auditory temporal discrimination in a noisy and dynamically changing environ

…challenge of receiving only uncertain sensory evidence, resulting from noisy input and noisy neural processes. Efficiently adapting one’s internal decision system including prior expectations and subsequent metacognitive assessments to these challenges is crucial in everyday life. However, the exact decision mechanisms and whether these represent modifiable states remain unknown in the general population and clinical patients with psychosis. Using data from a laboratory-based sample of healthy

Perceptual decision-making involves combining sensory evidence with available choices to arrive at an appropriate action to report the choice. When sensory evidence and potential actions to report choice are simultaneously available, neural activity in dorsal premotor (PMd) and lateral intraparietal (LIP) cortices covary with sensory evidence and choice. However, when sensory evidence and possible actions are separated in time, PMd and LIP show action selection related responses and only when the stimulus-response relationship is apparent. Thus, these areas are largely involved in action selec

We employed a dynamical systems perspective to bridge decision-related neural activity and decision-making behavior, a fundamentally unresolved problem. The dynamical systems approach posits that neural dynamics (X) are parameterized by a state equation (dX/dt=F(X)+U) with varying initial conditions (x0) and that evolves in time by combining at each time step, recurrent activity (F) and inputs (U). We considered different dynamical systems that make predictions about whether 1) initial conditions substantially predict subsequent dynamics and reaction time (RT) and/or choice, and 2) whether i

Perceptual decision-making is crucial for survival, requiring the integration of dynamic sensory evidence from the environment with internal state variables (e.g. context, priors, value) to choose an appropriate action plan. Neural correlates of perceptual decisions are more distributed than once thought, having been found within the cortex, striatum, and superior colliculus (SC). Recent work has suggested a potentially novel node in this network - the midbrain reticular nucleus (MRN) - which was one of the few regions that encoded visual and action selection signals in a brain-wide survey of

…to optimal predictions given available evidence and subj

…in the sensitivity to subsequent sensory evidence. It remains unknown what neural mechanisms give rise to these post-decision biases and whether continuous estimates and categorical choices are computed in a common cortical circuit. Here, we develop a neural network model that addresses these questions. We study the integration of continuous

Decisions are often guided by detecting subtle signals in a dynamic sensory environment. Although the brain must track such decision-relevant signals, how they are represented and transformed by neural activity across the sensorimotor axis remains poorly understood. Here, we recorded neural activity with Neuropixels probes across dozens of brain regions while mice performed a visual change-detection task. We trained mice to detect a sustained increase in temporal-frequency (TF) of a drifting grating stimulus, whose speed fluctuates stochastically around the mean of 1Hz. The task requires mice

…are present from low-level sensory areas to high-level areas. According to this view, the neural dynamics implements some forms of probabilistic message passing (i.e. neural sampling, probabilistic pop

Decision-making in noisy environments with constant sensory evidence involves integrating sequentially-sampled evidence, a strategy formalized by diffusion models which is supported by decades behavioral and neural findings. By contrast, it is unknown whether this strategy is also used during decision-making when the underlying sensory evidence is expected to change. Here, we trained monkeys to identify the dominant color of a dynamically refreshed checkerboard pattern that doesn't become informative until after a variable delay. Animals' behavioral responses were briefly suppressed after an a

…consistent strategy to integrate sensory evidence and form decisions during an experiment. In this talk, I aim to convince you that this common view is incorrect. I will show results from applying a latent variable framework, the “GLM-HMM”, to hundreds of thousands of trials of mouse choice data. Our analysis reveals that mice don’t lapse. Instead, mice switch back and forth between engaged and disengaged behavior within a single session, and each mode of behavior lasts tens to hundreds of trials.

…continuously tracking the appropriate sensory evidence while discarding noisy acoustic variations. Despite the apparent simplicity of this perceptual phenomenon, the neural basis of the extraction of relevant information in complex continuous streams for goal-directed behavior is