Ralf M. Haefner

This study analyzes 59 macaque studies to assess factors influencing choice probability, finding consistent patterns across tasks and regions with exceptions in bistable percepts and area V1.

This paper shows that Bayesian causal inference explains both perceptual and neuronal center-surround motion effects in area MT by linking motion integration and contextual response modulation.

The paper develops a Bayesian method to estimate perceptual biases in animals during task performance and adjusts rewards based on these estimates to minimize bias distortion.

The paper distinguishes between Bayesian Encoding and Decoding frameworks, showing they can coexist and explain neural activity differently.

This study shows how reference frame demands shape touch perception by integrating tactile and proprioceptive cues in a task-specific manner, with faster reaction times and lower decision thresholds in a Sternum-centric task.

The paper uses CNNs to analyze contextual modulation in visual cortex by synthesizing surround patterns, finding that pattern completion enhances responses while disruption suppresses them, consistent across mice and macaques.

The paper tests how task-uninformative visual stimuli affect auditory spatial discrimination, finding no significant increase in effect size with shorter stimuli.

The paper links task-induced neural covariability to approximate Bayesian inference, showing it reflects internal models and learning processes.

This paper provides theoretical and experimental evidence that choice-related neural signals depend on stimulus information content, introducing new methods and models to better assess neuron contributions to perceptual decisions.

The paper shows that task-related feedback changes spike-count correlations in V1 neurons, indicating feedback dynamics shape correlated variability rather than shared afferents.

The paper uses pupil size to infer decision confidence in monkeys, supporting an integration-to-bound model of perceptual decision-making.

The paper reinterprets neural correlations as influenced by internal variables and shows how these can be modeled using tuning functions to better understand sensory responses.

The paper extends the understanding of decision-related signals in sensory neurons by analyzing their dependence on stimuli, choice bias, and feedback, providing new analytical tools for studying perceptual decisions.