European Journal of Neuroscience
«Cross-Modal Transfer as a Window Into the Investigation of Recent Stimulus and Response History in Perceptual Decision-Making»
Fitze, Schlunegger & Mast (2026)
European Journal of Neuroscience, 63, e70436. https://doi.org/10.1111/ejn.70436
Abstract
Perceptual decisions are shaped by recent stimulus and response history, yet these history effects vary with experimental design and task structure. Their origins, the processing stages involved, the factors that determine their magnitude and the sources of individual differences require further investigation. We propose a multisensory approach that leverages cross-modal transfer of history effects as a diagnostic tool to address these open questions. Visual–vestibular stimuli are particularly suitable because both modalities contribute to estimating the body’s position in space and they likely share central processing stages. This approach allows for manipulation of response format, independently of sensory stimulation. By moving beyond isolated sensory channels and examine perceptual decisions as they occur in everyday multisensory environments, we provide a framework to investigate when, how and why recent history shapes perception.
iScience
«Probabilistic integration of preceding responses explains response bias in perceptual decision making»
Schlunegger & Mast (2023)
iScience, 26(7), 107123. https://doi.org/10.1016/j.isci.2023.107123
Abstract
Expectations of sensory information change not only how well but also what we perceive. Even in an unpredictable environment, the brain is by default constantly engaged in computing probabilities between sensory events. These estimates are used to generate predictions about future sensory events. Here, we investigated the predictability of behavioral responses using three different learning models in three different one-interval two-alternative forced choice experiments with either auditory, vestibular, or visual stimuli. Results indicate that recent decisions, instead of the sequence of generative stimuli, cause serial dependence. By bridging the gap between sequence learning and perceptual decision making, we provide a novel perspective on sequential choice effects. We propose that serial biases reflect the tracking of statistical regularities of the decision variable, offering a broader understanding of this phenomenon.
PLOS Computational Biology
«The Sense of Confidence during Probabilistic Learning: A Normative Account.»
Meyniel, Schlunegger, & Dehaene (2015)
PLoS Comput Biol 11(6): e1004305. doi:10.1371/journal.pcbi.1004305
Abstract
Learning in a stochastic environment consists of estimating a model from a limited amount of noisy data, and is therefore inherently uncertain. However, many classical models reduce the learning process to the updating of parameter estimates and neglect the fact that learning is also frequently accompanied by a variable “feeling of knowing” or confidence. The characteristics and the origin of these subjective confidence estimates thus remain largely unknown. Here we investigate whether, during learning, humans not only infer a model of their environment, but also derive an accurate sense of confidence from their inferences. In our experiment, humans estimated the transition probabilities between two visual or auditory stimuli in a changing environment, and reported their mean estimate and their confidence in this report. To formalize the link between both kinds of estimate and assess their accuracy in comparison to a normative reference, we derive the optimal inference strategy for our task. Our results indicate that subjects accurately track the likelihood that their inferences are correct. Learning and estimating confidence in what has been learned appear to be two intimately related abilities, suggesting that they arise from a single inference process. We show that human performance matches several properties of the optimal probabilistic inference. In particular, subjective confidence is impacted by environmental uncertainty, both at the first level (uncertainty in stimulus occurrence given the inferred stochastic characteristics) and at the second level (uncertainty due to unexpected changes in these stochastic characteristics). Confidence also increases appropriately with the number of observations within stable periods. Our results support the idea that humans possess a quantitative sense of confidence in their inferences about abstract non-sensory parameters of the environment. This ability cannot be reduced to simple heuristics; it seems instead a core property of the learning process.