In many everyday situations, our senses are bombarded by many different unisensory signals at any given time. To gain the most veridical, and least variable, estimate of environmental stimuli/properties, we need to combine the individual noisy unisensory perceptual estimates that refer to the same object, while keeping those estimates belonging to different objects or events separate. How, though, does the brain “know” which stimuli to combine? Traditionally, researchers interested in the crossmodal binding problem have focused on the roles that spatial and temporal factors play in modulating multisensory integration. However, crossmodal correspondences between various unisensory features (such as between auditory pitch and visual size) may provide yet another important means of constraining the crossmodal binding problem. A large body of research now shows that people exhibit consistent crossmodal correspondences between many stimulus features in different sensory modalities. For example, people consistently match high-pitched sounds with small, bright objects that are located high up in space. The literature reviewed here supports the view that crossmodal correspondences need to be considered alongside semantic and spatiotemporal congruency, among the key constraints that help our brains solve the crossmodal binding problem.

/pdf/crossmodal-correspondences-a-tutorial-review-20vme8gbi3.pdf

Crossmodal correspondences: A tutorial review

Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks. The results show that indeed humans can efficiently infer the causal structure as well as the location of causes. By combining insights from the study of causal inference with the ideal-observer approach to sensory cue combination, we show that the capacity to infer causal structure is not limited to conscious, high-level cognition; it is also performed continually and effortlessly in perception.

/pdf/causal-inference-in-multisensory-perception-57tf03r3ls.pdf

Causal inference in multisensory perception.

Computational learning models are critical for understanding mechanisms of adaptive behavior. However, the two major current frameworks, reinforcement learning (RL) and Bayesian learning, both have certain limitations. For example, many Bayesian models are agnostic of inter-individual variability and involve complicated integrals, making online learning difficult. Here, we introduce a generic hierarchical Bayesian framework for individual learning under multiple forms of uncertainty (e.g., environmental volatility and perceptual uncertainty). The model assumes Gaussian random walks of states at all but the first level, with the step size determined by the next higher level. The coupling between levels is controlled by parameters that shape the influence of uncertainty on learning in a subject-specific fashion. Using variational Bayes under a mean field approximation and a novel approximation to the posterior energy function, we derive trial-by-trial update equations which (i) are analytical and extremely efficient, enabling real-time learning, (ii) have a natural interpretation in terms of RL, and (iii) contain parameters representing processes which play a key role in current theories of learning, e.g., precision-weighting of prediction error. These parameters allow for the expression of individual differences in learning and may relate to specific neuromodulatory mechanisms in the brain. Our model is very general: it can deal with both discrete and continuous states and equally accounts for deterministic and probabilistic relations between environmental events and perceptual states (i.e., situations with and without perceptual uncertainty). These properties are illustrated by simulations and analyses of empirical time series. Overall, our framework provides a novel foundation for understanding normal and pathological learning that contextualizes RL within a generic Bayesian scheme and thus connects it to principles of optimality from probability theory.

/pdf/a-bayesian-foundation-for-individual-learning-under-1ysqhh9mrb.pdf

A Bayesian Foundation for Individual Learning Under Uncertainty

Rapid reaching to a target is generally accurate but also contains random and systematic error. Random errors result from noise in visual measurement, motor planning, and reach execution. Systematic error results from systematic changes in the mapping between the visual estimate of target location and the motor command necessary to reach the target (e.g., new spectacles, muscular fatigue). Humans maintain accurate reaching by recalibrating the visuomotor system, but no widely accepted computational model of the process exists. Given certain boundary conditions, a statistically optimal solution is a Kalman filter. We compared human to Kalman filter behavior to determine how humans take into account the statistical properties of errors and the reliability with which those errors can be measured. For most conditions, human and Kalman filter behavior was similar: Increasing measurement uncertainty caused similar decreases in recalibration rate; directionally asymmetric uncertainty caused different rates in different directions; more variation in systematic error increased recalibration rate. However, behavior differed in one respect: Inserting random error by perturbing feedback position causes slower adaptation in Kalman filters but had no effect in humans. This difference may be due to how biological systems remain responsive to changes in environmental statistics. We discuss the implications of this work.

The statistical determinants of adaptation rate in human reaching.

http://www.gatsby.ucl.ac.uk/~beierh/shams_beierholm10.pdf

Causal inference in perception

The purpose of the present experiment was to investigate the integration of sequences of visual and tactile events. Subjects were presented with sequences of visual flashes and tactile taps simultaneously and instructed to count either the flashes (Session 1) or the taps (Session 2). The number of flashes could differ from the number of taps by +/-1. For both sessions, the perceived number of events was significantly influenced by the number of events presented in the task-irrelevant modality. Touch had a stronger influence on vision than vision on touch. Interestingly, touch was the more reliable of the two modalities-less variable estimates when presented alone. For both sessions, the perceptual estimates were less variable when stimuli were presented in both modalities than when the task-relevant modality was presented alone. These results indicate that even when one signal is explicitly task irrelevant, sensory information tends to be automatically integrated across modalities. They also suggest that the relative weight of each sensory channel in the integration process depends on its relative reliability. The results are described using a Bayesian probabilistic model for multimodal integration that accounts for the coupling between the sensory estimates.

http://perso.unifr.ch/jean-pierre.bresciani/publications/Bresciani06.pdf

Vision and touch are automatically integrated for the perception of sequences of events.

Vision and touch are automatically integrated for the perception of sequences of events

http://perso.unifr.ch/jean-pierre.bresciani/publications/Bresciani08.pdf

Tri-modal integration of visual, tactile and auditory signals for the perception of sequences of events

We investigated the interactions between visual, tactile and auditory sensory signals for the perception of sequences of events. Sequences of flashes, taps and beeps were presented simultaneously. For each session, subjects were instructed to count the number of events presented in one modality (Target) and to ignore the stimuli presented in the other modalities (Background). The number of events presented in the background sequence could differ from the number of events in the target sequence. For each session, we quantified the Background-evoked bias by comparing subjects’ responses with and without Background (Target presented alone). Nine combinations between vision, touch and audition were tested. In each session but two, the Background significantly biased the Target. Vision was the most susceptible to Background-evoked bias and the least efficient in biasing the other two modalities. By contrast, audition was the least susceptible to Background-evoked bias and the most efficient in biasing the other two modalities. These differences were strongly correlated to the relative reliability of each modality. In line with this, the evoked biases were larger when the Background consisted of two instead of only one modality. These results show that for the perception of sequences of events: (1) vision, touch and audition are automatically integrated; (2) the respective contributions of the three modalities to the integrated percept differ; (3) the relative contribution of each modality depends on its relative reliability

Franziska Dammeier

Papers

Vision and touch are automatically integrated for the perception of sequences of events.

Vision and touch are automatically integrated for the perception of sequences of events

Tri-modal integration of visual, tactile and auditory signals for the perception of sequences of events

Research report Tri-modal integration of visual, tactile and auditory signals for the perception of sequences of events

Automatic integration of visual, tactile and auditory signals for the perception of sequences of events