Combining symbolic cues with sensory input and prior experience in an iterative bayesian framework

Relative cue precision and prior knowledge contribute to the preference of proximal and distal landmarks in human orientation

A prevailing argument posits that distal landmarks dominate over proximal landmarks as orientation cues. However, no studies have tested this argument or examined the underlying mechanisms. This project aimed to close this gap by examining the roles of relative cue precision and prior knowledge in cue preference. Participants learned object locations with proximal and distal landmarks in an immersive virtual environment. After walking a path without seeing objects or landmarks, participants disoriented themselves by spinning in place and pointed to the objects with the reappearance of a proximal landmark being rotated -50°, a distal landmark being rotated 50°, or both (Conflict). Heading errors were examined. Experiment 1 manipulated the relative cue precision. Results showed that in Conflict condition, the observed weight on the distal cue (exceeding 0.5) changed with but remained higher than the weight predicted by the relative cue precision. This indicates that besides the relative cue precision, prior knowledge of distal cue dominance also influences orientation cue usage. In Experiments 2 and 3, participants walked a path stopping at one object location. Participants were informed of it explicitly in Experiment 2 but not in Experiment 3. Results showed that distal cue dominance still occurred in Experiment 3. However, in Experiment 2, proximal cue dominance appeared, and it was not predicted by the relative cue precision. These results suggest that prior knowledge of proximal cue dominance might have been invoked by the instruction of locations. Consistent with the Bayesian inference model, human cue usage in orientation is determined by relative cue precision and prior knowledge. The choice of prior knowledge can be influenced by instructions.

Rapid calibration to dynamic temporal contexts

The prediction of future events and the preparation of appropriate behavioural reactions rely on an accurate perception of temporal regularities. In dynamic environments, temporal regularities are subject to slow and sudden changes, and adaptation to these changes is an important requirement for efficient behaviour. Bayesian models have proven a useful tool to understand the processing of temporal regularities in humans; yet an open question pertains to the degree of flexibility of the prior that is required for optimal modelling of behaviour. Here we directly compare dynamic models (with continuously changing prior expectations) and static models (a stable prior for each experimental session) with their ability to describe regression effects in interval timing. Our results show that dynamic Bayesian models are superior when describing the responses to slow, continuous environmental changes, whereas static models are more suitable to describe responses to sudden changes. In time perception research, these results will be informative for the choice of adequate computational models and enhance our understanding of the neuronal computations underlying human timing behaviour.

Introspective inference counteracts perceptual distortion

Introspective agents can recognize the extent to which their internal perceptual experiences deviate from the actual states of the external world. This ability, also known as insight, is critically required for reality testing and is impaired in psychosis, yet little is known about its cognitive underpinnings. We develop a Bayesian modeling framework and a psychophysics paradigm to quantitatively characterize this type of insight while people experience a motion after-effect illusion. People can incorporate knowledge about the illusion into their decisions when judging the actual direction of a motion stimulus, compensating for the illusion (and often overcompensating). Furthermore, confidence, reaction-time, and pupil-dilation data all show signatures consistent with inferential adjustments in the Bayesian insight model. Our results suggest that people can question the veracity of what they see by making insightful inferences that incorporate introspective knowledge about internal distortions.

Context-specific and context-invariant computations of interval timing

Introduction

An accurate sense of time is crucial in flexible sensorimotor control and other cognitive functions. However, it remains unknown how multiple timing computations in different contexts interact to shape our behavior.

Methods

We asked 41 healthy human subjects to perform timing tasks that differed in the sensorimotor domain (sensory timing vs. motor timing) and effector (hand vs. saccadic eye movement). To understand how these different behavioral contexts contribute to timing behavior, we applied a three-stage Bayesian model to behavioral data.

Results

Our results demonstrate that the Bayesian model for each effector could not describe bias in the other effector. Similarly, in each task the model-predicted data could not describe bias in the other task. These findings suggest that the measurement stage of interval timing is context-specific in the sensorimotor and effector domains. We also showed that temporal precision is context-invariant in the effector domain, unlike temporal accuracy.

Discussion

This combination of context-specific and context-invariant computations across sensorimotor and effector domains suggests overlapping and distributed computations as the underlying mechanism of timing in different contexts.

What determines location specificity or generalization of transsaccadic learning?

Humans incorporate knowledge of transsaccadic associations into peripheral object perception. Several studies have shown that learning of new manipulated transsaccadic associations leads to a presaccadic perceptual bias. However, there was still disagreement whether this learning effect was location specific (Herwig, Weiß, & Schneider, 2018) or generalizes to new locations (Valsecchi & Gegenfurtner, 2016). The current study investigated under what conditions location generalization of transsaccadic learning occurs. In all experiments, there were acquisition phases in which the spatial frequency (Experiment 1) or the size (Experiment 2 and 3) of objects was changed transsaccadically. In the test phases, participants judged the respective feature of peripheral objects. These could appear either at the location where learning had taken place or at new locations. All experiments replicated the perceptual bias effect at the old learning locations. In two experiments, transsaccadic learning remained location specific even when learning occurred at multiple locations (Experiment 1) or with the feature of size (Experiment 2) for which a transfer had previously been shown. Only in Experiment 3 was a transfer of the learning effect to new locations observable. Here, learning only took place for one object and not for several objects that had to be discriminated. Therefore, one can conclude that, when specific associations are learned for multiple objects, transsaccadic learning stays location specific and when a transsaccadic association is learned for only one object it allows a generalization to other locations.

Eliciting contextual temporal calibration: The effect of bottom-up and top-down information in reproduction tasks

Bayesian integration assumes that a current observation is integrated with previous observations. An example in the temporal domain is the central tendency effect: when a range of durations is presented, a regression towards the mean is observed. Furthermore, a context effect emerges if a partially overlapping lower and a higher range of durations is presented in a blocked design, with the overlapping durations pulled towards the mean duration of the block. We determine under which conditions this context effect is observed, and whether explicit cues strengthen the effect. Each block contained either two or three durations, with one duration present in both blocks. We provided either no information at the start of each block about the nature of that block, provided written ("short" / "long" or "A" / "B") categorizations, or operationalized pitch (low vs high) to reflect the temporal context. We demonstrate that (1) the context effect emerges as long as sufficiently distinct durations are presented; (2) the effect is not modulated by explicit instructions or other cues; (3) just a single additional duration is sufficient to produce a context effect. Taken together, these results provide information on the most efficient operationalization to evoke the context effect, allowing for highly economical experimental designs, and highlights the automaticity by which priors are constructed.

Sustained effects of corrupted feedback on perceptual inference

Feedback is central to most forms of learning, and its reliability is therefore critical. Here, we investigated the effects of corrupted, and hence unreliable, feedback on perceptual inference. Within the framework of Bayesian inference, we hypothesised that corrupting feedback in a demanding perceptual task would compromise sensory information processing and bias inference towards prior information if available. These hypotheses were examined by a simulation and in two behavioural experiments with visual detection (experiment 1) and discrimination (experiment 2) tasks. Both experiments consisted of two sessions comprising intervention runs with either corrupted or uncorrupted (correct) feedback, and pre- and post-intervention tests to assess the effects of feedback. In the tests alone, additional prior beliefs were induced through predictive auditory cues to assess sustained effects of feedback on the balance between sensory evidence and prior beliefs. Both experiments and the simulation showed the hypothesised decrease in performance and increased reliance on prior beliefs after corrupted but not uncorrupted feedback. Exploratory analyses indicated reduced confidence regarding perceptual decisions during delivery of corrupted feedback. Our results suggest that corrupted feedback on perceptual decisions leads to sustained changes in perceptual inference, characterised by a shift from sensory likelihood to prior beliefs when those are accessible.

Generalization of prior information for rapid Bayesian time estimation

To enable effective interaction with the environment, the brain combines noisy sensory information with expectations based on prior experience. There is ample evidence showing that humans can learn statistical regularities in sensory input and exploit this knowledge to improve perceptual decisions and actions. However, fundamental questions remain regarding how priors are learned and how they generalize to different sensory and behavioral contexts. In principle, maintaining a large set of highly specific priors may be inefficient and restrict the speed at which expectations can be formed and updated in response to changes in the environment. However, priors formed by generalizing across varying contexts may not be accurate. Here, we exploit rapidly induced contextual biases in duration reproduction to reveal how these competing demands are resolved during the early stages of prior acquisition. We show that observers initially form a single prior by generalizing across duration distributions coupled with distinct sensory signals. In contrast, they form multiple priors if distributions are coupled with distinct motor outputs. Together, our findings suggest that rapid prior acquisition is facilitated by generalization across experiences of different sensory inputs but organized according to how that sensory information is acted on.

Inference of perceptual priors from path dynamics of passive self-motion

The monitoring of one's own spatial orientation depends on the ability to estimate successive self-motion cues accurately. This process has become to be known as path integration. A feature of sequential cue estimation, in general, is that the history of previously experienced stimuli, or priors, biases perception. Here, we investigate how during angular path integration, the prior imparted by the displacement path dynamics affects the translation of vestibular sensations into perceptual estimates. Subjects received successive whole-body yaw rotations and were instructed to report their position within a virtual scene after each rotation. The overall movement trajectory either followed a parabolic path or was devoid of explicit dynamics. In the latter case, estimates were biased toward the average stimulus prior and were well captured by an optimal Bayesian estimator model fit to the data. However, the use of parabolic paths reduced perceptual uncertainty, and a decrease of the average size of bias and thus the weight of the average stimulus prior were observed over time. The produced estimates were, in fact, better accounted for by a model where a prediction of rotation magnitude is inferred from the underlying path dynamics on each trial. Therefore, when passively displaced, we seem to be able to build, over time, from sequential vestibular measurements an internal model of the vehicle's movement dynamics. Our findings suggest that in ecological conditions, vestibular afference can be internally predicted, even when self-motion is not actively generated by the observer, thereby augmenting both the accuracy and precision of displacement perception.