Biology subjects

Beierholm, U.

Publications and source records attributed to Beierholm, U..

10 recordsLinked to original sources

bioRxiv2026

Analyzing multisensory integration: dos and donts

Multisensory perception is a cornerstone paradigm for understanding how the brain constructs coherent representations of the world from noisy, fragmented sensory inputs. For decades, researchers have used the magnitude of crossmodal illusions, the width of the temporal binding window, and related behavioral indices as direct proxies for integration strength, and have leveraged these measures to compare multisensory function across developmental, clinical, and aging populations. Here we argue that this descriptive practice is fundamentally compromised: behavioral readouts of multisensory integration are composite measures jointly shaped by unisensory precision, amodal priors, and the binding process itself, and cannot be interpreted in isolation. Drawing on simulations within a Bayesian Causal Inference framework, we show how identical behavioral patterns can arise from very different underlying causes, leading to systematic misattribution of group differences to deficits or enhancements in integration. We review complementary computational frameworks, including drift diffusion, multisensory correlation detection, and statistical facilitation models, and outline their respective explanatory limits. Finally, we provide a model-based inference pipeline, from experimental design and unisensory baselines to parameter estimation and interpretation, that disentangles sensory fidelity, prior expectations, and integrative tendency. Adopting this normative approach is essential for cumulative progress in basic multisensory research and for its translation to neuropsychiatric assessment, lifespan research, and artificial perceptual systems.

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bioRxiv2025

Crossmodal Interaction of Flashes and Beeps Across Time and Number Follows Bayesian Causal Inference

Multisensory perception requires the brain to dynamically infer causal relationships between sensory inputs across various dimensions, such as temporal and spatial attributes. Traditionally, Bayesian Causal Inference (BCI) models have generally provided a robust framework for understanding sensory processing in unidimensional settings where stimuli across sensory modalities vary along one dimension such as spatial location, or numerosity (Samad et al., 2015). However, real-world sensory processing involves multidimensional cues, where the alignment of information across multiple dimensions influences whether the brain perceives a unified or segregated source. In an effort to investigate sensory processing in more realistic conditions, this study introduces an expanded BCI model that incorporates multidimensional information, specifically numerosity and temporal discrepancies. Using a modified sound-induced flash illusion (SiFI) paradigm with manipulated audiovisual disparities, we tested the performance of the enhanced BCI model. Results showed that integration probability decreased with increasing temporal discrepancies, and our proposed multidimensional BCI model accurately predicts multisensory perception outcomes under the entire range of stimulus conditions. This multidimensional framework extends the BCI models applicability, providing deeper insights into the computational mechanisms underlying multisensory processing and offering a foundation for future quantitative studies on naturalistic sensory processing.

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bioRxiv2024

BCI Toolbox: An Open-Source Python Package for the Bayesian Causal Inference Model

Psychological and neuroscientific research over the past two decades has shown that the Bayesian causal inference (BCI) is a potential unifying theory that can account for a wide range of perceptual and sensorimotor processes in humans. Therefore, we introduce the BCI Toolbox, a statistical and analytical tool in Python, enabling researchers to conveniently perform quantitative modeling and analysis of behavioral data. Additionally, we describe the algorithm of the BCI model and test its stability and reliability via parameter recovery. The present BCI toolbox offers a robust platform for BCI model implementation as well as a hands-on tool for learning and understanding the model, facilitating its widespread use and enabling researchers to delve into the data to uncover underlying cognitive mechanisms.

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bioRxiv2021

Perceptual clustering in auditory streaming

Perception is dependent on the ability to separate stimuli from different objects and causes in order to perform inference and further processing. We have models of how the human brain can perform such causal inference for simple binary stimuli (e.g., auditory and visual), but the complexity of the models increases dramatically with more than two stimuli. To characterize human perception with more complex stimuli, we developed a Bayesian inference model that takes into account a potentially unlimited number of stimulus sources: it is general enough to factor in any discrete sequential cues from any modality. Because the model employs a non-parametric prior, increased signal complexity does not necessitate the addition of more parameters. The model not only predicts the number of possible sources, but also specifies the source with which each signal is associated. As a test case, we demonstrate that such a model can explain several phenomena in the auditory stream perception literature, that it provides an excellent fit to experimental data, and that it makes novel predictions that we experimentally confirm. These findings have implications not just for human auditory temporal perception, but for a wide range of perceptual phenomena across unisensory and multisensory stimuli. Author summaryPerceiving the world requires humans to organize perceptual stimuli according to the likely sources that generated them, requiring inference about these sources and their relationship with the generated stimuli. As an example, the sound of two different species of song birds should be segregated in order to better identify them. In this paper we utilize ideas from statistics to propose a way for the mind to do the allocation of stimuli to sources, that is not restricted by the number of stimuli, and that instead combines information dynamically as the complexity grows. We show that this not only qualitatively explains known phenomena in auditory perception, but can also quantitatively explain behavior, and leads to an experimental prediction that we confirm.

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bioRxiv2021

Dopamine and reward-related vigor in younger and older human participants

Vigor reflects how motivated one is to respond to a stimulus. We previously showed that humans are more vigorous when more reward is available on average, and that this relationship is modulated by the dopamine precursor levodopa. Dopamine signalling and probabilistic reward learning degrade with age, so the relationship between vigor and reward should change with age. We test this and assess whether the relationship between vigor and reward correlates with D1 dopamine receptor availability measured using Positron Emission Tomography. We measured response times of 30 older and 30 younger subjects during an oddball discrimination task where rewards varied systematically between trial. Reward rate had a similar impact on the vigor of both groups. We observed a weak positive association across subjects between ventral striatal dopamine receptor availability and effect of average reward rate on response time, which was in the opposite direction to our prediction. Overall, the effect of reward on response vigor is similar between younger and older humans and is weakly sensitive to dopamine D1 receptor availability.

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bioRxiv2021

Central tendency biases must be accounted for to consistently capture Bayesian cue combination in continuous response data

Observers in perceptual tasks are often reported to combine multiple sensory cues in a weighted average that improves precision - in some studies, approaching statistically-optimal (Bayesian) weighting, but in others departing from optimality, or not benefitting from combined cues at all. To correctly conclude which combination rules observers use, it is crucial to have accurate measures of their sensory precision and cue weighting. Here, we present a new approach for accurately recovering these parameters in perceptual tasks with continuous responses. Continuous responses have many advantages, but are susceptible to a central tendency bias, where responses are biased towards the central stimulus value. We show such biases lead to inaccuracies in estimating both precision gains and cue weightings, two key measures used to assess sensory cue combination. We introduce a method that estimates sensory precision by regressing continuous responses on targets and dividing the variance of the residuals by the squared slope of the regression line, "correcting-out" the error introduced by the central bias and increasing statistical power. We also suggest a complementary analysis that recovers the sensory cue weights. Using both simulations and empirical data, we show that the proposed methods can accurately estimate sensory precision and cue weightings in the presence of central tendency biases. We conclude that central tendency biases should be (and can easily be) accounted for to consistently capture Bayesian cue combination in continuous response data.

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bioRxiv2019

Bayesian transfer in a complex spatial localisation task

Prior knowledge can help observers in various situations. Adults can simultaneously learn two location priors and integrate these with sensory information to locate hidden objects. Importantly, observers weight prior and sensory (likelihood) information differently depending on their respective reliabilities, in line with principles of Bayesian inference. Yet, there is limited evidence that observers actually perform Bayesian inference, rather than a heuristic, such as forming a look-up table. To distinguish these possibilities, we ask whether previously-learnt priors will be immediately integrated with a new, untrained likelihood. If observers use Bayesian principles, they should immediately put less weight on the new, less reliable, likelihood ("Bayesian transfer"). In an initial experiment, observers estimated the position of a hidden target, drawn from one of two distinct distributions, using sensory and prior information. The sensory cue consisted of dots drawn from a Gaussian distribution centred on the true location with either low, medium, or high variance; the latter introduced after block three of five to test for evidence of Bayesian transfer. Observers did not weight the cue (relative to the prior) significantly less in the high compared to medium variance condition, counter to Bayesian predictions. However, when explicitly informed of the different prior variabilities, observers placed less weight on the new high variance likelihood ("Bayesian transfer"), yet substantially diverged from ideal. Much of this divergence can be captured by a model that weights sensory information, according only to internal noise in using the cue. These results emphasise the limits of Bayesian models in complex tasks.

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bioRxiv2019

Using the past to estimate sensory uncertainty

To form the most reliable percept of the environment, the brain needs to represent sensory uncertainty. Current theories of perceptual inference assume that the brain computes sensory uncertainty instantaneously and independently for each stimulus.\n\nIn a series of psychophysics experiments human observers localized auditory signals that were presented in synchrony with spatially disparate visual signals. Critically, the visual noise changed dynamically over time with or without intermittent jumps. Our results show that observers integrate audiovisual inputs weighted by sensory reliability estimates that combine information from past and current signals as predicted by an optimal Bayesian learner or approximate strategies of exponential discounting\n\nOur results challenge classical models of perceptual inference where sensory uncertainty estimates depend only on the current stimulus. They demonstrate that the brain capitalizes on the temporal dynamics of the external world and estimates sensory uncertainty by combining past experiences with new incoming sensory signals.

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bioRxiv2018

Older adults sacrifice response speed to preserve multisensory integration performance

Ageing has been shown to impact multisensory perception, but the underlying computational mechanisms are unclear. For effective interactions with the environment, observers should integrate signals that share a common source, weighted by their reliabilities, and segregate those from separate sources. Observers are thought to accumulate evidence about the worlds causal structure over time until a decisional threshold is reached.\n\nCombining psychophysics and Bayesian modelling, we investigated how ageing affects audiovisual perception of spatial signals. Older and younger adults were comparable in their final localisation and common-source judgement responses under both speeded and unspeeded conditions, but were disproportionately slower for audiovisually incongruent trials.\n\nBayesian modelling showed that ageing did not affect the ability to arbitrate between integration and segregation under either unspeeded or speeded conditions. However, modelling the within-trial dynamics of evidence accumulation under speeded conditions revealed that older observers accumulate noisier auditory representations for longer, set higher decisional thresholds, and have impaired motor speed. Older observers preserve audiovisual localisation performance, despite noisier sensory representations, by sacrificing response speed.

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bioRxiv2017

A Non-Parametric Bayesian Prior For Causal Inference Of Auditory Streaming

Human perceptual grouping of sequential auditory cues has traditionally been modeled using a mechanistic approach. The problem however is essentially one of source inference - a problem that has recently been tackled using statistical Bayesian models in visual and auditory-visual modalities. Usually the models are restricted to performing inference over just one or two possible sources, but human perceptual systems have to deal with much more complex scenarios. To characterize human perception we have developed a Bayesian inference model that allows an unlimited number of signal sources to be considered: it is general enough to allow any discrete sequential cues, from any modality. The model uses a non-parametric prior, hence increased complexity of the signal does not necessitate more parameters. The model not only determines the most likely number of sources, but also specifies the source that each signal is associated with. The model gives an excellent fit to data from an auditory stream segregation experiment in which the pitch and presentation rate of pure tones determined the perceived number of sources.

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