scholarly journals Tactile length contraction as Bayesian inference

2016 ◽  
Vol 116 (2) ◽  
pp. 369-379 ◽  
Author(s):  
Jonathan Tong ◽  
Vy Ngo ◽  
Daniel Goldreich
Keyword(s):  
Bayesian Inference ◽  
Inference Process ◽  
Low Velocity ◽  
Temporal Separation ◽  
Length Contraction ◽  
Significant Length ◽  
Reference Distance ◽  
Fixed Reference ◽  
Observer Model ◽  
The Brain

To perceive, the brain must interpret stimulus-evoked neural activity. This is challenging: The stochastic nature of the neural response renders its interpretation inherently uncertain. Perception would be optimized if the brain used Bayesian inference to interpret inputs in light of expectations derived from experience. Bayesian inference would improve perception on average but cause illusions when stimuli violate expectation. Intriguingly, tactile, auditory, and visual perception are all prone to length contraction illusions, characterized by the dramatic underestimation of the distance between punctate stimuli delivered in rapid succession; the origin of these illusions has been mysterious. We previously proposed that length contraction illusions occur because the brain interprets punctate stimulus sequences using Bayesian inference with a low-velocity expectation. A novel prediction of our Bayesian observer model is that length contraction should intensify if stimuli are made more difficult to localize. Here we report a tactile psychophysical study that tested this prediction. Twenty humans compared two distances on the forearm: a fixed reference distance defined by two taps with 1-s temporal separation and an adjustable comparison distance defined by two taps with temporal separation t ≤ 1 s. We observed significant length contraction: As t was decreased, participants perceived the two distances as equal only when the comparison distance was made progressively greater than the reference distance. Furthermore, the use of weaker taps significantly enhanced participants' length contraction. These findings confirm the model's predictions, supporting the view that the spatiotemporal percept is a best estimate resulting from a Bayesian inference process.

scholarly journals Attention as a Bayesian inference process

2011 ◽  
Author(s):  
Sharat Chikkerur ◽  
Thomas Serre ◽  
Cheston Tan ◽  
Tomaso Poggio
Keyword(s):  
Bayesian Inference ◽  
Inference Process

scholarly journals The self as a prior for the other: social learning under paranoia

2021 ◽  
Author(s):  
Joseph M Barnby ◽  
Nichola Raihani ◽  
Peter Dayan
Keyword(s):  
Bayesian Inference ◽  
Social Interactions ◽  
Social Preferences ◽  
Value Orientation ◽  
Social Value ◽  
The Self ◽  
The Other ◽  
Social Value Orientation ◽  
Inference Process ◽  
Social Partners

To benefit from social interactions, people need to predict how their social partners will behave. Such predictions arise through integrating prior expectations with evidence from observations, but where the priors come from and whether they influence the integration is not clear. Furthermore, this process can be affected by factors such as paranoia, in which the tendency to form biased impressions of others is common. Using a modified social value orientation (SVO) task in a large online sample (n=697), we showed that participants used a Bayesian inference process to learn about partners, with priors that were based on their own preferences. Paranoia was associated with preferences for earning more than a partner and less flexible beliefs regarding a partner’s social preferences. Alignment between the preferences of participants and their partners was associated with better predictions and with reduced attributions of harmful intent to partners.

Surviving a Self-inflicted Transorbital Pen Intrusion to the Cerebellum: Case Report

2021 ◽  
Author(s):  
David Breuskin ◽  
Ralf Ketter ◽  
Joachim Oertel
Keyword(s):  
Case Report ◽  
Ct Scan ◽  
Foreign Bodies ◽  
Routine Procedure ◽  
Low Velocity ◽  
Foreign Objects ◽  
Surgical Exposure ◽  
The Right ◽  
The Brain ◽  
Do So

Abstract Background Although intracranial traumas by penetrating foreign objects are not absolute rarities, the nature of trauma, the kind of object, and its trajectory make them a one of a kind case every time they occur. Whereas high-velocity traumas mostly result in fatalities, it is the low-velocity traumas that demand an individualized surgical strategy. Methods We present a case report of a 33-year-old patient who was admitted to our department with a self-inflicted transorbital pen injury to the brain. The authors recall the incident and the technique of the pen removal. Results Large surgical exposure of the pen trajectory was considered too traumatic. Therefore, we opted to remove the pen and have an immediate postoperative computed tomography (CT) scan. Due to its fragility, the pen case could only be removed with a screwdriver, inserted into the case. Post-op CT scan showed a small bleeding in the right peduncular region, which was treated conservatively. The patient was transferred back to intensive care unit and woken up the next day. She lost visual function on her right eye, but suffered from no further neurologic deficit. Conclusion Surgical management of removal of intracranial foreign bodies is no routine procedure. Although some would favor a large surgical exposure, we could not think of an approach to do so without maximum surgical efforts. We opted for a minimal surgical procedure with immediate CT scan and achieved an optimal result. We find this case to be worth considering when deciding on a strategy in the future.

scholarly journals Changepoint detection versus reinforcement learning: Separable neural substrates approximate different forms of Bayesian inference

2019 ◽  
Author(s):  
Wolfgang M. Pauli ◽  
Matt Jones
Keyword(s):  
Bayesian Inference ◽  
Neural Substrates ◽  
Statistical Structure ◽  
Statistical Process ◽  
Future Events ◽  
Reward Contingencies ◽  
Foraging Task ◽  
The Brain ◽  
Adaptive Foraging ◽  
Previous Learning

AbstractAdaptive behavior in even the simplest decision-making tasks requires predicting future events in an environment that is generally nonstationary. As an inductive problem, this prediction requires a commitment to the statistical process underlying environmental change. This challenge can be formalized in a Bayesian framework as a question of choosing a generative model for the task dynamics. Previous learning models assume, implicitly or explicitly, that nonstationarity follows either a continuous diffusion process or a discrete changepoint process. Each approach is slow to adapt when its assumptions are violated. A new mixture of Bayesian experts framework proposes separable brain systems approximating inference under different assumptions regarding the statistical structure of the environment. This model explains data from a laboratory foraging task, in which rats experienced a change in reward contingencies after pharmacological disruption of dorsolateral (DLS) or dorsomedial striatum (DMS). The data and model suggest DLS learns under a diffusion prior whereas DMS learns under a changepoint prior. The combination of these two systems offers a new explanation for how the brain handles inference in an uncertain environment.One Sentence SummaryAdaptive foraging behavior can be explained by separable brain systems approximating Bayesian inference under different assumptions about dynamics of the environment.

scholarly journals Choice-induced biases in perception

2016 ◽  
Author(s):  
Long Luu ◽  
Alan A Stocker
Keyword(s):  
Discrimination Task ◽  
Sensory Information ◽  
Motion Direction ◽  
Great Opportunity ◽  
Perceptual Illusion ◽  
Psychophysical Data ◽  
Observer Model ◽  
Discrimination Judgment ◽  
Perceived Motion ◽  
The Brain

AbstractIllusions provide a great opportunity to study how perception is affected by both the observer's expectations and the way sensory information is represented1,2,3,4,5,6. Recently, Jazayeri and Movshon7 reported a new and interesting perceptual illusion, demonstrating that the perceived motion direction of a dynamic random dot stimulus is systematically biased when preceded by a motion discrimination judgment. The authors hypothesized that these biases emerge because the brain predominantly relies on those neurons that are most informative for solving the discrimination task8, but then is using the same neural weighting profile for generating the percept. In other words, they argue that these biases are “mistakes” of the brain, resulting from using inappropriate neural read-out weights. While we were able to replicate the illusion for a different visual stimulus (orientation), our new psychophysical data suggest that the above interpretation is likely incorrect: Biases are not caused by a read-out profile optimized for solving the discrimination task but rather by the specific choices subjects make in the discrimination task on any given trial. We formulate this idea as a conditioned Bayesian observer model and show that it can explain the new as well as the original psychophysical data. In this framework, the biases are not caused by mistake but rather by the brain's attempt to remain ‘self-consistent’ in its inference process. Our model establishes a direct connection between the current perceptual illusion and the well-known phenomena of cognitive consistency and dissonance9,10.

scholarly journals An uncertainty-based model of the effects of fixation on choice

2020 ◽  
Author(s):  
Zhiwei Li ◽  
Wei-Ji Ma
Keyword(s):  
Standard Deviation ◽  
Diffusion Model ◽  
Weighted Sum ◽  
Drift Diffusion Model ◽  
Inference Process ◽  
Time Data ◽  
Drift Diffusion ◽  
Posterior Mean ◽  
The Brain ◽  
Normative Account

When people view a consumable item for a longer amount of time, they choose it more frequently~\cite{krajbich_visual_2010}; this also seems to be the direction of causality~\cite{armel_biasing_2008}. The leading model of this effect is a drift-diffusion model with a fixation-based attentional bias. While this model accounts for the data, it is not normative, in the sense that it does not provide a rationale for this behavioral tendency. Here, we propose a partially normative account for the same data. This account is based on the notion that the brain builds a posterior belief over the value of an item in the same way it would over a sensory variable. As the agent gathers evidence about the item from sensory observations and from retrieved memories, the posterior distribution narrows. We further postulate that the utility of an item is a weighted sum of the posterior mean and the negative posterior standard deviation. Fixating for longer can increase or decrease the posterior mean, but will inevitably lower the posterior standard deviation. This model fits the data approximately as well as the attentional drift-diffusion model. We discuss the often overlooked technical challenges in fitting models simultaneously to choice and response time data in the absence of an analytical expression. Our results contribute to emerging accounts of valuation as an inference process.

scholarly journals Bayesian Comparison of Explicit and Implicit Causal Inference Strategies in Multisensory Heading Perception

2017 ◽  
Author(s):  
Luigi Acerbi ◽  
Kalpana Dokka ◽  
Dora E. Angelaki ◽  
Wei Ji Ma
Keyword(s):  
Causal Inference ◽  
Discrimination Task ◽  
Model Comparison ◽  
Inference Process ◽  
Computational Framework ◽  
Bayesian Model Comparison ◽  
Vestibular Cues ◽  
Multisensory Cues ◽  
The Brain ◽  
Rule Out

AbstractThe precision of multisensory heading perception improves when visual and vestibular cues arising from the same cause, namely motion of the observer through a stationary environment, are integrated. Thus, in order to determine how the cues should be processed, the brain must infer the causal relationship underlying the multisensory cues. In heading perception, however, it is unclear whether observers follow the Bayesian strategy, a simpler non-Bayesian heuristic, or even perform causal inference at all. We developed an efficient and robust computational framework to perform Bayesian model comparison of causal inference strategies, which incorporates a number of alternative assumptions about the observers. With this framework, we investigated whether human observers’ performance in an explicit cause attribution and an implicit heading discrimination task can be modeled as a causal inference process. In the explicit inference task, all subjects accounted for cue disparity when reporting judgments of common cause, although not necessarily all in a Bayesian fashion. By contrast, but in agreement with previous findings, data from the heading discrimination task only could not rule out that several of the same observers were adopting a forced-fusion strategy, whereby cues are integrated regardless of disparity. Only when we combined evidence from both tasks we were able to rule out forced-fusion in the heading discrimination task. Crucially, findings were robust across a number of variants of models and analyses. Our results demonstrate that our proposed computational framework allows researchers to ask complex questions within a rigorous Bayesian framework that accounts for parameter and model uncertainty.

scholarly journals Role of age-related alterations of the cerebral venous circulation in the pathogenesis of vascular cognitive impairment

2019 ◽  
Vol 316 (5) ◽  
pp. H1124-H1140 ◽  
Author(s):  
Gabor A. Fulop ◽  
Stefano Tarantini ◽  
Andriy Yabluchanskiy ◽  
Andrea Molnar ◽  
Calin I. Prodan ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Cerebral Arteries ◽  
Vascular Cognitive Impairment ◽  
Cerebral Veins ◽  
Older Individuals ◽  
Low Velocity ◽  
Venous Circulation ◽  
Age Related ◽  
The Brain

There has been an increasing appreciation of the role of vascular contributions to cognitive impairment and dementia (VCID) associated with old age. Strong preclinical and translational evidence links age-related dysfunction and structural alterations of the cerebral arteries, arterioles, and capillaries to the pathogenesis of many types of dementia in the elderly, including Alzheimer’s disease. The low-pressure, low-velocity, and large-volume venous circulation of the brain also plays critical roles in the maintenance of homeostasis in the central nervous system. Despite its physiological importance, the role of age-related alterations of the brain venous circulation in the pathogenesis of vascular cognitive impairment and dementia is much less understood. This overview discusses the role of cerebral veins in the pathogenesis of VCID. Pathophysiological consequences of age-related dysregulation of the cerebral venous circulation are explored, including blood-brain barrier disruption, neuroinflammation, exacerbation of neurodegeneration, development of cerebral microhemorrhages of venous origin, altered production of cerebrospinal fluid, impaired function of the glymphatics system, dysregulation of cerebral blood flow, and ischemic neuronal dysfunction and damage. Understanding the age-related functional and phenotypic alterations of the cerebral venous circulation is critical for developing new preventive, diagnostic, and therapeutic approaches to preserve brain health in older individuals.

scholarly journals Computational Approaches to Spatial Orientation: From Transfer Functions to Dynamic Bayesian Inference

2008 ◽  
Vol 100 (6) ◽  
pp. 2981-2996 ◽  
Author(s):  
Paul R. MacNeilage ◽  
Narayan Ganesan ◽  
Dora E. Angelaki
Keyword(s):  
Bayesian Inference ◽  
Spatial Orientation ◽  
Particle Filtering ◽  
Transfer Functions ◽  
Linear Acceleration ◽  
Frequency Domain Analysis ◽  
Internal Models ◽  
Velocity Storage ◽  
Computational Approaches ◽  
The Brain

Spatial orientation is the sense of body orientation and self-motion relative to the stationary environment, fundamental to normal waking behavior and control of everyday motor actions including eye movements, postural control, and locomotion. The brain achieves spatial orientation by integrating visual, vestibular, and somatosensory signals. Over the past years, considerable progress has been made toward understanding how these signals are processed by the brain using multiple computational approaches that include frequency domain analysis, the concept of internal models, observer theory, Bayesian theory, and Kalman filtering. Here we put these approaches in context by examining the specific questions that can be addressed by each technique and some of the scientific insights that have resulted. We conclude with a recent application of particle filtering, a probabilistic simulation technique that aims to generate the most likely state estimates by incorporating internal models of sensor dynamics and physical laws and noise associated with sensory processing as well as prior knowledge or experience. In this framework, priors for low angular velocity and linear acceleration can explain the phenomena of velocity storage and frequency segregation, both of which have been modeled previously using arbitrary low-pass filtering. How Kalman and particle filters may be implemented by the brain is an emerging field. Unlike past neurophysiological research that has aimed to characterize mean responses of single neurons, investigations of dynamic Bayesian inference should attempt to characterize population activities that constitute probabilistic representations of sensory and prior information.

Role of Skull Fracture on Load Transfer to Brain

2016 ◽  
Author(s):  
Timothy G. Zhang ◽  
Kimberly A. Thompson ◽  
Sikhanda S. Satapathy
Keyword(s):  
Load Transfer ◽  
Skull Fracture ◽  
Material Model ◽  
Rigid Wall ◽  
Frontal Impact ◽  
Low Velocity ◽  
Element Technique ◽  
The Brain ◽  
Skull Deformation

This study focuses on the effect of skull fracture on the load transfer to brain for low velocity frontal impact of head against a rigid wall. The skull was modeled as a cortical-trabecular-cortical layered structure in order to better capture the skull deformation and consequent failure. The skull components were modeled with an elastoplastic with failure material model. Different methods were explored to model the material response after failure, such as eroding element technique, conversion to fluid, and conversion to SPH particles. The transmitted pressure in the brain was observed to increase with skull fracture.

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