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Supervised calibration relies on the multisensory percept.

ABSTRACT: Multisensory plasticity enables us to dynamically adapt sensory cues to one another and to the environment. Without external feedback, "unsupervised" multisensory calibration reduces cue conflict in a manner largely independent of cue reliability. But environmental feedback regarding cue accuracy ("supervised") also affects calibration. Here we measured the combined influence of cue accuracy and cue reliability on supervised multisensory calibration, using discrepant visual and vestibular motion stimuli. When the less reliable cue was inaccurate, it alone got calibrated. However, when the more reliable cue was inaccurate, cues were yoked and calibrated together in the same direction. Strikingly, the less reliable cue shifted away from external feedback, becoming less accurate. A computational model in which supervised and unsupervised calibration work in parallel, where the former only relies on the multisensory percept, but the latter can calibrate cues individually, accounts for the observed behavior. In combination, they could ultimately achieve the optimal solution of both external accuracy and internal consistency.

SUBMITTER: Zaidel A

PROVIDER: S-EPMC3872253 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Supervised calibration relies on the multisensory percept.

Zaidel Adam A Ma Wei Ji WJ Angelaki Dora E DE

Neuron 20131127 6

Multisensory plasticity enables us to dynamically adapt sensory cues to one another and to the environment. Without external feedback, "unsupervised" multisensory calibration reduces cue conflict in a manner largely independent of cue reliability. But environmental feedback regarding cue accuracy ("supervised") also affects calibration. Here we measured the combined influence of cue accuracy and cue reliability on supervised multisensory calibration, using discrepant visual and vestibular motion ...[more]

PMID: 24290205

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Project description:ObjectiveElectronic health records (EHR), containing detailed longitudinal clinical information on a large number of patients and covering broad patient populations, open opportunities for comprehensive predictive modeling of disease progression and treatment response. However, since EHRs were originally constructed for administrative purposes not for research, in the EHR-linked studies, it is often not feasible to capture reliable information for analytical variables, especially in the survival setting, when both accurate event status and event times are needed for model building. For example, progression-free survival (PFS), a commonly used survival outcome for cancer patients, often involves complex information embedded in free-text clinical notes and cannot be extracted reliably. Proxies of PFS time such as time to the first mention of progression in the notes are at best good approximations to the true event time. This leads to difficulty in efficiently estimating event rates for an EHR patient cohort. Estimating survival rates based on error-prone outcome definitions can lead to biased results and hamper the power in the downstream analysis. On the other hand, extracting accurate event time information via manual annotation is time and resource intensive. The objective of this study is to develop a calibrated survival rate estimator using noisy outcomes from EHR data.Materials and methodsIn this paper, we propose a two-stage semi-supervised calibration of noisy event rate (SCANER) estimator that can effectively overcome censoring induced dependency and attains more robust performance (i.e., not sensitive to misspecification of the imputation model) by fully utilizing both a small-labeled set of gold-standard survival outcomes annotated via manual chart review and a set of proxy features automatically captured via EHR in the unlabeled set. We validate the SCANER estimator by estimating the PFS rates for a virtual cohort of lung cancer patients from one large tertiary care center and the ICU-free survival rates for COVID patients from two large tertiary care centers.ResultsIn terms of survival rate estimates, the SCANER had very similar point estimates compared to the complete-case Kaplan Meier estimator. On the other hand, other benchmark methods for comparison, which fail to account for the induced dependency between event time and the censoring time conditioning on surrogate outcomes, produced biased results across all three case studies. In terms of standard errors, the SCANER estimator was more efficient than the KM estimator, with up to 50% efficiency gain.ConclusionThe SCANER estimator achieves more efficient, robust, and accurate survival rate estimates compared to existing approaches. This promising new approach can also improve the resolution (i.e., granularity of event time) by using labels conditioning on multiple surrogates, particularly among less common or poorly coded conditions.

Project description:Brain-computer interfaces (BCIs) aim to restore independence to people with severe motor disabilities by allowing control of acursor on a computer screen or other effectors with neural activity. However, physiological and/or recording-related nonstationarities in neural signals can limit long-term decoding stability, and it would be tedious for users to pause use of the BCI whenever neural control degrades to perform decoder recalibration routines. We recently demonstrated that a kinematic decoder (i.e. a decoder that controls cursor movement) can be recalibrated using data acquired during practical point-and-click control of the BCI by retrospectively inferring users' intended movement directions based on their subsequent selections. Here, we extend these methods to allow the click decoder to also be recalibrated using data acquired during practical BCI use. We retrospectively labeled neural data patterns as corresponding to "click" during all time bins in which the click log-likelihood (decoded using linear discriminant analysis, or LDA) had been above the click threshold that was used during real-time neural control. We labeled as "non-click" those periods that the kinematic decoder's retrospective target inference (RTI) heuristics determined to be consistent with intended cursor movement. Once these neural activity patterns were labeled, the click decoder was calibrated using standard supervised classifier training methods. Combined with real-time bias correction and baseline firing rate tracking, this set of "retrospectively labeled" decoder calibration methods enabled a BrainGate participant with amyotrophic lateral sclerosis (T9) to type freely across 11 research sessions spanning 29days, maintaining high-performance neural control over cursor movement and click without needing to interrupt virtual keyboard use for explicit calibration tasks. By eliminating the need for tedious calibration tasks with prescribed targets and pre-specified click times, this approach advances the potential clinical utility of intracortical BCIs for individuals with severe motor disability.

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Supervised calibration relies on the multisensory percept.

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Supervised calibration relies on the multisensory percept.

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