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1.
J Neurosci ; 40(26): 5078-5089, 2020 06 24.
Artigo em Inglês | MEDLINE | ID: mdl-32424021

RESUMO

Human subjects of both sexes were asked to make a perceptual decision between multiple directions of visual motion. In addition to reporting a primary choice, they also had to report a second guess, indicating which of the remaining options they would rather bet on, assuming that they got their primary choice wrong. The second guess was clearly informed by the amounts of sensory evidence that were provided for the different options. A single computational integration-to-threshold model, based on the assumption that the second guess is determined by the rank ordering of accumulated evidence at or shortly after the time of the decision, was able to explain the distribution of primary choices, associated response times, and the distribution of second guesses. This suggests that the decision-maker has access to how well supported unchosen options are by the sensory evidence.SIGNIFICANCE STATEMENT Perceptual decisions require conversion of sensory evidence into a discrete choice. Computational models based on the accumulation of evidence to a decision threshold can explain the distribution of choices and associated decision times. Subjects are also able to report the level of confidence in their decision. Here we show that, when making decisions between more than two alternatives, the decision-maker can even report a second guess that is clearly informed by the sensory evidence. These second guesses show a distribution that is consistent with subjects having access to how much sensory evidence was accumulated for the unchosen options. The decision-maker therefore has knowledge about the outcome of the decision process that goes beyond just the choice and an associated confidence.


Assuntos
Encéfalo/fisiologia , Comportamento de Escolha/fisiologia , Simulação por Computador , Modelos Neurológicos , Feminino , Humanos , Masculino , Percepção/fisiologia , Adulto Jovem
2.
Biosens Bioelectron ; 237: 115422, 2023 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-37301179

RESUMO

ANALYSIS: of rare circulating extracellular vesicles (EV) from early cancers or different types of host cells requires extremely sensitive EV sensing technologies. Nanoplasmonic EV sensing technologies have demonstrated good analytical performances, but their sensitivity is often limited by EVs' diffusion to the active sensor surface for specific target EV capture. Here, we developed an advanced plasmonic EV platform with electrokinetically enhanced yields (KeyPLEX). The KeyPLEX system effectively overcomes diffusion-limited reactions with applied electroosmosis and dielectrophoresis forces. These forces bring EVs toward the sensor surface and concentrate them in specific areas. Using the keyPLEX, we showed significant improvements in detection sensitivity by ∼100-fold, leading to the sensitive detection of rare cancer EVs from human plasma samples in 10 min. The keyPLEX system could become a valuable tool for point-of-care rapid EV analysis.


Assuntos
Técnicas Biossensoriais , Vesículas Extracelulares , Neoplasias , Humanos , Neoplasias/diagnóstico , Eletro-Osmose
3.
Front Neurosci ; 11: 618, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-29176941

RESUMO

Perceptual decisions in the presence of decision-irrelevant sensory information require a selection of decision-relevant sensory evidence. To characterize the mechanism that is responsible for separating decision-relevant from irrelevant sensory information we asked human subjects to make judgments about one of two simultaneously present motion components in a random dot stimulus. Subjects were able to ignore the decision-irrelevant component to a large degree, but their decisions were still influenced by the irrelevant sensory information. Computational modeling revealed that this influence was not simply the consequence of subjects forgetting at times which stimulus component they had been instructed to base their decision on. Instead, residual irrelevant information always seems to be leaking through, and the decision process is captured by a net sensory evidence signal being accumulated to a decision threshold. This net sensory evidence is a linear combination of decision-relevant and irrelevant sensory information. The selection process is therefore well-described by a strong linear gain modulation, which, in our experiment, resulted in the relevant sensory evidence having at least 10 times more impact on the decision than the irrelevant evidence.

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