Dataset Information

Value associations bias ensemble perception.

ABSTRACT: Ensemble perception refers to awareness of average properties, e.g. size, of "noisy" elements that often comprise visual arrays in natural scenes. Here, we asked how ensemble perception might be influenced when some but not all array elements are associated with monetary reward. Previous studies show that reward associations can speed object processing, facilitate selection, and enhance working-memory maintenance, suggesting they may bias ensemble judgments. To investigate, participants reported the average element size of brief arrays of different-sized circles. In the learning phase, all circles had the same color, but different colors produced high or low performance-contingent rewards. Then, in an unrewarded test phase, arrays comprised three spatially inter-mixed subsets, each with a different color, including the high-reward color. In different trials, the mean size of the subset with the high-reward color was smaller, larger, or the same as the ensemble mean. Ensemble size estimates were significantly biased by the high-reward-associated subset, showing that value associations modulate ensemble perception. In the test phase of a second experiment, a pattern mask appeared immediately after array presentation to limit top-down processing. Not only was value-biasing eliminated, ensemble accuracy improved, suggesting that value associations distort consciously available ensemble representation via late high-level processing.

SUBMITTER: Dodgson DB

PROVIDER: S-EPMC6994430 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Value associations bias ensemble perception.

Dodgson Daniel B DB Raymond Jane E JE

Attention, perception & psychophysics 20200101 1

Ensemble perception refers to awareness of average properties, e.g. size, of "noisy" elements that often comprise visual arrays in natural scenes. Here, we asked how ensemble perception might be influenced when some but not all array elements are associated with monetary reward. Previous studies show that reward associations can speed object processing, facilitate selection, and enhance working-memory maintenance, suggesting they may bias ensemble judgments. To investigate, participants reported ...[more]

PMID: 31069635

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Project description:BackgroundIn the field of drug repositioning, it is assumed that similar drugs may treat similar diseases, therefore many existing computational methods need to compute the similarities of drugs and diseases. However, the calculation of similarity depends on the adopted measure and the available features, which may lead that the similarity scores vary dramatically from one to another, and it will not work when facing the incomplete data. Besides, supervised learning based methods usually need both positive and negative samples to train the prediction models, whereas in drug-disease pairs data there are only some verified interactions (positive samples) and a lot of unlabeled pairs. To train the models, many methods simply treat the unlabeled samples as negative ones, which may introduce artificial noises. Herein, we propose a method to predict drug-disease associations without the need of similarity information, and select more likely negative samples.ResultsIn the proposed EMP-SVD (Ensemble Meta Paths and Singular Value Decomposition), we introduce five meta paths corresponding to different kinds of interaction data, and for each meta path we generate a commuting matrix. Every matrix is factorized into two low rank matrices by SVD which are used for the latent features of drugs and diseases respectively. The features are combined to represent drug-disease pairs. We build a base classifier via Random Forest for each meta path and five base classifiers are combined as the final ensemble classifier. In order to train out a more reliable prediction model, we select more likely negative ones from unlabeled samples under the assumption that non-associated drug and disease pair have no common interacted proteins. The experiments have shown that the proposed EMP-SVD method outperforms several state-of-the-art approaches. Case studies by literature investigation have found that the proposed EMP-SVD can mine out many drug-disease associations, which implies the practicality of EMP-SVD.ConclusionsThe proposed EMP-SVD can integrate the interaction data among drugs, proteins and diseases, and predict the drug-disease associations without the need of similarity information. At the same time, the strategy of selecting more reliable negative samples will benefit the prediction.

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Value associations bias ensemble perception.

Publications

Value associations bias ensemble perception.

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