Completeness out of incompleteness: Inferences from regularities in imperfect information ensembles.

Handling imperfect information problems is fundamental to perception, learning, and decision-making. Ensemble perception may partially overcome imperfect information by providing global clues. However, if not all cluster elements are readily accessible, the observations required for computing statistics are incomplete.

Two discoveries, one principle: Using a two-stage Bayesian model to explain a dissociated working memory distraction effect.

How working memory (WM) resists perceptual distraction with its limited capacity is a fundamental question to understand its mechanism. To address this question, we used a continuous recall paradigm to directly compare the distraction effect during encoding and the delay periods. Across Experiments 1-3, we observed a substantial distraction-related cost on mnemonic fidelity when distractors presented during the delay (delay-distraction condition), but not if they were introduced at encoding (encoding-distraction condition) or across both periods (full-distraction condition).

Abdomen anatomic characteristics on CT scans as predictive markers for short-term complications following radical resection of colorectal cancer.

Background: Prediction and management of short-term postoperative complications in patients with colorectal cancer are essential in postoperative rehabilitation. Through CT scan images, we can easily measure some parameters of abdomen anatomic characteristics. This study aimed to assess whether there is a relationship between the abdomen anatomic characteristics and short-term postoperative complications.

The ROCK Tool: A Novel Method for the Structural Exploration of Schemata.

Information stored in the human memory is organized in the form of mental schemata. In this paper we report on the Reproduction of Categorical Knowledge (ROCK) tool, a novel method for uncovering the structure of mental schemata of memorized information. The tool applies serial reproduction and hierarchical clustering to magnify memory bias and uncover inner configurations of fragmented information, using strength of association.