We envision a machine capable of solving mathematical problems. Dividing the
quantitative reasoning system into two parts: thought processes and cognitive
processes, we provide probabilistic descriptions of the architecture.

我们构想了一种能够解决数学问题的机器，将定量推理系统分为思维过程和认知过程，并提供了对该体系结构的概率描述。

数学推理的概率结果与认知交替

Probabilistic Results on the Architecture of Mathematical Reasoning  Aligned by Cognitive Alternation

The continuity hypothesis of dreams suggests that the content of dreams is
continuous with the dreamer's waking experiences. Given the unprecedented
nature of the experiences during COVID-19, we studied the continuity hypothesis
in the context of the pandemic. We implemented a deep-learning algorithm that
can extract mentions of medical conditions from text and applied it to two
datasets collected during the pandemic: 2,888 dream reports (dreaming life
experiences), and 57M tweets mentioning the pandemic (waking life experiences).
The health expressions common to both sets were typical COVID-19 symptoms
(e.g., cough, fever, and anxiety), suggesting that dreams reflected people's
real-world experiences. The health expressions that distinguished the two sets
reflected differences in thought processes: expressions in waking life
reflected a linear and logical thought process and, as such, described
realistic symptoms or related disorders (e.g., nasal pain, SARS, H1N1); those
in dreaming life reflected a thought process closer to the visual and emotional
spheres and, as such, described either conditions unrelated to the virus (e.g.,
maggots, deformities, snakebites), or conditions of surreal nature (e.g., teeth
falling out, body crumbling into sand). Our results confirm that dream reports
represent an understudied yet valuable source of people's health experiences in
the real world.

研究探讨了 COVID-19 大流行期间梦境和现实生活经历间的连贯性，发现梦境内容反映了现实世界的体验，且两者表达的健康状况存在差异，反映了思维过程的不同。结果表明，梦境报告是了解人们健康体验的宝贵来源。