The sustained use of marijuana significantly impacts the lives and health of
people. In this study, we propose an interpretable novel framework called the
HOGAB (High-Order Attention Graph Attention Neural Networks) model to analyze
local abnormal brain activity in chronic marijuana users in two datasets. The
HOGAB integrates dynamic intrinsic functional networks with LSTM technology to
capture temporal patterns in fMRI time series of marijuana users. Moreover, we
use the high-order attention module in neighborhood nodes for information
fusion and message passing, enhancing community clustering analysis for
long-term marijuana users. Furthermore, we improve the overall learning ability
of the model by incorporating attention mechanisms, achieving an AUC of 85.1%
and an accuracy of 80.7% in multigraph classification. In addition, we compare
linear machine learning methods and evaluate the effectiveness of our proposed
HODAB model. Specifically, we identified the most relevant subnetworks and
cognitive regions that are negatively influenced by persistent marijuana use,
revealing that chronic marijuana use adversely affects cognitive control,
particularly within the Dorsal Attention and Frontoparietal networks, which are
essential for attentional, cognitive, and higher cognitive functions. The
results show that our proposed model is capable of accurately predicting
craving maps and identifying brain maps associated with long-term cravings, and
also pinpointing active areas that are important for analysis.

通过使用 HOGAB 模型，本研究发现长期使用大麻会对认知控制产生负面影响，特别是在注意力、认知和高级认知功能所需的脑网络中，以预测渴望图和识别与长期渴望相关的脑图，并确定对分析重要的活跃区域。

通过高阶关注脑网络分析大麻使用者的静息态 fMRI 数据

Analyzing Resting-State fMRI Data in Marijuana Users via High-Order  Attention Brain Network

Traditionally, theory and practice of Cognitive Control are linked via
literature reviews by human domain experts. This approach, however, is
inadequate to track the ever-growing literature. It may also be biased, and
yield redundancies and confusion.
Here we present an alternative approach. We performed automated text analyses
on a large body of scientific texts to create a joint representation of tasks
and constructs. More specifically, 385,705 scientific abstracts were first
mapped into an embedding space using a transformers-based language model.
Document embeddings were then used to identify a task-construct graph embedding
that grounds constructs on tasks and supports nuanced meaning of the constructs
by taking advantage of constrained random walks in the graph.
This joint task-construct graph embedding, can be queried to generate task
batteries targeting specific constructs, may reveal knowledge gaps in the
literature, and inspire new tasks and novel hypotheses.

本文采用了自动化文本分析的方法，创建一种联合任务 - 构建图嵌入表示，可以用于构建针对特定构建的任务电池，在文献中发现知识缺口并激发新的任务和新的假设。

通过科学文献的联合嵌入将认知科学的理论和方法联系起来：以认知控制为例

Linking Theories and Methods in Cognitive Sciences via Joint Embedding of the Scientific Literature: The Example of Cognitive Control

Planning is useful. It lets people take actions that have desirable long-term
consequences. But, planning is hard. It requires thinking about consequences,
which consumes limited computational and cognitive resources. Thus, people
should plan their actions, but they should also be smart about how they deploy
resources used for planning their actions. Put another way, people should also
"plan their plans". Here, we formulate this aspect of planning as a
meta-reasoning problem and formalize it in terms of a recursive Bellman
objective that incorporates both task rewards and information-theoretic
planning costs. Our account makes quantitative predictions about how people
should plan and meta-plan as a function of the overall structure of a task,
which we test in two experiments with human participants. We find that people's
reaction times reflect a planned use of information processing, consistent with
our account. This formulation of planning to plan provides new insight into the
function of hierarchical planning, state abstraction, and cognitive control in
both humans and machines.

通过两个实验，本文将计划的计划表述为元推理问题，并用一个递归贝尔曼目标来形式化计划。 我们的研究成果在规划、状态抽象和认知控制方面提供了新的见解。