Can AI and humans genuinely communicate? In this article, after giving some
background and motivating my proposal (sections 1 to 3), I explore a way to
answer this question that I call the "mental-behavioral methodology" (sections
4 and 5). This methodology follows the following three steps: First, spell out
what mental capacities are sufficient for human communication (as opposed to
communication more generally). Second, spell out the experimental paradigms
required to test whether a behavior exhibits these capacities. Third, apply or
adapt these paradigms to test whether an AI displays the relevant behaviors. If
the first two steps are successfully completed, and if the AI passes the tests
with human-like results, this constitutes evidence that this AI and humans can
genuinely communicate. This mental-behavioral methodology has the advantage
that we don't need to understand the workings of black-box algorithms, such as
standard deep neural networks. This is comparable to the fact that we don't
need to understand how human brains work to know that humans can genuinely
communicate. This methodology also has its disadvantages and I will discuss
some of them (section 6).

AI 和人类是否能够真正地进行沟通是这篇文章探讨的主题，作者提出了一种称为 “心理行为方法论” 的方式来回答这个问题，并通过三个步骤来证明：首先，明确人类沟通所需的心理能力；其次，明确测试这些能力的实验范例；最后，应用或调整这些范例，测试 AI 是否展现出相关的行为。如果前两个步骤成功完成，并且 AI 在测试中表现出与人类类似的结果，这就是 AI 和人类真正沟通的证据。

人工智能和人类能够真正交流吗？

Can AI and humans genuinely communicate?

This article summarizes a systematic review of the electroencephalography
(EEG)-based cognitive workload (CWL) estimation. The focus of the article is
twofold: identify the disparate experimental paradigms used for reliably
eliciting discreet and quantifiable levels of cognitive load and the specific
nature and representational structure of the commonly used input formulations
in deep neural networks (DNNs) used for signal classification. The analysis
revealed a number of studies using EEG signals in its native representation of
a two-dimensional matrix for offline classification of CWL. However, only a few
studies adopted an online or pseudo-online classification strategy for
real-time CWL estimation. Further, only a couple of interpretable DNNs and a
single generative model were employed for cognitive load detection till date
during this review. More often than not, researchers were using DNNs as
black-box type models. In conclusion, DNNs prove to be valuable tools for
classifying EEG signals, primarily due to the substantial modeling power
provided by the depth of their network architecture. It is further suggested
that interpretable and explainable DNN models must be employed for cognitive
workload estimation since existing methods are limited in the face of the
non-stationary nature of the signal.

通过系统综述了解了基于脑电图 (EEG) 的认知负荷估计。重点包括：确定可靠引发离散和量化认知负荷水平的实验范式，并探究在信号分类中常用的深度神经网络 (DNNs) 的输入形式的特点和表征结构。分析发现一些研究在离线认知负荷分类中使用脑电信号的二维矩阵原始表示，但只有少数研究采用了在线或伪在线的实时认知负荷估计策略。此外，在这项综述中，仅采用了数个可解释的 DNNs 和一个生成模型进行认知负荷检测。总的来说，由于其网络架构的深度提供了巨大的建模能力，DNNs 成为分类脑电信号的宝贵工具。进一步建议采用可解释且可解读的 DNN 模型进行认知负荷估计，因为现有方法在面对非平稳信号时存在局限性。

基于脑电图的认知负荷检测实验范式和深度神经网络的系统评述

Systematic Review of Experimental Paradigms and Deep Neural Networks for  Electroencephalography-Based Cognitive Workload Detection

Imaging neuroscience links brain activation maps to behavior and cognition
via correlational studies. Due to the nature of the individual experiments,
based on eliciting neural response from a small number of stimuli, this link is
incomplete, and unidirectional from the causal point of view. To come to
conclusions on the function implied by the activation of brain regions, it is
necessary to combine a wide exploration of the various brain functions and some
inversion of the statistical inference. Here we introduce a methodology for
accumulating knowledge towards a bidirectional link between observed brain
activity and the corresponding function. We rely on a large corpus of imaging
studies and a predictive engine. Technically, the challenges are to find
commonality between the studies without denaturing the richness of the corpus.
The key elements that we contribute are labeling the tasks performed with a
cognitive ontology, and modeling the long tail of rare paradigms in the corpus.
To our knowledge, our approach is the first demonstration of predicting the
cognitive content of completely new brain images. To that end, we propose a
method that predicts the experimental paradigms across different studies.

本文介绍了一种方法来累积关于观察到的脑活动和相应功能之间的双向联系的知识。我们的方法基于大量的影像研究和一个预测引擎。我们首次演示了预测完全新的脑图像的认知内容的方法。