Previous active inference accounts of emotion translate fluctuations in free
energy to a sense of emotion, mainly focusing on valence. However, in affective
science, emotions are often represented as multi-dimensional. In this paper, we
propose to adopt a Circumplex Model of emotion by mapping emotions into a
two-dimensional spectrum of valence and arousal. We show how one can derive a
valence and arousal signal from an agent's expected free energy, relating
arousal to the entropy of posterior beliefs and valence to utility less
expected utility. Under this formulation, we simulate artificial agents engaged
in a search task. We show that the manipulation of priors and object presence
results in commonsense variability in emotional states.

通过将情感映射到价值和唤起的二维谱上，本文采用情感的环形模型，利用主观 free energy 变化来推断唤起和价值信号，并通过先验和物体存在的操纵展示了情感状态的常识性变化。

情绪环模型中的自由能

Free Energy in a Circumplex Model of Emotion

Fluent human-robot collaboration requires a robot teammate to understand,
learn, and adapt to the human's psycho-physiological state. Such collaborations
require a computing system that monitors human physiological signals during
human-robot collaboration (HRC) to quantitatively estimate a human's level of
comfort, which we have termed in this research as comfortability index (CI) and
uncomfortability index (unCI). Subjective metrics (surprise, anxiety, boredom,
calmness, and comfortability) and physiological signals were collected during a
human-robot collaboration experiment that varied robot behavior. The emotion
circumplex model is adapted to calculate the CI from the participant's
quantitative data as well as physiological data. To estimate CI/unCI from
physiological signals, time features were extracted from electrocardiogram
(ECG), galvanic skin response (GSR), and pupillometry signals. In this
research, we successfully adapt the circumplex model to find the location
(axis) of 'comfortability' and 'uncomfortability' on the circumplex model, and
its location match with the closest emotions on the circumplex model. Finally,
the study showed that the proposed approach can estimate human
comfortability/uncomfortability from physiological signals.

人机协作中，通过生理信号来定量评估人的舒适度和不舒适度，本研究成功地将舒适度和不舒适度与情感圆坐标模型的最接近情感匹配，并且证明了从生理信号中评估人的舒适度和不舒适度的可行性。

工业人机协作任务的人体舒适性指数估计

Human Comfortability Index Estimation in Industrial Human-Robot  Collaboration Task

Automatic understanding of human affect using visual signals is a problem
that has attracted significant interest over the past 20 years. However, human
emotional states are quite complex. To appraise such states displayed in
real-world settings, we need expressive emotional descriptors that are capable
of capturing and describing this complexity. The circumplex model of affect,
which is described in terms of valence (i.e., how positive or negative is an
emotion) and arousal (i.e., power of the activation of the emotion), can be
used for this purpose. Recent progress in the emotion recognition domain has
been achieved through the development of deep neural architectures and the
availability of very large training databases. To this end, Aff-Wild has been
the first large-scale "in-the-wild" database, containing around 1,200,000
frames. In this paper, we build upon this database, extending it with 260 more
subjects and 1,413,000 new video frames. We call the union of Aff-Wild with the
additional data, Aff-Wild2. The videos are downloaded from Youtube and have
large variations in pose, age, illumination conditions, ethnicity and
profession. Both database-specific as well as cross-database experiments are
performed in this paper, by utilizing the Aff-Wild2, along with the RECOLA
database. The developed deep neural architectures are based on the joint
training of state-of-the-art convolutional and recurrent neural networks with
attention mechanism; thus exploiting both the invariant properties of
convolutional features, while modeling temporal dynamics that arise in human
behaviour via the recurrent layers. The obtained results show premise for
utilization of the extended Aff-Wild, as well as of the developed deep neural
architectures for visual analysis of human behaviour in terms of continuous
emotion dimensions.

利用扩展的 Aff-Wild 数据集和联合卷积 - 循环神经网络构架，本文分别在特定数据库和跨数据库实验中，发掘了这一神经网络模型对情感识别和分析中的应用前景，并引用了情感范畴的简述模型 circumplex model 进行表述。