Mental imagery refers to percept-like experiences in the absence of sensory
input. Brain imaging studies suggest common, modality-specific, neural
correlates imagery and perception. We associated abstract visual stimuli with
either visually presented or imagined monetary rewards and scrambled pictures.
Brain images for a group of 12 participants were collected using functional
magnetic resonance imaging. Statistical analysis showed that human midbrain
regions were activated irrespective of the monetary rewards being imagined or
visually present. A support vector machine trained on the midbrain activation
patterns to the visually presented rewards predicted with 75% accuracy whether
the participants imagined the monetary reward or the scrambled picture during
imagination trials. Training samples were drawn from visually presented trials
and classification accuracy was assessed for imagination trials. These results
suggest the use of machine learning technique for classification of underlying
cognitive states from brain imaging data.

研究使用机器学习技术对人脑成像数据进行分类，统计分析显示无论奖励是想象还是视觉呈现，人类中脑区域都会被激活。

利用脑部活动预测想象的内容

Predicting the imagined contents using brain activation

Humans can infer concepts from image pairs and apply those in the physical
world in a completely different setting, enabling tasks like IKEA assembly from
diagrams. If robots could represent and infer high-level concepts, it would
significantly improve their ability to understand our intent and to transfer
tasks between different environments. To that end, we introduce a computational
framework that replicates aspects of human concept learning. Concepts are
represented as programs on a novel computer architecture consisting of a visual
perception system, working memory, and action controller. The instruction set
of this "cognitive computer" has commands for parsing a visual scene, directing
gaze and attention, imagining new objects, manipulating the contents of a
visual working memory, and controlling arm movement. Inferring a concept
corresponds to inducing a program that can transform the input to the output.
Some concepts require the use of imagination and recursion. Previously learned
concepts simplify the learning of subsequent more elaborate concepts, and
create a hierarchy of abstractions. We demonstrate how a robot can use these
abstractions to interpret novel concepts presented to it as schematic images,
and then apply those concepts in dramatically different situations. By bringing
cognitive science ideas on mental imagery, perceptual symbols, embodied
cognition, and deictic mechanisms into the realm of machine learning, our work
brings us closer to the goal of building robots that have interpretable
representations and commonsense.

本文通过引入类人概念学习框架和新型计算机架构，将认知心理学的有关概念转移到机器学习领域中，实现了具备可解释性及直观感知能力的机器人。该认知计算机通过视觉感知系统、即时记忆和运动控制系统，实现一个命令集合，以学习新概念，并可将其应用于不同形势的物理世界中。