Using perceptual learning to understand and influence face recognition

感知学习是一种基本的认知技能。它可以被定义为一种增强的能力，以区分类似的刺激（否则将很难区分）作为一个结果，与他们的经验，或与刺激类似的目标刺激。拟议的项目侧重于这种现象的发展，作为我们识别人脸能力的关键因素。此外，通过使用一系列神经科学技术（EEG/ERP，tDCS，fMRI，TMS）与我开发的行为设计相结合，我将研究改善这种感知技能的方法，并定义负责控制和发展这种现象的特定大脑结构。因此，基本上，我的目标是找出我们如何提高区分事物（区分）的能力，并发现增强这种能力的方法。我将首先发展知觉学习作为面部识别中最强大的认知现象之一的关键贡献者，即复合面部效应。这指的是当合成物直立并对齐时，个体识别一张脸的上半部分与另一张脸的下半部分的能力比当两个半部分横向偏移（未对齐）时降低。通过使用新类别的原型定义的棋盘，参与者将预先暴露在研究过程中，我希望显示出类似的复合效果熟悉的棋盘，通常发现的面孔。这将是我们感知学习的指标。然后，该项目将继续使用不同的神经刺激技术（tDCS和TMS）来选择性地增加和减少棋盘和面孔的复合效应。通过tDCS和EEG技术的结合，该项目还将揭示通常针对面部发现的特定大脑反应、也可以针对熟悉的棋盘发现的特定大脑反应以及如何通过神经刺激进行改变。最后，tDCS和fMRI的结合将有助于更精确地定位与知觉学习有关的大脑结构。该项目将为我们提供对知觉学习的发展和控制机制的见解，并将知觉学习扩展到面部识别，通过展示参与者从未见过的刺激类别如何发现与通常发现的面部相似的效果进入实验室。这也将为知觉学习的神经认知基础提供证据。我们将利用这些结果来设计训练方法，以提高我们在各个领域的辨别能力。这将使我们能够定制培训计划（例如，涉及面部、指纹、子宫颈涂片、乳房X光照片等刺激），以在受训者的表现方面提供最大的好处。例如，这项研究将适用于提高前线和情报人员的面部识别技能，并且可以通过使用基于Starstim EEG/tDCS系统的移动的神经科学技术来选择性地提高这种培训的有效性。它还将让我们深入了解如何设计用于检测和识别此类刺激的人工系统，这将对专门从事计算机软件或面部识别应用程序的公司感兴趣。

Perceptual learning is a fundamental cognitive skill. It can be defined as an enhancement in the ability to distinguish between similar stimuli (that otherwise would be very hard to tell apart) as a consequence of experience with them, or with stimuli similar to the target stimuli. The proposed project focuses on the development of this phenomenon as a key factor to our ability to recognise faces. Further, by using a range of neuroscience techniques (EEG/ERP, tDCS, fMRI, TMS) in conjunction with the behavioural designs I have developed, I will investigate methods to improve this perceptual skill and define specific brain structures responsible for the control and development of this phenomenon. So, basically, I aim to find out how we improve at telling things apart (discrimination) and to discover ways of enhancing this ability.I will start by developing the case for perceptual learning as a key contributor to one of the most robust cognitive phenomenon in face recognition i.e. the composite face effect. This refers to individuals' decreased ability to recognise the top half of one face presented in composite with the bottom half of another face when the composite is upright and aligned than when the two halves are offset laterally (misalignment). By using novel categories of prototype-defined chequerboards which participants will be pre-exposed to during the study procedure, I would expect to show a similar composite effect for familiar chequerboards to that usually found with faces. This will be our index of perceptual learning.The project will then continue by using different neurostimulation techniques (tDCS and TMS) to selectively increase and decrease the composite effect for chequerboards and that for faces. Through a combination of tDCS and EEG techniques, this project will also reveal how specific brain responses usually found for faces, can also be found for familiar chequerboards and be altered by neurostimulation. Finally, a combination of tDCS and fMRI will help to localise more precisely the brain structures involved in perceptual learning.This project will provide us with insights into the mechanisms that characterise the development and control of perceptual learning and will extend perceptual learning to face recognition by showing how similar effects to those usually found for faces can be found for categories of stimuli that participants had never seen before entering the lab. It will also provide evidence for the neurocognitive basis of perceptual learning. We will use these results to devise methods of training that can enhance our discrimination abilities in various domains. It would allow us to tailor training programmes (e.g. involving stimuli such as faces, fingerprints, cervical smears, mammograms) to provide maximum benefit in terms of performance by the trainee. As an example, this research would be suitable for enhancing the face recognition skills of frontline and intelligence staff, and one might be able to selectively increase the effectiveness of this training through the use of mobile neuroscience techniques based on the Starstim EEG/tDCS system. It would also give us insights into how to design artificial systems for the detection and recognition of such stimuli, which would be of interest to companies specialised in computer software or apps for face recognition.