Memory for 'warning' patterns: a link to specific neural excitation

许多动物，被称为警戒（AP），表现出“警告信号”，明亮的图案，包括高对比度，斑点或条纹和鲜艳的颜色。虽然这使它们更容易被看到，但这些图案被认为是为了阻止猎物而进化的，这些是“接收者”，他们识别出那些不好吃或有毒的动物所展示的图案。这个博士项目的目的是研究警告信号模式对人类感知，注意力和记忆的影响，并探索这些模式如何具体影响感官和认知功能。为什么警告信号会对捕食者产生威慑作用？众所周知，某些类型的模式（例如。高对比度条纹）在某些个体中引起视觉不适（Conlon 99）。有人提出，这是因为这种模式是非常不寻常或异常的，很少在自然场景中发现。典型的AP模式（例如高对比度斑点/条纹）类似于导致视觉不适的模式。我们实验室（以及来自纽卡斯尔的动物学合作者）最近的工作使用鳞翅目模式数据库对AP模式进行数学建模，并开发具有相同特征的人工模式（Haplin 20）。我们将在这里使用这些模式。测试动物警告信号模式影响的研究表明，AP模式可以增强其他认知功能，如记忆和学习。在这里，我们将选择人类作为我们的研究接收者物种，因为我们是一个多面手的捕食者，因为在人类中，感知，注意力和记忆可以使用复杂的技术进行研究。在这个项目中，我们的目标是使用一系列的知觉，注意力和记忆的任务，沿着眼动的测量，行为上探测为什么AP模式是有效的。该项目的关键创新部分是开发一套注意力和记忆任务，可用于探测对视觉的已知影响是否延伸到注意力和记忆。有许多研究表明，情绪刺激可以提高注意力。我们将使用注意瞬脱或点探测技术来研究注意警告信号模式的时间限制。编码和提取的标准记忆研究操作，还将使我们能够确定警告信号模式是否比其他模式更容易记住，如果是这样，通过什么记忆机制增加记忆力。这些见解将用于调整最近开发的神经网络算法，以预测哪些模式将是令人难忘的（例如Isola等人，2014）。我们将在警告信号模式上运行这样的模型，以测试理论上的可记忆性。该项目是跨学科的，汇集了进化生物学，实验心理学和计算神经科学。它适合神经科学、动物学、心理学或物理科学/工程学的毕业生。在项目结束时，我们将使用一系列测试，从感知，通过注意力，到记忆，以了解警告信号模式如何内在地刺激人类的感知系统。我们将开发神经模型，帮助我们理解为什么大脑会产生这些行为。

Many animals, called aposematic (AP), exhibit 'warning signals', bright patterns that include high contrast, spots or stripes and vivid colours. Although this makes them easier to see, the patterns are thought to have evolved to deter prey, these are the 'receivers' who recognise the patterns as being exhibited by animals who are unpalatable or poisonous. The aim of this PhD project is to study the effects of warning signal patterns on perception, attention and memory in humans, and to explore how the patterns might specifically affect sensory and cognitive function. Why are warning signals a deterrent to predation? It is well known that some types of pattern (eg. high contrast stripes) cause visual discomfort in some individuals (Conlon 99). It has been proposed that this occurs because such patterning that is highly unusual or anomalous and rarely found in natural scenes. Typical AP patterns (eg high contrast spots/stripes) are similar to patterns that cause visual discomfort. Recent work from our lab (and zoology collaborators from Newcastle), has used a database of lepidoptera patterns to mathematically characterise AP patterns and develop artificial patterns with the same characteristics (Haplin 20). We will use those patterns here. Studies testing the effects of warning signal patterns in animals suggest that other cognitive function, like memory and learning could be enhanced by AP patterns. Here we will choose humans as our receiver species of study, because we are a generalist predator, and because in humans, perception, attention and memory can be studied using sophisticated techniques. In this project, we aim to use a range of perceptual, attention and memory tasks, along with measurement of eye movements, to behaviourally probe why AP patterns are effective. The key novel part of the project is to develop a suite of attention and memory tasks that can be used to probe whether the known effects on vision extend to attention and to memory. There are many studies showing how emotionally arousing stimuli can enhance attention. We will use attentional blink or dot-probe techniques to study the temporal limits of attention to warning signal patterns. Standard memory research manipulations at encoding and retrieval, will additionally allow us to determine whether warning signal patterns are more memorable than others, and if so, by what memory mechanisms this increased memorability arises. These insights will be used to adapt a recently developed neural network algorithm to predict what patterns will be memorable (e.g. Isola et al, 2014). We will run such models on warning signal patterns to test for theoretical memorability. The project is interdisciplinary, bringing together evolutionary biology, experimental psychology and computational neuroscience. It would suit a graduate in neuroscience, zoology, psychology or physical science/engineering. At the end of the project, we will have used a body of tests, from perception, through attention, to memory, to understand how warning signal patterns intrinsically stimulate the human perceptual system. We will develop neural models that can help us understand why the brain delivers these behaviours.