Python编程：实验心理学与认知神经科学的入门指南

需积分: 12 17 浏览量更新于2024-07-20 收藏 2.4MB PDF 举报

"Python for Experimental Psychologists 是一本面向没有编程经验的心理学家和认知神经科学家的指南，旨在教授如何使用Python进行实验设计和数据分析。书中涵盖了Python的基础语法、变量管理、刺激展示、响应处理、实验脚本编写、行为数据和轨迹数据的分析，以及眼动追踪技术。此外，书中还提供了配套网站，包含彩色图表、示例数据、脚本和便携式Windows Python安装包。" 本书的内容详细说明如下： 1. **Python基础**：介绍Python语言的基本概念，包括安装Python环境，理解Python的代码结构和基本语法，这对于初学者来说是入门的第一步。 2. **变量类型**：讲解Python中的各种数据类型，如整型、浮点型、字符串、列表、元组、字典等，以及如何进行类型转换和操作这些数据。 3. **创建和呈现刺激**：这部分将教读者如何利用Python在计算机屏幕上显示实验所需的图像、文本和其他视觉刺激，并控制它们的呈现方式。 4. **处理响应**：涵盖如何获取和处理来自键盘、鼠标等外围设备以及专业设备（如眼动仪）的输入数据，这是实验交互的核心部分。 5. **脚本实验**：教授如何编写完整的实验脚本，包括实验流程的控制、随机化和条件判断，使研究人员能够自主设计实验程序。 6. **分析行为数据**：介绍基本的数据分析方法，如统计测试、描述性统计和数据可视化，以便对实验结果进行初步解读。 7. **分析轨迹数据**：针对如瞳孔大小、眼动等连续变化的数据，讲解高级分析技术，如时间序列分析、信号处理和模式识别。 8. **眼动追踪**：专门讨论眼动追踪技术，包括数据采集、预处理和眼动数据分析，帮助研究人员理解视线移动与认知过程的关系。 9. **获取帮助**：指导读者如何查找和利用Python社区资源、文档和在线工具，解决编程和分析中遇到的问题。附录则列出了全书的所有代码清单，方便读者查阅和实践。通过本书的学习，心理学和认知神经科学的研究人员不仅能够掌握Python编程技能，还能将这些技能应用于实际的实验设计和数据分析中，从而提高研究效率和质量。配套网站提供的额外资源将进一步增强学习体验，确保读者能够顺利入门并深入掌握Python在实验心理学领域的应用。

Integers

Let’s start actually using these concepts. Fire up a Python interpreter, and start typing:

2 + 2

Expected result, right? 2 + 2 = 4. In your face, Radiohead! Now let’s try subtraction:

5 ‒ 3

3 ‒ 5

This should also be an expected result to you. Five minus three does indeed equal two. And three minus

five equals minus two. Let’s move on quickly:

3 * 2

Brilliant, Python can do multiplication. Would it be able to do exponentiation as well? Let’s try 3

(the

square of three, or three to the power of two):

** 2

The syntax might seem a bit weird, with the double asterisk. You might be used to exponentiating with

a caret or circumflex (^), e.g. in spreadsheet editors (LibreOffice Calc or Microsoft Excel) or Matlab.

In Python, the caret is used as a different operator (you will learn about it later on), so don’t use it for

exponentiation!

All right, now let’s check out division:

5 / 3

Wait, what? 5 / 3 = 1? Is Python joking? Not quite, as you have asked for an integer division of five by

three. In fact, five divided by three is one, with a remainder of two. When you divide two integers, Python

only reports the result and not the remainder. To report the remainder, use the % operator:

5 % 3

So 5 / 3 = 1, and 5 % 3 = 2. This actually makes sense! However, you were probably looking for an

answer like 5 / 3 = 1.6666666666666667. For this, we need to turn to floats.

Floats

We want to calculate how much 5 / 3 is, but we would like the answer to be an approximation rather than

an absolutely precise answer (because we are not an overly anal mathematician). For this, we are going

to use floats. You can use floats by writing numbers as a fraction:

5.0 / 3.0

Boom! 5.0 / 3.0 = 1.6666666666666667. The cool thing about floats is that they are contagious. If you

use only one in your calculation, the answer will automatically be a float. Example:

5 / 3.0

However, you should be careful with this logic: although using a float in a calculation will result in a float

answer, it will not force numbers in intermittent calculations to be floats! Example:

(5 / 3) * 2.0

The answer might seem wrong, but: 5 / 3 = 1, and 1 * 2.0 = 2.0, so (5 / 3) * 2.0 = 2.0. Now try the

alternative:

(5 / 3.0) * 2

5 / 3.0 = 1.6666666666666667, and 1.6666666666666667 * 2 = 3.3333333333333335, so (5 / 3.0) * 2 =

3.3333333333333335.

Now that you understand what a float is, you can use them in exponentiation. Aside from being a good

exercise, float exponentiation can also be useful. Let’s take the square of three (3

) as an example. In

Python, you write this as 3**2, and the result should be 9. The inverse of this calculation, is taking the

square root of 9, which is 3. Now, in mathematics, the square root of a number can also be expressed as

an exponentiation. In this example, the square root of 9 is the same as 9

. In Python, you can write this as

9**0.5. Try the following in an Interpreter to see for yourself:

3.0 ** 2

9 ** 0.5

Did that blow your mind? The square root of a number can be written in exponent notation! This is SO

exciting!

Remember the Pythagorean theorem? In a right triangle, the square of the hypotenuse (that’s the side

opposite the right angle) is equal to the summed squares of the other two sides. You can write this as a

= c

. The square root of c

will give you the length of the hypotenuse, so you can write .

Let’s say a = 3.0 and b = 4.0, and try this in Python!

(3.0**2 + 4.0**2) ** 0.5

BOOM! You got this number thing down!

Assigning variables

Variables are a key concept in programming. In fact, variables form the building blocks of all your future

scripts. They act like pointers, allowing you to access data via labels that you assign yourself. Variable

names can contain letters (upper and lower case), some characters (such as underscores), and numbers;

but they must always start with a letter. For example, you can make a variable with name test to refer

to the number 5, like so:

test = 5

You can now use this variable to do all sorts of freaky stuff. You could, for example, use it in calculations:

剩余227页未读，继续阅读

ramissue

粉丝: 354
资源: 1487

Python编程：实验心理学与认知神经科学的入门指南

MATLAB for Psychologists

Python for Experimental Psychologists pdf 0分

from tensorflow.python.eager.def_function import experimental_functions_run_eagerly ImportError: cannot import name 'experimental_functions_run_eagerly' from 'tensorflow.python.eager.def_function' (D:\anaconda\lib\site-packages\tensorflow\python\eager\def_function.py) >>>

Acer VR Util. for 360 WebVR(experimental)-crx插件

Acer VR Util. for Vivaldi WebVR(experimental)-crx插件

Acer VR Util. for QQ WebVR(experimental)-crx插件

module 'tensorflow.python.keras.layers' has no attribute 'experimental'

from tensorflow.compat.v2.experimental import dtensor as dtensor_api ImportError: cannot import name 'dtensor' from 'tensorflow.compat.v2.experimental' (D:\py\lib\site-packages\tensorflow\_api\v2\compat\v2\experimental\__init__.py)

AttributeError: module 'tensorflow.python.keras.layers' has no attribute 'experimental'

最新资源

from tensorflow.compat.v2.experimental import dtensor as dtensor_api ImportError: cannot import name 'dtensor' from 'tensorflow.compat.v2.experimental' (D:\py\lib\site-packages\tensorflow\_api\v2\compat\v2\experimental\init.py)