دانلود کتاب مقدمه‌ای بر سایکوفیزیک با پایتون

Psychophysics is at a unique intersection of psychology, neuroscience, and quantitative science – an irreverent approach that refuses to treat tradition as unquestionable authority and instead demands rigorous, reproducible measurement of perception. This book was written to inspire readers to challenge assumptions, embrace robust experimental design, and utilize computational tools to transform subjective experience into precise data. Chapter 1: History of Psychophysics. We revisit the pioneering work of Weber, Fechner, and Stevens, not to idolize their methods, but to understand how early ideas were challenged, refined, and ultimately replaced by more reliable practices. Through classical paradigms, the methods of limits, constant stimuli, and adjustment, we see how the field evolved by questioning its own foundations. Chapter 2: Understanding Sensory Systems. Armed with a critical mindset, we explore the anatomy and function of vision, audition, touch, taste, and smell. Each modality is illustrated with real-world examples and up-to-date research, highlighting where old models fell short and how modern techniques, such as color-opponent theory and binaural localization, advance our reproducible understanding. Chapter 3: Signal Detection Theory and Experimental Design. Here we introduce the statistical and methodological toolkit that keeps psychophysics honest. From experimental designs (between and within subjects) and common pitfalls to power analysis, signal detection theory, parametric and non-parametric tests, and psychometric function fitting, this chapter offers concrete recipes for reproducible research and ways to critically evaluate your own results. Chapter 4: Using PsychoPy to Build Experiments. In a hands-on finale, we strip away the boilerplate and guide you through building, running, and analyzing your own studies in PsychoPy. Annotated code snippets emphasize clarity, reproducibility, and version control, demonstrating how to move seamlessly from conceptual design to transparent, shareable data. Whether you are a psychology major, neuroscience researcher, or datacurious learner, this guide empowers you to approach perception with healthy skepticism, scientific rigor, and a commitment to reproducibility.