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Python for Experimental Psychologists equips researchers who have no prior programming experience with the essential knowledge to independently script experiments and analyses in the programming language Python. This book offers an excellent introduction, whether you are an undergraduate, a PhD candidate, or an established researcher. This updated edition is on Python 3 (the most current version). It starts by teaching the fundamentals of programming in Python and then offers several chapters on scripting experiments (displaying stimuli, obtaining and logging user input, precision timing, etc.) using the popular PsychoPy package. The remainder of the book is dedicated to data analysis and includes chapters on reading/writing to text files, time series, eye tracking, data visualisation, and statistics. Access to online support material enriches the learning experience with colour figures, example stimuli, datasets, scripts, and a portable Windows installation of Python. This book assumes no prior knowledge, and its informal and accessible tone helps readers with backgrounds in experimental psychology and cognitive neuroscience to quickly understand Python. It serves as a useful resource not only for researchers in these fields but also for lecturers instructing on methodology and data analysis. Python for Experimental Psychologists demystifies programming complexities and empowers researchers to proficiently conduct experiments and analyse their results.
Programming is an important part of experimental psychology and cognitive neuroscience, and Python is an ideal language for novices. It sports a very readable syntax, intuitive variable management, and a very large body of functionality that ranges from simple arithmetic to complex computing. Python for Experimental Psychologists provides researchers without prior programming experience with the knowledge they need to independently script experiments and analyses in Python. The skills it offers include: how to display stimuli on a computer screen; how to get input from peripherals (e.g. keyboard, mouse) and specialised equipment (e.g. eye trackers); how to log data; and how to control timing. In addition, it shows readers the basic principles of data analysis applied to behavioural data, and the more advanced techniques required to analyse trace data (e.g. pupil size) and gaze data. Written informally and accessibly, the book deliberately focuses on the parts of Python that are relevant to experimental psychologists and cognitive neuroscientists. It is also supported by a companion website where you will find colour versions of the figures, along with example stimuli, datasets and scripts, and a portable Windows installation of Python.
Programming is an important part of experimental psychology and cognitive neuroscience, and Python is an ideal language for novices. It sports a very readable syntax, intuitive variable management, and a very large body of functionality that ranges from simple arithmetic to complex computing. Python for Experimental Psychologists provides researchers without prior programming experience with the knowledge they need to independently script experiments and analyses in Python. The skills it offers include: how to display stimuli on a computer screen; how to get input from peripherals (e.g. keyboard, mouse) and specialised equipment (e.g. eye trackers); how to log data; and how to control timing. In addition, it shows readers the basic principles of data analysis applied to behavioural data, and the more advanced techniques required to analyse trace data (e.g. pupil size) and gaze data. Written informally and accessibly, the book deliberately focuses on the parts of Python that are relevant to experimental psychologists and cognitive neuroscientists. It is also supported by a companion website where you will find colour versions of the figures, along with example stimuli, datasets and scripts, and a portable Windows installation of Python.
Several Python programming books feature tools designed for experimental psychologists. What sets this book apart is its focus on eye-tracking. Eye-tracking is a widely used research technique in psychology and neuroscience labs. Research grade eye-trackers are typically faster, more accurate, and of course, more expensive than the ones seen in consumer goods or usability labs. Not surprisingly, a successful eye-tracking study usually requires sophisticated computer programming. Easy syntax and flexibility make Python a perfect choice for this task, especially for psychology researchers with little or no computer programming experience. This book offers detailed coverage of the Pylink library, a Python interface for the gold standard EyeLink ® eye-trackers, with many step-by-step example scripts. This book is a useful reference for eye-tracking researchers, but you can also use it as a textbook for graduate-level programming courses.
PsychoPy is an open-source software package for creating rich, dynamic experiments in psychology, neuroscience and linguistics. Written by its creator, this book walks you through the steps of building experiments in PsychoPy, from using images to discovering lesser-known features, and from analysing data to debugging your experiment. Divided into three parts and with unique extension exercises to guide you at whatever level you are at, this textbook is the perfect tool for teaching practical undergraduate classes on research methods, as well as acting as a comprehensive reference text for the professional scientist. Essential reading for anyone using PsychoPy software, the second edition has been fully updated and includes multiple new chapters about features included in recent versions of PsychoPy, including running studies online and collecting survey data. Part I teaches you all the basic skills you need (and some more advanced tips along the way) to design experiments in behavioral sciences. Each chapter introduces anew concept but will offer a series of working experiments that you can build on. Part II presents more details important for professional scientists intending to use PsychoPy for published research. This part is recommended reading for science professionals in any discipline. Part III covers a range of specialist topics, such as those doing fMRI research, or those studying visual perception. "This book fills an incredibly important gap in the field. Many users of PsychoPy will be excited to learn that there is now a highly accessible and well-designed written guide to refine their skills." – Susanne Quadflieg, University of Bristol
Programming is an important part of experimental psychology and cognitive neuroscience, and Python is an ideal language for novices. It sports a very readable syntax, intuitive variable management, and a very large body of functionality that ranges from simple arithmetic to complex computing. Python for Experimental Psychologistsprovides researchers without prior programming experience with the knowledge they need to independently script experiments and analyses in Python. The skills it offers include: how to display stimuli on a computer screen; how to get input from peripherals (e.g. keyboard, mouse) and specialised equipment (e.g. eye trackers); how to log data; and how to control timing. In addition, it shows readers the basic principles of data analysis applied to behavioural data, and the more advanced techniques required to analyse trace data (e.g. pupil size) and gaze data. Written informally and accessibly, the book deliberately focuses on the parts of Python that are relevant to experimental psychologists and cognitive neuroscientists. It is also supported by a companion website where you will find colour versions of the figures, along with example stimuli, datasets and scripts, and a portable Windows installation of Python.
Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.
This book integrates philosophy of science, data acquisition methods, and statistical modeling techniques to present readers with a forward-thinking perspective on clinical science. It reviews modern research practices in clinical psychology that support the goals of psychological science, study designs that promote good research, and quantitative methods that can test specific scientific questions. It covers new themes in research including intensive longitudinal designs, neurobiology, developmental psychopathology, and advanced computational methods such as machine learning. Core chapters examine significant statistical topics, for example missing data, causality, meta-analysis, latent variable analysis, and dyadic data analysis. A balanced overview of observational and experimental designs is also supplied, including preclinical research and intervention science. This is a foundational resource that supports the methodological training of the current and future generations of clinical psychological scientists.
Experimental psychology is a branch of psychology that employs scientific methods to study and understand psychological phenomena. The principal objective of experimental psychology is to investigate the underlying cognitive processes, emotions, behaviors, and social interactions through empirical observation and controlled experimentation. This chapter aims to provide an overview of the foundational principles that underpin experimental psychology, its objectives, and the significance of its methodologies in the broader context of psychological research. The foundation of experimental psychology lies in the belief that behaviors and mental processes can be quantified, measured, and manipulated in a systematic manner. This empirical investigation predominately stems from the principles of behaviorism and cognitive psychology, among others. The approach seeks to elucidate the cause-and-effect relationships between variables, thereby accumulating knowledge that can be generalized to broader populations and contexts. One of the foundational concepts of experimental psychology is hypothesis testing. Researchers formulate specific, testable predictions about the relationship between variables based on existing theories and literature. These hypotheses guide the experimental design, leading to the identification of independent variables (IVs), dependent variables (DVs), and control variables. The manipulation of IVs allows researchers to observe changes in DVs, thus uncovering psychological insights through structured experimentation.
Designing EEG Experiments for Studying the Brain: Design Code and Example Datasets details the design of various brain experiments using electroencephalogram (EEG). Providing guidelines for designing an EEG experiment, it is primarily for researchers who want to venture into this field by designing their own experiments as well as those who are excited about neuroscience and want to explore various applications related to the brain. The first chapter describes how to design an EEG experiment and details the various parameters that should be considered for success, while remaining chapters provide experiment design for a number of neurological applications, both clinical and behavioral. As each chapter is accompanied with experiment design codes and example datasets, those interested can quickly design their own experiments or use the current design for their own purposes. Helpful appendices provide various forms for one's experiment including recruitment forms, feedback forms, ethics forms, and recommendations for related hardware equipment and software for data acquisition, processing, and analysis. - Written to assist neuroscientists in experiment designs using EEG - Presents a step-by-step approach to designing both clinical and behavioral EEG experiments - Includes experiment design codes and example datasets - Provides inclusion and exclusion criteria to help correctly identify experiment subjects and the minimum number of samples - Includes appendices that provide recruitment forms, ethics forms, and various subjective tests associated with each of the chapters