Python for Experimental Psychologists
Python for Experimental Psychologists

Author: Edwin S. Dalmaijer

Publisher: Taylor & Francis

Published: 2024-11-11

Total Pages: 201

ISBN-13: 1040147275

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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.

Python for Experimental Psychologists
Python for Experimental Psychologists

Author: Edwin Dalmaijer

Publisher: Taylor & Francis

Published: 2016-11-03

Total Pages: 229

ISBN-13: 1317206444

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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.

Python for Experimental Psychologists
Python for Experimental Psychologists

Author: Edwin Dalmaijer

Publisher: Routledge

Published: 2016-11-03

Total Pages: 419

ISBN-13: 1317206436

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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.

Eye-Tracking with Python and Pylink
Eye-Tracking with Python and Pylink

Author: Zhiguo Wang

Publisher: Springer Nature

Published: 2021-11-26

Total Pages: 237

ISBN-13: 303082635X

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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.

Building Experiments in PsychoPy
Building Experiments in PsychoPy

Author: Jonathan Peirce

Publisher: SAGE

Published: 2022-01-12

Total Pages: 313

ISBN-13: 1529788692

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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

Python for Experimental Psychologists
Python for Experimental Psychologists

Author: Edwin S. Dalmaijer

Publisher:

Published: 2017

Total Pages: 215

ISBN-13: 9781315616933

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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 Cognitive Modeling
Bayesian Cognitive Modeling

Author: Michael D. Lee

Publisher: Cambridge University Press

Published: 2014-04-03

Total Pages: 279

ISBN-13: 1107653916

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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.

Python for Marketing Research and Analytics
Python for Marketing Research and Analytics

Author: Jason S. Schwarz

Publisher: Springer Nature

Published: 2020-11-03

Total Pages: 272

ISBN-13: 3030497208

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This book provides an introduction to quantitative marketing with Python. The book presents a hands-on approach to using Python for real marketing questions, organized by key topic areas. Following the Python scientific computing movement toward reproducible research, the book presents all analyses in Colab notebooks, which integrate code, figures, tables, and annotation in a single file. The code notebooks for each chapter may be copied, adapted, and reused in one's own analyses. The book also introduces the usage of machine learning predictive models using the Python sklearn package in the context of marketing research. This book is designed for three groups of readers: experienced marketing researchers who wish to learn to program in Python, coming from tools and languages such as R, SAS, or SPSS; analysts or students who already program in Python and wish to learn about marketing applications; and undergraduate or graduate marketing students with little or no programming background. It presumes only an introductory level of familiarity with formal statistics and contains a minimum of mathematics.

The Cambridge Handbook of Research Methods in Clinical Psychology
The Cambridge Handbook of Research Methods in Clinical Psychology

Author: Aidan G. C. Wright

Publisher: Cambridge University Press

Published: 2020-03-31

Total Pages: 600

ISBN-13: 9781316639528

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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
Experimental Psychology

Author: Prof. Dr. Bilal Semih Bozdemir

Publisher: Prof. Dr. Bilal Semih Bozdemir

Published:

Total Pages: 441

ISBN-13:

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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.