Introduction to Intricate Artificial Psychology with Python.

Format:

Book

Author/Creator:

Watson, Peter.

Language:

English

Subjects (All):

Psychology.

Artificial intelligence.

Physical Description:

1 online resource (449 pages)

Edition:

1st ed.

Place of Publication:

Chantilly : Elsevier Science & Technology, 2025.

Summary:

Introduction to Intricate Artificial Psychology with Python unlocks the mysteries of Intricate Artificial Psychology (iAp).This comprehensive guide takes readers through advanced cognitive frameworks and the complex landscape of artificial psychology using Python.

Contents:

Front Cover

Introduction to Intricate Artificial Psychology with Python

Copyright Page

Dedication

Contents

List of contributors

Preface

In the Age of the Artificial, Psychology Must Evolve

Acknowledgments

1 Introduction to intricate artificial psychology

From simple theory to complex theory

Psychological intricate network theory

Intricate approach on brain

Intricate mind: perception, cognition, and emotion are integrated

Toward intricate artificial psychology

References

2 Intricate mind: perception, cognition, and emotion are integrated in a new paradigm for a cognitive model

Introduction

Preliminary information

Pattern of human cognition

Pattern of human cognition as the first resource to model intelligent systems

Cognitive computing is calibrated to solve complex problems and provide accurate results

Reconfiguration required for cognitive computing: a single structure

Some existing literature models on cognition

Interdisciplinarity as a source to delineate the cognitive linguistic process as a unique structure for humans and machines

A single cognitive process across the branches of science

Features of the universal cognitive linguistic process

The cognitive framework involves constructing values to generate meaning

"Relationship" in modeling structures to generate cognition

Conclusion

3 Toward intricate thinking

From entanglement to intricacy

Distinguishing entanglement from intricacy

Entangled theories and network perspectives

4 Prediction in intricate artificial psychology

Importance of prediction with intricate data in psychology

Link prediction

Predicting links with traditional methods

Heuristic methods

Node embeddings

Subgraph method

Node prediction.

Importance of node prediction in psychological data

Traditional machine learning on graph features

Graph neural networks for node prediction

Node embedding methods

Hybrid methods for small data

Overview of methods for large-scale node prediction

Practical considerations for node prediction in small datasets

Conclusion and future perspectives on node prediction

5 Detecting implicit bias using fuzzy cognitive maps

Building the fuzzy cognitive map model

Choosing the initial activation values

Quasinonlinear reasoning mechanism

Implicit bias against young applicants

Implicit bias against female applicants

Implicit bias against foreign applicants

Summary and remarks

6 Forecasting in complex artificial psychology

The role of dynamics graph neural networks

Introducing dynamics graph neural networks

Finding a solution

7 Explaining neural networks in natural language

On the importance of Trustworthy Artificial Intelligence

Explaining deep neural networks with Shapley additive explanation tool

Explainable artificial intelligence-based system for mental health monitoring

Construction of fuzzy linguistic summaries

Practical examples using Python

About datasets and data preprocessing

Definition of fuzzy linguistic variables

Building predictive model

Generating fuzzy linguistic summaries about predictive models

Acknowledgement

8 Complex network analysis

Networks for cognition and emotion

Perception, attention, and emotion

Theory of mind

Features of complex networks

Topological features

Spatial features

Dynamic features

Connectome changes, evolution, and ageing

Network analysis of functional connectivity matrices.

The adjacency matrix or functional connectivity matrix

Functional/structural/effective connectivity

Microscale/mesoscale/macroscale connectome

Directed or undirected/binary or weighted

Types of brain parcellation (seed-to-seed correlation/seed-to-voxel correlation/independent component analysis)

Compute and display the connectivity matrix

Types of correlation

Network metrics

Node degree or centrality

Clustering coefficient

Motifs

Path length and distance

Community structure, modules, and hubs

Static or dynamic functional connectivity

Feature or edge selection methods

Visualization of brain networks in Python

9 Network approach in psychology

Network psychometrics

Network estimation

Network topology

Network properties

Network accuracy

Centrality indices accuracy

Edges accuracy

Exploratory graph analysis

Bootstrap exploratory graph

10 Deep learning techniques in neuroimaging

Introduction to deep learning in neuroimaging

Fundamentals of neural networks for image analysis

Convolutional neural networks for neuroimaging

Generative models: enhancing neuroimaging data

Transfer learning and pretrained models in neuroimaging

Interpretable deep learning in neuroimaging analysis

Advanced architectures and architectural innovations

Applications in neuroimaging: deep learning approaches for Alzheimer disease classification

Future frontiers: emerging trends in deep learning for neuroimaging

11 Machine learning techniques in neuroimaging

Introduction to machine learning in neuroimaging

Neuroimaging modalities and data types.

Core machine learning algorithms for neuroimaging

Data preprocessing in neuroimaging

Building and training machine learning models

Evaluating machine learning model performance

Case studies and applications in psychological research

Future directions in machine learning for neuroimaging

Practical implementation example in Python

12 Becoming a PsychoPythonista

Psychology and using Python programming

Statistical methods and data visualization

Machine learning and modeling

Data acquisition and preprocessing techniques

Neuroimaging data analysis in Python

Electroencephalography and functional magnetic resonance imaging data analysis with Python machine learning for cognitive tasks

Natural language processing with Python

Emotion recognition with Python

Data acquisition

Data cleaning and data preprocessing

Data analysis and visualization

Modeling

Hypothesis testing

Linear regression model

Classification model

Index

Back Cover.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

0-443-30249-9

9780443302497

OCLC:

1557604829