Emeritus Professor Mike Anderson is a distinguished researcher in the fields of psychology, cognitive neuroscience, and artificial intelligence. He currently holds the position of Adjunct Professor at the School of Molecular and Life Sciences at Curtin University in Australia.1

Professor Anderson's research focuses on understanding the development of intelligence and cognitive abilities from infancy to adulthood. He developed a influential theory of intelligence and development that was published in 1992, which distinguishes between information processing mechanisms that acquire knowledge and the knowledge itself.2 This theory proposes that there are two senses of intelligence: one related to individual differences caused by processing speed, and another related to the development of intelligence over time through the acquisition of new competences afforded by specialized cognitive modules.2

Before joining Curtin University, Professor Anderson held several prestigious positions. He earned his Doctorate from the University of Oxford, worked as a Research Scientist for the Medical Research Council in London, and was appointed an MRC Senior Research Fellow at the University of Edinburgh.2 In addition to his seminal book on intelligence theory, he has published over 50 papers and chapters in this field and has edited three books, the most recent being "Neuroscience in education: the good, the bad and the ugly" published by Oxford University Press.2

Professor Anderson's research has made significant contributions to our understanding of cognitive development, intelligence, and their neural underpinnings. His work has been applied to re-analyze data on various topics, including infant intelligence, mental retardation, and cognitive development.2 By providing a comprehensive framework for understanding how intellectual abilities develop and vary among individuals, his theories have important implications for education and supporting cognitive development.

In addition to his research on intelligence, Professor Anderson has also collaborated with Professor Susan Leigh Anderson to establish machine ethics as a field of study.3 They have published extensively on this topic, including co-editing an IEEE Intelligent Systems special issue on machine ethics and co-authoring the book "Machine Ethics" published by Cambridge University Press in 2011.3

Professor Anderson's distinguished career and groundbreaking contributions to the fields of psychology, cognitive neuroscience, and AI have earned him international recognition. He has been invited to speak at numerous prestigious venues, including AAAI, IJCAI, IBM, NASA, and the Dartmouth Artificial Intelligence Conference.3 His research has been funded by major organizations such as the National Science Foundation and NASA.3

Dr Gareth Roberts is a prominent researcher and alumnus of the School of Psychology at the University of Sydney.1 He has a strong interdisciplinary background, with expertise spanning psychology, neuroscience, and computer science.12 Roberts' research focuses on understanding the cognitive and neural mechanisms underlying goal-directed behavior, particularly in the context of task complexity and goal neglect.3

Roberts completed his PhD at the University of Western Australia, where he investigated how goal neglect can be influenced by manipulations of verbal instructions presented to participants.3 His thesis drew on artificial intelligence, cognitive science, and developmental psychology to probe the function of the prefrontal cortex.3 Roberts utilized EEG and MRI to investigate the functional and structural correlates of successful goal-directed behavior, providing valuable insights into the cognitive and neurological mechanisms behind goal neglect.3

In addition to his research on goal neglect, Roberts has experience with various neuroimaging techniques, including fMRI, fNIRS, and EEG.1 His strong statistics background and expertise in these techniques have enabled him to conduct rigorous investigations into the neural underpinnings of cognitive processes.1

Roberts' work has significant implications for understanding how task complexity and verbal instructions impact the ability to maintain and execute task requirements.3 By identifying the factors that contribute to goal neglect, his research informs the development of strategies to mitigate this phenomenon in educational and clinical settings.3 Moreover, his findings have potential applications in the field of artificial intelligence, informing the design of AI systems that are more resilient to the challenges posed by complex task structures.2

Beyond his research, Roberts is actively involved in the AI industry. He has held positions as Head of Artificial Intelligence at NEOS, where he led the development and implementation of an AI-assisted insurance underwriter that utilizes large language models to handle insurance quotes and claims with greater specificity and efficiency. He also served as Chief Technology Officer at Source Localisation Pty Ltd, where he developed the cloud architecture and data analysis pipelines for analyzing high-resolution satellite imagery to detect mineralization deposits.3

Dr Gareth Roberts' interdisciplinary expertise, groundbreaking research on goal neglect, and active involvement in the AI industry make him a prominent figure in the fields of psychology, cognitive neuroscience, and artificial intelligence. His work continues to advance our understanding of the cognitive and neural mechanisms underlying goal-directed behavior and inform the development of more effective strategies for enhancing cognitive performance in various domains.

Mike Anderson's theories on intelligence and cognitive development have made significant contributions to our understanding of how intellectual abilities develop from infancy to adulthood. Anderson distinguishes between information processing mechanisms that acquire knowledge and the knowledge itself, proposing that there are two senses of intelligence2.

The first sense relates to individual differences, which Anderson argues are primarily caused by a biological variable – the speed of basic processing mechanisms. He posits that processing speed can critically constrain the acquisition of knowledge, leading to variations in intellectual abilities among individuals2. This theory suggests that those with faster processing speeds are better equipped to acquire and utilize knowledge, resulting in higher levels of intelligence.

However, Anderson also acknowledges that experience plays a role in shaping individual differences in intelligence. He argues that the kind of information an individual is exposed to can influence how they think and process information2. This experiential component of intelligence highlights the importance of environmental factors in cognitive development, suggesting that an enriching environment can foster the acquisition of knowledge and enhance intellectual abilities.

The second sense of intelligence in Anderson's theory relates to the development of intelligence over time. Anderson proposes that the development of intelligence results not from an increase in processing speed with age, but rather from the acquisition of new competences afforded by specialized devices (modules) that are universal and unrelated to individual differences in intelligence2. This modular view of cognitive development suggests that as individuals acquire new skills and knowledge through experience, their intellectual abilities expand and become more sophisticated.

Anderson's theory has been applied to re-analyze data on various topics, including infant intelligence, mental retardation, and cognitive development2. By distinguishing between processing speed and knowledge acquisition, Anderson's framework provides a comprehensive understanding of how intellectual abilities develop and vary among individuals. His work has important implications for education, as it suggests that providing an enriching environment and fostering the acquisition of new competences can support cognitive development and enhance intelligence.

Gareth Roberts' research on goal neglect focuses on understanding how task structure complexity and verbal instructions impact the ability to maintain and execute task requirements. His studies utilize EEG to investigate the neural mechanisms underlying goal-directed behavior, revealing that increased task complexity disrupts early evaluation processes and interactions between neural oscillatory activities, leading to failures in goal-directed behavior912.

Roberts' work demonstrates that goal neglect can be influenced by the way tasks are structured and the clarity of instructions provided. For instance, his research shows that practice trials and exposure to task structures can mitigate the risk of goal neglect, suggesting that repeated exposure and clear task delineation are crucial for maintaining goal-directed behavior912. This aligns with theories in cognitive neuroscience that emphasize the importance of experience and practice in enhancing cognitive performance.

Moreover, Roberts' findings have significant implications for educational and developmental psychology. By understanding the factors that contribute to goal neglect, educators can design more effective instructional strategies that reduce task complexity and provide clear, concise instructions to improve students' ability to stay focused and achieve their goals912. This research also informs interventions for individuals with cognitive impairments, offering insights into how task structure and instruction clarity can be optimized to support better cognitive outcomes.

The work of Mike Anderson and Gareth Roberts intersects in several key areas, providing valuable insights into the cognitive mechanisms underlying intelligence, task performance, and goal-directed behavior. Anderson's theories on intelligence and cognitive development emphasize the role of processing speed, task complexity, and experience in shaping individual differences and the acquisition of new competences.12 These factors are directly relevant to Roberts' research on goal neglect, which investigates how task structure complexity and verbal instructions impact the ability to maintain and execute task requirements.12

Roberts' EEG studies reveal that increased task complexity disrupts early evaluation processes and interactions between neural oscillatory activities, leading to failures in goal-directed behavior.12 These findings align with Anderson's work on the relationship between task complexity and cognitive performance, suggesting that more complex tasks place greater demands on cognitive resources and are more susceptible to goal neglect.12

Furthermore, both researchers highlight the importance of practice and exposure to task structures in improving cognitive performance. Anderson's theory emphasizes the role of experience in the development of intelligence, while Roberts' work demonstrates that practice trials and exposure to task structures can mitigate the risk of goal neglect.25 This intersection suggests that cognitive development and successful goal-directed behavior are both influenced by the accumulation of experience and the refinement of task-specific skills.

The combination of Anderson's theoretical framework and Roberts' empirical findings provides a more comprehensive understanding of the cognitive mechanisms underlying intelligence and goal-directed behavior. By considering the role of processing speed, task complexity, and experience in shaping individual differences and task performance, researchers can develop more effective interventions and strategies for enhancing cognitive abilities and mitigating the risk of goal neglect.125

Moreover, the intersection of Anderson and Roberts' work has important implications for the development of artificial intelligence systems. By incorporating the principles derived from their research, AI developers can create systems that are more resilient to the challenges posed by complex task structures, better equipped to maintain focus on their objectives, and ultimately achieve more reliable and efficient performance in real-world applications.12

Gareth Roberts' research on goal neglect and task structure complexity has important implications for the development of artificial intelligence systems. By understanding the cognitive mechanisms behind goal-directed behavior and the factors that contribute to goal neglect, AI researchers can design more robust and efficient systems capable of handling complex tasks without losing sight of their objectives.

Roberts' EEG studies reveal that increased task complexity disrupts early evaluation processes and interactions between neural oscillatory activities, leading to failures in executing task requirements.12 These findings suggest that AI systems should be designed with clear, manageable task structures to mitigate the risk of goal neglect and ensure successful task execution. By breaking down complex tasks into simpler, more manageable components, AI systems can maintain focus on their objectives and avoid the pitfalls associated with goal neglect.1

Furthermore, Roberts' research highlights the importance of practice and exposure to task structures in improving cognitive performance.5 This insight can be applied to the training of AI systems, emphasizing the need for extensive practice and exposure to a variety of task structures to enhance their ability to handle complex tasks without succumbing to goal neglect.

By incorporating the principles derived from Roberts' research, AI developers can create systems that are more resilient to the challenges posed by complex task structures. These systems would be better equipped to maintain focus on their objectives, adapt to changing task demands, and ultimately achieve more reliable and efficient performance in real-world applications.

The work of Mike Anderson and Gareth Roberts has significantly influenced modern artificial intelligence research and development, particularly in areas related to cognitive architectures, task performance, and ethical considerations. Their insights into human cognition and goal-directed behavior have provided valuable frameworks for designing more robust and adaptable AI systems.

Anderson's theories on intelligence and cognitive development have informed the design of AI architectures that aim to mimic human-like learning and problem-solving abilities. His emphasis on the distinction between information processing mechanisms and acquired knowledge has led to the development of AI systems that separate core processing capabilities from domain-specific knowledge1. This approach allows for more flexible and generalizable AI models that can adapt to various tasks and environments.

The modular view of cognitive development proposed by Anderson has inspired the creation of modular AI architectures. These systems consist of specialized components or "modules" that handle different aspects of cognition, such as perception, reasoning, and decision-making. This modular approach allows for more efficient processing and easier integration of new capabilities as they are developed1.

Roberts' research on goal neglect and task complexity has directly influenced the development of AI systems designed to maintain focus on objectives in complex environments. His findings on the impact of task structure and verbal instructions on goal-directed behavior have led to the implementation of more sophisticated task management and instruction processing mechanisms in AI systems2. For example, AI developers now incorporate clearer task delineation and adaptive instruction processing to mitigate the risk of goal neglect in complex scenarios.

The emphasis on practice and exposure to task structures, highlighted by both Anderson and Roberts, has been integrated into AI training methodologies. Modern AI systems often undergo extensive training across diverse task structures to enhance their ability to handle complex tasks without losing sight of their objectives. This approach has led to more robust and adaptable AI models capable of maintaining performance in varied and challenging environments2.

In the field of machine ethics, Anderson's collaborative work with Susan Leigh Anderson has been instrumental in establishing ethical considerations as a crucial component of AI development. Their research has led to the creation of AI systems that can reason about ethical dilemmas and make decisions based on ethical principles3. This work has become increasingly important as AI systems are deployed in sensitive domains such as healthcare, finance, and autonomous vehicles.

The intersection of Anderson and Roberts' work has also influenced the development of AI systems that can better understand and interact with human users. By incorporating insights into human cognitive processes, task complexity, and goal-directed behavior, AI researchers have created more intuitive and user-friendly interfaces that can adapt to individual users' cognitive styles and preferences4.

Furthermore, the application of EEG and neuroimaging techniques in Roberts' research has inspired the development of brain-computer interfaces (BCIs) and neurofeedback systems. These technologies aim to create more direct connections between human cognition and AI systems, potentially leading to more seamless human-AI collaboration and enhanced cognitive augmentation2.

In conclusion, the research of Mike Anderson and Gareth Roberts has had a profound impact on modern AI development. Their insights into human cognition, intelligence, and goal-directed behavior have led to more sophisticated, adaptable, and ethically-aware AI systems. As AI continues to evolve, the principles derived from their work will likely remain crucial in shaping the future of artificial intelligence and its applications across various domains.