Dr Redmond O’ Connell is a cognitive neuroscientist from Trinity College Dublin, the college where I received my degree.

He has spent decades studying our decision making systems in the brain, how we build evidence in a manner shaped by our experience and perceptions and ultimately how we make heavily biased decisions.

Dr Redmond has won many awards and is a brilliant researcher and scientific communicator. Subscribe on youtube and follow on Spotify so you don’t miss the next neuroscience podcast, I hope you enjoy!

00:00 Intro
03:23 The Brain's Decision-Making Process
04:56 The Evidence Accumulation Process
11:58 Overcoming Biases through Education and Critical Thinking
15:15 Effective Communication to Address Biases
39:52 Introduction and Free Will
55:50 Limitations of Large Language Models
01:02:42 Mimicking the Human Brain in AI
01:10:31 Understanding Consciousness in AI
01:16:29 Exciting Advances and Concerns in the Next Decade of Science

The brain's decision-making process is still not fully understood, but there are some guiding principles that have been established. One principle is the evidence accumulation process, where the brain samples and weighs different pieces of information before making a decision. Expectations and biases also play a role in decision making, as they shape the thresholds for accepting evidence. Biases can be difficult to overcome, but education and critical thinking can help. Effective communication is crucial in addressing biases and promoting a better understanding of complex issues. The concept of free will is subjective and depends on individual definitions and perspectives.

In this conversation, Redmond O'Connell discusses the concept of free will and its relationship to neural activity and decision-making. He challenges the idea that neural activity preceding conscious decisions negates free will, arguing that it is a matter of quantity rather than a binary concept. O'Connell also explores the limitations of large language models like ChatGPT, highlighting their inability to reason and their reliance on statistical associations. He suggests that mimicking the human brain and incorporating embodied cognition may be key to developing more intelligent AI.

O'Connell emphasizes the importance of understanding consciousness and the potential ethical concerns surrounding AI and misinformation.