Chapter 1: Introduction to Biological Psychology

1.4: Payoffs of Biological Psychology

Biological psychology and related neuroscience fields are extremely active areas of research. Billions of dollars are invested annually and tens of thousands of researchers are studying the brain and behavior. The vast undertaking to understand the brain will help unravel the mysteries of human nature and our human experience. In addition, research on the brain has impactful applications–it helps to heal brain damage and psychological disorders and guides the development of artificial intelligence and brain-compatible social policies (Eagleman & Downar, 2016).

Healing the brain. Brain damage and psychological disorders affect tens of millions of people each year. Treatment and care of afflicted patients benefits greatly from basic brain science. Research on cellular and molecular mechanisms of the brain can lead to the development of new drugs, and ways to regrow neurons and increase neural plasticity. Researchers have developed mind-boggling new technology like transcranial magnetic stimulation that stimulates the brain with magnetic pulses and can treat depression, or deep brain stimulation that uses brain implants to treat diseases like Parkinson’s. Brain-computer interfaces that pick up brain signals, analyze them, and translate them into commands for an external device like a wheelchair or robotic arm can help people regain movement who have neuromuscular disorders including amyotrophic lateral sclerosis (ALS), spinal cord injury, or stroke (Figure 4).


Figure 4. Components of a typical system for Brain-Computer Interface that picks up brain signals, analyzes them, and translates them into commands to an external output device like a wheelchair or robotic arm.

Guiding Artificial Intelligence. Neuroscience is closely intertwined with artificial intelligence (AI). Understanding biological brains is important for building artificial brains and neuroscience has inspired the design of AI systems (Hassabis et al., 2017). Similar to neurons in the human brain, artificial neural networks have many interconnected units that work in parallel. Neuroscience provided early guidance for AI-system architecture (e.g., units organized in many layers) and algorithms (e.g., a “backpropagation algorithm” that adjusts the connections between units across multiple layers to enable learning). Another example of neuroscience-inspired AI is in attention—until recently artificial neural networks processed an entire image or video frame with equal priority given to all pixels. Conversely, humans focus on one very small aspect at a time and strategically shift our “spotlight of attention.” By incorporating a biologically-inspired approach with prioritized focus, AI-image processing has improved performance and efficiency (Hassabis et al., 2017).

AI has recently made dramatic advances thanks to breakthroughs in “deep learning” and “reinforcement learning” methods (wherein learning is optimized by reinforcing or rewarding when desired outcomes occur). By incorporating principles from neuroscience, AI researchers have been able to improve the efficiency and accuracy of AI algorithms, resulting in significant advances in areas like computer vision, decision-making, and natural language processing. Those AI assistants you might have used, like ChatGPT and LLaMa for text generation or Stable Diffusion and DALL-E for image generation, built on principles discovered in brain science. Finally, advances in AI that were inspired by neuroscience are now being applied back to neuroscience as tools to understand the brain. For example, AI and deep learning can be used to analyze neuroimaging data, build computational models to simulate and study the brain, and identify and predict the trajectory of psychological disorders or diseases like Alzheimer’s (Kreigeskorte & Douglas, 2018; Colliot, 2023).


Computer generated image of a brain.
Figure 5. AI-generated image from DALL-E 3 using the prompt “Artificial Intelligence computer system inspired by neurons and brains.”

Brain-compatible social policies. As outlined by Eagleman and Downar (2016), brain science can also have payoffs in guiding brain-compatible social policies. For example, psychology and neuroscience research has implications for how to view and address drug addiction. As brain science learns more about psychopharmacology and the brain mechanisms underlying addiction, it has become increasingly clear that addiction is driven by biological processes in the brain. As a result, policies that attack only drug supply are typically unsuccessful—if one point of supply is busted, another will pop up soon because the demand remains. Instead, policies should focus on attacking the demand for drugs—treatments that are rooted in interrupting the brain circuits that drive addiction will be more effective than an incarceration-focused approach.

Finally, a large portion of the prison population in the U.S. is mentally ill. The prevalence of serious mental illness in jail inmates is around 15% for men and 31% for women (Steadman et al., 2009). Jails and prisons have become mental health care facilities. For example, the Rikers Island prison is New York’s largest mental institution (Ransom & Harris, 2023), and more mentally ill persons are in jails and prisons than are in hospitals (Torrey et al., 2010). Brain science clearly demonstrates that psychological disorders are rooted in the brain, so one must ask whether policies that support and treat those with mental-health problems might keep them from committing crimes, and whether a treatment-approach could be more humane and cost-effective (Eagleman & Downar, 2016).


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Biological Psychology Copyright © 2024 by Michael J. Hove and Steven A. Martinez is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted.

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