4 Things You Need to Know about the NEW Cognitive Neuroscience of the Brain (Predictive “Bayesian” Brain)

If you are tuned into the cognitive sciences over the past several years you may be feeling the scientific foundations shifting beneath you. Breakthroughs in cognitive neuroscience are transforming much of what we know about how the brain functions. I highlight 4 important brain changes…

1. From PASSIVELY Awaiting Information to ACTIVELY Predicting it.

The conventional model of cognition has assumed a linear path of information flow — we perceive stimuli, we process it, we act on. Although this feels intuitively accurate, neuroscience has been questioning its biologically feasibility (Rao & Ballard, 1999).

Can you imagine if our brains had to consciously perceive and process everything we encountered? From a neurobiological perspective, it is impossible. So how does our brain account for the overwhelming sensory input?

According the new wave of research, when we interact with the world we are guided by our expectations and anticipations about the sensory input we will receive (Clark, 2013). Our brains are constantly active predicting the incoming streams of input before they arrive to prepare us for action. By “always being on”, our brain maintains continuous feedforward and feedback loops to optimize precious cognitive resources, avoid “representational bottlenecks” and attend to whats important in our immediate environment.

2. From passive MEMORY Models to active GENERATIVE Models.

How does the brain generate predictions, anticipations and expectations to manage the incoming sensory information? The answer is generative models. Generative models are simply means to organize sensory data into an accurate internal model of the outside world. As opposed to passive, memory-based cognitive processing, our brains are active, multilevel learning machines, continuously making predictions based on probabilistic features in the environment and updating the model.

The brain can be thought of as a statistical organ that learns a model of its environment (Friston, 2010, Helmholtz, 1866). They capture the statistical structure of the world by picking up regularity and patterns at many spatial and temporal scales (Clark, 2013).

3. From BIASES to PREDICTION ERROR MINIMIZATION.

Our brain continuously and automatically makes predictions about the environment. As we make predictions, anticipations and expectations about how our world unfolds in front of us 2 things can occur:

1. predictions can be confirmed (e.g., see the sun BRIGHT and sky LIGHT blue so I don’t wear a coat → Go outside and realize it was a good choice not to wear a coat)
2. prediction error (e.g., see the sun BRIGHT and sky DARK blue so I don’t wear coat → Go outside and “WOW its too cold, I need a coat”)

The goal of the brain is to minimize prediction error. From the example above, we would update the difference of the sky color to interpret warm weather. The take away is our brain constantly makes predictions (feedforward), the deviations or errors are fed back to update our generative models (feedback) and optimize future predictions.

4. From Memories to Anticipation.

Instead of our brain being memory-based, waiting to be triggered by a stimulus, our brain automatically and efficiently makes predictions about its relevant future. Our generative models automatically recombines past memories into predictions and anticipations (Buckner et al., 2007). A good way to conceptualize brain functioning is like Google search, constantly anticipating and predicting….

SUMMARY:

1. Proactive Brain doesn’t wait for information, its continuously predicting and anticipating.

2. Proactive Brain uses a generative model to capture the statistical structure and regularities of the world.

3. Proactive Brain is in the business of reducing errors to update its generative model.

4. Proactive Brain is future focused.

Citations:

1. Buckner, R. L., & Carroll, D. C. (2007). Self-projection and the brain. Trends in cognitive sciences.
2. Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and brain sciences.
3. Rao, R. P., & Ballard, D. H. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature neuroscience.
4. Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience,
5. Friston, K. (2009). The free-energy principle: a rough guide to the brain? Trends in cognitive sciences.
6. Helmholtz, H. V. (1962). Treatise on physiological optics. Helmholtz. NY: Dover.
7. Schacter, D. L., Addis, D. R., & Buckner, R. L. (2007). Remembering the past to imagine the future: the prospective brain. Nature Reviews Neuroscience.