The Brain and Cause and effect
Belief Formation Research Group
The Belief Formation Research Group incorporates a group of researchers within the Department of Cognitive Science, and their collaborators, investigating the cognitive and brain systems underlying our abilities to formulate and test beliefs about the world. We focus primarily on disorders of belief formation, such as delusion, and related symptoms, such as hallucinations, as well as the consequences of these symptoms for patients’ daily living. Our approach is based on the ‘two-factor’ account which draws a distinction between the factors that explain the generation of aberrant belief content and the factors that explain the adoption and persistence of delusional beliefs. Thus our work spans the study of “lower-order” processes involved in experiencing a sense of self, perceiving others and external reality, and “higher-order” processes involved in self-monitoring, conflict processing, suspension of belief and belief revision. Our cognitive approach applies to all forms of delusional thinking regardless of aetiology. This is why we study aberrant beliefs in a range of clinical conditions, such as schizophrenia, obsessive compulsive disorder, delusional disorder, head injury, and dementia, and we investigate related non-clinical phenomena. We also aim to better understand the impact of delusional thinking on individuals and society more generally, as well as the social and cultural factors that influence delusional thinking. Our clinical collaborators draw on these advances in understanding to develop and evaluate cognitive and psychological treatments for different disorders.
Belief formation – A driving force for brain evolution
The topic of belief has been neglected in the natural sciences for a long period of time. Recent neuroscience research in non-human primates and humans, however, has shown that beliefs are the neuropsychic product of fundamental brain processes that attribute affective meaning to concrete objects and events, enabling individual goal setting, decision making and maneuvering in the environment. With regard to the involved neural processes they can be categorized as empirical, relational, and conceptual beliefs. Empirical beliefs are about objects and relational beliefs are about events as in tool use and in interactions between subjects that develop below the level of awareness and are up-dated dynamically. Conceptual beliefs are more complex being based on narratives and participation in ritual acts. As neural processes are known to require computational space in the brain, the formation of inceasingly complex beliefs demands extra neural resources. Here, we argue that the evolution of human beliefs is related to the phylogenetic enlargement of the brain including the parietal and medial frontal cortex in humans.
Beliefs are our brain’s way of making sense of and navigating our complex world. They are mental representations of the ways our brains expect things in our environment to behave, and how things should be related to each other—the patterns our brain expects the world to conform to
As a prediction machine, it must take shortcuts for pattern recognition as it processes the vast amounts of information received from the environment by its sense organ outgrowths. Beliefs allow the brain to distill complex information, enabling it to quickly categorize and evaluate information and to jump to conclusions. For example, beliefs are often concerned with understanding the causes of things: If ‘b’ closely followed ‘a’, then ‘a’ might be assumed to have been the cause of ‘b’.
These shortcuts to interpreting and predicting our world often involve connecting dots and filling in gaps, making extrapolations and assumptions based on incomplete information and based on similarity to previously recognized patterns. In jumping to conclusions, our brains have a preference for familiar conclusions over unfamiliar ones. Thus, our brains are prone to error, sometimes seeing patterns where there are none. This may or may not be subsequently identified and corrected by error-detection mechanisms. It’s a trade-off between efficiency and accuracy.
TED Anil Seth
Imagine being a brain. You're locked inside a bony skull, trying to figure what's out there in the world. There's no lights inside the skull. There's no sound either. All you've got to go on is streams of electrical impulses which are only indirectly related to things in the world, whatever they may be. So perception -- figuring out what's there -- has to be a process of informed guesswork in which the brain combines these sensory signals with its prior expectations or beliefs about the way the world is to form its best guess of what caused those signals. The brain doesn't hear sound or see light. What we perceive is its best guess of what's out there in the world.
Now, I leave you with three implications of all this. First, just as we can misperceive the world, we can misperceive ourselves when the mechanisms of prediction go wrong. Understanding this opens many new opportunities in psychiatry and neurology, because we can finally get at the mechanisms rather than just treating the symptoms in conditions like depression and schizophrenia.
The idea of the brain as a prediction engine is very old. In neuroscience, it is often traced to the German physicist and physiologist Hermann von Helmholtz who first formalized the idea of perception as "unconscious inference." More recently, the idea has gained momentum in various new guises, like predictive coding, the Bayesian brain and the free energy principle — see Andy Clark's Surfing Uncertainty and Jakob Hohwy’s The Predictive Mind.
Muscle memory is a form of procedural memory that involves consolidating a specific motor task into memory through repetition, which has been used synonymously with motor learning. When a movement is repeated over time, the brain creates a long-term muscle memory for that task, eventually allowing it to be performed with little to no conscious effort. This process decreases the need for attention and creates maximum efficiency within the motor and memory systems. Muscle memory is found in many everyday activities that become automatic and improve with practice, such as riding bicycles, driving motor vehicles, playing ball sports, typing on keyboards, entering PINs, playing musical instruments, poker, martial arts, and dancing.