Where is the crack in the mind machine? Some insights from Bayesian theory into mental disorders.
Pavel Voinov and József Arató are PhD students in the Cognitive Sciences Programme at the Central European University, Budapest.
As cognitive scientists we keep asking ourselves what knowledge society expects from us and what the implications of this knowledge are. “Mind as a machine” is a good metaphor, as we feel it captures those expectations: to describe the principles of how the mind works, and, furthermore, to explain its failures and suggest ways to fix it.
In this post we will take the perspective of reverse mind-engineers. We will use one of the most prominent computational theories in contemporary cognitive science – Continue reading Where is the crack in the mind machine: some insights from Bayesian theory into mental disorders by Pavel Voinov & József Arató
Bayesian models of cognition can explain how people handle the uncertainty that is constantly present in the world. As we can never be sure what the true state of the world actually is, perception works by hypothesis testing. We continuously guess what we might expect to happen in the world. For these guesses we can use the accumulated knowledge we have acquired through past experience.
In other words, we evaluate newly incoming sensory information in the light of prior experience, and this is how we constantly make fresh predictions of what it is that we are seeing, hearing and feeling. What we actually perceive is not what is out there. Instead we perceive the most likely event out there. The most likely event is what we hypothesise given the new sensory signal (evidence) evaluated on the basis of the probabilities of past events (priors). We continuously update our expectations based on the differences between the predicted and experienced sensory stimuli (prediction error). The same model applies to belief formation: we update our beliefs based on the differences between expected and experienced events (Figure based on Van Boxtel & Lu).
Here’s an example how this works. Imagine you need to read a signboard at a distance. Even if you can’t discriminate individual letters, the context will help you, and you are likely to identify common words like “caution”, “danger”, “exit” etc., just by matching approximate length of a word or its salient features. Even if you can’t see any details at all, you may make a guess: this will be a case of relying solely on your priors. But when the context is not informative, and there are many probable alternatives, you will have to rely more on what your eyes deliver to the brain. Now you can’t guess the word and you need to distinguish the individual letters.
The Bayesian approach has been impressively powerful in modelling various aspects of human cognition: let’s see whether it has been useful for explaining malfunctions of the mind.
The most distinctive features of the schizophrenic mind are the so-called positive symptoms: delusions (abnormal beliefs) and hallucinations (false perceptions). Hence, a Bayesian framework is a promising tool for exploring these features. After all, it was developed to deal with belief formation and perception – and these are precisely the cognitive functions that are most affected in schizophrenia.
Various experiments have shown that something goes wrong in the combination of prior knowledge and incoming sensory evidence. For example a weaker reliance on prior expectations can explain why schizophrenics are less susceptible to some visual illusion. Not being tricked by an illusion means, that in some situations, they perceive the world more veridically. So why does this not happen in healthy people? Healthy perception is optimally adapted to the environment, with the brain inferring the most likely patterns from raw sensory inputs. In contrast, schizophrenic patients are more influenced by what their eyes see, and less by the higher-level expectations their brain has derived from past experience. Look at the picture with two masks (image from: Grosjean et al., 2012). Our strong perceptual bias (or ‘prior’) for natural convex faces overrides competing information (such as shadows) and makes a concave hollow mask (bottom) perceived as a convex face (top). Schizophrenic patients are generally less susceptible to this illusion.
A weaker influence from higher-level expectations is also shown in tasks which involve uncertainty – for example in playing stone-paper-scissors game. Here schizophrenic patients take less account of the past history of their contestant’s decisions and as a result follow strategies based on only the most recent evidence. Patients were also more confident about their decisions, and raised the stakes after smaller number of sequential winnings than healthy players (Joyce et al, 2013).
Another important feature of decision-making under uncertainty in schizophrenic patients is their reduced sensitivity to negative feedback once they have formed their belief. In Bayesian terms they do not use the prediction error correctly to update their models of the world.
These findings are especially intriguing when linked to our knowledge about the neurotransmitter dopamine. Dopamine plays the key role in learning based on the prediction error. Abnormally high levels of dopamine found in the brains of schizophrenic patients may underlie their failures to integrate sensory (and higher order) error signals appropriately. We are not surprised by outcomes of our actions because our brain foresees them. But absence or distortion of this prediction in the brain of schizophrenic patients may create strange experience and give rise to delusional beliefs about it (Fletcher & Frith, 2009).
The Bayesian approach is also promising in modelling how this lower-level prediction error could give rise to false beliefs at higher – ’more cognitive’ – levels. In this case corrupted output from lower levels would feed into higher cognitive functions leading to faulty beliefs of a higher order. Altogether, within a Bayesian picture of the mind one can build a coherent story, which would explain positive symptoms of schizophrenia with strong links between neural, cognitive, and behavioural levels.
Autism spectrum disorder has a clinical picture distinctively different from schizophrenia. The characteristic symptoms of this condition are repetitive behaviours and problems in social interaction and communication. Furthermore, autism is usually accompanied by a range of sensory peculiarities. The origin of these differences and its relation to the core symptoms of autism is still a mystery for psychologists. Atypical features of autistic perception include hyper-sensitivity to ordinary incoming stimuli, enhanced focus on details, and more fragmented perception. On the picture with kiwi fruits people with autism would likely see three separate objects rather than a triangle.
Recently, Pellicano and Burr (2012) have proposed a Bayesian account of abnormal perception in autism. According to this account, perception in autism is shaped by weak priors. As we remember, a prior basically means accumulated information about the environment, and a weak prior means that less account is taken of this accumulated information. Hence we can say that the brain is not tuned appropriately to the environment. As a consequence, the perceived reality would be dominated by new incoming stimuli, and would be less influenced by internal information (priors). This would explain why autistic children are less susceptible to visual illusions and are often better at accurate copying of unusual images.
On the other hand, weak priors would also explain the phenomenon of sensory overload, that is hyper-sensitivity to ordinary sensory stimuli, like human voices or lamplight. While our sensitivity is modulated by the context, – we would be surprised if we heard a human voice in the middle of a forest, but not in the street. This contextual information does not seem to work for people living with autism. For ordinary people the brain would filter out most of incoming information. For autistic people everything from the sensory stream would be preserved, and this might lead to blurring of the figure with the background in their perception.
However, a theory has low scientific value if it can only incorporate already known facts, but can’t make predictions for new findings. What empirical prediction can the Bayesian theory make? The weak priors hypothesis can be tested against the sensory enhancement hypothesis: while the former suggests reduced influence of contextual information on perception, the latter predicts less noise in the sensory input for autistics. Joshua Skewes and colleagues (2014) tested this prediction in an experiment probing acuity of visual perception in ordinary people. The experiment demonstrated that people with higher scores on a scale measuring autistic traits, the AQ, were less sensitive to the context information, but were as sensitive to the noise in stimuli as the control group. This result favours the weak priors hypothesis. However, it has not yet been tested with autistic individuals.
Conclusion. If you read the two stories carefully you can see surprising similarities between them: faulty functioning of prior knowledge is suggested as an explanatory cause for both conditions, and this cause is described in almost identical words. This is the weak part of the story: the theory seems to be so unspecified at the moment that it apparently fails to account for qualitative differences between two conditions, which a clinician would never confuse. We seem to be at the stage of trying to embed things in vague and extensively general principles, but small pieces of the big puzzle are yet to be defined. Perhaps this would sound disappointing if you expected a story like “There is a function in the normal brain which checks whether or not our beliefs are realistic. The part of the brain responsible for this function is broken in schizophrenics and that’s why you believe I’m an alien”. Unfortunately, we are not even close to the mechanical description of the “Mind as a machine”. But our first steps indicate that we’re on the right path…