The Biology of Scepticism (part 2)

Assuming that scepticism has biological roots, the next step consists on identifying the biological mechanisms that support the critical appraisal of the signals and their containing messages.

Evolutionary explanations make sense only in the light of genetics, which means, for a trait to be passed on to the next generation, it is a requirement that the code for that trait is passed on. But if there is a genetic code, what is it coding for?

In the light of our present knowledge in neurobiology, we know that the neural pathways and neurotransmitters that act in the brain are genetically determined, but during ontogeny and life development the inputs from the surrounding environment can also modulate the expression of these genes. Recent discussions in epigenetics question if these acquired traits can be passed on to the next generation. This would require that changes that occur in the brain of an individual could somehow be inserted in the gamete DNA as new information. At present it is difficult to see how somatic changes due to mental learning processes  during a life time could inserted and passed on through the genes of gametes. But it is reasonable to assume that there may be genes whose expression depends on the environmental stimuli.

For scepticism to be a genetically transmitted trait, it must rely on the morphology of brain areas, neuronal pathways or in the expression of neurotransmitters and the respective enzymatic and membrane protein machinery of the neurons and supporting brain cells.

How do we know which neurons makes us question the reliability of information? Does the brain have some sort of cognitive module that evaluates plausibility and information reliability?

Before delving in the neurobiology mechanisms that support scepticism, we must first delve into the psychological pathways involved in cognition and communication and to do so we need to go back to simpler examples as models to understand how the signals is deciphered and evaluated. How do animals “know” what information to ignore and what to take in?

Let us start with a simple model.

An individual exposed to the world is constantly sampling information which is stored in the memory.

As the memory becomes overloaded, the storage of information relies mainly on identifying common features of all the information that reach the senses and categorizing it in classes which shares common features.

When new information is perceived, it is compared against the data-base of cognitive categories.

Once it is included in a category, a refinement process occurs to distinguish the particularities of each piece of information in relation to the common denominator of the category. This is just like the process used by taxonomists to identify species.

If new information arrives which is not consistent with the stored memory database of categories, then two things can happen. It can be rejected or a new category is formed to accommodate it. However a category is persistent is it contains many members. As the category gets filled up it is frequently assessed by the mind’s categorization processes. If the category contains few members, its use for comparison purposes decreases and the neural pathways that refer to that category get weakened. In opposition the cognitive pathways which lead to categories that are frequently used as comparison standards get reinforced. Any inconsistent information will then take longer to be categorised. If it fits one of these mental categories it may be readily accepted, if not, it may require a further comparison with other existing categories, some of which may be fairly used by the cognitive comparison system.This could be eventually modelled by computers. (more about this later)

The work of  Grèzes et al., 2004; Lissek et al., 2008 suggested that three brain regions are active when deceptive acts are correctly (rather than incorrectly) detected.

• the orbitofrontal cortex (involved in understanding other people’s mental states),
• the anterior cingulate cortex (associated with monitoring inconsistencies
• the amygdala (associated with detecting threats;).

But the results of Grèzes study can be interpreted in many different ways, for example, the activation of these brain areas may relate more to the perception of fairness than deception.

As I suggested in my earlier posting ( The biology of scepticism) , detection of deception relies on the ability to detect inconsistencies and comparing what is known and stored in the memory against the content of the novel information. So I hypothesise that structures involved with memory and categorization of information are more likely to be involved in detection of false information than structures involved with language.

Evolutionary significance of honesty

Scepticism is no more than a fancy word for the evolutionary mechanism for detection of deception and it protects us against making our decision based on unreliable or dishonest information. Wrong decisions can be costly.

Research on deception has focused mainly on two approaches:

1. Detection of false propositions
2. False belief task which is a prototypical task used in ToM which requires the subject to predict where a character will look for an object that has been displaced by another character unbeknownst to the first character.

Studies adopting the first approach where the  subject is required to detect explicit lies fall short of supporting this notion; 54% accuracy provides little protection from manipulation by deceivers, especially given that this above-chance accuracy is driven by the accurate detection of truths (mean accuracy = 61%), not lies (mean accuracy = 47%; Bond & DePaulo, 2006).

This result may be due to the fact that detection of deception is an ancient evolutionary mechanism, which has evolved well before language. Detection of deception may rely more on Theory of Mind and the evolution of intentionality than on language.

( to be continued)

————————————–

Cited papers

Bond, C. F., & DePaulo, B. M. (2006)- Accuracy of deception judgements. Personality and Social Psychology Review, 10, 214–234.

Grèzes, J., Frith, C., & Passingham, R. E. (2004)- Brain mechanisms for inferring deceit in the actions of others. The Journal of Neuroscience, 24, 5500–5505.

Lissek, S., et al.(2008)-Cooperation anddeception recruit different subsets of the theory-of-mind network. PLoS ONE, 3(4), Article e2023. Retrieved from http://www.plosone.org/article/info:doi/10.1371/journal.pone.0002023

Advertisement