Fig. 8.

Molecular mechanisms for integrating information.

A) Top: Rats are playing against either an intermediate (C2) or a strong competitor (C3) who is trying to predict their choice between two food ports. Animals are only rewarded on trials where the computer does not match their choice. The difference between the competitors is that the strong competitor is better able to detect any patterns in the animal’s choices and exploit them. In other words, if there are any statistical regularities, i.e. if the rats’ behaviour is predictable, the competitor will use this information to prevent the rats from getting rewards. Bottom: Playing against a stronger competitor leads to more random choices (decreased choice predictability). Inactivation of anterior cingulate cortex (ACC) using a GABA agonist produces increasingly random choices in rats playing against C2, but not in the rats playing against C3 who already exhibit strongly random choice behaviour. This effect is mediated via noradrenergic (NA) input from the locus coeruleus (LC) onto ACC because the same effect is observed when LC inputs to ACC are stimulated pharmacologically or optogenetically. This suggests that the level of NA in the ACC controls the balance between using a model (trying to predict the opponent) and random choices. Adapted from [122]. B) Magnetic mesonance spectroscopy (MRS) was used to examine the balance of excitation (glutamate) and inhibition (GABA) in the dACC when choices relied on learnt and explicitly cued information. Top: A model parameter that captured how much participants relied on learnt relative to new information showed a positive relationship with Glutamate and a negative relationship with GABA. Bottom: dACC encoded the information to be learnt and this signal increased as a function of the E/I balance in the region. Thus, consistent with [122], dACC controls how much learnt information influences behaviour and this might be achieved by regulating its E/I-balance. Adapted from [126]. C) In a similar task, a partial NMDA agonist made participants rely more strongly on an optimal non-linear strategy for combining different pieces of information to calculate integrated value (here reward probability * magnitude). Choices in the placebo group were guided predominantly by the simpler linear computation (here reward probability + magnitude). Adapted from [209]. This suggests that NMDA receptors play a role in non-linear integration of information.