There are 3 types of decoy effects – compromise, attraction and similarity – which are determined by the attribute values of the decoys themselves (green dots). Our model accounts for all 3 effects. [10/n]pic.twitter.com/LW514FoyI3
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Therefore, we ask what is the role of dACC and interconnected regions in adaptive gain control. We conducted an #fMRI study using the more complex flanker task (where target and distractor strength and variance changed between each trial) [20/n]pic.twitter.com/OfbfnthNAe
We found that the BOLD signal in dACC, AIC and SPL were best explained by the context-modulated decision variable predicted by our model (compared to alternative models). This remains true after we partial out the influence of RT on BOLD signal. [21/n]pic.twitter.com/Xy6JbjpaiW
So in summary: irrelevant information influences decisions in multiple ways. Our adaptive gain model accounts for all of these different effects. Neurally, signals in several brain regions reflect the predictions of the model. [22/n]
The adaptive gain model emphasises the benefit of having consistent context (relevant or irrelevant info). This is consistent to the view that our neural system code efficiently to maximises sensitivity towards expected features like the Efficient Coding hypothesis [23/n]
To find out more details, check out the open access paper here: http://www.pnas.org/content/early/2018/08/29/1805224115 … [24/24]
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