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When participants are given less time to deliberate, they tend to make more “suboptimal” decisions – i.e. more influenced by the decoy. Our model is also able to account for this effect. 1st&3rd column reprinted from Pettibone (2012) and Trueblood et al. (2014) [12/n]pic.twitter.com/aL57cwCUHV
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One key feature of our model is that the degree of neural sharpening depends on info variability. Therefore, we devised a flanker task (pps respond to target tilt, ignoring the irrelevant flankers) where the surrounding flankers can be non-identical. [13/n]pic.twitter.com/GGHwJD0kBR
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In this task, we sampled the flanker tilts from a normal distribution with different dispersions. [14/n]
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Our model correctly predicts that the conflict effect disappears when the flankers are variable (Blue & Green lines). This effect is driven by congruent trials (‘cong’; when target and flankers agree). [15/n]pic.twitter.com/bXWT7ImaNE
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In other words, having consistent, congruent flankers brings a relative benefit on performance (red lines). [16/n]
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We moved to a more complex design in which the strength of the decision-relevant information (target orientations) and the strength of the decision irrelevant information (flanker mean orientations) vary independently. [17/n]
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Our model counterintuitively predicts that under certain circumstances, there is a reversal of the conflict effect – that is, you are slower on congruent rather than incongruent trials (top-left corner).[18/n]pic.twitter.com/UEtIECZZM9
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Previous work showed that the
#dACC is involved in many things, such as conflict monitoring, signalling the proximity of the decision value from the category boundary, the relative value of the unchosen to the chosen option, the value of switching into a new context. [19/n]Prikaži ovu nit -
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
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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
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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]
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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]
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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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