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summerfieldlab joined the Oxford rally today in support of better working conditions in UK higher education. Here's Adam campaigning in support of Leurers (whatever they are)pic.twitter.com/52M24KJpQx
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this is a fascinating paper, thank you. So I'm >800 times more likely to obtain a significant result in my fMRI analysis if I use FSL rather than SPM? really???
@tschonbergpic.twitter.com/4fTAyg4gAH
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@OpenAI solve one of machine learning's timeless problems: how to avoid being peturbed if you are attacked by a stuffed giraffe:https://youtu.be/QyJGXc9WeNo?t=72 … -
many congratulations to
@yinan_cao_meg on a successful PhD viva!! and thanks to@K_M_M_Walker and Uta Noppeney for providing the hard questions.pic.twitter.com/P10NPyzYJh
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Agreed calculations can be complex. Here's one site I found helpful: http://www.ecopassenger.org . I'm going to Marseille on wednesday - here are the estimated emissions by rail, road & airpic.twitter.com/I7sLKdgxsK
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Congratulations to the lab’s newest Dr
@vickieCL_Li !pic.twitter.com/gbDHBkBA0K
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Tomorrow at
#SBDM2019 Poster 68: Mira@atomsrivet will show that distractors exert a multiplicative influence in perceptual decisions consistent with an adaptive gain account of feature encoding - come check it out!pic.twitter.com/NxauwWM3Gj
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Interestingly, simply assuming that more than one value dimension contributes to the agent’s distance-to-goal estimation (eq. 2) yields diminishing marginal utility and convex indifference curves over value dimensions. 10/13pic.twitter.com/pknxUvAHlO
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The key intuition is that the agent represents an abstracted, multi-dimensional value map on which it defines its current position and goals. Thus, the agent will seek to minimise its distance to goal through interactions with the environment. 8/13pic.twitter.com/7Vd9tH5rEn
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Distance to goal signals in a wider sense have recently been reported by us and many others across a range of regions in the medial prefrontal cortex, indicating that these signals are a ubiquitously tracked quantity in humans. 6/13pic.twitter.com/jKR6znuWZB
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In this framework, rewards are “sensed” by the agent via dedicated input channels as if the environment furnished a ground-truth reward signal which the agent can interpret and maximize according to its preferences. 3/13pic.twitter.com/elmrEYG0uA
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Explaining AI to a curious dog (and some curious people too)pic.twitter.com/PLfGgNBHYP
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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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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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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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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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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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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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It turns out participants do not always exhibit all three decoy effects and have stereotypical intercorrelation pattern on the decoy effects. Our model is able to replicate the effect. Panel A-D reprinted from Berkowitsch et al. (2014) [11/n]pic.twitter.com/8uqdcveLwH
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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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