In link go all the way to the bottom and click on Faculty of Science to find my ad. My email if you click on my name. Happy to receive queries.

Our new EEG+modelling paper is out now, led by Christina Dimitriadou, which links P300 and beta power to probabilistic beliefs and inferences in the beads task. See below for link.

🧠 Using computational models, we found that a misguided belief about future options (a "biased prior") best explains this error. Rather than being lazy or impulsive, people genuinely expect the next options won't be much better—even when they are!

Please visit our poster today at BNA and see @scaramozzinoFr 's work with DDM, TMS and delusion proneness, M-LRZ-126

Other approaches to delusions imply that evidence unduly overpowers weak priors. E.g., some studies of draws to decision and graded estimates beads tasks, aberrant salience, abnormal prediction error learning, corollary discharge theories, etc. 4/5

This “strong prior view” can also explain another common symptom of psychosis: hallucinations. Here, we illustrate one example study using Bayesian modelling to show heightened prior in hallucinations. 3/5

Our review describes how studies often focus on one of these elements. For example, studies like the example illustrated here show delusions are associated with heightened reliance on prior belief over new evidence. 2/5

Two elements define delusions: They are swayed by weak evidence yet incorrigible to strong evidence. Our preprint by @Furllab @maxcoltheart, Ryan McKay at psyarxiv.com/e37c2 reviews how literature on psychosis embodies these elements. 1/5

Modern science (Twitter)

This work was funded by @ESRC and the Bial Foundation. Paper is open access sciencedirect.com/science/ar… and open data and code are at openneuro.org/datasets/ds002…. 12/12.

Representational similarity analysis is yet another method to localize dissimilarity space. Widespread visual responses including occipital face area, V5 and superior temporal sulcus reflected participants’ dissimilarity perception (i.e., their ratings). 10/12

Repetition suppression is another method for localizing dissimilarity spaces. Visual responses, including occipital face area and V5 reflected the same dissimilarity structure as the caricatured faces. 9/12

Every movement displaces a certain distance (max), at a certain speed (slope), at a certain time (mid). Responses in face- and motion-sensitive regions of interest covaried with these putative dimensions of motion-based face space. 8/12

A popular formulation of face space represents faces as deviations from a central tendency (i.e., norm). We found evidence for deviation from the motion-based norm throughout visual cortex, including all face- and motion-sensitive regions of interest. 7/12