Biology subjects

Feurra, M.

Publications and source records attributed to Feurra, M..

3 recordsLinked to original sources

bioRxiv2023

Posterior parietal cortex is causally involved in reward valuation but not probability weighting during risky choice

This study provides evidence that the posterior parietal cortex (PPC) is causally involved in risky decision making via the processing of reward values but not reward probabilities. In the within-group experimental design, participants performed a binary lottery choice task following transcranial magnetic stimulation of the right PPC, left PPC and a right PPC sham (placebo) stimulation. Both, mean-variance and the prospect theory approach to risky choice showed that the PPC stimulation changed participants preferences towards greater risk aversion compared to sham. On the behavioral level, after the PPC stimulation the likelihood of choosing a safer option became more sensitive to the difference in standard deviations between lotteries, compared to sham, indicating greater risk avoidance within the meanvariance framework. We also estimated the shift in prospect theory parameters of risk preferences after PPC stimulation. The hierarchical Bayesian approach showed moderate evidence (BF = 7.44 and 5.41 for right and left PPC respectively) for a credible change in risk aversion parameter towards lower marginal reward value (and, hence, lower risk tolerance), while no credible change in probability weighting was observed. Additionally, we observed anecdotal evidence (BF = 2.9) for a credible increase in the consistency of responses after the left PPC stimulation compared to sham.

neuroscience↗

bioRxiv2022

The influence of learning on robustness of working memory to distractors

Inhibition of irrelevant information is a crucial process within the working memory (WM) system. In the present study, we explored how inhibition of distractors is learned within a Sternberg-like WM task and how this learning process affects readout performance. We introduced distractors of varying strength as well as novel stimuli to a classic Sternberg task. Our findings show that the length of the training period affected obstructiveness of distractors as well as differences between WM loads. Significant differences between conditions observed early on in accuracy became void after general ceiling performance was reached. Moreover, accuracy and reaction times, which are commonly used as readouts of WM performance, were affected differently by training. While both improved significantly, only accuracy exhibited differential dependency of experimental conditions on training. It is essential to consider these dynamics when introducing a WM task which requires a long training period.

neuroscience↗

bioRxiv2020

Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning

We aimed to replicate a published effect of transcranial direct-current stimulation (tDCS)-induced recognition enhancement over the human ventrolateral prefrontal cortex [1] and analyse the data with machine learning. We investigated effects over an adjacent region, the dorsolateral PFC. We found weak or absent effects over the VLPFC and DLPFC. We conducted machine learning studies to examine the effects of semantic and phonetic features on memorization, which revealed no effect of VLPFC tDCS on the original dataset or the current data. The highest contributing factor to memory performance was individual differences in memory not explained by word features, tDCS group, or sample size, while semantic, phonetic, and orthographic word characteristics did not contribute significantly. To our knowledge, this is the first tDCS study to investigate cognitive effects with machine learning, and future studies may benefit from studying physiological as well as cognitive effects with data-driven approaches and computational models. Author summaryNon-Invasive Brain Stimulation techniques (in our case, transcranial direct current stimulation) are widely used among neuroscientists to map cognitive processes, for example, memory, decision making, emotional processing. In many cases, the sample size is limited, or methods applied for the data analysis are questionable. Here we propose a replication study aiming at confirmation of memory enhancement as a result of the application of anodal transcranial direct stimulation over the ventrolateral prefrontal cortex. Additionally, the involvement of the dorsolateral prefrontal cortex into episodic memory was analyzed. We used a larger sample size and applied different data analysis methods, including machine learning techniques. Surprisingly, we did not replicate the results of the original study, and we found weak effects of memory impairment after stimulating the DLPFC. However, what is most remarkable is that we have observed no significant effect of tDCS involvement on memory performance. Machine learning methods revealed no effect of linguistic factors on tDCS effect for both the original study and the replication at the level of individual trials and participants. Our findings highlight the importance of considering individual differences and data on the level of a single trial; in our data, participants memory responses resembled guessing behavior when recognition performance was measured by AUROC and highlight the need for modifications in the memory test and the use of other performance measures.

neuroscience↗