Search bioRxiv⌕ Search

Biology subjects

Baths, V.

Publications and source records attributed to Baths, V..

2 recordsLinked to original sources

Doing what's not wanted: Conflict in incentives and misallocation of behavioural control lead to drug-seeking despite adverse outcomes

Despite being aware of negative consequences and wanting to quit, long-term addicts find it difficult to quit seeking and consuming drugs. This inconsistency between the (often compulsive) behavioural patterns and the explicit knowledge of negative consequences represents a cognitive conflict which is a central characteristic of addiction. Neurobiologically, differential cue-induced activity in distinct striatal subregions, as well as the dopamine connectivity spiraling from ventral striatal regions to the dorsal regions, play critical roles in compulsive drug seeking. The focus of this work is to illustrate the mechanisms that lead to a cognitive conflict and its impact on actions taken i.e. addictive choices. We propose an algorithmic model that captures how the action choices that the agent makes when reinforced with drug-rewards become incongruent with the presence of negative consequences that often follow those choices. We advance the understanding of having a decision hierarchy in representing "cognitive control" and how lack of such control at higher-level in the hierarchy could potentially lead to consolidated drug-seeking habits. We further propose a cost-benefit based arbitration scheme, which mediates the allocation of control across different levels of the decision-making hierarchy. Lastly, we discuss how our work on extending a computational model to an algorithmic one, could in turn also helps us improve the understanding of how drugs hijack the dopamine-spiralling circuit at an implementation level.

neuroscience↗

Decoding the Neural Signatures of Valence and Arousal From Portable EEG Headset

Emotion classification using electroencephalography (EEG) data and machine learning techniques has been on the rise in the recent past. However, past studies uses data from medical-grade EEG setup with long set-up time and environment constraints. This paper focuses on classifying emotions on the valence-arousal plane using various feature extraction, feature selection and machine learning techniques. We evaluate different feature extraction and selection techniques and propose the optimal set of features and electrodes for emotion recognition. The images from the OASIS image dataset were used to elicit valence and arousal emotions, and the EEG data was recorded using the Emotiv Epoc X mobile EEG headset. The analysis is carried out on publicly available datasets: DEAP and DREAMER for benchmarking. We propose a novel feature ranking technique and incremental learning approach to analyze performance dependence on the number of participants. Leave-one-subject-out cross-validation was carried out to identify subject bias in emotion elicitation patterns. The importance of different electrode locations was calculated, which could be used for designing a headset for emotion recognition. The collected dataset and pipeline are also published. Our study achieved a root mean square score (RMSE) of 0.905 on DREAMER, 1.902 on DEAP, and 2.728 on our dataset for valence label and a score of 0.749 on DREAMER, 1.769 on DEAP and 2.3 on our proposed dataset for arousal label respectively.

animal behavior and cognition↗