Reversing food preference through multisensory exposure.

Experiencing food craving is nearly ubiquitous and has several negative pathological impacts prompting an increase in recent craving-related research. Food cue-reactivity tasks are often used to study craving, but most paradigms ignore the individual food preferences of participants, which could confound the findings. We explored the neuropsychological correlates of food craving preference using psychophysical tasks on human participants considering their individual food preferences in a multisensory food exposure set-up. Participants were grouped into Liked Food Exposure (LFE), Disliked Food Exposure (DFE), and Neutral Control (NEC) based on their preference for sweet and savory items. Participants reported their momentary craving for the displayed food stimuli through the desire scale and bidding scale (willingness to pay) pre and post multisensory exposure. Participants were exposed to food items they either liked or disliked. Our results asserted the effect of the multisensory food exposure showing a statistically significant increase in food craving for DFE participants postexposure to disliked food items. Using computational models and statistical methods, we also show that the desire for food does not necessarily translate to a willingness to pay every time, and instantaneous subjective valuation of food craving is an important parameter for subsequent action. Our results further demonstrate the role of parietal N200 and centro-parietal P300 in reversing food preference and possibly point to the decrease of inhibitory control in up-regulating craving for disliked food.

Wisdom of crowds benefits perceptual decision making across difficulty levels.

Decades of research on collective decision making has claimed that aggregated judgment of multiple individuals is more accurate than expert individual judgement. A longstanding problem in this regard has been to determine how decisions of individuals can be combined to form intelligent group decisions. Our study consisted of a random target detection task in natural scenes, where human subjects (18 subjects, 7 female) detected the presence or absence of a random target as indicated by the cue word displayed prior to stimulus display. Concurrently the neural activities (EEG signals) were recorded. A separate behavioural experiment was performed by different subjects (20 subjects, 11 female) on the same set of images to categorize the tasks according to their difficulty levels. We demonstrate that the weighted average of individual decision confidence/neural decision variables produces significantly better performance than the frequently used majority pooling algorithm. Further, the classification error rates from individual judgement were found to increase with increasing task difficulty. This error could be significantly reduced upon combining the individual decisions using group aggregation rules. Using statistical tests, we show that combining all available participants is unnecessary to achieve minimum classification error rate. We also try to explore if group aggregation benefits depend on the correlation between the individual judgements of the group and our results seem to suggest that reduced inter-subject correlation can improve collective decision making for a fixed difficulty level.

How Our Perception and Confidence Are Altered Using Decision Cues.

Understanding how individuals utilize social information while making perceptual decisions and how it affects their decision confidence is crucial in a society. To date, very little has been known about perceptual decision-making in humans and the associated neural mediators under social influence. The present study provides empirical evidence of how individuals are manipulated by others' decisions while performing a face/car identification task. Subjects were significantly influenced by what they perceived as the decisions of other subjects, while the cues, in reality, were manipulated independently from the stimulus. Subjects, in general, tend to increase their decision confidence when their individual decision and the cues coincide, while their confidence decreases when cues conflict with their individual judgments, often leading to reversal of decision. Using a novel statistical model, it was possible to rank subjects based on their propensity to be influenced by cues. This was subsequently corroborated by an analysis of their neural data. Neural time series analysis revealed no significant difference in decision-making using social cues in the early stages, unlike neural expectation studies with predictive cues. Multivariate pattern analysis of neural data alludes to a potential role of the frontal cortex in the later stages of visual processing, which appeared to code the effect of cues on perceptual decision-making. Specifically, the medial frontal cortex seems to play a role in facilitating perceptual decision preceded by conflicting cues.

A computationally efficient order statistics based outlier detection technique for EEG signals.

Detecting artifacts in EEG data produced by muscle activity, eye blinks and electrical noise is a common and important problem in EEG applications. We present a novel outlier detection method based on order statistics. We propose a 2 step procedure comprising of detecting noisy EEG channels followed by detection of noisy epochs in the outlier channels. The performance of our method is tested systematically using simulated and real EEG data. Our technique produces significant improvement in detecting EEG artifacts over state-of-the-art outlier detection technique used in EEG applications. The proposed method can serve as a general outlier detection tool for different types of noisy signals.