PsychoPy2: experiments in behavior made easy

PsychoPy is an application for the creation of experiments in behavioral science (psychology, neuroscience, linguistics, etc.) with precise spatial control and timing of stimuli. It now provides a choice of interface; users can write scripts Python if they choose, while those that prefer to construct experiments graphically can use the new Builder interface. Here we describe the features that have been added over the last 10 years of its development. The most notable addition has been that Builder interface, allowing users to create studies with minimal or no programming, while also allowing the insertion of Python code for maximal flexibility. We also present some of the other new features, including further stimulus options, asynchronous timestamped hardware polling, and better support for open science and reproducibility. Tens of thousands of users now launch PsychoPy every month and more than 90 people have contributed to the code. We discuss the current state of the project, as well as plans for the future

A reusable benchmark of brain-age prediction from M/EEG resting-state signals

Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R^2 scores between 0.60-0.71. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints

Delta activity encodes taste information in the human brain

The categorization of food via sensing nutrients or toxins is crucial to the survival of any organism. On ingestion, rapid responses within the gustatory system are required to identify the oral stimulus to guide immediate behavior (swallowing or expulsion). The way in which the human brain accomplishes this task has so far remained unclear. Using multivariate analysis of 64-channel scalp EEG recordings obtained from 16 volunteers during tasting salty, sweet, sour, or bitter solutions, we found that activity in the delta-frequency range (1–4 Hz; delta power and phase) has information about taste identity in the human brain, with discriminable response patterns at the single-trial level within 130 ms of tasting. Importantly, the latencies of these response patterns predicted the point in time at which participants indicated detection of a taste by pressing a button. Furthermore, taste pattern discrimination was independent of motor-related activation and encoded taste identity rather than other taste features such as intensity and valence. On comparison with our previous findings from a delayed taste-discrimination task (Crouzet et al., 2015), taste-specific neural representations emerged earlier during this speeded taste-detection task, suggesting a goal-dependent flexibility in gustatory response coding. Together, these findings provide the first evidence of a role of delta activity in taste-information coding in humans. Crucially, these neuronal response patterns can be linked to the speed of simple gustatory perceptual decisions – a vital performance index of nutrient sensing

Higher sensitivity to sweet and salty taste in obese compared to lean individuals

Although putatively taste has been associated with obesity as one of the factors governing food intake, previous studies have failed to find a consistent link between taste perception and Body Mass Index (BMI). A comprehensive comparison of both thresholds and hedonics for four basic taste modalities (sweet, salty, sour, and bitter) has only been carried out with a very small sample size in adults. In the present exploratory study, we compared 23 obese (OB; BMI > 30), and 31 lean (LN; BMI < 25) individuals on three dimensions of taste perception – recognition thresholds, intensity, and pleasantness – using different concentrations of sucrose (sweet), sodium chloride (NaCl; salty), citric acid (sour), and quinine hydrochloride (bitter) dissolved in water. Recognition thresholds were estimated with an adaptive Bayesian staircase procedure (QUEST). Intensity and pleasantness ratings were acquired using visual analogue scales (VAS). It was found that OB had lower thresholds than LN for sucrose and NaCl, indicating a higher sensitivity to sweet and salty tastes. This effect was also reflected in ratings of intensity, which were significantly higher in the OB group for the lower concentrations of sweet, salty, and sour. Calculation of Bayes factors further corroborated the differences observed with null-hypothesis significance testing (NHST). Overall, the results suggest that OB are more sensitive to sweet and salty, and perceive sweet, salty, and sour more intensely than LN

The capacity and organization of gustatory working memory

Remembering a particular taste is crucial in food intake and associative learning. We investigated whether taste can be dynamically encoded, maintained, and retrieved on short time scales consistent with working memory (WM). We use novel single and multi-item taste recognition tasks to show that a single taste can be reliably recognized despite repeated oro-sensory interference suggesting active and resilient maintenance (Experiment 1, N = 21). When multiple tastes were presented (Experiment 2, N = 20), the resolution with which these were maintained depended on their serial position, and recognition was reliable for up to three tastes suggesting a limited capacity of gustatory WM. Lastly, stimulus similarity impaired recognition with increasing set size, which seemed to mask the awareness of capacity limitations. Together, the results advocate a hybrid model of gustatory WM with a limited number of slots where items are stored with varying precision