Reading and Calculation Neural Systems and Their Weighted Adaptive Use for Programming Skills

Ayahuasca-enhanced extinction of fear behaviour: Role of infralimbic cortex 5-HT2A and 5-HT1A receptors

BACKGROUND AND PURPOSE

Ayahuasca (AYA) is a botanical psychedelic with promising results in observational and small clinical trials for depression, trauma and drug use disorders. Its psychoactive effects primarily stem from N,N-dimethyltryptamine (DMT). However, there is a lack of research on how and where AYA acts in the brain. This study addressed these questions by examining the extinction of aversive memories in AYA-treated rats.

EXPERIMENTAL APPROACH

We focused on the 5-HT1A and 5-HT2A receptors, as DMT exhibits a high affinity for both of them, along with the infralimbic cortex in which activity and plasticity play crucial roles in regulating the mnemonic process under analysis.

KEY RESULTS

A single oral treatment with AYA containing 0.3 mg·kg-1 of DMT increased the within-session extinction of contextual freezing behaviour without affecting its recall. This protocol, when repeated twice on consecutive days, enhanced extinction recall. These effects were consistent for both 1- and 21-day-old memories in males and females. AYA effects on fear extinction were independent of changes in anxiety and general exploratory activity: AYA- and vehicle-treated animals showed no differences when tested in the elevated plus-maze. The 5-HT2A receptor antagonist MDL-11,939 and the 5-HT1A receptor antagonist WAY-100635 infused into the infralimbic cortex respectively blocked within- and between-session fear extinction effects resulting from repeated oral administration of AYA.

CONCLUSION AND IMPLICATIONS

Our findings highlight complementary mechanisms by which AYA facilitates the behavioural suppression of aversive memories in the rat infralimbic cortex. These results suggest potential beneficial effects of AYA or DMT in stress-related disorders.

On the accuracy of code complexity metrics: A neuroscience-based guideline for improvement

Complexity is the key element of software quality. This article investigates the problem of measuring code complexity and discusses the results of a controlled experiment to compare different views and methods to measure code complexity. Participants (27 programmers) were asked to read and (try to) understand a set of programs, while the complexity of such programs is assessed through different methods and perspectives: (a) classic code complexity metrics such as McCabe and Halstead metrics, (b) cognitive complexity metrics based on scored code constructs, (c) cognitive complexity metrics from state-of-the-art tools such as SonarQube, (d) human-centered metrics relying on the direct assessment of programmers' behavioral features (e.g., reading time, and revisits) using eye tracking, and (e) cognitive load/mental effort assessed using electroencephalography (EEG). The human-centered perspective was complemented by the subjective evaluation of participants on the mental effort required to understand the programs using the NASA Task Load Index (TLX). Additionally, the evaluation of the code complexity is measured at both the program level and, whenever possible, at the very low level of code constructs/code regions, to identify the actual code elements and the code context that may trigger a complexity surge in the programmers' perception of code comprehension difficulty. The programmers' cognitive load measured using EEG was used as a reference to evaluate how the different metrics can express the (human) difficulty in comprehending the code. Extensive experimental results show that popular metrics such as V(g) and the complexity metric from SonarSource tools deviate considerably from the programmers' perception of code complexity and often do not show the expected monotonic behavior. The article summarizes the findings in a set of guidelines to improve existing code complexity metrics, particularly state-of-the-art metrics such as cognitive complexity from SonarSource tools.

Software Bug Detection Causes a Shift From Bottom-Up to Top-Down Effective Connectivity Involving the Insula Within the Error-Monitoring Network

The neural correlates of software programming skills have been the target of an increasing number of studies in the past few years. Those studies focused on error-monitoring during software code inspection. Others have studied task-related cognitive load as measured by distinct neurophysiological measures. Most studies addressed only syntax errors (shallow level of code monitoring). However, a recent functional MRI (fMRI) study suggested a pivotal role of the insula during error-monitoring when challenging deep-level analysis of code inspection was required. This raised the hypothesis that the insula is causally involved in deep error-monitoring. To confirm this hypothesis, we carried out a new fMRI study where participants performed a deep source-code comprehension task that included error-monitoring to detect bugs in the code. The generality of our paradigm was enhanced by comparison with a variety of tasks related to text reading and bugless source-code understanding. Healthy adult programmers (N = 21) participated in this 3T fMRI experiment. The activation maps evoked by error-related events confirmed significant activations in the insula [p(Bonferroni) < 0.05]. Importantly, a posterior-to-anterior causality shift was observed concerning the role of the insula: in the absence of error, causal directions were mainly bottom-up, whereas, in their presence, the strong causal top-down effects from frontal regions, in particular, the anterior cingulate cortex was observed.