Evolutionary Psychology’s next challenge: Solving modern problems using a mismatch perspective

As acceptance of evolutionary perspectives in mainstream psychology grows, it becomes increasingly pertinent to ask what evolutionary psychology can do to solve real-world problems and better our lives. Answers to this important question will more than likely require an understanding and application of the evolutionary mismatch framework. This powerful framework suggests that many of our contemporary problems—ranging from diabetes and depression to low fertility and sustainability—stem from a mismatch between our evolved psychological mechanisms, which are designed to be adaptive in ancestral contexts, and modern environments, which present novel stimuli that these mechanisms are not well suited to handle. By providing a better understanding of the functions of our evolved mechanisms and how they are incompatible with modern environments, the mismatch perspective can help with the generation of more enlightened and effective strategies to tackle modern problems than would otherwise be the case. We describe this perspective and discuss its potential efficacy and promise. (PsycInfo Database Record (c) 2020 APA, all rights reserved) Public Significance Statement—This article describes evolutionary mismatch – a process that likely underlies many of the problems that humans face in the modern world. As discussed, human minds are not designed for and thus, not well-suited to handle, modern environments. Accordingly, solving the various problems of the modern world will require researchers and policymakers to understand mismatch and how to work around it

Decoding study-independent mind-wandering from EEG using convolutional neural networks

Objective. Mind-wandering is a mental phenomenon where the internal thought process disengages from the external environment periodically. In the current study, we trained EEG classifiers using convolutional neural networks (CNNs) to track mind-wandering across studies. Approach. We transformed the input from raw EEG to band-frequency information (power), single-trial ERP (stERP) patterns, and connectivity matrices between channels (based on inter-site phase clustering). We trained CNN models for each input type from each EEG channel as the input model for the meta-learner. To verify the generalizability, we used leave-N-participant-out cross-validations (N = 6) and tested the meta-learner on the data from an independent study for across-study predictions. Main results. The current results show limited generalizability across participants and tasks. Nevertheless, our meta-learner trained with the stERPs performed the best among the state-of-the-art neural networks. The mapping of each input model to the output of the meta-learner indicates the importance of each EEG channel. Significance. Our study makes the first attempt to train study-independent mind-wandering classifiers. The results indicate that this remains challenging. The stacking neural network design we used allows an easy inspection of channel importance and feature maps.</p

Distinguishing Vigilance Decrement and Low Task Demands from Mind-wandering:A Machine Learning Analysis of EEG

Mind-wandering is a ubiquitous mental phenomenon that is defined as self-generated thought irrelevant to the ongoing task. Mind-wandering tends to occur when people are in a low-vigilance state or when they are performing a very easy task. In the current study, we investigated whether mind-wandering is completely dependent on vigilance and current task demands, or whether it is an independent phenomenon. To this end, we trained support vector machine (SVM) classifiers on EEG data in conditions of low and high vigilance, as well as under conditions of low and high task demands, and subsequently tested those classifiers on participants' self-reported mind-wandering. Participants' momentary mental state was measured by means of intermittent thought probes in which they reported on their current mental state. The results showed that neither the vigilance classifier nor the task demands classifier could predict mind-wandering above-chance level, while a classifier trained on self-reports of mind-wandering was able to do so. This suggests that mind-wandering is a mental state different from low vigilance or performing tasks with low demands—both which could be discriminated from the EEG above chance. Furthermore, we used dipole fitting to source-localize the neural correlates of the most import features in each of the three classifiers, indeed finding a few distinct neural structures between the three phenomena. Our study demonstrates the value of machine-learning classifiers in unveiling patterns in neural data and uncovering the associated neural structures by combining it with an EEG source analysis technique

The effect of a sport-based intervention to prevent juvenile delinquency in at-risk adolescents

Despite the wide implementation of sport-based crime prevention programs, there is a lack of empirical knowledge on the effectiveness of these interventions. This study evaluated a Dutch sport-based program in N = 368 youth at risk for juvenile delinquency. Intervention effects were tested in a quasi-experimental study, comparing the intervention group with a comparison group using multiple sources of information. The study was conducted under conditions that resemble real-life implementation, thereby enhancing the relevance of this contribution to practitioners. The primary outcome was juvenile delinquency, measured by official police data. The secondary outcomes were risk and protective factors for delinquency, assessed with self- and teacher reports. A significant effect was found on one delinquency measure. The intervention group consisted of fewer youth with police registrations as a suspect than the comparison group (d = −0.34). We did not find an intervention effect on the number of registrations as a suspect in each group. In addition, no significant intervention effects were found on the secondary outcomes. Implications for theory and practice concerning the use of sport-based crime prevention programs are discussed

Mapping working memory retrieval in space and in time:A combined electroencephalography and electrocorticography approach

In this study, we investigated the time course and neural correlates of the retrieval process underlying visual working memory. We made use of a rare dataset in which the same task was recorded using both scalp electroencephalography (EEG) and Electrocorticography (ECoG), respectively. This allowed us to examine with great spatial and temporal detail how the retrieval process works, and in particular how the medial temporal lobe (MTL) is involved. In each trial, participants judged whether a probe face had been among a set of recently studied faces. With a method that combines hidden semi-Markov models and multivariate pattern analysis, the neural signal was decomposed into a sequence of latent cognitive stages with information about their durations on a trial-by-trial basis. Analyzed separately, EEG and ECoG data yielded converging results on discovered stages and their interpretation, which reflected 1) a brief pre-attention stage, 2) encoding the stimulus, 3) retrieving the studied set, and 4) making a decision. Combining these stages with the high spatial resolution of ECoG suggested that activity in the temporal cortex reflected item familiarity in the retrieval stage; and that once retrieval is complete, there is active maintenance of the studied face set in the decision stage in the MTL. During this same period, the frontal cortex guides the decision by means of theta coupling with the MTL. These observations generalize previous findings on the role of MTL theta from long-term memory tasks to short-term memory tasks