Psychology needs to get tired of winning

The State of Open Science Practices in Psychometric Studies of Suicide: A Systematic Review

The adoption of open science practices (OSPs) is crucial for promoting transparency and robustness in research. We conducted a systematic review to assess the frequency and trends of OSPs in psychometric studies focusing on measures of suicidal thoughts and behavior. We analyzed publications from two international databases, examining the use of OSPs such as open access publication, preregistration, provision of open materials, and data sharing. Our findings indicate a lack of adherence to OSPs in psychometric studies of suicide. The majority of manuscripts were published under restricted access, and preregistrations were not utilized. The provision of open materials and data was rare, with limited access to instruments and analysis scripts. Open access versions (preprints/postprints) were scarce. The low adoption of OSPs in psychometric studies of suicide calls for urgent action. Embracing a culture of open science will enhance transparency, reproducibility, and the impact of research in suicide prevention efforts.

Assessing processing-based measures of implicit statistical learning: Three serial reaction time experiments do not reveal artificial grammar learning

Implicit statistical learning, whereby predictable relationships between stimuli are detected without conscious awareness, is important for language acquisition. However, while this process is putatively implicit, it is often assessed using measures that require explicit reflection and conscious decision making. Here, we conducted three experiments combining an artificial grammar learning paradigm with a serial reaction time (SRT-AGL) task, to measure statistical learning of adjacent and nonadjacent dependencies implicitly, without conscious decision making. Participants viewed an array of six visual stimuli and were presented with a sequence of three auditory (nonsense words, Expt. 1; names of familiar objects, Expt. 2) or visual (abstract shapes, Expt. 3) cues and were asked to click on the corresponding visual stimulus as quickly as possible. In each experiment, the final stimulus in the sequence was predictable based on items earlier in the sequence. Faster responses to this predictable final stimulus compared to unpredictable stimuli would provide evidence of implicit statistical learning, without requiring explicit decision making or conscious reflection. Despite previous positive results (Christiansen et al. 2009 and Misyak et al. 2010) we saw little evidence of implicit statistical learning in any of the experiments, suggesting that in this case, these SRT-AGL tasks were not an effective measure implicit statistical learning.

No support for a causal role of primary motor cortex in construing meaning from language: An rTMS study

Embodied cognition theories predict a functional involvement of sensorimotor processes in language understanding. In a preregistered experiment, we tested this idea by investigating whether interfering with primary motor cortex (M1) activation can change how people construe meaning from action language. Participants were presented with sentences describing actions (e.g., "turning off the light") and asked to choose between two interpretations of their meaning, one more concrete (e.g., "flipping a switch") and another more abstract (e.g., "going to sleep"). Prior to this task, participants' M1 was disrupted using repetitive transcranial magnetic stimulation (rTMS). The results yielded strong evidence against the idea that M1-rTMS affects meaning construction (BF01 > 30). Additional analyses and control experiments suggest that the absence of effect cannot be accounted for by failure to inhibit M1, lack of construct validity of the task, or lack of power to detect a small effect. In sum, these results do not support a causal role for primary motor cortex in building meaning from action language.

Neurobiological Causal Models of Language Processing

The language faculty is physically realized in the neurobiological infrastructure of the human brain. Despite significant efforts, an integrated understanding of this system remains a formidable challenge. What is missing from most theoretical accounts is a specification of the neural mechanisms that implement language function. Computational models that have been put forward generally lack an explicit neurobiological foundation. We propose a neurobiologically informed causal modeling approach which offers a framework for how to bridge this gap. A neurobiological causal model is a mechanistic description of language processing that is grounded in, and constrained by, the characteristics of the neurobiological substrate. It intends to model the generators of language behavior at the level of implementational causality. We describe key features and neurobiological component parts from which causal models can be built and provide guidelines on how to implement them in model simulations. Then we outline how this approach can shed new light on the core computational machinery for language, the long-term storage of words in the mental lexicon and combinatorial processing in sentence comprehension. In contrast to cognitive theories of behavior, causal models are formulated in the "machine language" of neurobiology which is universal to human cognition. We argue that neurobiological causal modeling should be pursued in addition to existing approaches. Eventually, this approach will allow us to develop an explicit computational neurobiology of language.

Measuring the menu, not the food: "psychometric" data may instead measure "lingometrics" (and miss its greatest potential)

This is a review of a range of empirical studies that use digital text algorithms to predict and model response patterns from humans to Likert-scale items, using texts only as inputs. The studies show that statistics used in construct validation is predictable on sample and individual levels, that this happens across languages and cultures, and that the relationship between variables are often semantic instead of empirical. That is, the relationships among variables are given a priori and evidently computable as such. We explain this by replacing the idea of "nomological networks" with "semantic networks" to designate computable relationships between abstract concepts. Understanding constructs as nodes in semantic networks makes it clear why psychological research has produced constant average explained variance at 42% since 1956. Together, these findings shed new light on the formidable capability of human minds to operate with fast and intersubjectively similar semantic processing. Our review identifies a categorical error present in much psychological research, measuring representations instead of the purportedly represented. We discuss how this has grave consequences for the empirical truth in research using traditional psychometric methods.

A multi-sample evaluation of the measurement structure and function of the modified monetary incentive delay task in adolescents

Interpreting the neural response elicited during task functional magnetic resonance imaging (fMRI) remains a challenge in neurodevelopmental research. The monetary incentive delay (MID) task is an fMRI reward processing task that is extensively used in the literature. However, modern psychometric tools have not been used to evaluate measurement properties of the MID task fMRI data. The current study uses data for a similar task design across three adolescent samples (N = 346 [Agemean 12.0; 44 % Female]; N = 97 [19.3; 58 %]; N = 112 [20.2; 38 %]) to evaluate multiple measurement properties of fMRI responses on the MID task. Confirmatory factor analysis (CFA) is used to evaluate an a priori theoretical model for the task and its measurement invariance across three samples. Exploratory factor analysis (EFA) is used to identify the data-driven measurement structure across the samples. CFA results suggest that the a priori model is a poor representation of these MID task fMRI data. Across the samples, the data-driven EFA models consistently identify a six-to-seven factor structure with run and bilateral brain region factors. This factor structure is moderately-to-highly congruent across the samples. Altogether, these findings demonstrate a need to evaluate theoretical frameworks for popular fMRI task designs to improve our understanding and interpretation of brain-behavior associations.

Child and adolescent mental health and psychosocial support interventions: An evidence and gap map of low- and middle-income countries

Background

Mental disorders affect about one in seven children and adolescents worldwide. Investment in effective child and adolescent mental health prevention, promotion and care is essential. To date, however, the evidence from this field is yet to be comprehensively collected and mapped.

Objectives

The objective of this evidence and gap map (EGM) is to provide an overview of the existing evidence on the effectiveness of interventions aimed at promoting mental health and reducing or preventing mental health conditions among children and adolescents in lower-middle-income countries (LMICs).

Search Methods

We searched for studies from a wide range of bibliographic databases, libraries and websites. All searches were conducted in December 2021 and covered the period between 2010 and 2021.

Selection Criteria

We included evidence on the effectiveness of any Mental Health and Psychosocial Support (MHPSS) interventions targeting children and adolescents from 0 to 19 years of age in LMICs. The map includes systematic reviews and effectiveness studies in the form of randomised control trials and quasi-experimental studies, and mixed-methods studies with a focus on intervention effectiveness.

Data Collection and Analysis

A total of 63,947 records were identified after the search. A total of 19,578 records were removed using machine learning. A total of 7545 records were screened independently and simultaneously by four reviewers based on title and abstract and 2721 full texts were assessed for eligibility. The EGM includes 697 studies and reviews that covered 78 LMICs.

Main Results

School-based interventions make up 61% of intervention research on child and adolescent mental health and psychosocial support. Most interventions (59%) focusing on treating mental health conditions rather than preventing them or promoting mental health. Depression (40%, N = 282) was the most frequently researched outcome sub-domain analysed by studies and reviews, followed by anxiety disorders (32%, N = 225), well-being (21%, N = 143), and post-traumatic stress disorder (18%, N = 125). Most included studies and reviews investigated the effectiveness of mental health and psychosocial support interventions in early (75%, N = 525) and late adolescence (64%, N = 448).

Conclusions

The body of evidence in this area is complex and it is expanding progressively. However, research on child and adolescent MHPSS interventions is more reactive than proactive, with most evidence focusing on addressing mental health conditions that have already arisen rather than preventing them or promoting mental health. Future research should investigate the effectiveness of digital mental health interventions for children and adolescents as well as interventions to address the mental health and psychosocial needs of children in humanitarian settings. Research on early childhood MHPSS interventions is urgently needed. MHPSS research for children and adolescents lacks diversity. Research is also needed to address geographical inequalities at the regional and national level. Important questions also remain on the quality of the available research-is child and adolescent MHPSS intervention research locally relevant, reliable, well-designed and conducted, accessible and innovative? Planning research collaborations with decision-makers and involving experts by experience in research is essential.

Is psychological science progressing? Explained variance in PsycINFO articles during the period 1956 to 2022

We aimed to numerically assess the progress of modern psychological science. Average explained variance in 1565 included articles was 42.8 percent, and this was constant during 1956 to 2022. We explored whether this could be explained by a combination of methodological conventions with the semantic properties of the involved variables. Using latent semantic analysis (LSA) on a random sample of 50 studies from the 1,565, we were able to replicate the possible semantic factor structures of 205 constructs reported in the corresponding articles. We argue that the methodological conventions pertaining to factor structures will lock the possible explained variance within mathematical constraints that will make most statistics cluster around 40 percent explained variance. Hypotheses with close to 100 percent semantic truth value will never be part of any assumed empirical study. Nor will hypotheses approaching zero truth value. Hypotheses with around 40 percent truth value will probably be experienced as empirical and plausible and, consequently, as good candidates for psychological research. Therefore, to the extent that the findings were indeed produced by semantic structures, they could have been known without collecting data. Finally, we try to explain why psychology had to abandon an individual, causal method and switch to studying whether associations among variables at the group level differ from chance. Psychological processes take place in indefinitely complex and irreversibly changing contexts. The prevalent research paradigm seems bound to producing theoretical statements that explain each other to around 40%. Any theoretical progress would need to address and transcend this barrier.

Revisiting the left ear advantage for phonetic cues to talker identification

Previous research suggests that learning to use a phonetic property [e.g., voice-onset-time, (VOT)] for talker identity supports a left ear processing advantage. Specifically, listeners trained to identify two "talkers" who only differed in characteristic VOTs showed faster talker identification for stimuli presented to the left ear compared to that presented to the right ear, which is interpreted as evidence of hemispheric lateralization consistent with task demands. Experiment 1 (n = 97) aimed to replicate this finding and identify predictors of performance; experiment 2 (n = 79) aimed to replicate this finding under conditions that better facilitate observation of laterality effects. Listeners completed a talker identification task during pretest, training, and posttest phases. Inhibition, category identification, and auditory acuity were also assessed in experiment 1. Listeners learned to use VOT for talker identity, which was positively associated with auditory acuity. Talker identification was not influenced by ear of presentation, and Bayes factors indicated strong support for the null. These results suggest that talker-specific phonetic variation is not sufficient to induce a left ear advantage for talker identification; together with the extant literature, this instead suggests that hemispheric lateralization for talker-specific phonetic variation requires phonetic variation to be conditioned on talker differences in source characteristics.