The Influence of the BDNF Val66Met Polymorphism on Mechanisms of Semantic Priming: Analyses with Drift-Diffusion Models of Masked and Unmasked Priming

Abstract

Automatic and strategic processes in semantic priming can be investigated with masked and unmasked priming tasks. Unmasked priming is thought to enable strategic processes due to the conscious processing of primes, while masked priming exclusively depends on automatic processes due to the invisibility of the prime. Besides task properties, interindividual differences may alter priming effects. In a recent study, masked and unmasked priming based on mean response time (RT) and error rate (ER) differed as a function of the BDNF Val66Met polymorphism (Sanwald et al., 2020). The BDNF Val66Met polymorphism is related to the integrity of several cognitive executive functions and might thus influence the magnitude of priming. In the present study, we reanalyzed this data with drift-diffusion models. Drift-diffusion models conjointly analyze single trial RT and ER data and serve as a framework to elucidate cognitive processes underlying priming. Masked and unmasked priming effects were observed for the drift rates ν, presumably reflecting semantic preactivation. Priming effects on nondecision time t0 were especially pronounced in unmasked priming, suggesting additional conscious processes to be involved in the t0 modulation. Priming effects on the decision thresholds a may reflect a speed-accuracy tradeoff. Considering the BDNF Val66Met polymorphism, we found lowered drift rates and decision thresholds for Met allele carriers, possibly reflecting a superficial processing style in Met allele carriers. The present study shows that differences in cognitive tasks between genetic groups can be elucidated using drift-diffusion modeling.

Cumulative Genetic Score of DRD2 Polymorphisms Is Associated with Impulsivity and Masked Semantic Priming

“…We found no genotype effect regarding unmasked semantic priming. This is in line with previous studies on BDNF Val66Met and semantic priming which used similar behavioral data (Berger et al 2021;Sanwald et al 2020). It is possible that visible primes induce the application of strategic priming mechanisms masking the small effects of genotype on semantic priming (Sanwald et al 2020).…”

“…One-hundred eighty-eight native German speakers with normal or corrected-to-normal vision participated in the study. Data from this sample was analyzed in previous studies with regard to a BDNF polymorphism (Berger et al 2021;Sanwald et al 2020). Sixteen participants were later excluded because they reported a diagnosis of a neurological or psychiatric disorder.…”

“…Given this association between EF and semantic priming, genes impacting EF might also be able to explain variance in interindividual differences in semantic priming. There are to date only three studies investigating the molecular genetics of semantic priming examining polymorphisms of genes formerly associated with differences in EF (Berger et al 2021;Reuter et al 2009;Sanwald et al 2020). One study found Met carriers of the brain-derived neurotrophic factor (BDNF) Val66Met polymorphism to show less RT and stronger ER priming in a masked semantic priming paradigm (Sanwald et al 2020).…”

“…One study found Met carriers of the brain-derived neurotrophic factor (BDNF) Val66Met polymorphism to show less RT and stronger ER priming in a masked semantic priming paradigm (Sanwald et al 2020). When reanalyzing this data by means of drift diffusion models (Berger et al 2021), drift rate and decision thresholds were lower in Met than in Val carriers suggesting a more superficial processing style in Met carriers. Finally, the catechol-o-methyl transferase (COMT) gene, coding for the postsynaptic enzyme that methylates released dopamine, has been suggested to be important for prefrontal functioning (Weinberger et al 2001).…”

Task cue influences on lexical decision performance and masked semantic priming effects: The role of cue-task compatibility

“…Drift-diffusion models (Ratcliff, 1978 ; Voss, Nagler, & Lerche, 2013a ) use single-trial RT and ER data to map task performance to different parameters associated with cognitive processes. We focused on analyses of drift rates and non-decision times, as the drift rate reliably indexes semantic priming (Berger et al, 2021 ; Lerche & Voss, 2017 ; Voss, Rothermund, et al, 2013b ), whereas the non-decision time is related to task-switching costs (Ging-Jehli & Ratcliff, 2020 ; Schmitz & Voss, 2012 , 2014 ) as well as priming processes (Berger et al, 2021 ; Gomez et al, 2013 ). Drift-diffusion models possibly have the power to elaborate in more detail the processes triggered in task-cue only and induction-task trials and to permit disentangling influences on semantic priming from influences on general task performance.…”