Advances in Cognitive Psychology最新文献

Even though effects of emotion and motivation on cognition are well documented, the interaction of all three factors is rarely investigated. Here, we used electroencephalography (EEG) to examine the effects of self-determined choice-as an experimental manipulation of intrinsic motivation - and emotional stimulus content on task preparation and engagement in a temporal production task. Behavioral results indicated a modulation of time processing depending on choice and emotional content. Underlying EEG signals revealed differential modulations by choice on the contingent negative variation (CNV) during task and response preparation and by emotional content on the late positive potential (LPP) in response to the onset of an emotional picture during temporal production. Also, we obtained preliminary evidence for interaction effects of choice and emotional content on the LPP. The feedback-related negativity (FRN) in response to information regarding temporal production success was also affected by interactions of choice and emotional content. These findings indicate that besides separate effects of motivation and emotion, there may be time windows during task engagement in which both factors jointly affect cognitive processing. These results are interpreted as dynamic modulations of attentional resource allocation.

Cognitive and physical training have been shown to be effective in improving older adults' cognition. However, it is not yet clear whether combined cognitive and physical training offers an advantage compared to cognitive training alone. Twenty-two older adults performed cognitive or combined cognitive and physical training in order to compare their effects on working memory event-related potentials (ERPs) and on working memory and executive function performance. Before and after eight weeks of training, performance in Plus Minus, Flanker, Updated Span, and Complex Span tasks was measured, and ERPs were registered during performance of an n-back task (0-back, 2-back, and 3-back). Post-training behavioural improvement was observed in Updated Span, Complex Span, and n-back tasks. During the n-back task, the N2/P3 complex was modulated by training, with a decrease in N2 amplitude and an increase in P3 amplitude in the posttraining session compared to the pretraining session. These changes in ERP components suggest that both types of training potentially reduce the need for attentional control to perform the tasks correctly and increase working memory capacity. Thus, based on our data, no conclusion can be reached on the direct advantage of combined training, either at behavioural or at neural level. However, the present study might suggest an indirect advantage of such a combined training, because the cognitive benefit was found to be highly similar in both types of training. Using combined cognitive and physical training may produce a potential improvement in general fitness and an increased appeal of training.

Previous research suggests that selective spatial attention is a determining factor for unconscious processing under continuous flash suppression (CFS), and specifically, that inattention toward stimulus location facilitates its unconscious processing by reducing the depth of CFS (Eo et al., 2016). The aim of our study was to further examine this modulation-by-attention model of CFS using a number priming paradigm. Participants (N = 26) performed a number comparison task on a visible target number ("compare target to five"). Prime-target pairs were either congruent (both smaller or larger than five) or incongruent. Spatial attention toward the primes was varied by manipulating the uncertainty of the primes' location. Based on the modulation-by-attention model, we hypothesized the following: In trials with uncertain prime location, RTs for congruent prime-target pairs should be faster than for incongruent ones. In trials with certain prime location, RTs for congruent versus incongruent prime-target pairs should not differ. We analyzed our data with sequential Bayes factors (BFs). Our data showed no effect of location uncertainty on unconscious priming under CFS (BF0+ = 5.16). However, even visible primes only weakly influenced RTs. Possible reasons for the absence of robust number priming effects in our study are discussed. Based on exploratory analyses, we conclude that the numerical order of prime and target resulted in a response conflict and interfered with the predicted priming effect.

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.