This special issue of the 12th volume of Advances in Cognitive Psychology is devoted to the Neuronus conference that took place in Kraków in 2015. In this editorial letter, we will focus on a selection of the materials and some follow-up research that was presented during this conference. We will also briefly introduce the conference contributions that successfully passed an external reviewing process.

New analysis techniques of the electroencephalogram (EEG) such as wavelet analysis open the possibility to address questions that may largely improve our understanding of the EEG and clarify its relation with related potentials (ERPs). Three issues were addressed. 1) To what extent can early ERP components be described as transient evoked oscillations in specific frequency bands? 2) Total EEG power (TP) after a stimulus consists of pre-stimulus baseline power (BP), evoked power (EP), and induced power (IP), but what are their respective contributions? 3) The Phase Reset model proposes that BP predicts EP, while the evoked model holds that BP is unrelated to EP; which model is the most valid one? EEG results on NoGo trials for 123 individuals that took part in an experiment with emotional facial expressions were examined by computing ERPs and by performing wavelet analyses on the raw EEG and on ERPs. After performing several multiple regression analyses, we obtained the following answers. First, the P1, N1, and P2 components can by and large be described as transient oscillations in the α and θ bands. Secondly, it appears possible to estimate the separate contributions of EP, BP, and IP to TP, and importantly, the contribution of IP is mostly larger than that of EP. Finally, no strong support was obtained for either the Phase Reset or the Evoked model. Recent models are discussed that may better explain the relation between raw EEG and ERPs.

We investigated the contribution of familiarity and recollection to associative retrieval of word pairs depending on the extent to which the pairs have been unitized through task instructions in the encoding phase. Participants in the unitization condition encoded word pairs in the context of a definition that tied them together such that they were treated as a coherent new item, while in the control condition word pairs were inserted into a sentence frame in which each word remained an individual unit. Contrasting event-related potentials (ERPs) elicited in a subsequent recognition test by old (intact) and recombined (a new combination of two words from different study pairs) word pairs, an early frontal effect, the putative ERP correlate of familiarity-based retrieval, was apparent in the unitization condition. The left parietal old/new effect, reflecting recollection-based retrieval, was elicited only in the control condition. This suggests that in the unitization condition only, familiarity was sufficiently diagnostic to distinguish old from recombined pairs, while in the control condition, recollection contributed to associative recognition. Our findings add to a body of literature suggesting that unitization of associations increases the relative contribution of familiarity to subsequent associative retrieval.

Motor imagery is generally thought to share common mechanisms with motor execution. In the present study, we examined to what extent learning a fine motor skill by motor imagery may substitute physical practice. Learning effects were assessed by manipulating the proportion of motor execution and motor imagery trials. Additionally, learning effects were compared between participants with an explicit motor imagery instruction and a control group. A Go/NoGo discrete sequence production (DSDSP) task was employed, wherein a five-stimulus sequence presented on each trial indicated the required sequence of finger movements after a Go signal. In the case of a NoGo signal, participants either had to imagine carrying out the response sequence (the motor imagery group), or the response sequence had to be withheld (the control group). Two practice days were followed by a final test day on which all sequences had to be executed. Learning effects were assessed by computing response times (RTRTs) and the percentages of correct responses (PCs). The electroencephalogram (EEG) was additionally measured on this test day to examine whether motor preparation and the involvement of visual short term memory (VSTM) depended on the amount of physical/mental practice. Accuracy data indicated strong learning effects. However, a substantial amount of physical practice was required to reach an optimal speed. EEG results suggest the involvement of VSTM for sequences that had less or no physical practice in both groups. The absence of differences between the motor imagery and the control group underlines the possibility that motor preparation may actually resemble motor imagery.

Mental representations of numbers are spatially organized along a Mental Number Line (MNL). One widely proven manifestation of this relationship is the Spatial Numerical Association of Response Codes (SNARC) effect. It refers to the phenomenon of faster responses to numbers when there is congruency between the reaction side and the number position on the MNL. Although long-term memory is considered to house the MNL, short-term memory (STM) load may also modulate responses to numbers and the SNARC effect. Our question, however, was not how STM content modulates the SNARC effect observed in responses to digits, but rather how the MNL representation affects the number retrieval from STM. Each trial began with four digits presented horizontally in a spatial sequence (prime stimuli), which were then replaced by one of the priming digits as a single target. The task required participants to recall the exact location of the target. The SNARC effect occurred only in the retrieval of left-sided digits, most likely because of the generally better processing of right-sided ones, as well as in reaction to digits presented more laterally. Moreover, memory processing was more efficient with low-magnitude numbers, which may suggest that they trigger attention shifting. We conclude that the MNL affects not only the responses to numbers obtained in typical SNARC-induction tasks, such as number detection, parity judgment or magnitude comparison, but also memorization and retrieval of them. Importantly, this effect seems to be dependent on the exact position of a digit in STM.

Working memory (WM) is a primary cognitive function that corresponds to the ability to update, stably maintain, and manipulate short-term memory (STM) rapidly to perform ongoing cognitive tasks. A prevalent neural substrate of WM coding is persistent neural activity, the property of neurons to remain active after having been activated by a transient sensory stimulus. This persistent activity allows for online maintenance of memory as well as its active manipulation necessary for task performance. WM is tightly capacity limited. Therefore, selective gating of sensory and internally generated information is crucial for WM function. While the exact neural substrate of selective gating remains unclear, increasing evidence suggests that it might be controlled by modulating ongoing oscillatory brain activity. Here, we review experiments and models that linked selective gating, persistent activity, and brain oscillations, putting them in the more general mechanistic context of WM. We do so by defining several operations necessary for successful WM function and then discussing how such operations may be carried out by mechanisms suggested by computational models. We specifically show how oscillatory mechanisms may provide a rapid and flexible active gating mechanism for WM operations.

We are used to dealing with concepts which provide explanations for how cognitive processes are initiated. But we hardly ever spend time on trying to explain how such processes are turned off again and, thus, do not compromise subsequent processes.