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Consciousness requires subjective experience in the "now." Establishing "now," however, necessitates temporal processing. In the current article, we review one method of altering consciousness, anaesthetic drug administration, and its effects on perceived duration. We searched PubMed, PsycInfo, and ScienceDirect databases, and article reference sections, for combinations of anaesthetic drugs and time perception tasks, finding a total of 36 articles which met our inclusion criteria. We categorised these articles with regard to whether they altered the felt passage of time, short or long interval timing, or were motor timing tasks. We found that various drugs alter the perceived passage of time; ketamine makes time subjectively slow down while GABAergic drugs make time subjectively speed up. At a short interval there is little established evidence of a shift in time perception, though temporal estimates appear more variable. Similarly, when asked to use time to optimise responses (i.e., in motor timing tasks), various anaesthetic agents make timing more variable. Longer durations are estimated as lasting longer than their objective duration, though there is some variation across articles in this regard. We conclude by proposing further experiments to examine time perception under altered states of consciousness and ask whether it is possible to perceive the passage of time of events which do not necessarily reach the level of conscious perception. The variety of methods used raises the need for more systematic investigations of time perception under anaesthesia. We encourage future investigations into the overlap of consciousness and time perception to advance both fields. [ABSTRACT FROM AUTHOR]

More experience results in better performance, usually. In most tasks, the more chances to learn we have, the better we are at it. This does not always appear to be the case in time perception however. In the current article, we use three different methods to investigate the role of the number of standard example durations presented on performance on three timing tasks: rhythm continuation, deviance detection, and final stimulus duration judgement. In Experiments 1a and 1b, rhythms were produced with the same accuracy whether one, two, three, or four examples of the critical duration were presented. In Experiment 2, participants were required to judge which of four stimuli had a different duration from the other three. This judgement did not depend on which of the four stimuli was the deviant one. In Experiments 3a and 3b, participants were just as accurate at judging the duration of a final stimulus in comparison to the prior stimuli regardless of the number of standards presented prior to the final stimulus. In summary, we never found any systematic effect of the number of standards presented on performance on any of the three timing tasks. In the discussion, we briefly relate these findings to three theories of time perception. [ABSTRACT FROM AUTHOR]

In cognitive neuroscience research, computational models of event-related potentials (ERP) can provide a means of developing explanatory hypotheses for the observed waveforms. However, researchers trained in cognitive neurosciences may face technical challenges in implementing these models. This paper provides a tutorial on developing recurrent neural network (RNN) models of ERP waveforms in order to facilitate broader use of computational models in ERP research. To exemplify the RNN model usage, the P3 component evoked by target and non-target visual events, measured at channel Pz, is examined. Input representations of experimental events and corresponding ERP labels are used to optimize the RNN in a supervised learning paradigm. Linking one input representation with multiple ERP waveform labels, then optimizing the RNN to minimize mean-squared-error loss, causes the RNN output to approximate the grand-average ERP waveform. Behavior of the RNN can then be evaluated as a model of the computational principles underlying ERP generation. Aside from fitting such a model, the current tutorial will also demonstrate how to classify hidden units of the RNN by their temporal responses and characterize them using principal component analysis. Statistical hypothesis testing can also be applied to these data. This paper focuses on presenting the modelling approach and subsequent analysis of model outputs in a how-to format, using publicly available data and shared code. While relatively less emphasis is placed on specific interpretations of P3 response generation, the results initiate some interesting discussion points. [ABSTRACT FROM AUTHOR]

People produced time intervals of 500 to 1250 ms, with accurate feedback in ms provided after each production. The mean times produced tracked the target times closely, and the coefficient of variation (standard deviation/mean) declined with increasing target time. The mean absolute change from one trial to another, and its standard deviation, measures of trial-by-trial change, also increased with target time. A model of feedback was fitted to all four measures. It assumed that the time produced resulted from a combination of a scalar timing process and a non-timing process. Although the non-timing process was on average invariant with target time, the timing process was assumed to be sensitive to feedback, in two different ways. If the previous production was close to the target the model repeated it (a repeat process), but if it was further away the next production was adjusted by an amount related to the discrepancy between the previous production and the target (an adjust process). The balance between the two was governed by a threshold, which was on average constant, and it was further assumed that the relative variability of the repeat process was lower than that of the adjust process. The model produced output which fitted three of the four measures well (average deviation of 3 or 4%) but fitted the standard deviation of change less well. Reducing the magnitude of the non-timing process produced output which conformed approximately to scalar timing, and the model could also mimic data resulting from the provision of inaccurate feedback. [ABSTRACT FROM AUTHOR]

Objective. Event-related potential (ERP) sensitivity to faces is predominantly characterized by an N170 peak that has greater amplitude and shorter latency when elicited by human faces than images of other objects. We aimed to develop a computational model of visual ERP generation to study this phenomenon which consisted of a three-dimensional convolutional neural network (CNN) connected to a recurrent neural network (RNN). Approach. The CNN provided image representation learning, complimenting sequence learning of the RNN for modeling visually-evoked potentials. We used open-access data from ERP Compendium of Open Resources and Experiments (40 subjects) to develop the model, generated synthetic images for simulating experiments with a generative adversarial network, then collected additional data (16 subjects) to validate predictions of these simulations. For modeling, visual stimuli presented during ERP experiments were represented as sequences of images (time x pixels). These were provided as inputs to the model. By filtering and pooling over spatial dimensions, the CNN transformed these inputs into sequences of vectors that were passed to the RNN. The ERP waveforms evoked by visual stimuli were provided to the RNN as labels for supervised learning. The whole model was trained end-to-end using data from the open-access dataset to reproduce ERP waveforms evoked by visual events. Main results. Cross-validation model outputs strongly correlated with open-access ( r = 0.98) and validation study data ( r = 0.78). Open-access and validation study data correlated similarly ( r = 0.81). Some aspects of model behavior were consistent with neural recordings while others were not, suggesting promising albeit limited capacity for modeling the neurophysiology of face-sensitive ERP generation. Significance. The approach developed in this work is potentially of significant value for visual neuroscience research, where it may be adapted for multiple contexts to study computational relationships between visual stimuli and evoked neural activity., (Creative Commons Attribution license.)

Active research-driven approaches that successfully incorporate new technology are known to catalyze student learning. Yet achieving these objectives in neuroscience education is especially challenging due to the prohibitive costs and technical demands of research-grade equipment. Here we describe a method that circumvents these factors by leveraging consumer EEG-based neurogaming technology to create an affordable, scalable, and highly portable teaching laboratory for undergraduate courses in neuroscience. This laboratory is designed to give students hands-on research experience, consolidate their understanding of key neuroscience concepts, and provide a unique real-time window into the working brain. Survey results demonstrate that students found the lab sessions engaging. Students also reported the labs enhanced their knowledge about EEG, their course material, and neuroscience research in general.