Cognition of Time and Thinking Beyond.

A common research protocol in cognitive neuroscience is to train subjects to perform deliberately designed experiments while recording brain activity, with the aim of understanding the brain mechanisms underlying cognition. However, how the results of this protocol of research can be applied in technology is seldom discussed. Here, I review the studies on time processing of the brain as examples of this research protocol, as well as two main application areas of neuroscience (neuroengineering and brain-inspired artificial intelligence). Time processing is a fundamental dimension of cognition, and time is also an indispensable dimension of any real-world signal to be processed in technology. Therefore, one may expect that the studies of time processing in cognition profoundly influence brain-related technology. Surprisingly, I found that the results from cognitive studies on timing processing are hardly helpful in solving practical problems. This awkward situation may be due to the lack of generalizability of the results of cognitive studies, which are under well-controlled laboratory conditions, to real-life situations. This lack of generalizability may be rooted in the fundamental unknowability of the world (including cognition). Overall, this paper questions and criticizes the usefulness and prospect of the abovementioned research protocol of cognitive neuroscience. I then give three suggestions for future research. First, to improve the generalizability of research, it is better to study brain activity under real-life conditions instead of in well-controlled laboratory experiments. Second, to overcome the unknowability of the world, we can engineer an easily accessible surrogate of the object under investigation, so that we can predict the behavior of the object under investigation by experimenting on the surrogate. Third, the paper calls for technology-oriented research, with the aim of technology creation instead of knowledge discovery.

A new era in cognitive neuroscience: the tidal wave of artificial intelligence (AI).

Translating artificial intelligence techniques into the realm of cognitive neuroscience holds promise for significant breakthroughs in our ability to probe the intrinsic mechanisms of the brain. The recent unprecedented development of robust AI models is changing how and what we understand about the brain. In this Editorial, we invite contributions for a BMC Neuroscience Collection on "AI and Cognitive Neuroscience".

Models need mechanisms, but not labels.

The target article proposes a model involving the important but not well-investigated topics of curiosity and creativity. The model, however, falls short of providing convincing explanations of the basic mechanisms underlying these phenomena. We outline the importance of mechanistic thinking in dealing with the concepts outlined in this article specifically and within psychology and cognitive neuroscience in general.

Getting closer to social interactions using electroencephalography in developmental cognitive neuroscience.

The field of developmental cognitive neuroscience is advancing rapidly, with large-scale, population-wide, longitudinal studies emerging as a key means of unraveling the complexity of the developing brain and cognitive processes in children. While numerous neuroscientific techniques like functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) have proved advantageous in such investigations, this perspective proposes a renewed focus on electroencephalography (EEG), leveraging underexplored possibilities of EEG. In addition to its temporal precision, low costs, and ease of application, EEG distinguishes itself with its ability to capture neural activity linked to social interactions in increasingly ecologically valid settings. Specifically, EEG can be measured during social interactions in the lab, hyperscanning can be used to study brain activity in two (or more) people simultaneously, and mobile EEG can be used to measure brain activity in real-life settings. This perspective paper summarizes research in these three areas, making a persuasive argument for the renewed inclusion of EEG into the toolkit of developmental cognitive and social neuroscientists.

Functional neuroimaging as a catalyst for integrated neuroscience.

Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.

Neural correlates of the inverse base rate effect.

The inverse base rate effect (IBRE) is a nonrational behavioral phenomenon in predictive learning. Canonically, participants learn that the AB stimulus compound leads to one outcome and that AC leads to another outcome, with AB being presented three times as often as AC. When subsequently presented with BC, the outcome associated with AC is preferentially selected, in opposition to the underlying base rates of the outcomes. The current leading explanation is based on error-driven learning. A key component of this account is prediction error, a concept previously linked to a number of brain areas including the anterior cingulate, the striatum, and the dorsolateral prefrontal cortex. The present work is the first fMRI study to directly examine the IBRE. Activations were noted in brain areas linked to prediction error, including the caudate body, the anterior cingulate, the ventromedial prefrontal cortex, and the right dorsolateral prefrontal cortex. Analyzing the difference in activations for singular key stimuli (B and C), as well as frequency matched controls, supports the predictions made by the error-driven learning account.

Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience.

In the last decades, non-invasive and portable neuroimaging techniques, such as functional near infrared spectroscopy (fNIRS), have allowed researchers to study the mechanisms underlying the functional cognitive development of the human brain, thus furthering the potential of Developmental Cognitive Neuroscience (DCN). However, the traditional paradigms used for the analysis of infant fNIRS data are still quite limited. Here, we introduce a multivariate pattern analysis for fNIRS data, xMVPA, that is powered by eXplainable Artificial Intelligence (XAI). The proposed approach is exemplified in a study that investigates visual and auditory processing in six-month-old infants. xMVPA not only identified patterns of cortical interactions, which confirmed the existent literature; in the form of conceptual linguistic representations, it also provided evidence for brain networks engaged in the processing of visual and auditory stimuli that were previously overlooked by other methods, while demonstrating similar statistical performance.

Nostalgia enhances detection of death threat: neural and behavioral evidence.

An experiment examined the potency of nostalgia-a sentimental longing for one's past-to facilitate detection of death-related stimuli, using functional magnetic resonance imaging (fMRI) and behavioral techniques (i.e., judgmental accuracy, reaction times). We hypothesized and found that, at the neural level, nostalgic (relative to control) participants evinced more intense activation in right amygdala in response to death-related (vs. neutral) words. We also hypothesized and found that, at the behavioral level, nostalgic (relative to control) participants manifested greater accuracy in judging whether two death-related (vs. neutral) words belonged in the same category. Exploratory analyses indicated that nostalgic (relative to control) participants did not show faster reaction times to death-related (vs. neutral) words. In all, nostalgia appeared to aid in death threat detection. We consider implications for the relevant literatures.

Individualized cognitive neuroscience needs 7T: Comparing numerosity maps at 3T and 7T MRI.

The field of cognitive neuroscience is weighing evidence about whether to move from the current standard field strength of 3 Tesla (3T) to ultra-high field (UHF) of 7T and above. The present study contributes to the evidence by comparing a computational cognitive neuroscience paradigm at 3T and 7T. The goal was to evaluate the practical effects, i.e. model predictive power, of field strength on a numerosity task using accessible pre-processing and analysis tools. Previously, using 7T functional magnetic resonance imaging and biologically-inspired analyses, i.e. population receptive field modelling, we discovered topographical organization of numerosity-selective neural populations in human parietal cortex. Here we show that these topographic maps are also detectable at 3T. However, averaging of many more functional runs was required at 3T to reliably reconstruct numerosity maps. On average, one 7T run had about four times the model predictive power of one 3T run. We believe that this amount of scanning would have made the initial discovery of the numerosity maps on 3T highly infeasible in practice. Therefore, we suggest that the higher signal-to-noise ratio and signal sensitivity of UHF MRI is necessary to build mechanistic models of the organization and function of our cognitive abilities in individual participants.

Protocol for building a cognitive map of structural knowledge in humans by integrating abstract relationships from separate experiences.

Humans are adept at learning the latent structure of the relationship between abstract concepts and can build a cognitive map from limited experiences. However, examining internal representations of the cognitive map is challenging because they are unobservable and differ across individuals. Here, we introduce a behavioral training protocol designed for human participants to implicitly build a map of two-dimensional social hierarchies while making a series of binary choices and analytic tools for measuring the internal representation of this structural knowledge. For complete details on the use and execution of this protocol, please refer to Park et al. (2020a, 2020b).

Individual differences among deep neural network models.

Deep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as a modeling framework for neural computations in the primate brain. Just like individual brains, each DNN has a unique connectivity and representational profile. Here, we investigate individual differences among DNN instances that arise from varying only the random initialization of the network weights. Using tools typically employed in systems neuroscience, we show that this minimal change in initial conditions prior to training leads to substantial differences in intermediate and higher-level network representations despite similar network-level classification performance. We locate the origins of the effects in an under-constrained alignment of category exemplars, rather than misaligned category centroids. These results call into question the common practice of using single networks to derive insights into neural information processing and rather suggest that computational neuroscientists working with DNNs may need to base their inferences on groups of multiple network instances.

Impressions on the current state of cognitive neuroscience in occupational measurement.

Cognitive neuroscience is currently finding itself as a marketing trend in occupational science, particularly in terms of workplace assessment and measurement. However, the field has historically had little to do with occupational applications and has generally remained focused on the clinical and academic relevance of its research. We will explore several frontiers where research methods and theory established in cognitive neuroscience are beginning to produce meaningful applications in the workplace. Given that this application is likely to be unfamiliar with many in brain research, we look to outline concepts that should be perceived as key considerations when applying innovative measures to the workplace. Relating to these key considerations are several challenges that currently stand in the way of cognitive neuroscience progressing beyond a marketing trend into a steadfast perspective in occupational science.

Disinhibition and Detachment in Adolescence: A Developmental Cognitive Neuroscience Perspective on the Alternative Model for Personality Disorders.

Personality pathology often emerges during adolescence, but attempts to understand its neurocognitive basis have traditionally been undermined by problems associated with the categorical classification of personality disorders. In contrast, dimensional models of personality pathology, such as the Alternative Model for Personality Disorders (AMPD) in DSM-5, may provide a stronger foundation for neurobiological investigations of maladaptive individual differences in personality. As an example, we review studies of the adolescent development of reward processing and cognitive control and connect these systems to the normal personality hierarchy and to two dimensions included in the AMPD - Detachment and Disinhibition. We argue that by linking developmental changes in these systems to the AMPD, researchers will be better positioned to understand the relationship between neurocognitive development and the expression of personality pathology in adolescence and early adulthood.

Brain meta-state transitions demarcate thoughts across task contexts exposing the mental noise of trait neuroticism.

Researchers have observed large-scale neural meta-state transitions that align to narrative events during movie-viewing. However, group or training-derived priors have been needed to detect them. Here, we introduce methods to sample transitions without any priors. Transitions detected by our methods predict narrative events, are similar across task and rest, and are correlated with activation of regions associated with spontaneous thought. Based on the centrality of semantics to thought, we argue these transitions serve as general, implicit neurobiological markers of new thoughts, and that their frequency, which is stable across contexts, approximates participants' mentation rate. By enabling observation of idiosyncratic transitions, our approach supports many applications, including phenomenological access to the black box of resting cognition. To illustrate the utility of this access, we regress resting fMRI transition rate and movie-viewing transition conformity against trait neuroticism, thereby providing a first neural confirmation of mental noise theory.

Enhancing Psychosis Risk Prediction Through Computational Cognitive Neuroscience.

Research suggests that early identification and intervention with individuals at clinical high risk (CHR) for psychosis may be able to improve the course of illness. The first generation of studies suggested that the identification of CHR through the use of specialized interviews evaluating attenuated psychosis symptoms is a promising strategy for exploring mechanisms associated with illness progression, etiology, and identifying new treatment targets. The next generation of research on psychosis risk must address two major limitations: (1) interview methods have limited specificity, as recent estimates indicate that only 15%-30% of individuals identified as CHR convert to psychosis and (2) the expertise needed to make CHR diagnosis is only accessible in a handful of academic centers. Here, we introduce a new approach to CHR assessment that has the potential to increase accessibility and positive predictive value. Recent advances in clinical and computational cognitive neuroscience have generated new behavioral measures that assay the cognitive mechanisms and neural systems that underlie the positive, negative, and disorganization symptoms that are characteristic of psychotic disorders. We hypothesize that measures tied to symptom generation will lead to enhanced sensitivity and specificity relative to interview methods and the cognitive intermediate phenotype measures that have been studied to date that are typically indicators of trait vulnerability and, therefore, have a high false positive rate for conversion to psychosis. These new behavioral measures have the potential to be implemented on the internet and at minimal expense, thereby increasing accessibility of assessments.

Putting the Nonsocial Into Social Neuroscience: A Role for Domain-General Priority Maps During Social Interactions.

Whether on a first date or during a team briefing at work, people's daily lives are inundated with social information, and in recent years, researchers have begun studying the neural mechanisms that support social-information processing. We argue that the focus of social neuroscience research to date has been skewed toward specialized processes at the expense of general processing mechanisms with a consequence that unrealistic expectations have been set for what specialized processes alone can achieve. We propose that for social neuroscience to develop into a more mature research program, it needs to embrace hybrid models that integrate specialized person representations with domain-general solutions, such as prioritization and selection, which operate across all classes of information (both social and nonsocial). To illustrate our central arguments, we first describe and then evaluate a hybrid model of information processing during social interactions that (a) generates novel and falsifiable predictions compared with existing models; (b) is predicated on a wealth of neurobiological evidence spanning many decades, methods, and species; (c) requires a superior standard of evidence to substantiate domain-specific mechanisms of social behavior; and (d) transforms expectations of what types of neural mechanisms may contribute to social-information processing in both typical and atypical populations.

Bioacoustics in cognitive research: Applications, considerations, and recommendations.

The multifaceted ability to produce, transmit, receive, and respond to acoustic signals is widespread in animals and forms the basis of the interdisciplinary science of bioacoustics. Bioacoustics research methods, including sound recording and playback experiments, are applicable in cognitive research that centers around the processing of information from the acoustic environment. We provide an overview of bioacoustics techniques in the context of cognitive studies and make the case for the importance of bioacoustics in the study of cognition by outlining some of the major cognitive processes in which acoustic signals are involved. We also describe key considerations associated with the recording of sound and its use in cognitive applications. Based on these considerations, we provide a set of recommendations for best practices in the recording and use of acoustic signals in cognitive studies. Our aim is to demonstrate that acoustic recordings and stimuli are valuable tools for cognitive researchers when used appropriately. In doing so, we hope to stimulate opportunities for innovative cognitive research that incorporates robust recording protocols. This article is categorized under: Neuroscience > Cognition Psychology > Theory and Methods Neuroscience > Behavior Neuroscience > Cognition.

Does higher sampling rate (multiband + SENSE) improve group statistics - An example from social neuroscience block design at 3T.

Multiband (MB) or Simultaneous multi-slice (SMS) acquisition schemes allow the acquisition of MRI signals from more than one spatial coordinate at a time. Commercial availability has brought this technique within the reach of many neuroscientists and psychologists. Most early evaluation of the performance of MB acquisition employed resting state fMRI or the most basic tasks. In this study, we tested whether the advantages of using MB acquisition schemes generalize to group analyses using a cognitive task more representative of typical cognitive neuroscience applications. Twenty-three subjects were scanned on a Philips 3 â€‹T scanner using five sequences, up to eight-fold acceleration with MB-factors 1 to 4, SENSE factors up to 2 and corresponding TRs of 2.45s down to 0.63s, while they viewed (i) movie blocks showing complex actions with hand object interactions and (ii) control movie blocks without hand object interaction. Data were processed using a widely used analysis pipeline implemented in SPM12 including the unified segmentation and canonical HRF modelling. Using random effects group-level, voxel-wise analysis we found that all sequences were able to detect the basic action observation network known to be recruited by our task. The highest t-values were found for sequences with MB4 acceleration. For the MB1 sequence, a 50% bigger voxel volume was needed to reach comparable t-statistics. The group-level t-values for resting state networks (RSNs) were also highest for MB4 sequences. Here the MB1 sequence with larger voxel size did not perform comparable to the MB4 sequence. Altogether, we can thus recommend the use of MB4 (and SENSE 1.5 or 2) on a Philips scanner when aiming to perform group-level analyses using cognitive block design fMRI tasks and voxel sizes in the range of cortical thickness (e.g. 2.7 â€‹mm isotropic). While results will not be dramatically changed by the use of multiband, our results suggest that MB will bring a moderate but significant benefit.

A rapid, neural measure of implicit recognition memory using fast periodic visual stimulation.

Fast periodic visual stimulation (FPVS) has recently emerged as a powerful new tool in cognitive neuroscience. Capable of measuring a range of cognitive functions in single subjects in just minutes of recording time, it has been adapted to measure visual, semantic and linguistic processing. We present a new adaptation of the FPVS approach to measure recognition memory via old/new contrasts. Twenty one subjects (23 (±6) yrs, 7 males) completed an FPVS-oddball paradigm that assessed their spontaneous ability to differentiate between rapidly presented images on the basis of a pre-FPVS encoding task, i.e. oddball stimuli were only defined by the subject's experimentally induced memory of them. A clear oddball detection response reflecting recognition memory was observed within one minute of EEG recording time, simply through the passive viewing of stimuli, i.e. subjects received no task instructions and provided no behavioural response. Performance on a subsequent behavioural recognition task showed high levels of recognition of the oddball stimuli. As such, the FPVS approach returned an objective, non-verbal measure of recognition memory in just one minute of recording time, free from the confounds of behavioural recognition tasks. This finding reinforces the adaptability of the FPVS approach for the examination of higher-level cognition and provides a new method for the neural measurement of recognition memory.