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This paper introduces a novel controlled adaptive mental causal network model addressing how dreams overnight can influence creativity in waking life. The network model depicts in a causal, dynamic, and generic manner which adaptive mental processes underlie the connection between dreams and creativity and is shown to be validated with the existing cognitive neuroscience literature. [ABSTRACT FROM AUTHOR]

The paper addresses the issue of determining the parameter values of the agent-based model of social campaign message adoption relying on Bass's classical innovation diffusion model. The problem concerns the parameters that reflect the influence of advertising and social communication on the adoption of the idea conveyed in the social campaign. Due to the fact that the factors influencing the behavior of message recipients are conditioned by their personality, circumstances or reaction to the environment, the conventional methods for estimating these parameters may not deliver a model reproducing reality with the required accuracy. Therefore, this paper proposes a procedure for determining the values of agent-based model parameters that relies on an experimental data acquisition procedure using a combination of cognitive neuroscience techniques and a survey method. The presented research examines a social campaign promoting road safety. The obtained results prove the suitability of the suggested solution for estimating the parameters of the agent-based model of social campaign message adoption. The proposed approach contributes to the methodology of data collection and parameter estimation in building agent-based models, although it is not without some limitations. [ABSTRACT FROM AUTHOR]

Decision making has evolved throughout the years, nowadays harnessing massive amounts and types of data through the unprecedented capabilities of data science, analytics, machine learning, and artificial intelligence. This has potentially led to higher quality and more informed decisions based on the collaborative rationality between humans and machines, no longer bounded by the cognitive capacity and limited rationality of each on their own. However, the multiplicity of modes of collaboration and interaction between humans and machines has also increased the complexity of decision making, consequentially complicating ex-ante and ex-post decision evaluation. Nevertheless, evaluation remains crucial to enable human and machine learning, rationalization, and sensemaking. This paper addresses the need for more research on why and how to evaluate collaborative rationality-based decisions, setting the stage for future studies in developing holistic evaluation solutions. By analyzing four relevant streams of literature: 1) classical decision theory and organizational management, 2) cognitive and neuroscience, 3) AI and ML, and 4) data-driven decision making, we highlight the limitations of current literature in considering a holistic evaluation perspective. Finally, we elaborate the theoretical underpinnings from the knowledge base on how humans and machines evaluate decisions, and the considerations for evaluating collaborative rationality-based decisions. [ABSTRACT FROM AUTHOR]

The primary objective of the paper is to present the essence of the new paradigm in the management science, which is neuroentrepreneurship. This objective determined the paper's layout. In the first part, the theories of entrepreneurship are presented, including the context of research on entrepreneurship. In the second part, the techniques of cognitive neuroscience are described, which are used to determine the impact of thought processes on the recognition and use of a business opportunity by an entrepreneur. This part also analyses the new paradigm of management sciences, i.e. neuroentrepreneurship. In this respect, the most important achievements of science in diagnosing the impact of neuronal impulses on the entrepreneurial process are presented. [ABSTRACT FROM AUTHOR]

This paper delves into the concept of archetypes, universal patterns of behavior and cognition, and proposes a novel tripartite model distinguishing between structural, regulatory, and representational archetypes. Drawing on insights from code biology, neuroscience, genetics, and epigenetics, the model provides a nuanced framework for understanding archetypes and their role in shaping cognition and behavior. The paper also explores the interplay between these elements to express representational archetypes. Furthermore, it addresses the informational capacity of the genome and its influence on post-natal development and the psyche. The paper concludes by discussing the future trajectory of psychology, emphasizing the need for an integrative approach that combines our understanding of social constructs with insights into our inherent organizational propensities or archetypes. This exploration holds the potential to advance our understanding of the human condition. [ABSTRACT FROM AUTHOR]

The neuron model as a computational unit not only determines the performance of widely used deep neural networks and emerging quantum neural networks, but in turn facilitates research on biological neurons. Current three generations of models, i.e., McCulloch–Pitts, perceptron, and spiking, are mainly based on action potentials at the overall structural level, however, since chemical transmission plays a more significant role in the activity of biological neurons, potential-based neuron models do not provide a chemical explanation for biological plausibility and neglect the possible quantum behaviors. In this paper, we present a Quantum model of Biological Neurons (QBN) to characterize information communication on three key components of dendrite, soma, and synapse with a quantum circuit. The input into the dendrite is taken as qubits whose amplitudes indicate the stimulus intensity and whose phases imply external geometry information such as object surface curvatures. The soma is built as a variational quantum circuit, where an ion channel is a quantum wire that tunes the amplitude and phase of its corresponding qubit through universal gates with parameters, and the qubit entanglement within the receptive field is achieved by controlled gates between wires. The synapses are seen as the amplitude regulator to learn activation thresholds. The output adopts the measurement expectations for a single qubit or state vectors in the Hilbert space as the cognitive feature. On the neural network constructed with the QBN as a unit, we complete the MNIST classification experiment and the results show the effectiveness of the QBN. [ABSTRACT FROM AUTHOR]

Important questions have arisen about the capacity of traditional questioned document methodology to differentiate between genuine and forged exemplars of an author's handwriting. This paper does not address that dispute. The paper describes the first part of a research project investigating whether imitators (forgers) can reliably differentiate between genuine samples and forgeries. This paper takes a different, cognitive neuroscience approach and investigates the overlooked topic of the mental processing of forgeries by forgers. The paper tried to examine the neural mechanisms of imitators' (forgers') attentive processing of forged and genuine exemplars. The data in this initial phase of the study showed imitators experienced more difficulty evaluating their own forgeries perhaps because the forgeries included both the features they had consciously copied and some of their own handwriting characteristics that they could not completely suppress. A subsequent phase of the study will use self-reporting and eye movement tracking studies, in this phase we shall attempt to identify the specific types of features the imitators relied on in correctly classifying the exhibit as a forgery. We shall then enlist the services of experienced questioned document examiners endeavor to determine whether those characteristics appear in genuine exemplars of the forgers' handwriting. The identification of those categories of features may hold the potential for improving both the detection of forgeries and the identification of the forger. [ABSTRACT FROM AUTHOR]

Highlights • Paper focusses on statistical analysis of ERPs in cognitive Neuroscience. • Current practices do not account for stimulus as random effect. • Simulations show high Type I error rates of such analyses. • Alternative methods are proposed, their validity is demonstrated. • Failure to change current practices fosters the replication crisis. Abstract Background Event-related potentials (ERPs) are increasingly used in cognitive science. With their high temporal resolution, they offer a unique window into cognitive processes and their time course. In this paper, we focus on ERP experiments whose designs involve selecting participants and stimuli amongst many. Recently, Westfall et al. (2017) highlighted the drastic consequences of not considering stimuli as a random variable in fMRI studies with such designs. Most ERP studies in cognitive psychology suffer from the same drawback. New method We advocate the use of the Quasi-F or Mixed-effects models instead of the classical ANOVA/by-participant F1 statistic to analyze ERP datasets in which the dependent variable is reduced to one measure per trial (e.g., mean amplitude). We combine Quasi-F statistic and cluster mass tests to analyze datasets with multiple measures per trial. Doing so allows us to treat stimulus as a random variable while correcting for multiple comparisons. Results Simulations show that the use of Quasi-F statistics with cluster mass tests allows maintaining the family wise error rates close to the nominal alpha level of 0.05. Comparison with existing methods Simulations reveal that the classical ANOVA/F1 approach has an alarming FWER, demonstrating the superiority of models that treat both participant and stimulus as random variables, like the Quasi-F approach. Conclusions Our simulations question the validity of studies in which stimulus is not treated as a random variable. Failure to change the current standards feeds the replicability crisis. [ABSTRACT FROM AUTHOR]

Highlights • We validate a Virtual Apartment-based Stroop task. • The typical Stroop effect pattern was found in the 3-dimensional virtual Stroop. • The Virtual Apartment Stroop is an attentional processing measure. • The Virtual Stroop provides unique data not tapped by other Stroop modalities. Abstract Background Measurement of supervisory attentional control is typically performed by placing task-relevant information in conflict with task-irrelevant information. A number of paper-and-pencil versions of the Stroop task have been developed to assess executive functioning and inhibitory control through the presentation of blocks of multiple Stroop stimuli on a card. While multi-item paper-and-pencil Stroop tasks are often used, there are instances when a single-item presentation of Stroop stimuli may be preferable. For example, there may be situations when neuropsychologists are interested in precise analysis of reaction times for individual stimuli. A limitation of these modalities is that they may not reflect the cognitive load found in everyday activities that are replete with distractors. New method A potential answer to this issue is to embed Stroop stimuli into a virtual environment that includes distractors. Comparison with existing method We compared the performance of 91 healthy undergraduates on a virtual apartment-based Stroop with traditional (multi-item) and computerized (single item) modalities. Results Results revealed that the classic "Stroop pattern" found in traditional modalities was observed in the Virtual Apartment Stroop task. Furthermore, participants performed more poorly on the Virtual Apartment Stroop task when distractors were present. Conclusions These results suggest the potential of the Virtual Apartment Stroop task to distinguish between prepotent response inhibition and resistance to distractor inhibition in young adults. [ABSTRACT FROM AUTHOR]

This paper addresses the question of managing the existential risk potential of general Artificial Intelligence (AI), as well as the more near-term yet hazardous and disruptive implications of specialised AI, from the perspective of a particular research project that could make a significant contribution to the development of Artificial Intelligence (AI): the Human Brain Project (HBP), a ten-year Future and Emerging Technologies Flagship of the European Commission. The HBP aims to create a digital research infrastructure for brain science, cognitive neuroscience, and brain-inspired computing. This paper builds on work undertaken in the HBP’s Ethics and Society subproject (SP12). Collaborators from two activities in SP12, Foresight and Researcher Awareness on the one hand, and Ethics Management on the other, use the case of machine intelligence to illustrate key aspects of the dynamic processes through which questions of ethics and society, including existential risks, are approached in the organisational context of the HBP. The overall aim of the paper is to provide practice-based evidence, enriched by self-reflexive assessment of the approach used and its limitations, for guiding policy makers and communities who are, and will be, engaging with such questions. [ABSTRACT FROM AUTHOR]

This paper aims to integrate some key constructs in the cognitive neuroscience of cognitive control and executive function by formalising the notion of cognitive (or mental) effort in terms of active inference. To do so, we call upon a task used in neuropsychology to assess impulse inhibition—a Stroop task. In this task, participants must suppress the impulse to read a colour word and instead report the colour of the text of the word. The Stroop task is characteristically effortful, and we unpack a theory of mental effort in which, to perform this task accurately, participants must overcome prior beliefs about how they would normally act. However, our interest here is not in overt action, but in covert (mental) action. Mental actions change our beliefs but have no (direct) effect on the outside world—much like deploying covert attention. This account of effort as mental action lets us generate multimodal (choice, reaction time, and electrophysiological) data of the sort we might expect from a human participant engaging in this task. We analyse how parameters determining cognitive effort influence simulated responses and demonstrate that—when provided only with performance data—these parameters can be recovered, provided they are within a certain range. [Display omitted] • This paper offers a formalisation of 'cognitive effort' under the active inference framework. • Cognitive effort is formulated as a deviation from prior beliefs about mental (covert) action—i.e., effort is exerted to overcome a mental habit. • A computational model of the Stroop task—a characteristically effortful task—is developed to illustrate this notion of effort. • We demonstrate that it is possible to recover combinations of effort-related model parameters from simulated data. [ABSTRACT FROM AUTHOR]

Non-Invasive Brain Stimulation (NIBS) techniques and in particular, repetitive Transcranial Magnetic Stimulation (rTMS), are developing beyond mere clinical application. Although originally purposed for the treatment of resistant neuropsychiatric disorders, NIBS is also contributing to a deeper understanding of psychiatric disorders. rTMS is also changing the model of the disorder itself, from "mental" to one of neural connectivity. TMS allows the assessment of brain circuit excitability and eventually, of plastic changes affecting these circuits. While a clinical translational approach is, at the present time, the most adequate to meet the dimensional-circuit base model of the disorder, it refines the standard categorical classification of psychiatric disorders. The discovery of the fundamental importance of the balance between neuroplasticity and inflammation is also now explored through neuro-modulation findings consistently with the evidence of anti-inflammatory actions of the magnetic pulses. rTMS may activate, inhibit, or otherwise interfere with the activity of neuronal cortical networks, depending on stimulus frequency and intensity of brain-induced electric field. Of particular interest, yet still unclear, is how the relatively unspecific nature of TMS stimulation may lead to specific neuronal reorganization, as well as a definition of the TMS-triggered reorganization of functional brain modules, raising attention on the importance of the active participation of the patient to the treatment.. Configuration and state of consciousness of the subject have made subjective experience under treatment regain importance in the neuro-scientific Psychiatry based on the requirement of United States National Institute of Health (NIH) and the substantial importance of the consciousness state in the efficacy of the TMS treatment. By focusing on the subjective experience, a renaissance of the phenomenology offers Psychiatry an opportunity to become proficient and to distinguish itself from other disciplines. For all these reasons, TMS should be included in the cluster of the sub-specialties as a new "Super-Specialty" and an appropriate training course has to be inaugurated. Psychiatrists are nowadays multi-specialists, moving from a specialty to another, vs super-specialist. The cultivation of a properly trained cohort of TMS psychiatrists will better meet the challenges of treatment-resistant psychiatric conditions (disorders of connectivity), through appropriate and ethical practice, meanwhile facilitating an informed development and integration of additional emerging neuro-modulation techniques. The aim of this consensus paper is to underline the interdisciplinary nature of NIBS, that also encompasses the subjective experience and to point out the necessity of a neuroscience-applied approach to NIBS in the context of the European College of Neuro-psychopharmacology (ECNP). [ABSTRACT FROM AUTHOR]

In this paper, joint decision making processes are studied and the role of cognitive metaphors as mental models in them. A second-order self-modeling network model is introduced based on mechanisms known from cognitive and social neuroscience and cognitive metaphor and mental model literature. The cognitive metaphors were modeled as specific forms of mental models providing a form of modulation within the joint decision making process. The model addresses not only the use of these mental models in the decision making, but also their hebbian learning and the control over the learning. The obtained self-modeling network model was applied to two types of metaphors that affect joint decision making in different manners: a cooperative metaphor and a competitive metaphor. By a number of scenarios it was shown how the obtained self-modeling network model can be used to simulate and analyze joint decision processes and how they are influenced by such cognitive metaphors. [ABSTRACT FROM AUTHOR]

This paper draws on a practice-based interdisciplinary research project to examine ‘kinaesthetic empathy’ as an intersubjective phenomenon within clinical practice. Research participants included both experienced movers (a group formed by qualified dance movement psychotherapists and dance artists) and non-experienced movers (a group comprising multidisciplinary clinicians within a National Health Service mental health unit). The investigative work was grounded in embodied interdisciplinary research and was informed by dance movement psychotherapy (DMP), phenomenology and cognitive neuroscience. Research outcomes included: (i) A measure of motor cortex involvement in movement processing; (ii) phenomenological analysis of participants’ accounts and (iii) embodied performance work. This paper straddles art, science and clinical practice boundaries and contributes to discourses of embodied empathy and intersubjectivity within clinical contexts. [ABSTRACT FROM AUTHOR]

The detection of event-related potentials (ERPs) in the electroencephalogram (EEG) signal is a fundamental component in non-invasive brain-computer interface (BCI) research, and in modern cognitive neuroscience studies. Whereas the grand average response across trials provides an estimation of essential characteristics of a brain-evoked response, an estimation of the differences between trials for a particular type of stimulus can provide key insight about the brain dynamics and possible origins of the brain response. The research in ERP single-trial detection has been mainly driven by applications in biomedical engineering, with an interest from machine learning and signal processing groups that test novel methods on noisy signals. Efficient single-trial detection techniques require processing steps that include temporal filtering, spatial filtering, and classification. In this paper, we review the current state-of-the-art methods for single-trial detection of event-related potentials with applications in BCI. Efficient single-trial detection techniques should embed simple yet efficient functions requiring as few hyper-parameters as possible. The focus of this paper is on methods that do not include a large number of hyper-parameters and can be easily implemented with datasets containing a limited number of trials. A benchmark of different classification methods is proposed on a database recorded from sixteen healthy subjects during a rapid serial visual presentation task. The results support the conclusion that single-trial detection can be achieved with an area under the ROC curve superior to 0.9 with less than ten sensors and 20 trials corresponding to the presentation of a target. Whereas the number of sensors is not a key element for efficient single-trial detection, the number of trials must be carefully chosen for creating a robust classifier. [ABSTRACT FROM AUTHOR]

Cognitive neuroscience techniques have become a great complement to traditional methods in marketing research. However, many difficulties have arisen, such as the collection of reliable data, processing and proper analysis. The paper presents good practices of collecting neurophysiological data for the evaluation of media messages in social campaigns. For this purpose, the most common difficulties during signal recording for devices such as EEG, GSR, HR, Eye Tracker were identified. The problems that arose were assigned to three main groups that could be affected, i.e.the examing person, the examing enviroment and the examing equipment. In the final stage, solutions were proposed to reduce or eliminate them. [ABSTRACT FROM AUTHOR]

Multimodal sentiment analysis is one of the most attractive interdisciplinary research topics in artificial intelligence (AI). Different from other classification issues, multimodal sentiment analysis of human is a much finer classification problem. However, most current work accept all multimodalities as the input together and then output final results at one time after fusion and decision processes. Rare models try to divide their models into more than one fusion modules with different fusion strategies for better adaption of different tasks. Additionally, most recent multimodal sentiment analysis methods pay great focuses on binary classification, but the accuracy of multi-classification still remains difficult to improve. Inspired by the emotional processing procedure in cognitive science, both binary and multi-classification abilities are improved in our method by dividing the complicated problem into smaller issues which are easier to be handled. In this paper, we propose a Hierarchal Attention-BiLSTM (Bidirectional Long-Short Term Memory) model based on Cognitive Brain limbic system (HALCB). HALCB splits the multimodal sentiment analysis into two modules responsible for two tasks, the binary classification and the multi-classification. The former module divides the input items into two categories by recognizing their polarity and then sends them to the latter module separately. In this module, Hash algorithm is utilized to improve the retrieve accuracy and speed. Correspondingly, the latter module contains a positive sub-net dedicated for positive inputs and a negative sub-nets dedicated for negative inputs. Each of these binary module and two sub-nets in multi-classification module possesses different fusion strategy and decision layer for matching its respective function. We also add a random forest at the final link to collect outputs from all modules and fuse them at the decision-level at last. Experiments are conducted on three datasets and compare the results with baselines on both binary classification and multi-classification tasks. Our experimental results surpass the state-of-the-art multimodal sentiment analysis methods on both binary and multi-classification by a big margin. [ABSTRACT FROM AUTHOR]

• The present paper reviews research relevant to a computational psychiatry of juvenile OCD and relates it to findings in adult OCD. • We show that juvenile OCD is mainly characterised by the disruption of complex reasoning systems. • A new neurocomputational framework brings together reviewed findings and illustrates how observed alterations may arise with development. • We make concrete predictions for future studies and highlight areas that need further research. Obsessive-Compulsive Disorder (OCD) most often emerges during adolescence, but we know little about the aberrant neural and cognitive developmental mechanisms that underlie its emergence during this critical developmental period. To move towards a computational psychiatry of juvenile OCD, we review studies on the computational, neuropsychological and neural alterations in juvenile OCD and link these findings to the adult OCD and cognitive neuroscience literature. We find consistent difficulties in tasks entailing complex decision making and set shifting, but limited evidence in other areas that are altered in adult OCD, such as habit and confidence formation. Based on these findings, we establish a neurocomputational framework that illustrates how cognition can go awry and lead to symptoms of juvenile OCD. We link these possible aberrant neural processes to neuroimaging findings in juvenile OCD and show that juvenile OCD is mainly characterised by disruptions of complex reasoning systems. [ABSTRACT FROM AUTHOR]

Bodily self-consciousness has become an important topic in cognitive neuroscience aiming to understand how the brain creates a unified sensation of the self in a body. Specifically, full body illusion (FBI) in which changes in bodily self-consciousness are experimentally introduced by using visual–tactile stimulation has led to improve understanding of these mechanisms. This paper introduces a novel approach to the classic FBI paradigm using a robotic master–slave system which allows us to examine interactions between action and the sense of body ownership in behavioral and MRI experiments. In the proposed approach, the use of the robotic master–slave system enables unique stimulation in which experimental participants can administer tactile cues on their own back using active self-touch. This active self-touch has never been employed in FBI experiments and it allows to test the role of sensorimotor integration and agency (the feeling of control over our actions) in FBI paradigms. The objective of this study is to propose a robotic–haptic platform allowing a new FBI paradigm including the active self-touch in MRI environments. This paper, first, describes the design concept and the performance of the prototype device in the fMRI environment (for 3 T and 7 T MRI scanners). In addition, the prototype device is applied to a classic FBI experiment, and we verify that the use of the prototype device succeeded in inducing the FBI. These results indicate that the proposed approach has a potential to drive advances in our understanding of human body ownership and agency by allowing novel manipulation and paradigms. [ABSTRACT FROM AUTHOR]

Abstract: Phenomenological research indicates that disturbance of the basic sense of self may be a core phenotypic marker of schizophrenia spectrum disorders. Basic self-disturbance refers to disruption of the sense of ownership of experience and agency of action and is associated with a variety of anomalous subjective experiences. Little is known about the neurocognitive underpinnings of basic self-disturbance. In these two theoretical papers (of which this is Part 2), we review some recent phenomenological and neurocognitive research and point to a convergence of these approaches around the concept of self-disturbance. Specifically, we propose that subjective anomalies associated with basic self-disturbance may be associated with: 1. source monitoring deficits, which may contribute particularly to disturbances of “ownership” and “mineness” (the phenomenological notion of presence or self-affection) and 2. aberrant salience, and associated disturbances of memory, prediction and attention processes, which may contribute to hyper-reflexivity, disturbed “grip” or “hold” on perceptual and conceptual fields, and disturbances of intuitive social understanding (“common sense”). In this paper (Part 2) we focus on aberrant salience. Part 1 (this issue) addressed source monitoring deficits. Empirical studies are required in a variety of populations in order to test these proposed associations between phenomenological and neurocognitive aspects of self-disturbance in schizophrenia. An integration of findings across the phenomenological and neurocognitive “levels” would represent a significant advance in the understanding of schizophrenia and possibly enhance early identification and intervention strategies. [Copyright &y& Elsevier]

Abstract: Phenomenological research indicates that disturbance of the basic sense of self may be a core phenotypic marker of schizophrenia spectrum disorders. Basic self-disturbance refers to disruption of the sense of ownership of experience and agency of action and is associated with a variety of anomalous subjective experiences. Little is known about the neurocognitive underpinnings of basic self-disturbance. In these two theoretical papers (of which this is Part 1), we review some recent phenomenological and neurocognitive research and point to a convergence of these approaches around the concept of self-disturbance. Specifically, we propose that subjective anomalies associated with basic self-disturbance may be associated with: 1. source monitoring deficits, which may contribute particularly to disturbances of “ownership” and “mineness” (the phenomenological notion of presence or self-affection) and 2. aberrant salience, and associated disturbances of memory, prediction and attention processes, which may contribute to hyper-reflexivity, disturbed “grip” or “hold” on the perceptual and conceptual field, and disturbances of intuitive social understanding (“common sense”). In this paper (Part 1) we focus on source monitoring deficits. Part 2 (this issue) addresses aberrant salience. Empirical studies are required in a variety of populations in order to test these proposed associations between phenomenological and neurocognitive aspects of self-disturbance in schizophrenia. An integration of findings across the phenomenological and neurocognitive “levels” would represent a significant advance in the understanding of schizophrenia and possibly enhance early identification and intervention strategies. [Copyright &y& Elsevier]

Abstract: Recent progress in cognitive neuroscience suggests that alpha activity may reflect selective cortical inhibition involved in signal amplification, rather than neural idling. Unfortunately, these theoretical advances remain largely ignored in affective neuroscience. To address this limitation the present paper proposes a novel research avenue aimed at using alpha to elucidate cortical inhibitory mechanisms involved in affective processes. The proposal is illustrated by developing inhibitory accounts of affective attention and affective tuning phenomena. The emergent predictions were tested using event-related perturbations from 73 students evaluating affective and nonaffective aspects of five types of emotional images. The results revealed that upper alpha power was increased by affective content in general and aversive stimuli in particular from 350ms at posterior and from 575ms at central sites. The evaluation task interacted with affective content only at a liberal statistical significance level in late posterior alpha. These results are generally in line with the proposed inhibitory accounts of affective attention and tuning, although the evidence is preliminary rather than conclusive. As confirmation of functional origins of alpha in affect remains beyond the scope of a single study, this paper aims to inspire further extrapolation of the inhibitory account of alpha within affective neuroscience. [Copyright &y& Elsevier]

Abstract: The ability to generate and sustain an internal train of thought unrelated to external reality frees an agent from the constraints of only acting on immediate, environmentally triggered events. The current paper proposes that such thought is produced through cooperation between autobiographical information provided by the default mode network and a frontal–parietal control network which helps sustain and buffer internal trains of thought against disruption by the external world. This hypothesis explains at least two features of the literature on internally guided thought. First, access to the top-down control system is a generally accepted prerequisite of conscious experience; this explains why activation of this system and default mode activity is often observed together during periods of internally guided thought. Second, because the top-down attentional control system has a limited capacity, internally and externally driven streams can come into conflict, with the result that perceptual information must be denied attentional amplification if the internal stream is to be maintained. This explains why internal thought is routinely associated with a state of perceptual decoupling, reflected in both measured anticorrelations between the default mode network and sensory areas and the manner in which task unrelated thoughts compromise task performance. This paper offers a hypothesis that should help to constrain and guide interpretations, investigations, and analyses of the neural processes involved in internally driven cognition. This article is part of a Special Issue entitled Special Issue The Cognitive Neuroscience. [Copyright &y& Elsevier]

• Review of motivational and cognitive impairments in depression. • Impairments in key components of motivation contribute to cognitive control deficits in depression. • Proposed framework focuses on the reduced value of control in depression. • Simulations provide predictions about the influence of motivational impairments on cognitive control. Depression is linked to deficits in cognitive control and a host of other cognitive impairments arise as a consequence of these deficits. Despite of their important role in depression, there are no mechanistic models of cognitive control deficits in depression. In this paper we propose how these deficits can emerge from the interaction between motivational and cognitive processes. We review depression-related impairments in key components of motivation along with new cognitive neuroscience models that focus on the role of motivation in the decision-making about cognitive control allocation. Based on this review we propose a unifying framework which connects motivational and cognitive control deficits in depression. This framework is rooted in computational models of cognitive control and offers a mechanistic understanding of cognitive control deficits in depression. [ABSTRACT FROM AUTHOR]

Abstract Inspired by the concept of memory mechanism and predictive coding from the cognitive neuroscience, this paper presents a deep memory and prediction neural network (DMPNet) for video prediction. Correspondingly, memory and error propagation units are designed in DMPNet to capture the previous spatial-temporal information and compute current predictive error which is forwarded to the prediction unit for correcting the subsequent video prediction. Subsequently, prediction unit takes the information stored in memory unit and predictive error of previous frame as input to predict the next frame. We evaluate our method on two public real-world datasets and demonstrate that the proposed DMPNet outperforms some state-of-the-art methods quantitatively and qualitatively. [ABSTRACT FROM AUTHOR]

Abstract Research at the intersection of social neuroscience and cognitive effort is an interesting new area for exploration. There is great potential to broaden our understanding of how social context and cognitive effort processes, currently addressed in disparate literatures, interact with one another. In this paper, we briefly review the literature on cognitive effort, focusing on effort-linked valuation and the gap in the literature regarding cognitive effort in the social domain. Next, we present a study designed to explore valuation processes linked to cognitive effort within the social context of an inequality manipulation. More specifically, we created monetary inequality among the participant (SELF, endowed with $50) and two confederates: one also endowed with $50 (OTHER HIGH) and another with only $5 (OTHER LOW). We then scanned participants using fMRI as they attempted to earn bonus payments for themselves and others through a cognitively effortful feedback-based learning task. Positive feedback produced significantly greater activation than negative feedback in key valuation regions, the ventral striatum (VS) and ventromedial prefrontal cortex (vmPFC), both when participants were performing the task on their own behalf and when earning rewards for others. While reward-related activity in the VS was exaggerated for SELF compared to OTHER HIGH for both positive and negative feedback, activity in the vmPFC did not distinguish between recipients in the group-level results. Furthermore, participants naturally fell into two groups: those most engaged when playing for themselves and those who reported engagement for others. While Self-Engaged participants showed differences between the SELF and both OTHER conditions in the VS and vmPFC, Other-Engaged participants only showed an attenuated response to negative feedback for OTHER HIGH compared to SELF in the VS and no differences between recipient conditions in the vmPFC. Together, this work shows the importance of individual differences and the fragility of advantageous inequality aversion in the face of cognitive effort, highlighting the need to study cognitive effort in the social domain. Highlights • A gap in the literature exists on the value of cognitive effort in the social domain. • We investigated self and vicarious reward responses after an inequality manipulation. • Greater reward responses were found for rewards earned for oneself than for others. • Individual differences in responses for rewards for others emerged. • Prosocial preferences may be fragile in the face of cognitive effort. [ABSTRACT FROM AUTHOR]

Abstract: This paper aimed to review the limited, but growing literature on prospective memory (PM) following closed head injury (CHI). Search of two commonly used databases yielded studies that could be classified as: self- or other-report of PM deficits; behavioral PM measures in adults with CHI, behavioral PM measures in children and adolescents with CHI, and treatment of PM in adults with CHI. The methodology and findings of these studies were critically reviewed and discussed. Because of the small number of studies, meta-analysis was only conducted for studies that used behavioral PM measures in adults to integrate findings. PM deficits were found to be commonly reported by patients with CHI and their significant others and they could be identified using behavioral measures in adults, children and adolescents with CHI. However, more work is needed to clarify the nature and mechanisms of these deficits. Although some promising results have been reported by studies that evaluated PM treatment, most studies lack tight experimental control and used only a small number of participants. The paper concluded with some suggestions for future research. [Copyright &y& Elsevier]

Abstract The task of the daily box office prediction model is to build a dynamic prediction model to rolling forecast daily box office. It is a complex task as the movie box office has a short life cycle, and the static data and dynamic data that affect the trend of box office are heterogeneous. This paper proposes an end-to-end deep learning model for daily box office prediction, called Deep-DBP which consists of temporal component and static characteristics component. The temporal component is the main component which uses LSTM to learn the temporal dependencies between data points. The static characteristics component is an auxiliary component and it integrates static characteristics to improve prediction effect. The Deep-DBP can overcome the problems that the ARIMA and traditional ANN model cannot solve. The structure of input and output proposed in the model can well handle short time series prediction problem. It is a successful case in dealing with multi-source and multi-view data, addition of static characteristics component reduces the prediction error by 7%. The prediction error of Deep-DBP is 30.1%, which is better than that of the previous model. The experiment proved that the more training data collected, the better the prediction effect. [ABSTRACT FROM AUTHOR]

An intrinsic difficulty in studying cognitive processes is that they are unobservable states that exist in between observable responses to the sensory environment. Cognitive states must be inferred from indirect behavioral measures. Neuroscience potentially provides the tools necessary to measure cognitive processes directly, but it is challenged on two fronts. First, neuroscientific measures often lack the spatiotemporal resolution to identify the neural computations that underlie a cognitive process. Second, the activity of a single neuron, which is the fundamental building block of neural computation, is too noisy to provide accurate measurements of a cognitive process. In this paper, I examine recent developments in neurophysiological recording and analysis methods that provide a potential solution to these problems. Highlights Recent advances in analytic methods and high-channel count recordings have raised the possibility of reading out cognitive processes directly from the brain, as opposed to inferring cognitive processes indirectly from behavior. Decoding neural activity has been used to understand decision making by using place cell activity in the hippocampus or value-selective neural responses in orbitofrontal cortex. Decoding could have broad applications for measuring other cognitive processes directly from neural activity, such as attention, working memory and reasoning. [ABSTRACT FROM AUTHOR]

Abstract Episodic memory is a critical component of any computational representation of cognition. While declarative and procedural memory have been extensively studied by the cognitive modeling community, episodic memory has only recently been considered as an important component of cognitive architecture (CA) development. Human neurological evidence supports the concept that memory is stored in the mind in different forms and locations, with episodic memory being critical for learning temporal sequences of events and associating context to learned information. Recent neurological evidence supports the idea that episodic memory is distinct from semantic memory and procedural memory. This paper reviews the current research on episodic memory in neurology and CAs and argues for its inclusion in the Standard Model of Mind. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a fast and reliable approach for the localization of an iris image using neural networks by comparing some of the existing localization methods. Normally any biometric recognition system is used for identification of an individual and verified with the available database to check whether the person is authorized or not. Nowadays, iris recognition systems are considered the best authentication method compared to other biometric systems due to the unique characteristic feature of the human iris. In iris recognition systems, the important and difficult step is to locate or segment the iris from the input eye image which is responsible for success rate of the iris recognition. Hence this paper suggests a efficient approach to locate the iris using neural networks in order to improve the efficiency of recognition systems. Further, the paper compares the existing iris localization methods such as Daugman algorithm, Hough transform and Canny edge detector algorithm. The best localization algorithm is chosen for training a network and simulating for efficient and fast segmentation of irises with good success rate. [Copyright &y& Elsevier]

Abstract: In this paper it is aimed to classify sleep apnea syndrome (SAS) by using discrete wavelet transforms (DWT) and an artificial neural network (ANN). The abdominal and thoracic respiration signals are separated into spectral components by using multi-resolution DWT. Then the energy of these spectral components are applied to the inputs of the ANN. The neural network was configured to give three outputs to classify the SAS situation of the subject. The apnea can be mainly classified into three types: obstructive sleep apnea (OSA), central sleep apnea (CSA) and mixed sleep apnea (MSA). During OSA, the airway is blocked while respiratory efforts continue. During CSA the airway is open, however, there are no respiratory efforts. In this paper we aim to classify sleep apnea in one of three basic types: obstructive, central and mixed. A significant result was obtained. [Copyright &y& Elsevier]

This paper presents selected findings of doctoral research exploring how design could be used to influence user behaviour towards more sustainable practices. It describes three strategies for changing user behaviour through design drawn from literature and outlines the methodology and findings of a case study exploring the application of these strategies in sustainable design. Drawing on the perceptions of design professionals interviewed in response to one of the concepts generated, the paper goes on to explore the perceived acceptability and effectiveness of these strategies. It concludes by commenting on the wider implications of these perceptions for ongoing research. [Copyright &y& Elsevier]

The paper presents a pilot interdisciplinary research study carried out as a step towards understanding the neurological basis of design thinking. The study involved functional magnetic resonance imaging (fMRI) of volunteers while performing design and problem-solving tasks. The findings suggest that design and problem solving involve distinct cognitive functions associated with distinct brain networks. The paper introduces the methodology, presents the findings, and discusses the potential role of brain imaging in design research. [Copyright &y& Elsevier]

Abstract: Booming sound is one of the important sounds in a passenger car. The aim of the paper is to develop the objective evaluation method of interior booming sound. The development method is based on the sound metrics and ANN (artificial neural network). The developed method is called the booming index. Previous work maintained that booming sound quality is related to loudness and sharpness – the sound metrics used in psychoacoustics – and that the booming index is developed by using the loudness and sharpness for a signal within whole frequency between 20Hz and 20kHz. In the present paper, the booming sound quality was found to be effectively related to the loudness at frequencies below 200Hz; thus the booming index is updated by using the loudness of the signal filtered by the low pass filter at frequency under 200Hz. The relationship between the booming index and sound metric is identified by an ANN. The updated booming index has been successfully applied to the objective evaluation of the booming sound quality of mass-produced passenger cars. [Copyright &y& Elsevier]

Abstract: This paper outlines an algorithm for incrementally growing Artificial Neural Networks. The algorithm allows the network to expand by adding new sub-networks or modules to an existing structure; the modules are trained using an Evolutionary Algorithm. Only the latest module added to the network is trained, the previous structure remains fixed. The algorithm allows information from different data domains to be integrated into the network and because the search space in each iteration is small, large and complex networks with a modular structure can emerge naturally. The paper describes an application of the algorithm to a legged robot and discusses its biological inspiration. [Copyright &y& Elsevier]

Abstract: In this paper, we present a general class of neural networks with discontinuous neuron activations and varying coefficients, where the neuron activation function is a discontinuous monotone increasing and bounded function. By using the fixed point theorem in differential inclusion theory and constructing suitable Lyapunov functions, a condition is derived which ensures the existence and global exponential stability of a unique periodic solution for the neural network. Furthermore, under certain conditions global convergence in finite time of the state is investigated. The obtained results show that Forti’s conjecture for neural networks without delays is true. Finally, two numerical examples are given to demonstrate the effectiveness of the results obtained in this paper. [Copyright &y& Elsevier]

Abstract: In this paper, global exponential stability is studied for a class of high-order bidirectional associative memory (BAM) neural networks with time-varying delays. An approach combining the Lyapunov functional with the Linear Matrix Inequality (LMI) is taken to study the problems. Several sufficient conditions are presented for ensuring the system to be globally exponentially stable. Three typical examples are presented to show the application of the criteria obtained in this paper. [Copyright &y& Elsevier]

Abstract: In this paper, a class of fuzzy cellular neural networks (FCNNs) with distributed delays and variable coefficients is discussed. By applying the matrix theory and the inequality analysis technique, some sufficient conditions on the existence, global exponential stability and weak global exponential stability of periodic solutions are established. Particularly, differently from some previous works, in this paper we do not require the activation functions in the system are bounded and . [Copyright &y& Elsevier]

This paper presents a theory of how general-purpose learning-based intelligence is achieved in the mammal brain, and how we can replicate it. It reviews four generations of ever more powerful general-purpose learning designs in Adaptive, Approximate Dynamic Programming (ADP), which includes reinforcement learning as a special case. It reviews empirical results which fit the theory, and suggests important new directions for research, within the scope of NSF’s recent initiative on Cognitive Optimization and Prediction. The appendices suggest possible connections to the realms of human subjective experience, comparative cognitive neuroscience, and new challenges in electric power. The major challenge before us today in mathematical neural networks is to replicate the “mouse level”, but the paper does contain a few thoughts about building, understanding and nourishing levels of general intelligence beyond the mouse. [Copyright &y& Elsevier]

Abstract: This paper studies the shunting cellular neural networks with variable coefficients and time-varying delays. By applying Dini derivative and introducing many real parameters, a series of new and useful criteria on the existence and local exponential stability for general shunting inhibitory cellular neural networks have been derived. Those results obtained in this paper extend and generalize the corresponding results existing in the previous literature. [Copyright &y& Elsevier]

Abstract: In this paper, the design problem of state estimator for a class of neural networks of neutral-type with interval time-varying delays is studied. The interval time-varying delay does not have constraint that its derivative is less than 1. The constraint is widely used to deal with time-varying delays in many papers. A delay-dependent linear matrix inequality (LMI) criterion for existence of the estimator is proposed by using Lyapunov method. The criterion can be easily solved by various convex optimization algorithms. A numerical example is given to show the effectiveness of proposed method. [Copyright &y& Elsevier]

Abstract: In this paper, the global stability and almost periodicity are investigated for Hopfield neural networks with neutral time-varying delays. Some sufficient conditions are obtained for the existence and globally exponential stability of almost periodic solution by employing fixed point theorem and differential inequality techniques. The results of this paper are new and they complement previously known results. [Copyright &y& Elsevier]

Abstract: This paper presents a semi-automatic method using an unsupervised neural network to analyze geomorphometric features as landform elements. The Shuttle Radar Topography Mission (SRTM) provided detailed digital elevation models (DEMs) for all land masses between 60°N and 57°S. Exploiting these data for recognition and extraction of geomorphometric features is a challenging task. Results obtained with two methods, Wood''s morphometric parameterization and the Self Organizing Map (SOM), are presented in this paper. Four morphometric parameters (slope, minimum curvature, maximum curvature and cross-sectional curvature) were derived by fitting a bivariate quadratic surface with a window size of 5 by 5 to the SRTM DEM. These parameters were then used as input to the two methods. Wood''s morphometric parameterization provides point-based features (peak, pit and pass), line-based features (channel and ridge) and area-based features (planar). Since point-based features are defined as having a very small slope when their neighbors are considered, two tolerance values (slope tolerance and curvature tolerance) are introduced. Selection of suitable values for the tolerance parameters is crucial for obtaining useful results. The SOM as an unsupervised neural network algorithm is employed for the classification of the same morphometric parameters into ten classes characterized by morphometric position (crest, channel, ridge and plan area) subdivided by slope ranges. These terrain features are generic landform element and can be used to improve mapping and modeling of soils, vegetation, and land use, as well as ecological, hydrological and geomorphological features. These landform elements are the smallest homogeneous divisions of the land surface at the given resolution. The result showed that the SOM is an efficient scalable tool for analyzing geomorphometric features as meaningful landform elements, and uses the full potential of morphometric characteristics. [Copyright &y& Elsevier]

Abstract: This paper proposes the blind separation of complex signals using a novel neural network architecture based on an adaptive nonlinear bi-dimensional activation function (AF); the separation is obtained maximizing the output joint entropy. Avoiding the restriction due to the Louiville''s theorem, the AF is composed of a couple of bi-dimensional spline functions, one for the real and one for the imaginary part of the signal. The surface of this function is flexible and it is adaptively modified according to the learning process performed by a gradient-based technique. The use of the bi-dimensional spline defines a new class of flexible AFs which are bounded and locally analytic. This paper aims to demonstrate that this novel bi-dimensional complex AF outperforms the separation in every environment in which the real and imaginary parts of the complex signal are not decorrelated. This situation is realistic in a large number of cases. [Copyright &y& Elsevier]

Abstract: An improved EHM-based hysteresis model is proposed in this paper. In this scheme, neural networks are employed to implement the mapping between the input and output of hysteresis. The so-called elementary hysteresis model (EHM) is introduced to construct the transformation to transform the multi-valued mapping of hysteresis into a one-to-one mapping. In order to construct the EHM, parabolas are chosen as monotone curves in the EHM-based NN model. In this paper, a new method for determining the coefficients of the parabolas is proposed. The coefficients of the parabolas are different from each other in the improved EHM-based NN model. Finally, both the numerical and experimental examples of using the proposed modeling scheme are presented. [Copyright &y& Elsevier]

Abstract: In this paper, the issue of th moment exponential stability of stochastic recurrent neural network with time-varying delays is investigated in detail. Employing the method of variation parameter and inequality techniques, several sufficient conditions ensuring th moment exponential stability are obtained. Compared with the previous methods, our method does not resort to any Lyapunov function, and the results derived in this paper improve and generalize some earlier works reported in the literature. Two numerical examples are given to illustrate the effectiveness of our results. [Copyright &y& Elsevier]

Abstract: In 2002, we published a paper [Brock, J., Brown, C., Boucher, J., Rippon, G., 2002. The temporal binding deficit hypothesis of autism. Development and Psychopathology 142, 209–224] highlighting the parallels between the psychological model of ‘central coherence’ in information processing [Frith, U., 1989. Autism: Explaining the Enigma. Blackwell, Oxford] and the neuroscience model of neural integration or ‘temporal binding’. We proposed that autism is associated with abnormalities of information integration that is caused by a reduction in the connectivity between specialised local neural networks in the brain and possible overconnectivity within the isolated individual neural assemblies. The current paper updates this model, providing a summary of theoretical and empirical advances in research implicating disordered connectivity in autism. This is in the context of changes in the approach to the core psychological deficits in autism, of greater emphasis on ‘interactive specialisation’ and the resultant stress on early and/or low-level deficits and their cascading effects on the developing brain [Johnson, M.H., Halit, H., Grice, S.J., Karmiloff-Smith, A., 2002. Neuroimaging of typical and atypical development: a perspective from multiple levels of analysis. Development and Psychopathology 14, 521–536].We also highlight recent developments in the measurement and modelling of connectivity, particularly in the emerging ability to track the temporal dynamics of the brain using electroencephalography (EEG) and magnetoencephalography (MEG) and to investigate the signal characteristics of this activity. This advance could be particularly pertinent in testing an emerging model of effective connectivity based on the balance between excitatory and inhibitory cortical activity [Rubenstein, J.L., Merzenich M.M., 2003. Model of autism: increased ratio of excitation/inhibition in key neural systems. Genes, Brain and Behavior 2, 255–267; Brown, C., Gruber, T., Rippon, G., Brock, J., Boucher, J., 2005. Gamma abnormalities during perception of illusory figures in autism. Cortex 41, 364–376]. Finally, we note that the consequence of this convergence of research developments not only enables a greater understanding of autism but also has implications for prevention and remediation. [Copyright &y& Elsevier]

Abstract: In this paper, cellular neural networks with complex deviating arguments are considered. Sufficient conditions for the existence of the periodic solutions are established by using the coincidence degree theorem. The results of this paper are new and complement previously known results. [Copyright &y& Elsevier]

Abstract: In this paper the shunting inhibitory cellular neural networks (SICNNs) with time-varying delays are considered. Without assuming the global Lipschitz and bounded conditions of activation functions, sufficient conditions for the existence of the almost periodic solutions are established by using a fixed point theorem. The results of this paper are new and complement previously known results. [Copyright &y& Elsevier]