Work In Neuroscience Research Articles

Radical Predictive Processing

AbstractRecent work in computational and cognitive neuroscience depicts the brain as an ever‐active prediction machine: an inner engine continuously striving to anticipate the incoming sensory barrage. I briefly introduce this class of models before contrasting two ways of understanding the implied vision of mind. One way (Conservative Predictive Processing) depicts the predictive mind as an insulated inner arena populated by representations so rich and reconstructive as to enable the organism to ‘throw away the world’. The other (Radical Predictive Processing) stresses the use of fast and frugal, action‐involving solutions of the kind highlighted by much work in robotics and embodied cognition. But it goes further, by showing how predictive schemes can combine frugal and more knowledge‐intensive strategies, switching between them fluently and continuously as task and context dictate. I end by exploring some parallels with work in enactivism, and by noting a certain ambivalence concerning internal representations and their role in the predictive mind.

Subjective Probability as Sampling Propensity

Subjective probability plays an increasingly important role in many fields concerned with human cognition and behavior. Yet there have been significant criticisms of the idea that probabilities could actually be represented in the mind. This paper presents and elaborates a view of subjective probability as a kind of sampling propensity associated with internally represented generative models. The resulting view answers to some of the most well known criticisms of subjective probability, and is also supported by empirical work in neuroscience and behavioral psychology. The repercussions of the view for how we conceive of many ordinary instances of subjective probability, and how it relates to more traditional conceptions of subjective probability, are discussed in some detail.

How Does Neuroscience Work in Advertising?

![Figure][1] It doesn't quite yet fall into the category of an “empirical generalization,” but it's absolutely certain that neuroscience will be an integral part of the future of marketing research. Not only will it open new doors to academic research, but it also will provide new sets of

Navigating life.

The discoveries of "place cells" in the hippocampus and "grid cells" in the entorhinal cortex are landmark achievements in relating behavior to neural activity, permitting analysis of a powerful system for spatial representation in the brain. The contributions of this work include not only the empirical findings but also the approach this work pioneered of examining neural activity in complex behaviors with real ecological validity in freely moving animals, and of attempting to place the findings in the larger context of how the neural representations of space are used in service of real-world behavior, namely what the Nobel committee described as permitting us to "navigate our way through a complex environment." These discoveries and approaches have had far-ranging impact on and implications for work in human cognitive neuroscience, where we see (1) confirmation in humans that the hippocampus and overlying MTL cortex are critically engaged in supporting a relational representation of space, and that it can be used for flexible spatial navigation and (2) evidence that these regions are also critically involved in aspects of relational memory not limited to space, and in the flexible use of hippocampal memory extending beyond spatial navigation. Recent work, using tasks that emphasize the requirement for the active use of memory in online processing, just as spatial navigation has long placed such a requirement on rodents, suggests that the hippocampus and related MTL cortex can support the navigating of environments even more complex than what is needed in spatial navigation. It allows us to use memory in guiding upcoming actions and choices to act optimally in and on the world, permitting us to navigate life in all its beautiful complexity.

Book Review: “Cognitive neuroscience of language”

The 19th century has brought to linguistics “Broca's problem” (Boeckx, 2009), the question of how knowledge of language is implemented in the brain. Since then neuroscientific work on language was mostly driven by aphasiology, a state of affairs that has changed drastically with the advent of cognitive neuroscience toward the end of the 1970s and 1980s (Gazzaniga, 2015) and the subsequent further technical advancement and establishment of neuroimaging. With the publication of the textbook Cognitive Neuroscience of Language David Kemmerer aims at and succeeds in closing a long-standing gap in the available neurolinguistics literature, taking into account that cognitive neuroscience has come of age and progressed considerably in addressing Broca's problem in the relatively short timespan since its establishment.

Towards an empirically grounded predictive coding account of action understanding.

Recent work in cognitive and systems neuroscience suggests that the brain is a prediction machine ([Clark, 2013][1]), continually attempting to predict the external causes of sensory information. This idea is formulated in the predictive coding framework, a modern theory of brain function ([Friston

Welcome to eNeurologicalSci (eNS)!

Welcome to eNeurologicalSci (eNS)!

Connecting emotions and words: the referential process

This paper outlines the process of verbal communication of emotion as this occurs through the phases of the referential process, including arousal of an emotion schema; detailed and specific descriptions of images and episodes that are exemplars of emotion schemas; and reflection and reorganization, which may include emotion labels and other types of categorical terms. The concepts of emotion schemas and the referential process are defined in the theoretical framework of multiple code theory which includes subsymbolic sensory, visceral and motoric processes, symbolic images and words. Emotion schemas are defined as clusters of representations of events incorporating similar bodily, sensory and motoric processes activated in relation to different people in different contexts. Through the referential process subsymbolic components of a schema that have been activated in a speaker or writer and that may be connected only partially to words may be evoked in a listener or reader. The concept of the emotion schemas is examined in relation to current work in emotion theory and neuroscience. The unique effects of detailed descriptions of episodes in conveying complex aspects of emotional experience are discussed, as recognized by writers, and as demonstrated in empirical research. Computerized measures of the phases of the referential process are presented, focusing particularly on the central measure, the Weighted Referential Activity Dictionary (WRAD) which identifies points of narrative and imagery. The operation of the function words that dominate the WRAD are examined in relation to the structure of narrative expression underlying the verbal representation of emotion schemas.

Rosemary L Farmer, Neuroscience and social work: The missing link

Rosemary L Farmer, <i>Neuroscience and social work: The missing link</i>

Between the bench, the bedside and the office: The need to build bridges between working neuroscientists and ethicists.

This paper presents findings from an empirical study that explored the meaning of ethics in the everyday work of neuroscientists. Observation and interviews were carried out in one neuroscience research group that was involved in bench-to-bedside translational research. We focus here specifically on the scientists’ perceptions of bioethics. Interviewees were often unfamiliar with bioethics as a discipline, particularly the more junior members of the group. Those who were aware of its existence largely viewed it as something distant from them, and as either too abstract, not relevant or an alien imposition on their work. Some interviewees themselves pointed to the need for better ‘bridge building’ between ethical principles and real-world examples drawn from scientific practice, and we argue that this space is where a more empirically grounded ethics may be useful in terms of actually engaging scientists at both the bench and the bedside.

Evolutionary Theory and Neuroscience: An Explanatory Theory for Social Work

Evolutionary theory and neuroscience are recommended as a foundational theory for social work. Advantages of evolutionary theory include explanatory power, suggestion of interventions to beginning social work practitioners, and accommodation of more specific theories of human behavior. The explanatory power of neuroscience is also discussed, along with its evidence base and compatibility with the strengths perspective and destigmatization work of the profession. Connections between evolutionary theory, neuroscience, and social work values are also discussed.

Human creativity, evolutionary algorithms, and predictive representations: The mechanics of thought trials.

Creative thinking is arguably the pinnacle of cerebral functionality. Like no other mental faculty, it has been omnipotent in transforming human civilizations. Probing the neural basis of this most extraordinary capacity, however, has been doggedly frustrated. Despite a flurry of activity in cognitive neuroscience, recent reviews have shown that there is no coherent picture emerging from the neuroimaging work. Based on this, we take a different route and apply two well established paradigms to the problem. First is the evolutionary framework that, despite being part and parcel of creativity research, has no informed experimental work in cognitive neuroscience. Second is the emerging prediction framework that recognizes predictive representations as an integrating principle of all cognition. We show here how the prediction imperative revealingly synthesizes a host of new insights into the way brains process variation-selection thought trials and present a new neural mechanism for the partial sightedness in human creativity. Our ability to run offline simulations of expected future environments and action outcomes can account for some of the characteristic properties of cultural evolutionary algorithms running in brains, such as degrees of sightedness, the formation of scaffolds to jump over unviable intermediate forms, or how fitness criteria are set for a selection process that is necessarily hypothetical. Prospective processing in the brain also sheds light on how human creating and designing - as opposed to biological creativity - can be accompanied by intentions and foresight. This paper raises questions about the nature of creative thought that, as far as we know, have never been asked before.

A social neuroscience perspective on clinical empathy.

Clinical empathy is an important element of quality health care. Empathic communication is associated with improved patient satisfaction, increased adherence to treatment, and fewer malpractice complaints (1). Patients' perceptions of their physicians' empathy are positively related to more favorable health outcomes (2–4). In addition to improving patient outcomes, clinical empathy is associated with increased overall well-being for the physician (5). High levels of practitioner empathy have been associated with decreased burnout, personal distress, depression and anxiety, along with increased life satisfaction and psychological well-being (6,7). Despite increasing appreciation of the value of empathy, medical educators continue to struggle with how best to educate students and residents on empathy maintenance. There have been several promising creative approaches that have shown demonstrable short-term success (8). However, there is a lack of evidence for enduring success, that is, for interventions during medical education that will enable physicians to sustain empathy throughout their careers. A more comprehensive and precise understanding of the subcomponents of empathy and how they are influenced by stress and anxiety is needed in order to design targeted interventions.

A simple biophysically plausible model for long time constants in single neurons.

Recent work in computational neuroscience and cognitive psychology suggests that a set of cells that decay exponentially could be used to support memory for the time at which events took place. Analytically and through simulations on a biophysical model of an individual neuron, we demonstrate that exponentially decaying firing with a range of time constants up to minutes could be implemented using a simple combination of well-known neural mechanisms. In particular, we consider firing supported by calcium-controlled cation current. When the amount of calcium leaving the cell during an interspike interval is larger than the calcium influx during a spike, the overall decay in calcium concentration can be exponential, resulting in exponential decay of the firing rate. The time constant of the decay can be several orders of magnitude larger than the time constant of calcium clearance, and it could be controlled externally via a variety of biologically plausible ways. The ability to flexibly and rapidly control time constants could enable working memory of temporal history to be generalized to other variables in computing spatial and ordinal representations.

Neurofeminism and feminist neurosciences: a critical review of contemporary brain research.

To date, feminist approaches to neurosciences have evaluated the debates surrounding practices of knowledge production within and research results of contemporary brain research. Consequently, neurofeminist scholars have critically examined gendered impacts of neuroscientific research. Feminist neuroscientists also develop research approaches for a more gender-appropriate neuroscientific research on several levels. Based on neurofeminist critique feminist neuroscientists aim to enrich neuroscientific work by offering methodological suggestions for a more differentiated setup of categories and experimental designs, for reflective result presentations and interpretations as well as for the analysis of result validity. Reframing neuro-epistemologies by including plasticity concepts works to uncover social influences on the gendered development of the brain and of behavior. More recently, critical work on contemporary neurocultures has highlighted the entanglements of neuroscientific research within society and the implications of ‘neurofacts’ for gendered cultural symbolisms, social practices, and power relations. Not least, neurofeminism critically analyses the portrayal of neuro-knowledge in popular media. This article presents on overview on neurofeminist debates and on current approaches of feminist neurosciences. The authors conclude their review by calling for a more gender-appropriate research approach that takes into account both its situatedness and reflections on the neuroscientific agenda, but also questions neurofeminist discourse in regards to uses and misuses of its concepts.

A hierarchical model of vision (HMAX) can also recognize speech

HMAX is a well-known computational model of visual recognition in cortex consisting of just two computational operations – a “template match” and non-linear pooling – alternating in a feedforward hierarchy in which receptive fields exhibit increasing specificity and invariance [1]. Interestingly, auditory recognition problems (such as speech recognition) share similar computational requirements, and recent work in auditory neuroscience suggests that auditory and visual cortex share similar anatomical and functional organization. Based on these similarities, we tested whether HMAX could support an auditory recognition task (specifically, word spotting). To test HMAX on word spotting, recorded speech samples from the TIMIT corpus [2] were first converted into time-frequency spectrograms using a computational model of the auditory periphery [3]. These spectrograms were then split into 750 ms frames and input to a standard HMAX model [4]. Based on observed similarities between the receptive fields in primary auditory cortex (spectro-temporal receptive fields, or STRFs) and primary visual cortex (typically modeled as oriented Gabor filters), we used S1 filters identical to those used in vision [4]. Similarly, S2 “patches” were randomly selected from C1 representations of speech sounds drawn from an independent speech corpus. One vs. all linear support vector machines (SVMs) were then trained to discriminate frames that contain a target word from those that did not. These SVMs were then tested on a novel set of test sentences using a sliding frame approach (750 ms frame size, 20 ms step size). For each frame in a sentence, the SVM produced a distance from the hyperplane, and a threshold value was applied to produce a binary classification whether or not the target word was present in the sentence. When tested on target words that appeared in a fixed context (i.e. SA sentences in TIMIT), performance was highly robust, with ROC areas consistently above 0.9. When tested on target words that appeared in variable contexts (i.e., SI sentences in TIMIT), performance was somewhat decreased with ROC areas around 0.8. This decrease in performance is likely due to the inclusion of “clutter” (i.e., target irrelevant features) within the frame, also commonly observed when HMAX is applied to visual object recognition tasks [1]. These results are novel in that they provide support for the hypothesis that the simple computational framework implemented in HMAX – consisting of a feedforward hierarchy of only two alternating computational operations – may generalize beyond vision to support auditory recognition as well. It is possible that such a representation could give rise to stable neural encodings that are invariant to behaviorally irrelevant characteristics as seen in higher order visual and auditory cortices [5,6]. While it is likely that this auditory version of the HMAX model would benefit from the use of more auditory-specific filters based on STRF models [7], the Gabor features used here are largely compatible with previous computational models based on STRFs up to the level of primary auditory cortex [8]. Additional benefit may also be gained by learning sparse representations from natural sounds, at both the S1 and S2 levels [9].

Reconciling embodied and distributional accounts of meaning in language.

Over the past 15 years, there have been two increasingly popular approaches to the study of meaning in cognitive science. One, based on theories of embodied cognition, treats meaning as a simulation of perceptual and motor states. An alternative approach treats meaning as a consequence of the statistical distribution of words across spoken and written language. On the surface, these appear to be opposing scientific paradigms. In this review, we aim to show how recent cross-disciplinary developments have done much to reconcile these two approaches. The foundation to these developments has been the recognition that intralinguistic distributional and sensory-motor data are interdependent. We describe recent work in philosophy, psychology, cognitive neuroscience, and computational modeling that are all based on or consistent with this conclusion. We conclude by considering some possible directions for future research that arise as a consequence of these developments.

Mean-field behavior as a result of noisy local dynamics in self-organized criticality: Neuroscience implications

Motivated by recent experiments in neuroscience which indicate that neuronal avalanches exhibit scale invariant behavior similar to self-organized critical systems, we study the role of noisy (nonconservative) local dynamics on the critical behavior of a sandpile model which can be taken to mimic the dynamics of neuronal avalanches. We find that despite the fact that noise breaks the strict local conservation required to attain criticality, our system exhibits true criticality for a wide range of noise in various dimensions, given that conservation is respected on the average. Although the system remains critical, exhibiting finite-size scaling, the value of critical exponents change depending on the intensity of local noise. Interestingly, for a sufficiently strong noise level, the critical exponents approach and saturate at their mean-field values, consistent with empirical measurements of neuronal avalanches. This is confirmed for both two and three dimensional models. However, the addition of noise does not affect the exponents at the upper critical dimension (D = 4). In addition to an extensive finite-size scaling analysis of our systems, we also employ a useful time-series analysis method to establish true criticality of noisy systems. Finally, we discuss the implications of our work in neuroscience as well as some implications for the general phenomena of criticality in nonequilibrium systems.

Relation between belief and performance in perceptual decision making.

In an uncertain and ambiguous world, effective decision making requires that subjects form and maintain a belief about the correctness of their choices, a process called meta-cognition. Prediction of future outcomes and self-monitoring are only effective if belief closely matches behavioral performance. Equality between belief and performance is also critical for experimentalists to gain insight into the subjects' belief by simply measuring their performance. Assuming that the decision maker holds the correct model of the world, one might indeed expect that belief and performance should go hand in hand. Unfortunately, we show here that this is rarely the case when performance is defined as the percentage of correct responses for a fixed stimulus, a standard definition in psychophysics. In this case, belief equals performance only for a very narrow family of tasks, whereas in others they will only be very weakly correlated. As we will see it is possible to restore this equality in specific circumstances but this remedy is only effective for a decision-maker, not for an experimenter. We furthermore show that belief and performance do not match when conditioned on task difficulty – as is common practice when plotting the psychometric curve – highlighting common pitfalls in previous neuroscience work. Finally, we demonstrate that miscalibration and the hard-easy effect observed in humans' and other animals' certainty judgments could be explained by a mismatch between the experimenter's and decision maker's expected distribution of task difficulties. These results have important implications for experimental design and are of relevance for theories that aim to unravel the nature of meta-cognition.

Does Multitasking Improve Performance? Evidence from the Emergency Department

This paper examines the effect of multitasking on overall worker performance, as measured by processing time, throughput rate, and output quality using microlevel operational data from the field. Specifically, we study the multitasking behavior of physicians in a busy hospital emergency department (ED). By drawing on recent findings in the experimental psychology literature and the nascent work in cognitive neuroscience, we develop several hypotheses for the effect of multitasking on worker performance. We first examine how multitasking affects a physician's processing time. We find that the total time taken to discharge a given number of patients has a U-shaped response to the level of physician multitasking; that is, multitasking initially helps to reduce the time taken, but only up to a certain threshold level, after which it increases in the level of multitasking. In addition, multitasking significantly impacts quality of care. Although lower levels of multitasking are associated with improved quality of care, at higher levels, additional multitasking leads to a smaller number of detected diagnoses and an increased likelihood of a 24-hour revisit rate to the ED. These findings have important implications for the design and organization of work in general and for the delivery of critical care in particular.