Neural Levels Of Analysis Research Articles

Neural Aspects of Prospective Control through Resonating Taus in an Interceptive Timing Task.

High-density electroencephalography from visual and motor cortices in addition to kinematic hand and target movement recordings were used to investigate τ-coupling between brain activity patterns and physical movements in an interceptive timing task. Twelve adult participants were presented with a target car moving towards a destination at three constant accelerations, and an effector dot was available to intercept the car at the destination with a swift movement of the finger. A τ-coupling analysis was used to investigate involvement of perception and action variables at both the ecological scale of behavior and neural scale. By introducing the concept of resonance, the underlying dynamics of interceptive actions were investigated. A variety of one- and two-scale τ-coupling analyses showed significant differences in distinguishing between slow, medium, and fast target speed when car motion and finger movement, VEP and MRP brain activity, VEP and car motion, and MRP and finger movement were involved. These results suggested that the temporal structure present at the ecological scale is reflected at the neural scale. The results further showed a strong effect of target speed, indicating that τ-coupling constants k and kres increased with higher speeds of the moving target. It was concluded that τ-coupling can be considered a valuable tool when combining different types of variables at both the ecological and neural levels of analysis.

Context-sensitive computational mechanistic explanation in cognitive neuroscience.

Mainstream cognitive neuroscience aims to build mechanistic explanations of behavior by mapping abilities described at the organismal level via the subpersonal level of computation onto specific brain networks. We provide an integrative review of these commitments and their mismatch with empirical research findings. Context-dependent neural tuning, neural reuse, degeneracy, plasticity, functional recovery, and the neural correlates of enculturated skills each show that there is a lack of stable mappings between organismal, computational, and neural levels of analysis. We furthermore highlight recent research suggesting that task context at the organismal level determines the dynamic parcellation of functional components at the neural level. Such instability prevents the establishment of specific computational descriptions of neural function, which remains a central goal of many brain mappers – including those who are sympathetic to the notion of many-to-many mappings between organismal and neural levels. This between-level instability presents a deep epistemological challenge and requires a reorientation of methodological and theoretical commitments within cognitive neuroscience. We demonstrate the need for change to brain mapping efforts in the face of instability if cognitive neuroscience is to maintain its central goal of constructing computational mechanistic explanations of behavior; we show that such explanations must be contextual at all levels.

A Computational Model of Hopelessness and Active-Escape Bias in Suicidality.

Currently, psychiatric practice lacks reliable predictive tools and a sufficiently detailed mechanistic understanding of suicidal thoughts and behaviors (STB) to provide timely and personalized interventions. Developing computational models of STB that integrate across behavioral, cognitive and neural levels of analysis could help better understand STB vulnerabilities and guide personalized interventions. To that end, we present a computational model based on the active inference framework. With this model, we show that several STB risk markers - hopelessness, Pavlovian bias and active-escape bias - are interrelated via the drive to maximize one's model evidence. We propose four ways in which these effects can arise: (1) increased learning from aversive outcomes, (2) reduced belief decay in response to unexpected outcomes, (3) increased stress sensitivity and (4) reduced sense of stressor controllability. These proposals stem from considering the neurocircuits implicated in STB: how the locus coeruleus - norepinephrine (LC-NE) system together with the amygdala (Amy), the dorsal prefrontal cortex (dPFC) and the anterior cingulate cortex (ACC) mediate learning in response to acute stress and volatility as well as how the dorsal raphe nucleus - serotonin (DRN-5-HT) system together with the ventromedial prefrontal cortex (vmPFC) mediate stress reactivity based on perceived stressor controllability. We validate the model by simulating performance in an Avoid/Escape Go/No-Go task replicating recent behavioral findings. This serves as a proof of concept and provides a computational hypothesis space that can be tested empirically and be used to distinguish planful versus impulsive STB subtypes. We discuss the relevance of the proposed model for treatment response prediction, including pharmacotherapy and psychotherapy, as well as sex differences as it relates to stress reactivity and suicide risk.

Brain connectivity-based prediction of real-life creativity is mediated by semantic memory structure.

Associative theories of creativity argue that creative cognition involves the abilities to generate remote associations and make useful connections between unrelated concepts in one’s semantic memory. Yet, whether and how real-life creative behavior relies on semantic memory structure and its neural substrates remains unclear. We acquired multi-echo functional magnetic resonance imaging data while participants underwent a semantic relatedness judgment task. These ratings were used to estimate their individual semantic memory networks, whose properties significantly predicted their real-life creativity. Using a connectome predictive modeling approach, we identified patterns of task-based functional connectivity that predicted creativity-related semantic memory network properties. Furthermore, these properties mediated the relationship between functional connectivity and real-life creativity. These results provide new insights into how brain connectivity patterns support real-life creative behavior via the structure of semantic memory. We also show how computational network science can be used to couple behavioral, cognitive, and neural levels of analysis.

Different methods of fear reduction are supported by distinct cortical substrates.

Understanding how learned fear can be reduced is at the heart of treatments for anxiety disorders. Tremendous progress has been made in this regard through extinction training in which the aversive outcome is omitted. However, current progress almost entirely rests on this single paradigm, resulting in a very specialized knowledgebase at the behavioural and neural level of analysis. Here, we used a dual-paradigm approach to show that different methods that lead to reduction in learned fear in rats are dissociated in the cortex. We report that the infralimbic cortex has a very specific role in fear reduction that depends on the omission of aversive events but not on overexpectation. The orbitofrontal cortex, a structure generally overlooked in fear, is critical for downregulating fear when novel predictions about upcoming aversive events are generated, such as when fear is inflated or overexpected, but less so when an expected aversive event is omitted.

The impact of affective information on working memory: A pair of meta-analytic reviews of behavioral and neuroimaging evidence.

Everyday life is defined by goal states that are continuously reprioritized based on available, often affective information. To pursue these goals, individuals need to process and maintain goal-relevant information, while ignoring potentially salient information that distracts resources from these goals. Empirically, this ability has typically been operationalized as working memory (WM) capacity. A growing body of research is investigating the impact of information’s affective salience on WM capacity. In the present review we address this question by exploring the potential differential impact of affective compared with neutral information on WM, and the underlying neural substrates. One-hundred and 65 studies (N = 7,433) were included in the meta-analysis. Results showed negligible to small (d̂ = −.07–.20) effects of affective information on behavioral measures of WM in healthy individuals (n = 4,936) that varied as a function of valence and task-relevance. Heterogeneity analyses were significant, demonstrating the need to identify further study-specific factors and individual differences that moderate affective WM. At the neural level (33 studies; n = 683), processing affective versus neutral material during WM tasks was associated with more frequent recruitment of the vlPFC, the amygdala, and the temporo-occipital cortex. In contrast to healthy individuals, across behavioral studies those suffering from mental health problems (n = 2,041) showed impaired WM accuracy (d̂ = −0.21) in the presence of affective material. These findings highlight the importance of integrating behavioral and neural levels of analysis. Finally, these findings suggest that affective WM capacity may be a transdiagnostic mechanism associated with poor mental health.

Self-compassion and responses to negative social feedback: The role of fronto-amygdala circuit connectivity

ABSTRACTSelf-compassion has been shown to have significant relationships with psychological health and well-being. Despite the increasing growth of research on the topic, no studies to date have investigated how self-compassion relates to neural responses to threats to the self. To investigate whether self-compassion relates to threat-regulatory mechanisms at the neural level of analysis, we conducted a functional MRI study in a sample of college-aged students. We hypothesized that self-compassion would relate to greater negative connectivity between the ventromedial prefrontal cortex (VMPFC) and amygdala during a social feedback task. Interestingly, we found a negative correlation between self-compassion and VMPFC-amygdala functional connectivity as predicted; however, this seemed to be due to low levels of self-compassion relating to greater positive connectivity in this circuit (rather than high levels of self-compassion relating to more negative connectivity). We also found significant relationships with multiple subcomponents of self-compassion (Common Humanity, Self-Judgment). These results shed light on how self-compassion might affect neural responses to threat and informs our understanding of the basic psychological regulatory mechanisms linking a lack of self-compassion with poor mental health.

The Challenges of Integrating Behavioral and Neural Data: Bridging and Breaking Boundaries Across Levels of Analysis.

We describe here two approaches introduced by Abrahamsen (1987) that can be used by behavior analysts to interpret neuroscientific data. The first is a "boundary-bridging" approach aimed at understanding the interdisciplinary interactions between the behavioral and the neural levels of analysis while keeping the two domains independent. When presenting the boundary-bridging approach, we describe neuroplasticity, a perspective that describes how changes at the brain level can be understood by examining behavioral factors. In the second part of the paper, we contrast two "boundary-breaking" perspectives: neuropsychology and behavior analytic neuroscience. In neuropsychology, localized brain activation is used to explain behavior. In behavior analytic neuroscience, brain responses are interpreted as behavior. We discuss the conditions under which brain responses can be considered behavior and propose that including brain responses within a behavioral framework may allow carrying out a more sophisticated and temporally detailed behavior analysis.

The role of neuroscience within psychology: A call for inclusiveness over exclusiveness.

In the present article, we appraise the increasingly prominent role of neuroscience within psychology and offer cautions and recommendations regarding the future of psychology as a field. We contend that the conflict between eliminative reductionism (the belief that the neural level of analysis will eventually render the psychological level of analysis superfluous) and emergent properties (the assumption that higher-order mental functions are not directly reducible to neural processes) is critical if we are to identify the optimal role for neuroscience within psychology. We argue for an interdisciplinary future for psychology in which the considerable strengths of neuroscience complement and extend the strengths of other subfields of psychology. For this goal to be achieved, a balance must be struck between an increasing focus on neuroscience and the continued importance of other areas of psychology. We discuss the implications of the growing prominence of neuroscience for the broader profession of psychology, especially with respect to funding agency priorities, hiring practices in psychology departments, methodological rigor, and the training of future generations of students. We conclude with recommendations for advancing psychology as both a social science and a natural science. (PsycINFO Database Record

Foundations of children's numerical and mathematical skills: the roles of symbolic and nonsymbolic representations of numerical magnitude.

Numerical and mathematical skills are critical predictors of academic success. The last three decades have seen a substantial growth in our understanding of how the human mind and brain represent and process numbers. In particular, research has shown that we share with animals the ability to represent numerical magnitude (the total number of items in a set) and that preverbal infants can process numerical magnitude. Further research has shown that similar processing signatures characterize numerical magnitude processing across species and developmental time. These findings suggest that an approximate system for nonsymbolic (e.g., dot arrays) numerical magnitude representation serves as the basis for the acquisition of cultural, symbolic (e.g., Arabic numerals) representations of numerical magnitude. This chapter explores this hypothesis by reviewing studies that have examined the relation between individual differences in nonsymbolic numerical magnitude processing and symbolic math abilities (e.g., arithmetic). Furthermore, we examine the extent to which the available literature provides strong evidence for a link between symbolic and nonsymbolic representations of numerical magnitude at the behavioral and neural levels of analysis. We conclude that claims that symbolic number abilities are grounded in the approximate system for the nonsymbolic representation of numerical magnitude are not strongly supported by the available evidence. Alternative models and future research directions are discussed.

Comment: Constructionism is a Multilevel Framework for Affective Science

We point out that constructionist models from experimental psychology account for the sociocultural, psychological, and neural levels of analysis in emotion. Individual constructionist models form a “metamodel” that integrates the levels of analysis important to a science of emotion. By clarifying the multilevel nature of constructionism, we hope to help lay a strong foundation for future cross-disciplinary collaborations.

Diagnostic Value Underlies Asymmetric Updating of Impressions in the Morality and Ability Domains

While positive behavioral information is diagnostic when evaluating a person's abilities, negative information is diagnostic when evaluating morality. Although social psychology has considered these two domains as orthogonal and distinct from one another, we demonstrate that this asymmetry in diagnosticity can be explained by a single parsimonious principle--the perceived frequency of behaviors in these domains. Less frequent behaviors (e.g., high ability and low morality) are weighed more heavily in evaluations. We show that this statistical principle of frequency-derived diagnosticity is evident in human participants at both behavioral and neural levels of analysis. Specifically, activity in right ventrolateral prefrontal cortex increased preferentially when participants updated impressions based on diagnostic behaviors, and further, activity in this region covaried parametrically with the perceived frequency of behaviors. Activity in left ventrolateral PFC, left inferior frontal gyrus, and left superior temporal sulcus showed similar patterns of diagnosticity and sensitivity, though additional analyses confirmed that these regions responded primarily to updates based on immoral behaviors.

Linking mathematical modeling with human neuroimaging to segregate verbal working memory maintenance processes from stimulus encoding.

A fundamental dissociation for most working memory (WM) theories involves the separation of sensory-perceptual encoding of stimulus information from the maintenance of this information. The present paper reports tests of this separability hypothesis for visually presented pseudowords at both mathematical and neuroimaging levels of analysis. Levels of analysis were linked by two experimental manipulations-visual degradation and pseudoword length variation-that coupled findings from a mathematical modeling study of WM performed in a separate sample to findings from an event-related functional MRI (fMRI) study reported in the present paper. Results from the mathematical modeling study generated parametric signatures of stimulus encoding and WM rehearsal and displacement. These signatures led to specific predictions about neurophysiological responses to study manipulations in a priori regions of interest (ROI). Results demonstrated predicted dissociations of activation signatures in several ROIs. Significant patterns of brain response mirroring the encode signature were observed only during the task encode interval and only in the visual cortex and posterior fusiform gyrus. In contrast, significant brain response mirroring the rehearsal/displacement signature was observed only in the dorsolateral prefrontal cortex, inferior frontal gyrus, and supramarginal gyrus. Present findings support the separability hypothesis insofar as brain regions that underlie sensory-perceptual processes demonstrated encode signatures whereas brain regions that support WM maintenance demonstrated the rehearsal/displacement signature. These results also provide evidence for the utility of combining mathematical modeling with fMRI to integrate information across cognitive and neural levels of analysis.

Older brains are not all alike: Individual differences in brain activation patterns during working memory performance

Event Abstract Back to Event Older brains are not all alike: Individual differences in brain activation patterns during working memory performance Ulman Lindenberger1* 1 Max Planck Institute for Human Development, Germany Individual differences in cognitive performance increase from early to late adulthood. In this talk, I will examine this heterogeneity at genetic and neural levels of analysis. First, I will present the hypothesis that losses in neurochemical and anatomical brain resources in normal aging modulate the effects of common genetic variations on cognitive functioning. This hypothesis is based on the assumption that the function relating brain resources to cognition is nonlinear, so that genetic differences exert increasingly large effects on cognition as resources recede from high to medium levels in the course of aging. I will present empirical support for this hypothesis involving the effects of the COMT and BDNF genes on working memory and episodic memory. Second, I will note that performance heterogeneity generally has been largely neglected in fMRI aging studies of working memory. As a result, age differences in activation patterns are often confounded with individual differences in performance level. I will report data from two studies addressing this issue by comparing younger and older adults whose working memory performance is either low or high. In the first study (Nagel et al., 2009), 30 younger (20-30 years) and 30 older (60-70 years) healthy adults were tested on a spatial WM task with three load levels. In both age groups, a region-of-interest (ROI) analysis revealed marked differences in the activation patterns between high and low performers. Critically, among the older adults, a more “youth-like” load-dependent modulation of the BOLD signal was associated with higher levels of spatial WM performance. In the second study (Nagel et al., submitted), 30 younger adults (21-31) and 30 older adults (60-71) were tested in a parametric letter n-back task, again with three load levels. Across age groups, individual differences in the WM network’s responsivity to increasing task difficulty were related to individual differences in WM performance, with a more responsive BOLD signal predicting greater WM proficiency. Furthermore, individuals with higher WM performance showed greater change in connectivity between left dorsolateral prefrontal cortex and left premotor cortex with load. The results of both studies underscore the need of taking performance level into account when studying adult age differences in functional brain activation patterns. Our results suggest that a more responsive and connectivity-modulated WM network contributes to higher WM performance, regardless of adult age.

Emotion Regulation and the Anxiety Disorders: An Integrative Review.

The construct of emotion regulation has been increasingly investigated in the last decade, and this work has important implications for advancing anxiety disorder theory. This paper reviews research demonstrating that: 1) emotion (i.e., fear and anxiety) and emotion regulation are distinct, non-redundant, constructs that can be differentiated at the conceptual, behavioral, and neural levels of analysis; 2) emotion regulation can augment or diminish fear, depending on the emotion regulation strategy employed; and 3) measures of emotion regulation explain incremental variance in anxiety disorder symptoms above and beyond the variance explained by measures of emotional reactivity. The authors propose a model by which emotion regulation may function in the etiology of anxiety disorders. The paper concludes with suggestions for future research.

A neurocomputational model of stochastic resonance and aging

Stochastic resonance (SR) is fundamental to physical and biological processes. Here, we use a stochastic gain-tuning model to investigate interactions between aging-related increase of endogenous neuronal noise and external input noise in affecting SR. Compared to networks that have optimal system gain parameter of the activation function, networks with attenuated endogenous gain tuning at the system level, simulating aging neurocognitive systems with more intrinsic neuronal noise but less plasticity, continue to exhibit the general SR effect; however, this effect is smaller and requires more external noise. This set of finding suggests that determining the optimal proportion of resonance-inducing external noise as a function of internal-system stochastic gain tuning properties promotes unified theorizing about sensory and cognitive aging at behavioral and neural levels of analysis.

Human Functional Neuroimaging of Brain Changes Associated with Practice

The discovery that experience-driven changes in the human brain can occur from a neural to a cortical level throughout the lifespan has stimulated a proliferation of research into how neural function changes in response to experience, enabled by neuroimaging methods such as positron emission tomography and functional magnetic resonance imaging. Studies attempt to characterize these changes by examining how practice on a task affects the functional anatomy underlying performance. Results are incongruous, including patterns of increases, decreases and functional reorganization of regional activations. Following an extensive review of the practice-effects literature, we distinguish a number of factors affecting the pattern of practice effects observed, including the effects of task domain, changes at the level of behavioural and cognitive processes, the time-window of imaging and practice, and of a number of other influences and miscellaneous confounding factors. We make a novel distinction between patterns of reorganization and redistribution as effects of task practice on brain activation, and emphasize the need for careful attention to practice-related changes occurring on the behavioural, cognitive and neural levels of analysis. Finally, we suggest that functional and effective connectivity analyses may make important contributions to our understanding of changes in functional anatomy occurring as a result of practice on tasks.

Intuition: a social cognitive neuroscience approach.

This review proposes that implicit learning processes are the cognitive substrate of social intuition. This hypothesis is supported by (a) the conceptual correspondence between implicit learning and social intuition (nonverbal communication) and (b) a review of relevant neuropsychological (Huntington's and Parkinson's disease), neuroimaging, neurophysiological, and neuroanatomical data. It is concluded that the caudate and putamen, in the basal ganglia, are central components of both intuition and implicit learning, supporting the proposed relationship. Parallel, but distinct, processes of judgment and action are demonstrated at each of the social, cognitive, and neural levels of analysis. Additionally, explicit attempts to learn a sequence can interfere with implicit learning. The possible relevance of the computations of the basal ganglia to emotional appraisal, automatic evaluation, script processing, and decision making are discussed.