The dorsal anterior cingulate cortex (dACC) has a near-ubiquitous presence in the neuroscience of cognitive control. It has been implicated in a diversity of functions, from reward processing and ...performance monitoring to the execution of control and action selection. Here, we propose that this diversity can be understood in terms of a single underlying function: allocation of control based on an evaluation of the expected value of control (EVC). We present a normative model of EVC that integrates three critical factors: the expected payoff from a controlled process, the amount of control that must be invested to achieve that payoff, and the cost in terms of cognitive effort. We propose that dACC integrates this information, using it to determine whether, where and how much control to allocate. We then consider how the EVC model can explain the diverse array of findings concerning dACC function.
The dorsal anterior cingulate cortex (dACC) has been implicated in diverse functions related to cognitive control. Here, Shenhav et al. propose a unifying view of dACC function where dACC integrates information about the expected value of control to determine whether, where and how much control to allocate.
Humans are remarkably limited in: (i) how many control-dependent tasks they can execute simultaneously, and (ii) how intensely they can focus on a single task. These limitations are universal ...assumptions of most theories of cognition. Yet, a rationale for why humans are subject to these constraints remains elusive. This feature review draws on recent insights from psychology, neuroscience, and machine learning, to suggest that constraints on cognitive control may result from a rational adaptation to fundamental, computational dilemmas in neural architectures. The reviewed literature implies that limitations in multitasking may result from a trade-off between learning efficacy and processing efficiency and that limitations in the intensity of commitment to a single task may reflect a trade-off between cognitive stability and flexibility.
To explain human behavior, most general theories of cognition assume, rather than explain, limitations in: (i) the number of control-dependent tasks that can be performed simultaneously (i.e., multitasked); and (ii) the amount of cognitive control that can be allocated to a single task.Limitations in the capability to multitask can be explained by representation sharing between tasks. Computational modeling suggests that neural systems trade the benefits of shared representation for rapid learning and generalization (a mechanism increasingly exploited in machine learning) against constraints on multitasking performance.Experimental studies posit a trade-off between cognitive stability and cognitive flexibility. Computational analyses of this trade-off suggest that adaptations to high demands for flexibility limit the amount of control that can be allocated to a single task.
Real-world complex systems may be mathematically modeled as graphs, revealing properties of the system. Here we study graphs of functional brain organization in healthy adults using resting state ...functional connectivity MRI. We propose two novel brain-wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships. These graphs contain many subgraphs in good agreement with known functional brain systems. Other subgraphs lack established functional identities; we suggest possible functional characteristics for these subgraphs. Further, graph measures of the areal network indicate that the default mode subgraph shares network properties with sensory and motor subgraphs: it is internally integrated but isolated from other subgraphs, much like a “processing” system. The modified voxelwise graph also reveals spatial motifs in the patterning of systems across the cortex.
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► Areal and modified voxelwise graph definitions are proposed ► Subgraphs reflect known and unknown brain systems ► Default mode, sensory, and motor systems share network properties ► Functional systems are patterned across the cortex with spatial regularities
Debates over the function(s) of dorsal anterior cingulate cortex (dACC) have persisted for decades. So too have demonstrations of the region's association with cognitive control. Researchers have ...struggled to account for this association and, simultaneously, dACC's involvement in phenomena related to evaluation and motivation. We describe a recent integrative theory that achieves this goal. It proposes that dACC serves to specify the currently optimal allocation of control by determining the overall expected value of control (EVC), thereby licensing the associated cognitive effort. The EVC theory accounts for dACC's sensitivity to a wide array of experimental variables, and their relationship to subsequent control adjustments. Finally, we contrast our theory with a recent theory proposing a primary role for dACC in foraging-like decisions. We describe why the EVC theory offers a more comprehensive and coherent account of dACC function, including dACC's particular involvement in decisions regarding foraging or otherwise altering one's behavior.
Attention is commonly thought to be manifest through local variations in neural gain. However, what would be the effects of brain-wide changes in gain? We hypothesized that global fluctuations in ...gain modulate the breadth of attention and the degree to which processing is focused on aspects of the environment to which one is predisposed to attend. We found that measures of pupil diameter, which are thought to track levels of locus coeruleus norepinephrine activity and neural gain, were correlated with the degree to which learning was focused on stimulus dimensions that individual human participants were more predisposed to process. In support of our interpretation of this effect in terms of global changes in gain, we found that the measured pupillary and behavioral variables were strongly correlated with global changes in the strength and clustering of functional connectivity, as brain-wide fluctuations of gain would predict.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The ability to acquire abstract knowledge is a hallmark of human intelligence and is believed by many to be one of the core differences between humans and neural network models. Agents can be endowed ...with an inductive bias towards abstraction through meta-learning, where they are trained on a distribution of tasks that share some abstract structure that can be learned and applied. However, because neural networks are hard to interpret, it can be difficult to tell whether agents have learned the underlying abstraction, or alternatively statistical patterns that are characteristic of that abstraction. In this work, we compare the performance of humans and agents in a meta-reinforcement learning paradigm in which tasks are generated from abstract rules. We define a novel methodology for building “task metamers” that closely match the statistics of the abstract tasks but use a different underlying generative process, and evaluate performance on both abstract and metamer tasks. We find that humans perform better at abstract tasks than metamer tasks whereas common neural network architectures typically perform worse on the abstract tasks than the matched metamers. This work provides a foundation for characterizing differences between humans and machine learning that can be used in future work towards developing machines with more human-like behavior.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In spite of its familiar phenomenology, the mechanistic basis for mental effort remains poorly understood. Although most researchers agree that mental effort is aversive and stems from limitations in ...our capacity to exercise cognitive control, it is unclear what gives rise to those limitations and why they result in an experience of control as costly. The presence of these control costs also raises further questions regarding how best to allocate mental effort to minimize those costs and maximize the attendant benefits. This review explores recent advances in computational modeling and empirical research aimed at addressing these questions at the level of psychological process and neural mechanism, examining both the limitations to mental effort exertion and how we manage those limited cognitive resources. We conclude by identifying remaining challenges for theoretical accounts of mental effort as well as possible applications of the available findings to understanding the causes of and potential solutions for apparent failures to exert the mental effort required of us.
Cognitive control has long been one of the most active areas of computational modeling work in cognitive science. The focus on computational models as a medium for specifying and developing theory ...predates the PDP books, and cognitive control was not one of the areas on which they focused. However, the framework they provided has injected work on cognitive control with new energy and new ideas. On the occasion of the books' anniversary, we review computational modeling in the study of cognitive control, with a focus on the influence that the PDP approach has brought to bear in this area. Rather than providing a comprehensive review, we offer a framework for thinking about past and future modeling efforts in this domain. We define control in terms of the optimal parameterization of task processing. From this vantage point, the development of control systems in the brain can be seen as responding to the structure of naturalistic tasks, through the filter of the brain systems with which control directly interfaces. This perspective lays open a set of fascinating but difficult research questions, which together define an important frontier for future computational research.
Researchers and educators have long wrestled with the question of how best to teach their clients be they humans, non-human animals or machines. Here, we examine the role of a single variable, the ...difficulty of training, on the rate of learning. In many situations we find that there is a sweet spot in which training is neither too easy nor too hard, and where learning progresses most quickly. We derive conditions for this sweet spot for a broad class of learning algorithms in the context of binary classification tasks. For all of these stochastic gradient-descent based learning algorithms, we find that the optimal error rate for training is around 15.87% or, conversely, that the optimal training accuracy is about 85%. We demonstrate the efficacy of this 'Eighty Five Percent Rule' for artificial neural networks used in AI and biologically plausible neural networks thought to describe animal learning.
Understanding how antibiotics impact bacterial metabolism may provide insight into their mechanisms of action and could lead to enhanced therapeutic methodologies. Here, we profiled the metabolome of ...Escherichia coli after treatment with three different classes of bactericidal antibiotics (β-lactams, aminoglycosides, quinolones). These treatments induced a similar set of metabolic changes after 30 min that then diverged into more distinct profiles at later time points. The most striking changes corresponded to elevated concentrations of central carbon metabolites, active breakdown of the nucleotide pool, reduced lipid levels, and evidence of an elevated redox state. We examined potential end-target consequences of these metabolic perturbations and found that antibiotic-treated cells exhibited cytotoxic changes indicative of oxidative stress, including higher levels of protein carbonylation, malondialdehyde adducts, nucleotide oxidation, and double-strand DNA breaks. This work shows that bactericidal antibiotics induce a complex set of metabolic changes that are correlated with the buildup of toxic metabolic by-products.
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•Diverse bactericidal antibiotics induced similar metabolic changes•Bactericidal antibiotics induced biomarkers of oxidative stress•Bactericidal antibiotics induced dsDNA breaks
Belenky et al. profile the metabolome of Escherichia coli after treatment with different classes of bactericidal antibiotics and find that these treatments induce a similar set of metabolic changes. Antibiotic-treated cells were also found to exhibit cytotoxic changes indicative of oxidative stress. This insight could lead to enhanced therapeutic methodologies.
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