Current Opinion in Behavioral Sciences最新文献

Very few diencephalic structures project directly to the hippocampus. Two diencephalic nuclei, however, with pronounced direct projections to the hippocampus, are the nucleus reuniens (RE) of the midline thalamus and the supramammillary nucleus (SuM) of the caudal hypothalamus. As reviewed herein, RE and SuM distribute to separate sites in the hippocampus and accordingly largely exert distinct effects on the hippocampus. Specifically, RE is a major interface between the hippocampus and the medial prefrontal cortex (mPFC) and thus mainly serves functions associated with the hippocampal formation and the mPFC, namely, working memory, executive functions, and affective behaviors, primarily fear. By comparison, the SUM projects prominently to the dentate gyrus (DG) of the hippocampus, the major recipient zone of afferents from the entorhinal cortex (EC), and accordingly the SuM serves to enhance the activity of DG cells rendering them more responsive to inputs from the EC. This serves to promote the transfer, encoding, and storage of information from the EC to the DG — supporting learning and memory processes of the hippocampus. In sum, the RE and SuM exert a powerful influence on the hippocampus in the modulation/control of numerous affective, cognitive, and mnemonic functions.

Classically, the cerebellum is viewed as a sensorimotor structure devolved essentially to the control and coordination of smooth movements. However, recent findings strongly suggest that the cerebellum may also make a substantial contribution to a variety of cognitive and affective functions, including reinforcement learning, social preference and attention. Concomitantly, rapid progress in technology has enabled the discovery of novel feedforward and feedback projections and a variety of electrophysiological mechanisms governing information transfer from the cerebellar cortex to the cerebellar nuclei, which serve as the primary output from the cerebellum. These discoveries are driving the current shift in cerebellar research from the cortex to the nuclei, and call for reevaluating the potential computational capacity of these nuclei.

Cognitive stability and flexibility are regarded as key ingredients of goal-directed behavior. This review introduces dynamical systems as a theoretical framework for studying cognitive flexibility and stability. Following a gentle introduction to dynamical systems theory, we discuss how cognitive flexibility and stability can be operationalized and examined through the lens of such models. Drawing from recent advances in dynamical systems theory, we argue that various models of cognitive flexibility and stability, ranging from models of spiking neurons to models of human task switching to models of collective animal behavior, can be understood in terms of the same mathematical principles of low-dimensional dynamical systems. These principles suggest a trade-off between cognitive flexibility and stability inherent to dynamical system models of varying complexity. We conclude by discussing the consequences of this unified view and examine its explanatory scope in terms of behavioral and neural correlates of cognitive flexibility and stability.

The need to cope in a complex social environment has been suggested as the reason for the evolutionary development of our large brains. In contrast to this claim, growing evidence suggests that having a cortex is not a necessary condition for demonstrating prosocial behavior. Here, we suggest examining this issue through theoretical perspectives considering the relations between subcortical and cortical mechanisms. According to Ashby et al., (2007)'s SPEED (Subcortical Pathways Enable Expertise Development) model, the development of automaticity is characterized by a transfer of control from subcortical regions to faster cortical–cortical projections. Thus, subcortical regions may be perceived as trainers for the cortex. We apply this model to the social domain, suggesting that the automaticity associated with social processes begins in subcortical regions that direct attention to relevant events, allowing cortical regions to take control. We discuss this perspective in the context of face perception, prosociality, and social deficit disorders.

Neural networks trained with deep reinforcement learning can perform many complex tasks at similar levels to humans. However, unlike people, neural networks converge to a fixed solution during optimisation, limiting their ability to adapt to new challenges. In this opinion, we highlight three key new methods that allow neural networks to be posed as models of human cognitive flexibility. In the first, neural networks are trained in ways that allow them to learn complementary ‘habit’ and ‘goal’-based policies. In another, flexibility is ‘meta-learned’ during pre-training from large and diverse data, allowing the network to adapt ‘in context’ to novel inputs. Finally, we discuss work in which deep networks are meta-trained to adapt their behaviour to the level of control they have over the environment. We conclude by discussing new insights about cognitive flexibility obtained from the training of large generative models with reinforcement learning from human feedback.

A key aspect of cognitive flexibility is to efficiently make use of earlier experience to attain one’s goals. This requires learning, but also a modular, and more specifically hierarchical, structure. We hold that both are required, but combining them leads to several computational challenges that brains and artificial agents (learn to) deal with. In a hierarchical structure, meta-decisions must be made, of which two types can be distinguished. First, a (meta-)decision may involve choosing which (lower-level) modules to select (module choice). Second, it may consist of choosing appropriate parameter settings within a module (parameter tuning). Furthermore, prediction error monitoring may allow determining the right meta-decision (module choice or parameter tuning). We discuss computational challenges and empirical evidence relative to how these two meta-decisions may be implemented to support learning for cognitive flexibility.

Empirical evidence suggests a critical, but little-understood, contribution of the basal forebrain (BF) to motivational aspects of social cognition. Therefore, we review the current literature on reward and punishment processing in the BF, including social information, in both animals and more recently human imaging studies. This also includes interactions with other subcortical structures, especially the ventral striatum and substantia nigra/ventral tegmental area, which are part of the mesolimbic system. Importantly, the BF typically degenerates during healthy aging and shows abnormalities in autistic spectrum disorders, which may help to further understand its role in social information processing. Finally, we suggest a model of cortical and subcortical social information processing bringing together BF contributions in concert with the dopaminergic midbrain, medial temporal lobe, and prefrontal cortex to promote social cognition.

The use of ‘offline’ transcranial ultrasound stimulation (TUS) protocols is of particular interest in the rapidly growing field of low-intensity TUS. Offline TUS can modulate neural activity up to several hours after stimulation, suggesting the induction of early-phase neuroplasticity. Studies in both humans and nonhuman primates have shown spatially specific changes in both the neuromodulation target and in a distributed network of regions associated with it. These changes suggest that excitatory or inhibitory effects are a result of a complex interaction between the protocol used and the underlying brain region and state. Understanding how early-phase neuroplasticity is induced by offline TUS could open avenues for influencing late-phase neuroplasticity and therapeutic applications in a wide range of brain disorders.

Non-invasive brain stimulation (NIBS) approaches such as repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) are rapidly emerging as effective and well-tolerated treatments for depression. With the most recent head-to-head studies demonstrating equivalence in clinical efficacy between rTMS, tDCS and widely used pharmaceutical antidepressants, there is urgent need for a paradigm shift towards its inclusion as a low-cost, low-risk frontline treatment for depression. Here, we provide a narrative review outlining barriers currently impeding translation of NIBS approaches into large-scale clinical use, with a view to developing a neural biomarker that could provide early stratification of patients as potential responders or non-responders. We describe how the TMS-evoked potential provides a marker of cortical excitability that could be used as a baseline predictor of whether the NIBS user will derive benefit from approaches tailored to achieve neuromodulation.

The slaughter of animals comprises the induction of unconsciousness followed by bleeding to cause death. Today’s slaughter practices are chosen to avoid animal suffering, but what does science tell us about animal suffering? Do animals have emotions? Consciousness? How to study consciousness? Experiments suggest strongly that animals have emotions and are conscious, although many aspects of consciousness are still not understood. However, various brain areas involved in consciousness have been identified and the mechanical, electrical and gaseous stunning techniques used at slaughter, cause dysfunction of one or several of these areas, in different manners to induce unconsciousness.