277 research outputs found

    Reward prediction error and declarative memory

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    Learning based on reward prediction error (RPE) was originally proposed in the context of nondeclarative memory. We postulate that RPE may support declarative memory as well. Indeed, recent years have witnessed a number of independent empirical studies reporting effects of RPE on declarative memory. We provide a brief overview of these studies, identify emerging patterns, and discuss open issues such as the role of signed versus unsigned RPEs in declarative learning

    The impact of prediction errors on perception and learning: a systems approach

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    Neurophysiological correlates of purchase decision-making

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    Economic decisions are characterized by their uncertainty and the lack of explicit feedback that indicates the correctness of decisions at the time they are made. Nevertheless, very little is known about the neural mechanisms involved in this process. Our study sought to identify the neurophysiological correlates of purchase decision-making in situations where the optimal purchase time is not known. EEG was recorded in 24 healthy subjects while they were performing a new experimental paradigm that simulates real economic decisions. At the time of price presentation, we found an increase in the P3 Event-Related Potential and induced theta and alpha oscillatory activity when participants chose to buy compared to when they decided to wait for a better price. These results reflect the engagement of attention and executive function in purchase decision-making and might help in the understanding of brain mechanisms underlying economic decisions in uncertain scenarios

    State-dependencies of learning across brain scales

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    Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous “spontaneous” shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, particularly during sleep, are essential for learning and memory consolidation. Oscillatory activity plays a crucial role on several spatial scales, for example in plasticity at a synaptic level or in communication across brain areas. However, the underlying mechanisms and computational rules linking brain states and rhythms to learning, though relevant for our understanding of brain function and therapeutic approaches in brain disease, have not yet been elucidated. Here we review known mechanisms of how brain states mediate and modulate learning by their characteristic rhythmic signatures. To understand the critical interplay between brain states, brain rhythms, and learning processes, a wide range of experimental and theoretical work in animal models and human subjects from the single synapse to the large-scale cortical level needs to be integrated. By discussing results from experiments and theoretical approaches, we illuminate new avenues for utilizing neuronal learning mechanisms in developing tools and therapies, e.g., for stroke patients and to devise memory enhancement strategies for the elderly

    Decoding cognition from spontaneous neural activity

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    In human neuroscience, studies of cognition are rarely grounded in non-task-evoked, ‘spontaneous’ neural activity. Indeed, studies of spontaneous activity tend to focus predominantly on intrinsic neural patterns (for example, resting-state networks). Taking a ‘representation-rich’ approach bridges the gap between cognition and resting-state communities: this approach relies on decoding task-related representations from spontaneous neural activity, allowing quantification of the representational content and rich dynamics of such activity. For example, if we know the neural representation of an episodic memory, we can decode its subsequent replay during rest. We argue that such an approach advances cognitive research beyond a focus on immediate task demand and provides insight into the functional relevance of the intrinsic neural pattern (for example, the default mode network). This in turn enables a greater integration between human and animal neuroscience, facilitating experimental testing of theoretical accounts of intrinsic activity, and opening new avenues of research in psychiatry

    An embedded process perspective

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    It remains a dogma in cognitive neuroscience to separate human attention and memory into distinct modules and processes. Here we propose that brain rhythms reflect the embedded nature of these processes in the human brain, as evident from their shared neural signatures: gamma oscillations (30–90 Hz) reflect sensory information processing and activated neural representations (memory items). The theta rhythm (3–8 Hz) is a pacemaker of explicit control processes (central executive), structuring neural information processing, bit by bit, as reflected in the theta-gamma code. By representing memory items in a sequential and time-compressed manner the theta-gamma code is hypothesized to solve key problems of neural computation: (1) attentional sampling (integrating and segregating information processing), (2) mnemonic updating (implementing Hebbian learning), and (3) predictive coding (advancing information processing ahead of the real time to guide behavior). In this framework, reduced alpha oscillations (8–14 Hz) reflect activated semantic networks, involved in both explicit and implicit mnemonic processes. Linking recent theoretical accounts and empirical insights on neural rhythms to the embedded-process model advances our understanding of the integrated nature of attention and memory – as the bedrock of human cognition

    Selective Theta-Synchronization of Choice-Relevant Information Subserves Goal-Directed Behavior

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    Theta activity reflects a state of rhythmic modulation of excitability at the level of single neuron membranes, within local neuronal groups and between distant nodes of a neuronal network. A wealth of evidence has shown that during theta states distant neuronal groups synchronize, forming networks of spatially confined neuronal clusters at specific time periods during task performance. Here, we show that a functional commonality of networks engaging in theta rhythmic states is that they emerge around decision points, reflecting rhythmic synchronization of choice-relevant information. Decision points characterize a point in time shortly before a subject chooses to select one action over another, i.e., when automatic behavior is terminated and the organism reactivates multiple sources of information to evaluate the evidence for available choices. As such, decision processes require the coordinated retrieval of choice-relevant information including (i) the retrieval of stimulus evaluations (stimulus–reward associations) and reward expectancies about future outcomes, (ii) the retrieval of past and prospective memories (e.g., stimulus–stimulus associations), (iii) the reactivation of contextual task rule representations (e.g., stimulus–response mappings), along with (iv) an ongoing assessment of sensory evidence. An increasing number of studies reveal that retrieval of these multiple types of information proceeds within few theta cycles through synchronized spiking activity across limbic, striatal, and cortical processing nodes. The outlined evidence suggests that evolving spatially and temporally specific theta synchronization could serve as the critical correlate underlying the selection of a choice during goal-directed behavior
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