Cognitive Neuroscience最新文献

Parr, et al., explore the problem of non-Markovian pro cesses, in which the future state of a system depends not only on its present state but also on its past states. The authors suggest that the success of transformer networks in dealing with sequential data, such as language, stems from their ability to address this non-Markovian nature through the use of attention mechanisms. This commentary builds on their discussion, aiming to link it to some notions in Husserlian phenomenology and explore the implications for understanding meaning, context, and the nature of knowledge.

Processing natural language syntax requires a negotiation between symbolic and subsymbolic representations. Building on the recent representation, operation, structure, encoding (ROSE) neurocomputational architecture for syntax that scales from single units to inter-areal dynamics, I discuss the prospects of reconciling the neural code for hierarchical syntax with predictive processes. Here, the higher levels of ROSE provide instructions for symbolic phrase structure representations (S/E), while the lower levels provide probabilistic aspects of linguistic processing (R/O), with different types of cross-frequency coupling being hypothesized to interface these domains. I argue that ROSE provides a possible infrastructure for flexibly implementing distinct types of minimalist grammar parsers for the real-time processing of language. This perspective helps furnish a more restrictive 'core language network' in the brain than contemporary approaches that isolate general sentence composition. I define the language network as being critically involved in executing specific parsing operations (i.e. establishing phrasal categories, tree-structure depth, resolving dependencies, and retrieving proprietary lexical representations), capturing these network-defining operations jointly with probabilistic aspects of parsing. ROSE offers a 'mesoscopic protectorate' for natural language; an intermediate level of emergent organizational complexity that demands multi-scale modeling. By drawing principled relations across computational, algorithmic and implementational Marrian levels, ROSE offers new constraints on what a unified neurocomputational settlement for natural language syntax might look like, providing a tentative scaffold for a 'Universal Neural Grammar' - a species-specific format for neurally organizing the construction of compositional syntactic structures, which matures in accordance with a genetically determined biological matrix.

The brain learns statistical regularities in sensory sequences, enhancing behavioral performance for predictable stimuli while impairing behavioral performance for unpredictable stimuli. While previous research has shown that violations of non-informative regularities hinder task performance, it remains unclear whether predictable but task-irrelevant structures can facilitate performance. In a tone duration discrimination task, we manipulated the task-irrelevant pitch dimension by varying transition probabilities (TP) between successive tone frequencies. Participants judged duration, while pitch sequences were either deterministic (a rule-governed pitch pattern, TP = 1) or stochastic (no discernible pitch pattern, TP = 1/number of pitch levels). The tone pitch was task-irrelevant and it did not predict duration. Results showed that reaction times (RTs) were significantly faster for deterministic sequences, suggesting that predictability in a task-irrelevant dimension still facilitates task performance. RTs were also faster in two-tone sequences compared to eight-tone sequences, likely due to reduced memory load. These findings suggest that statistical learning benefits extend beyond task-relevant dimensions, supporting a predictive coding framework in which the brain integrates predictable sensory input to optimize cognitive processing.

With recent developments in artificial intelligence (AI), there is great interest in how mechanisms of human cognitive processing may be instantiated in those models and how those models may help us better understand human cognitive and neural processes. Recent research suggests predictive coding theories and associated generative models may help explain the processes of visual perception and language production, while newer AI models include mechanisms akin to human memory and attention. This special issue of Cognitive Neuroscience: Current Debates, Research & Reports presents 16 new papers that highlight important topics and present exciting new data, models, and controversies. The articles include a new discussion paper by Parr, Pezzulo, and Friston exploring how transformer architectures utilize non-Markovian generative models and how an attention-like process is critical for processing complex sequential data. This is followed by seven insightful commentaries and a reply from the authors. A discussion paper on a new neurocomputational model of syntax is provided by Murphy, in which predictive processes are integrated in a multi-level, hierarchical syntax architecture. This is followed by five commentaries suggesting important evolutionary and developmental perspectives and ways to explore and test the model. Finally, an empirical article by Bastug, Roeber, and Schröger on auditory perception presents new evidence suggesting that distracting information requires less cognitive processing when it is predictable. The topics of this special issue are evolving rapidly and promise to be at the heart of future developments in artificial learning systems and theories of the brain mechanisms that mediate cognitive processes.

Despite the overtly discrete nature of language, the use of semantic embedding spaces is pervasive in modern computational linguistics and machine learning for natural language. I argue that this is intelligible if language is viewed as an interface into a general-purpose system of concepts, in which metric spaces capture rich relationships. At the same time, language embeddings can be regarded, at least heuristically, as equivalent to parameters of distributions over word-word relationships.

Parr et al., 2025 examines how auto-regressive and deep temporal models differ in their treatment of non-Markovian sequence modelling. Building on this, we highlight the need for dissociating model architectures-i.e., how the predictive distribution factorises-from the computations invoked at inference. We demonstrate that deep temporal computations are mimicked by autoregressive models by structuring context access during iterative inference. Using a transformer trained on next-token prediction, we show that inducing hierarchical temporal factorisation during iterative inference maintains predictive capacity while instantiating fewer computations. This emphasises that processes for constructing and refining predictions are not necessarily bound to their underlying model architectures.