Cognitive Neuroscience最新文献

Dissociating model architectures from inference computations. 从推理计算中分离模型架构。

IF 2 4区医学

Cognitive Neuroscience Pub Date : 2025-07-17 DOI: 10.1080/17588928.2025.2532604

Noor Sajid, Johan Medrano

引用次数: 0

ROSE: A Universal Neural Grammar. 柔丝：通用神经语法。

IF 2 4区医学

Cognitive Neuroscience Pub Date : 2025-07-14 DOI: 10.1080/17588928.2025.2523875

Elliot Murphy

{"title":"ROSE: A Universal Neural Grammar.","authors":"Elliot Murphy","doi":"10.1080/17588928.2025.2523875","DOIUrl":"https://doi.org/10.1080/17588928.2025.2523875","url":null,"abstract":"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.","PeriodicalId":10413,"journal":{"name":"Cognitive Neuroscience","volume":" ","pages":"1-32"},"PeriodicalIF":2.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144625415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Embeddings as Dirichlet counts: Attention is the tip of the iceberg. Dirichlet计算的嵌入：注意力是冰山一角。

IF 2 4区医学

Cognitive Neuroscience Pub Date : 2025-07-09 DOI: 10.1080/17588928.2025.2530430

Alexander Bernard Kiefer

引用次数: 0

Non-Markovian systems, phenomenology, and the challenges of capturing meaning and context - comment on Parr, Pezzulo, and Friston (2025). 非马尔可夫系统，现象学，以及捕捉意义和上下文的挑战——评论Parr， Pezzulo, and Friston（2025）。

IF 2 4区医学

Cognitive Neuroscience Pub Date : 2025-07-04 DOI: 10.1080/17588928.2025.2523889

Mahault Albarracin, Dalton A R Sakthivadivel

引用次数: 0

Paying attention to process. 注重过程。

IF 2 4区医学

Cognitive Neuroscience Pub Date : 2025-07-03 DOI: 10.1080/17588928.2025.2520313

Ryan Singh, Alexander Tschantz, Christopher L Buckley

引用次数: 0

Beyond prediction: comments on the format of natural intelligence. 超越预测：评论自然智能的形式。

IF 2 4区医学

Cognitive Neuroscience Pub Date : 2025-06-18 DOI: 10.1080/17588928.2025.2521403

Elliot Murphy

引用次数: 0

Beyond individuals: Collective predictive coding for memory, attention, and the emergence of language. 超越个体：记忆、注意力和语言出现的集体预测编码。

IF 2 4区医学

Cognitive Neuroscience Pub Date : 2025-06-17 DOI: 10.1080/17588928.2025.2518942

Tadahiro Taniguchi

引用次数: 0

How deep will you go? Hierarchy in predictive coding and transformers. 你会走多远？预测编码和变压器的层次结构。

IF 2 4区医学

Cognitive Neuroscience Pub Date : 2025-06-17 DOI: 10.1080/17588928.2025.2518945

Jeffrey F Queißer, Henrique Oyama, Jun Tani

引用次数: 0

Auditory facilitation in deterministic versus stochastic worlds. 确定性与随机世界中的听觉促进。

IF 2 4区医学

Cognitive Neuroscience Pub Date : 2025-04-29 DOI: 10.1080/17588928.2025.2497762

Berfin Bastug, Urte Roeber, Erich Schröger

{"title":"Auditory facilitation in deterministic versus stochastic worlds.","authors":"Berfin Bastug, Urte Roeber, Erich Schröger","doi":"10.1080/17588928.2025.2497762","DOIUrl":"https://doi.org/10.1080/17588928.2025.2497762","url":null,"abstract":"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.","PeriodicalId":10413,"journal":{"name":"Cognitive Neuroscience","volume":" ","pages":"1-7"},"PeriodicalIF":2.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143969935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beyond Markov: Transformers, memory, and attention. 超越马尔科夫：变形金刚、记忆和注意力。

IF 2 4区医学

Cognitive Neuroscience Pub Date : 2025-04-15 DOI: 10.1080/17588928.2025.2484485

Thomas Parr, Giovanni Pezzulo, Karl Friston

{"title":"Beyond Markov: Transformers, memory, and attention.","authors":"Thomas Parr, Giovanni Pezzulo, Karl Friston","doi":"10.1080/17588928.2025.2484485","DOIUrl":"https://doi.org/10.1080/17588928.2025.2484485","url":null,"abstract":"This paper asks what predictive processing models of brain function can learn from the success of transformer architectures. We suggest that the reason transformer architectures have been successful is that they implicitly commit to a non-Markovian generative model - in which we need memory to contextualize our current observations and make predictions about the future. Interestingly, both the notions of working memory in cognitive science and transformer architectures rely heavily upon the concept of attention. We will argue that the move beyond Markov is crucial in the construction of generative models capable of dealing with much of the sequential data - and certainly language - that our brains contend with. We characterize two broad approaches to this problem - deep temporal hierarchies and autoregressive models - with transformers being an example of the latter. Our key conclusions are that transformers benefit heavily from their use of embedding spaces that place strong metric priors on an implicit latent variable and utilize this metric to direct a form of attention that highlights the most relevant, and not only the most recent, previous elements in a sequence to help predict the next.","PeriodicalId":10413,"journal":{"name":"Cognitive Neuroscience","volume":" ","pages":"1-19"},"PeriodicalIF":2.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143984568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0