Computational brain & behavior最新文献_第8页

What's Surprising About Surprisal. 惊喜的惊喜之处。

Computational brain & behavior Pub Date : 2025-01-01 Epub Date: 2025-02-21 DOI: 10.1007/s42113-025-00237-9

Sophie Slaats, Andrea E Martin

{"title":"What's Surprising About Surprisal.","authors":"Sophie Slaats, Andrea E Martin","doi":"10.1007/s42113-025-00237-9","DOIUrl":"10.1007/s42113-025-00237-9","url":null,"abstract":"In the computational and experimental psycholinguistic literature, the mechanisms behind syntactic structure building (e.g., combining words into phrases and sentences) are the subject of considerable debate. Much experimental work has shown that surprisal is a good predictor of human behavioral and neural data. These findings have led some authors to model language comprehension in a purely probabilistic way. In this paper, we use simulation to exemplify why surprisal works so well to model human data and to illustrate why exclusive reliance on it can be problematic for the development of mechanistic theories of language comprehension, particularly those with emphasis on meaning composition. Rather than arguing for the importance of structural or probabilistic information to the exclusion or exhaustion of the other, we argue more emphasis should be placed on understanding how the brain leverages both types of information (viz., statistical and structured). We propose that probabilistic information is an important cue to the structure in the message, but is not a substitute for the structure itself-neither computationally, formally, nor conceptually. Surprisal and other probabilistic metrics must play a key role as theoretical objects in any explanatory mechanistic theory of language processing, but that role remains in the service of the brain's goal of constructing structured meaning from sensory input.Supplementary information: The online version contains supplementary material available at 10.1007/s42113-025-00237-9.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"8 2","pages":"233-248"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12125142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparing Activation Typicality and Sparsity in a Deep CNN to Predict Facial Beauty. 比较深度CNN的激活典型性和稀疏性来预测面部美。

Computational brain & behavior Pub Date : 2024-12-11 eCollection Date: 2025-06-01 DOI: 10.1007/s42113-024-00231-7

Sonia Tieo, Melvin Bardin, Roland Bertin-Johannet, Nicolas Dibot, Tamra C Mendelson, William Puech, Julien P Renoult

{"title":"Comparing Activation Typicality and Sparsity in a Deep CNN to Predict Facial Beauty.","authors":"Sonia Tieo, Melvin Bardin, Roland Bertin-Johannet, Nicolas Dibot, Tamra C Mendelson, William Puech, Julien P Renoult","doi":"10.1007/s42113-024-00231-7","DOIUrl":"10.1007/s42113-024-00231-7","url":null,"abstract":"Processing fluency, which describes the subjective sensation of ease with which information is processed by the sensory systems and the brain, has become one of the most popular explanations of aesthetic appreciation and beauty. Two metrics have recently been proposed to model fluency: the sparsity of neuronal activation, which describes the concentration of activity in a subset of neurons, and the statistical typicality of activations, which describes how well the encoding of a stimulus matches a reference representation of stimuli of the category to which it belongs. Using convolutional neural networks (CNNs) as a model for the human visual system, this study compares the ability of these metrics to explain variation in facial attractiveness. Our findings show that the sparsity of neuronal activations is a more robust predictor of facial attractiveness than statistical typicality. Refining the reference representation to a single ethnicity or gender does not increase the explanatory power of statistical typicality. However, statistical typicality and sparsity predict facial beauty based on different layers of the CNNs, suggesting that they describe different neural mechanisms underlying fluency.Supplementary information: The online version contains supplementary material available at 10.1007/s42113-024-00231-7.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"8 2","pages":"249-261"},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298650/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning Temporal Relationships Between Symbols with Laplace Neural Manifolds. 用拉普拉斯神经流形学习符号间的时间关系。

Computational brain & behavior Pub Date : 2024-12-05 eCollection Date: 2025-06-01 DOI: 10.1007/s42113-024-00230-8

Marc W Howard, Zahra Gh Esfahani, Bao Le, Per B Sederberg

{"title":"Learning Temporal Relationships Between Symbols with Laplace Neural Manifolds.","authors":"Marc W Howard, Zahra Gh Esfahani, Bao Le, Per B Sederberg","doi":"10.1007/s42113-024-00230-8","DOIUrl":"10.1007/s42113-024-00230-8","url":null,"abstract":"Firing across populations of neurons in many regions of the mammalian brain maintains a temporal memory, a neural timeline of the recent past. Behavioral results demonstrate that people can both remember the past and anticipate the future over an analogous internal timeline. This paper presents a mathematical framework for building this timeline of the future. We assume that the input to the system is a time series of symbols-sparse tokenized representations of the present-in continuous time. The goal is to record pairwise temporal relationships between symbols over a wide range of time scales. We assume that the brain has access to a temporal memory in the form of the real Laplace transform. Hebbian associations with a diversity of synaptic time scales are formed between the past timeline and the present symbol. The associative memory stores the convolution between the past and the present. Knowing the temporal relationship between the past and the present allows one to infer relationships between the present and the future. With appropriate normalization, this Hebbian associative matrix can store a Laplace successor representation and a Laplace predecessor representation from which measures of temporal contingency can be evaluated. The diversity of synaptic time constants allows for the learning of non-stationary statistics as well as joint statistics between triplets of symbols. This framework synthesizes a number of recent neuroscientific findings including results from dopamine neurons in the mesolimbic forebrain.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"8 2","pages":"211-232"},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

What Makes a Good Theory? Interdisciplinary Perspectives. 什么是好的理论？跨学科的视角。

Computational brain & behavior Pub Date : 2024-11-13 eCollection Date: 2024-12-01 DOI: 10.1007/s42113-024-00225-5

Iris van Rooij, Berna Devezer, Joshua Skewes, Sashank Varma, Todd Wareham

引用次数: 0

Leveraging Psychophysics to Infer the Mechanisms of Encoding Change in Vision. 利用心理物理学来推断视觉编码变化的机制。

Computational brain & behavior Pub Date : 2024-11-08 eCollection Date: 2025-06-01 DOI: 10.1007/s42113-024-00227-3

Jason S Hays, Fabian A Soto

{"title":"Leveraging Psychophysics to Infer the Mechanisms of Encoding Change in Vision.","authors":"Jason S Hays, Fabian A Soto","doi":"10.1007/s42113-024-00227-3","DOIUrl":"10.1007/s42113-024-00227-3","url":null,"abstract":"The use of population encoding models has come to dominate the study of human vision, serving as a primary tool for making inferences about neuroscience studies neural code changes based on indirect measurements. A popular approach in computational neuroimaging is to use such models to obtain estimates of neural population responses via IEM. Recent research suggests that this approach may be prone to identifiability problems, with multiple mechanisms of encoding change producing similar changes in the estimated population responses. Psychophysical data might be able to provide additional constraints to infer the encoding change mechanism underlying some behavior of interest. Here, we used simulation to explore exactly which of a number of changes in neural population codes could be differentiated from observed changes in psychophysical thresholds. Eight mechanisms of encoding change were under study: specific and nonspecific gain, specific and nonspecific tuning, specific suppression, specific suppression plus gain, and inward and outward tuning shifts. We simulated psychophysical thresholds as a function of both external noise (TvN curves) or stimulus value (TvS curves) for a number of variations of each one of the models. With the exception of specific gain and specific tuning, all mechanisms produced qualitatively different patterns of change in the TvN and TvS curves, suggesting that psychophysical studies can be used as a complement to IEM, and provide constraints on inferences based on the latter. We use our results to provide recommendations for researchers and to re-interpret previous psychophysical data in terms of mechanisms of encoding change.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"8 2","pages":"262-285"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Retrieved Context Model of Serial Recall and Free Recall. 序列回忆与自由回忆的检索上下文模型。

Computational brain & behavior Pub Date : 2024-11-08 eCollection Date: 2025-03-01 DOI: 10.1007/s42113-024-00221-9

Lynn J Lohnas

{"title":"A Retrieved Context Model of Serial Recall and Free Recall.","authors":"Lynn J Lohnas","doi":"10.1007/s42113-024-00221-9","DOIUrl":"10.1007/s42113-024-00221-9","url":null,"abstract":"A full characterization of memory must include how participants use exogenous and endogenous cues to guide retrieval. In free recall, in which endogenous cues play a large role, retrieved context theories have emerged as a leading explanation of data on the dynamics of memory search (Lohnas & Healey, Psychology of Learning and Motivation, 75, 157-199, 2021). More recently, Logan and colleagues have advanced a retrieved context model to explain data on serial recall and motor production (Logan, Psychological Review, 125(4), 453-485, 2018, Psychological Review, 128(1), 1-44, 2021; Logan & Cox, Psychological Review, 128(6), 1197-1205, 2021, Psychological Review, 130(6), 1672-1687, 2023; Osth & Hurlstone, Psychological Review, 130(2), 213-245, 2023). Comparisons of recall transitions have further highlighted similarities among these tasks (e.g., Bhatarah et al., Memory & Cognition, 36(1), 20-34, 2008; Golomb et al., Memory & Cognition, 36(5), 947-956, 2008). Here, I evaluate retrieved context theory's ability to simultaneously account for data from these classic recall procedures. I show how a serial version of the context maintenance and retrieval model (termed sCMR) can account for dissociations between serial position curves and temporal clustering effects. I also show how sCMR can account for grouping effects using similar assumptions across recall procedures. The sCMR model provides a common theoretical framework to harmonize the disparate phenomena studied using these classic memory procedures, but also reveals the distinctions between serial and free recall through their relative dependence on different model-based mechanisms.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"8 1","pages":"1-35"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction: Towards a Generalized Bayesian Model of Reconstructive Memory. 更正：关于重建记忆的广义贝叶斯模型。

Computational brain & behavior Pub Date : 2024-11-06 eCollection Date: 2025-03-01 DOI: 10.1007/s42113-024-00226-4

Zihao Xu, Pernille Hemmer, Qiong Zhang

引用次数: 0

The Problem-Ladenness of Theory. 理论的问题负担性。

Computational brain & behavior Pub Date : 2024-10-16 eCollection Date: 2024-12-01 DOI: 10.1007/s42113-024-00219-3

Daniel Levenstein, Aniello De Santo, Saskia Heijnen, Manjari Narayan, Freek J W Oude Maatman, Jonathan Rawski, Cory Wright

{"title":"The Problem-Ladenness of Theory.","authors":"Daniel Levenstein, Aniello De Santo, Saskia Heijnen, Manjari Narayan, Freek J W Oude Maatman, Jonathan Rawski, Cory Wright","doi":"10.1007/s42113-024-00219-3","DOIUrl":"10.1007/s42113-024-00219-3","url":null,"abstract":"The cognitive sciences are facing questions of how to select from competing theories or develop those that suit their current needs. However, traditional accounts of theoretical virtues have not yet proven informative to theory development in these fields. We advance a pragmatic account by which theoretical virtues are heuristics we use to estimate a theory's contribution to a field's body of knowledge and the degree to which it increases that knowledge's ability to solve problems in the field's domain or problem space. From this perspective, properties that are traditionally considered epistemic virtues, such as a theory's fit to data or internal coherence, can be couched in terms of problem space coverage, and additional virtues come to light that reflect a theory's alignment with problem-having agents and the context in a societally embedded scientific system. This approach helps us understand why the needs of different fields result in different kinds of theories and allows us to formulate the challenges facing cognitive science in terms that we hope will facilitate their resolution through further theoretical development.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"7 4","pages":"548-571"},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From Empirical Problem-Solving to Theoretical Problem-Finding Perspectives on the Cognitive Sciences. 从认知科学的实证问题解决到理论问题发现视角。

Computational brain & behavior Pub Date : 2024-10-14 eCollection Date: 2024-12-01 DOI: 10.1007/s42113-024-00216-6

Federico Adolfi, Laura van de Braak, Marieke Woensdregt

引用次数: 0

Validation and Comparison of Non-stationary Cognitive Models: A Diffusion Model Application. 非平稳认知模型的验证与比较：扩散模型的应用。

Computational brain & behavior Pub Date : 2024-10-08 eCollection Date: 2025-06-01 DOI: 10.1007/s42113-024-00218-4

Lukas Schumacher, Martin Schnuerch, Andreas Voss, Stefan T Radev

{"title":"Validation and Comparison of Non-stationary Cognitive Models: A Diffusion Model Application.","authors":"Lukas Schumacher, Martin Schnuerch, Andreas Voss, Stefan T Radev","doi":"10.1007/s42113-024-00218-4","DOIUrl":"10.1007/s42113-024-00218-4","url":null,"abstract":"Cognitive processes undergo various fluctuations and transient states across different temporal scales. Superstatistics are emerging as a flexible framework for incorporating such non-stationary dynamics into existing cognitive model classes. In this work, we provide the first experimental validation of superstatistics and formal comparison of four non-stationary diffusion decision models in a specifically designed perceptual decision-making task. Task difficulty and speed-accuracy trade-off were systematically manipulated to induce expected changes in model parameters. To validate our models, we assess whether the inferred parameter trajectories align with the patterns and sequences of the experimental manipulations. To address computational challenges, we present novel deep learning techniques for amortized Bayesian estimation and comparison of models with time-varying parameters. Our findings indicate that transition models incorporating both gradual and abrupt parameter shifts provide the best fit to the empirical data. Moreover, we find that the inferred parameter trajectories closely mirror the sequence of experimental manipulations. Posterior re-simulations further underscore the ability of the models to faithfully reproduce critical data patterns. Accordingly, our results suggest that the inferred non-stationary dynamics may reflect actual changes in the targeted psychological constructs. We argue that our initial experimental validation paves the way for the widespread application of superstatistics in cognitive modeling and beyond.Supplementary information: The online version contains supplementary material available at 10.1007/s42113-024-00218-4.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"8 2","pages":"191-210"},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298658/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0