{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:31:17Z","timestamp":1778081477179,"version":"3.51.4"},"reference-count":59,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>According to a recent experimental phenomenology\u2013information processing theory, the sensory strength, or vividness, of visual mental images self-reported by human observers reflects the intensive variation in subjective time duration during the process of generation of said mental imagery. The primary objective of this study was to test the hypothesis that a biologically plausible essential multilayer perceptron (MLP) architecture can validly map the phenomenological categories of subjective time duration onto levels of subjectively self-reported vividness. A secondary objective was to explore whether this type of neural network cognitive modeling approach can give insight into plausible underlying large-scale brain dynamics. To achieve these objectives, vividness self-reports and reaction times from a previously collected database were reanalyzed using multilayered perceptron network models. The input layer consisted of six levels representing vividness self-reports and a reaction time cofactor. A single hidden layer consisted of three nodes representing the salience, task positive, and default mode networks. The output layer consisted of five levels representing Vittorio Benussi\u2019s subjective time categories. Across different models of networks, Benussi\u2019s subjective time categories (Level 1 = very brief, 2 = brief, 3 = present, 4 = long, 5 = very long) were predicted by visual imagery vividness level 1 (=no image) to 5 (=very vivid) with over 90% success in classification accuracy, precision, recall, and F1-score. This accuracy level was maintained after 5-fold cross validation. Linear regressions, Welch\u2019s t-test for independent coefficients, and Pearson\u2019s correlation analysis were applied to the resulting hidden node weight vectors, obtaining evidence for strong correlation and anticorrelation between nodes. This study successfully mapped Benussi\u2019s five levels of subjective time categories onto the activation patterns of a simple MLP, providing a novel computational framework for experimental phenomenology. Our results revealed structured, complex dynamics between the task positive network (TPN), the default mode network (DMN), and the salience network (SN), suggesting that the neural mechanisms underlying temporal consciousness involve flexible network interactions beyond the traditional triple network model.<\/jats:p>","DOI":"10.3390\/make7030082","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T15:53:27Z","timestamp":1755100407000},"page":"82","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multilayer Perceptron Mapping of Subjective Time Duration onto Mental Imagery Vividness and Underlying Brain Dynamics: A Neural Cognitive Modeling Approach"],"prefix":"10.3390","volume":"7","author":[{"given":"Matthew","family":"Sheculski","sequence":"first","affiliation":[{"name":"Neuroscience of Imagination Cognition and Emotion Research (NICER) Lab, Department of Neuroscience, Carleton University, Ottawa, ON K1S 5B6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8295-9850","authenticated-orcid":false,"given":"Amedeo","family":"D\u2019Angiulli","sequence":"additional","affiliation":[{"name":"Neuroscience of Imagination Cognition and Emotion Research (NICER) Lab, Department of Neuroscience, Carleton University, Ottawa, ON K1S 5B6, Canada"},{"name":"Children\u2019s Hospital of Eastern Ontario Research Institute, Ottawa, ON K1H 8L1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Arstila, V., and Lloyd, D. (2014). Subjective Duration in the Laboratory and the World Outside. Subjective Time: The Philosophy, Psychology, and Neuroscience of Temporality, The MIT Press.","DOI":"10.7551\/mitpress\/8516.001.0001"},{"key":"ref_2","unstructured":"Zalta, E.N., and Nodelman, U. (2024). Temporal Consciousness. The Stanford Encyclopedia of Philosophy, Metaphysics Research Lab, Stanford University. Available online: https:\/\/plato.stanford.edu\/archives\/fall2024\/entries\/consciousness-temporal\/."},{"key":"ref_3","first-page":"321","article-title":"Forms of Completion","volume":"50","author":"Albertazzi","year":"1995","journal-title":"Grazer Philos. Stud."},{"key":"ref_4","unstructured":"Benussi, V. (1913). Psychologie Der Zeitauffassung, Available online: https:\/\/onlinebooks.library.upenn.edu\/webbin\/book\/lookupid?key=ha005762281."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Albertazzi, L. (2017). Vittorio Benussi (1878\u20131927). The School of Alexius Meinong, Routledge.","DOI":"10.4324\/9781315237176-4"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.cortex.2017.09.014","article-title":"The Neural Correlates of Visual Imagery Vividness\u2014An fMRI Study and Literature Review","volume":"105","author":"Fulford","year":"2018","journal-title":"Cortex"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.3758\/BF03213401","article-title":"Generating Visual Mental Images: Latency and Vividness Are Inversely Related","volume":"30","author":"Reeves","year":"2002","journal-title":"Mem. Cognit."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lefebvre, E., and D\u2019Angiulli, A. (2019). Imagery-Mediated Verbal Learning Depends on Vividness\u2013Familiarity Interactions: The Possible Role of Dualistic Resting State Network Activity Interference. Brain Sci., 9.","DOI":"10.3390\/brainsci9060143"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1196\/annals.1440.011","article-title":"The Brain\u2019s Default Network","volume":"1124","author":"Buckner","year":"2008","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1073\/pnas.98.2.676","article-title":"A Default Mode of Brain Function","volume":"98","author":"Raichle","year":"2001","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1111\/nyas.12360","article-title":"The Default Network and Self-Generated Thought: Component Processes, Dynamic Control, and Clinical Relevance","volume":"1316","author":"Smallwood","year":"2014","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4689","DOI":"10.1523\/JNEUROSCI.23-11-04689.2003","article-title":"Functional Organization of Human Intraparietal and Frontal Cortex for Attending, Looking, and Pointing","volume":"23","author":"Astafiev","year":"2003","journal-title":"J. Neurosci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1038\/nrn755","article-title":"Control of Goal-Directed and Stimulus-Driven Attention in the Brain","volume":"3","author":"Corbetta","year":"2002","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1523\/JNEUROSCI.5587-06.2007","article-title":"Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control","volume":"27","author":"Seeley","year":"2007","journal-title":"J. Neurosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1007\/s00429-010-0262-0","article-title":"Saliency, Switching, Attention and Control: A Network Model of Insula Function","volume":"214","author":"Menon","year":"2010","journal-title":"Brain Struct. Funct."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1648","DOI":"10.1016\/j.neuroimage.2010.01.051","article-title":"Dissociating the Roles of the Default-Mode, Dorsal, and Ventral Networks in Episodic Memory Retrieval","volume":"50","author":"Kim","year":"2010","journal-title":"NeuroImage"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neucom.2020.07.079","article-title":"Neural Antagonistic Mechanism between Default-Mode and Task-Positive Networks","volume":"417","author":"Cheng","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1016\/j.neuroimage.2009.07.058","article-title":"Overlapping Brain Activity between Episodic Memory Encoding and Retrieval: Roles of the Task-Positive and Task-Negative Networks","volume":"49","author":"Kim","year":"2010","journal-title":"NeuroImage"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1007\/s00429-022-02467-0","article-title":"Tasks Activating the Default Mode Network Map Multiple Functional Systems","volume":"227","author":"Mancuso","year":"2022","journal-title":"Brain Struct. Funct."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"12569","DOI":"10.1073\/pnas.0800005105","article-title":"A Critical Role for the Right Fronto-Insular Cortex in Switching between Central-Executive and Default-Mode Networks","volume":"105","author":"Sridharan","year":"2008","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Levine, D.S. (2000). Introduction to Neural and Cognitive Modeling, Psychology Press.","DOI":"10.4324\/9781410605504"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1037\/0033-295X.94.2.148","article-title":"Seeing and Imagining in the Cerebral Hemispheres: A Computational Approach","volume":"94","author":"Kosslyn","year":"1987","journal-title":"Psychol. Rev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1037\/0096-3445.120.4.339","article-title":"A Computational Model of Semantic Memory Impairment: Modality Specificity and Emergent Category Specificity","volume":"120","author":"Farah","year":"1991","journal-title":"J. Exp. Psychol. Gen."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1037\/0033-295X.100.4.571","article-title":"Dissociated Overt and Covert Recognition as an Emergent Property of a Lesioned Neural Network","volume":"100","author":"Farah","year":"1993","journal-title":"Psychol. Rev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1162\/jocn.1994.6.4.377","article-title":"Mechanisms of Spatial Attention: The Relation of Macrostructure to Microstructure in Parietal Neglect","volume":"6","author":"Cohen","year":"1994","journal-title":"J. Cogn. Neurosci."},{"key":"ref_26","unstructured":"Schwartz, E.L. (1990). Broken Brains and Normal Minds: Why Humpty-Dumpty Needs a Skeleton. Computational Neuroscience, MIT Press."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Polk, T.A., and Seifert, C.M. (2002). Neuropsychological Inference with an Interactive Brain: A Critique of the \u201cLocality\u201d Assumption. Cognitive Modeling, A Bradford Book; MIT Press.","DOI":"10.7551\/mitpress\/1888.001.0001"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2727","DOI":"10.1016\/j.neuron.2021.07.002","article-title":"Single Cortical Neurons as Deep Artificial Neural Networks","volume":"109","author":"Beniaguev","year":"2021","journal-title":"Neuron"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5164","DOI":"10.1038\/s41467-021-25427-4","article-title":"Deep Neural Networks Using a Single Neuron: Folded-in-Time Architecture Using Feedback-Modulated Delay Loops","volume":"12","author":"Stelzer","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_30","first-page":"62","article-title":"NeuCube: A Spiking Neural Network Architecture for Mapping, Learning and Understanding of Spatio-Temporal Brain Data","volume":"52","author":"Kasabov","year":"2014","journal-title":"Neural Netw. Off. J. Int. Neural Netw. Soc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/BF02551274","article-title":"Approximation by Superpositions of a Sigmoidal Function","volume":"2","author":"Cybenko","year":"1989","journal-title":"Math. Control Signals Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Aleksander, I. (2014). Impossible Minds: My Neurons, My Consciousness (Revised Edition), World Scientific.","DOI":"10.1142\/p971"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Albantakis, L., Hintze, A., Koch, C., Adami, C., and Tononi, G. (2014). Evolution of Integrated Causal Structures in Animats Exposed to Environments of Increasing Complexity. PLoS Comput. Biol., 10.","DOI":"10.1371\/journal.pcbi.1003966"},{"key":"ref_34","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3326","DOI":"10.1109\/TMI.2021.3083984","article-title":"Estimating Effective Connectivity by Recurrent Generative Adversarial Networks","volume":"40","author":"Ji","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1007\/s13246-025-01543-z","article-title":"Estimating Brain Effective Connectivity from Time Series Using Recurrent Neural Networks","volume":"48","author":"Dai","year":"2025","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.compbiomed.2019.05.012","article-title":"A Self-Organized Recurrent Neural Network for Estimating the Effective Connectivity and Its Application to EEG Data","volume":"110","author":"Abbasvandi","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1038\/s41583-021-00473-5","article-title":"Biological Constraints on Neural Network Models of Cognitive Function","volume":"22","author":"Tomasello","year":"2021","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.neuroimage.2019.05.037","article-title":"Topological Correction of Infant White Matter Surfaces Using Anatomically Constrained Convolutional Neural Network","volume":"198","author":"Sun","year":"2019","journal-title":"NeuroImage"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.neuroimage.2014.05.052","article-title":"The Salience Network Is Responsible for Switching between the Default Mode Network and the Central Executive Network: Replication from DCM","volume":"99","author":"Goulden","year":"2014","journal-title":"NeuroImage"},{"key":"ref_41","first-page":"82","article-title":"What Does the Visual Buffer Tell the Mind\u2019s Eye?","volume":"8","author":"Reeves","year":"2003","journal-title":"Abstr. Psychon. Soc."},{"key":"ref_42","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4806","DOI":"10.1109\/ACCESS.2019.2962617","article-title":"The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling","volume":"8","author":"Ho","year":"2020","journal-title":"IEEE Access"},{"key":"ref_44","unstructured":"Korhonen, A., Traum, D., and M\u00e0rquez, L. (August, January 28). Bayes Test of Precision, Recall, and F1 Measure for Comparison of Two Natural Language Processing Models. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladeni\u010d, D., and Skowron, A. (2007, January 17\u201321). Efficient AUC Optimization for Classification. Proceedings of the Knowledge Discovery in Databases: PKDD 2007, Warsaw, Poland.","DOI":"10.1007\/978-3-540-74976-9"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1080\/00031305.2018.1543135","article-title":"Three Recommendations for Improving the Use of p-Values","volume":"73","author":"Benjamin","year":"2019","journal-title":"Am. Stat."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"25","DOI":"10.4097\/kja.21209","article-title":"Receiver Operating Characteristic Curve: Overview and Practical Use for Clinicians","volume":"75","author":"Nahm","year":"2022","journal-title":"Korean J. Anesthesiol."},{"key":"ref_48","unstructured":"Toga, A.W. (2015). Bayesian Model Inference. Brain Mapping, Academic Press."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.neuroimage.2016.05.048","article-title":"Decoupling of Large-Scale Brain Networks Supports the Consolidation of Durable Episodic Memories","volume":"153","author":"Sneve","year":"2017","journal-title":"NeuroImage"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"9673","DOI":"10.1073\/pnas.0504136102","article-title":"The Human Brain Is Intrinsically Organized into Dynamic, Anticorrelated Functional Networks","volume":"102","author":"Fox","year":"2005","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Marks, D.F. (2023). Phenomenological Studies of Visual Mental Imagery: A Review and Synthesis of Historical Datasets. Vision, 7.","DOI":"10.3390\/vision7040067"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"James, W. (1890). The Principles of Psychology, Henry Holt and Co.","DOI":"10.1037\/10538-000"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2459","DOI":"10.1097\/00001756-200411150-00005","article-title":"Neural Network for Encoding Immediate Memory in Phonological Processing","volume":"15","author":"Li","year":"2004","journal-title":"Neuroreport"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"133","DOI":"10.31887\/DCNS.2018.20.2\/smarek","article-title":"The Frontoparietal Network: Function, Electrophysiology, and Importance of Individual Precision Mapping","volume":"20","author":"Marek","year":"2018","journal-title":"Dialogues Clin. Neurosci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.neubiorev.2017.02.009","article-title":"The Task Novelty Paradox: Flexible Control of Inflexible Neural Pathways during Rapid Instructed Task Learning","volume":"81","author":"Cole","year":"2017","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"5243","DOI":"10.1523\/JNEUROSCI.1273-20.2021","article-title":"The Ventral and Dorsal Default Mode Networks Are Dissociably Modulated by the Vividness and Valence of Imagined Events","volume":"41","author":"Lee","year":"2021","journal-title":"J. Neurosci. Off. J. Soc. Neurosci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1016\/j.neuropsychologia.2008.10.026","article-title":"Constructive Episodic Simulation of the Future and the Past: Distinct Subsystems of a Core Brain Network Mediate Imagining and Remembering","volume":"47","author":"Addis","year":"2009","journal-title":"Neuropsychologia"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ribeiro da Costa, C., Soares, J.M., Oliveira-Silva, P., Sampaio, A., and Coutinho, J.F. (2022). Interplay between the Salience and the Default Mode Network in a Social-Cognitive Task toward a Close Other. Front. Psychiatry, 12.","DOI":"10.3389\/fpsyt.2021.718400"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.tics.2011.08.003","article-title":"Large-Scale Brain Networks and Psychopathology: A Unifying Triple Network Model","volume":"15","author":"Menon","year":"2011","journal-title":"Trends Cogn. Sci."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/3\/82\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:26:19Z","timestamp":1760034379000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/3\/82"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,13]]},"references-count":59,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["make7030082"],"URL":"https:\/\/doi.org\/10.3390\/make7030082","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,13]]}}}