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Zhou X, Zhu J, Yao Y, Yang X, Shen Z, Wang Y. Diminished representational momentum for physical states in patients with depressive disorder. J Psychiatr Res 2024; 175:103-107. [PMID: 38718440 DOI: 10.1016/j.jpsychires.2024.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 04/30/2024] [Accepted: 05/02/2024] [Indexed: 09/06/2024]
Abstract
The representational momentum for physical state changes refers to the fact that we remember objects as more changed in physical state than they actually were. It has been well documented that depressive disorder is associated with impairment of time perception. Thus, the present work was conducted with the aim to investigate the representational momentum for physical state changes in patients with depressive disorder, firstly. Forty patients with depressive disorder and forty-two healthy controls were administrated with the task of representational momentum for physical state changes and task of time perception. The stimulus comprises 10 videos showing object state changes (e.g., ice melting). During playing, the video would stop and immediately be obscured by mosaics at a specific point. Participants select from three detection images (the exact frame of the video just be masked, the frame after that, and the frame before that) to test representational momentum and estimate the duration of the video in seconds to test time perception. The depressive group showed diminished representational momentum (i.e., lower scores on the task of representational momentum) than normal controls (U = 215.00, Z = -5.83, p < 0.001). In addition, the scores on the task of representational momentum were significantly positively related with the scores on the task of time perception among depressive group (r = 0.446, p = 0.004). The findings indicate that patients with depressive disorder may exhibit diminished representational momentum for physical states, which may be correlated with their impairment of time perception.
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Affiliation(s)
- Xiangyi Zhou
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui Province, People's Republic of China
| | - Jiamin Zhu
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui Province, People's Republic of China
| | - Yuke Yao
- College of Information Engineer, China Jiliang University, Hangzhou, Zhejiang Province, People's Republic of China
| | - Xiaohuanghao Yang
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui Province, People's Republic of China
| | - Zhihua Shen
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, People's Republic of China; Zhejiang Provincial Institute of Drug Abuse Research, Hangzhou, Zhejiang Province, China
| | - Yongguang Wang
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui Province, People's Republic of China; Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, People's Republic of China; Zhejiang Provincial Institute of Drug Abuse Research, Hangzhou, Zhejiang Province, China.
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Leroy N, Majerus S, D'Argembeau A. Working memory capacity for continuous events: The root of temporal compression in episodic memory? Cognition 2024; 247:105789. [PMID: 38583322 DOI: 10.1016/j.cognition.2024.105789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 02/05/2024] [Accepted: 04/01/2024] [Indexed: 04/09/2024]
Abstract
Remembering the unfolding of past episodes usually takes less time than their actual duration. In this study, we evaluated whether such temporal compression emerges when continuous events are too long to be fully held in working memory. To do so, we asked 90 young adults to watch and mentally replay video clips showing people performing a continuous action (e.g., turning a car jack) that lasted 3, 6, 9, 12, or 15 s. For each clip, participants had to carefully watch the event and then to mentally replay it as accurately and precisely as possible. Results showed that mental replay durations increased with event duration but in a non-linear manner: they were close to the actual event duration for short videos (3-9 s), but significantly smaller for longer videos (12 and 15 s). These results suggest that working memory is temporally limited in its capacity to represent continuous events, which could in part explain why the unfolding of events is temporally compressed in episodic memory.
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Zahid U, Guo Q, Fountas Z. Predictive Coding as a Neuromorphic Alternative to Backpropagation: A Critical Evaluation. Neural Comput 2023; 35:1881-1909. [PMID: 37844326 DOI: 10.1162/neco_a_01620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 08/01/2023] [Indexed: 10/18/2023]
Abstract
Backpropagation has rapidly become the workhorse credit assignment algorithm for modern deep learning methods. Recently, modified forms of predictive coding (PC), an algorithm with origins in computational neuroscience, have been shown to result in approximately or exactly equal parameter updates to those under backpropagation. Due to this connection, it has been suggested that PC can act as an alternative to backpropagation with desirable properties that may facilitate implementation in neuromorphic systems. Here, we explore these claims using the different contemporary PC variants proposed in the literature. We obtain time complexity bounds for these PC variants, which we show are lower bounded by backpropagation. We also present key properties of these variants that have implications for neurobiological plausibility and their interpretations, particularly from the perspective of standard PC as a variational Bayes algorithm for latent probabilistic models. Our findings shed new light on the connection between the two learning frameworks and suggest that in its current forms, PC may have more limited potential as a direct replacement of backpropagation than previously envisioned.
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Affiliation(s)
- Umais Zahid
- Huawei Technologies R&D, London N19 3HT, U.K.
| | - Qinghai Guo
- Huawei Technologies R&D, Shenzhen 518129, China
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Muzik O, Diwadkar VA. Depth and hierarchies in the predictive brain: From reaction to action. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2023; 14:e1664. [PMID: 37518831 DOI: 10.1002/wcs.1664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 05/18/2023] [Accepted: 07/10/2023] [Indexed: 08/01/2023]
Abstract
The human brain is a prediction device, a view widely accepted in neuroscience. Prediction is a rational and efficient response that relies on the brain's ability to create and employ generative models to optimize actions over unpredictable time horizons. We argue that extant predictive frameworks while compelling, have not explicitly accounted for the following: (a) The brain's generative models must incorporate predictive depth (i.e., rely on degrees of abstraction to enable predictions over different time horizons); (b) The brain's implementation scheme to account for varying predictive depth relies on dynamic predictive hierarchies formed using the brain's functional networks. We show that these hierarchies incorporate the ascending processes (driven by reaction), and the descending processes (related to prediction), eventually driving action. Because they are dynamically formed, predictive hierarchies allow the brain to address predictive challenges in virtually any domain. By way of application, we explain how this framework can be applied to heretofore poorly understood processes of human behavioral thermoregulation. Although mammalian thermoregulation has been closely tied to deep brain structures engaged in autonomic control such as the hypothalamus, this narrow conception does not translate well to humans. In addition to profound differences in evolutionary history, the human brain is bestowed with substantially increased functional complexity (that itself emerged from evolutionary differences). We argue that behavioral thermoregulation in humans is possible because, (a) ascending signals shaped by homeostatic sub-networks, interject with (b) descending signals related to prediction (implemented in interoceptive and executive sub-networks) and action (implemented in executive sub-networks). These sub-networks cumulatively form a predictive hierarchy for human thermoregulation, potentiating a range of viable responses to known and unknown thermoregulatory challenges. We suggest that our proposed extensions to the predictive framework provide a set of generalizable principles that can further illuminate the many facets of the predictive brain. This article is categorized under: Neuroscience > Behavior Philosophy > Action Psychology > Prediction.
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Affiliation(s)
- Otto Muzik
- Department of Pediatrics, Wayne State University School of Medicine, Children's Hospital of Michigan, Michigan, USA
| | - Vaibhav A Diwadkar
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, Michigan, USA
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Kumar M, Goldstein A, Michelmann S, Zacks JM, Hasson U, Norman KA. Bayesian Surprise Predicts Human Event Segmentation in Story Listening. Cogn Sci 2023; 47:e13343. [PMID: 37867379 DOI: 10.1111/cogs.13343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 08/28/2023] [Accepted: 09/01/2023] [Indexed: 10/24/2023]
Abstract
Event segmentation theory posits that people segment continuous experience into discrete events and that event boundaries occur when there are large transient increases in prediction error. Here, we set out to test this theory in the context of story listening, by using a deep learning language model (GPT-2) to compute the predicted probability distribution of the next word, at each point in the story. For three stories, we used the probability distributions generated by GPT-2 to compute the time series of prediction error. We also asked participants to listen to these stories while marking event boundaries. We used regression models to relate the GPT-2 measures to the human segmentation data. We found that event boundaries are associated with transient increases in Bayesian surprise but not with a simpler measure of prediction error (surprisal) that tracks, for each word in the story, how strongly that word was predicted at the previous time point. These results support the hypothesis that prediction error serves as a control mechanism governing event segmentation and point to important differences between operational definitions of prediction error.
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Affiliation(s)
- Manoj Kumar
- Princeton Neuroscience Institute, Princeton University
| | - Ariel Goldstein
- Department of Cognitive and Brain Sciences and Business School, Hebrew University
- Google Research, Tel-Aviv
| | | | - Jeffrey M Zacks
- Department of Psychological & Brain Sciences, Washington University in St. Louis
| | - Uri Hasson
- Princeton Neuroscience Institute, Princeton University
- Department of Psychology, Princeton University
| | - Kenneth A Norman
- Princeton Neuroscience Institute, Princeton University
- Department of Psychology, Princeton University
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Marković D, Reiter AMF, Kiebel SJ. Revealing human sensitivity to a latent temporal structure of changes. Front Behav Neurosci 2022; 16:962494. [PMID: 36325156 PMCID: PMC9621332 DOI: 10.3389/fnbeh.2022.962494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 09/26/2022] [Indexed: 11/29/2022] Open
Abstract
Precisely timed behavior and accurate time perception plays a critical role in our everyday lives, as our wellbeing and even survival can depend on well-timed decisions. Although the temporal structure of the world around us is essential for human decision making, we know surprisingly little about how representation of temporal structure of our everyday environment impacts decision making. How does the representation of temporal structure affect our ability to generate well-timed decisions? Here we address this question by using a well-established dynamic probabilistic learning task. Using computational modeling, we found that human subjects' beliefs about temporal structure are reflected in their choices to either exploit their current knowledge or to explore novel options. The model-based analysis illustrates a large within-group and within-subject heterogeneity. To explain these results, we propose a normative model for how temporal structure is used in decision making, based on the semi-Markov formalism in the active inference framework. We discuss potential key applications of the presented approach to the fields of cognitive phenotyping and computational psychiatry.
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Affiliation(s)
- Dimitrije Marković
- Department of Psychology, Technische Universität Dresden, Dresden, Germany
- *Correspondence: Dimitrije Marković
| | - Andrea M. F. Reiter
- Department of Psychology, Technische Universität Dresden, Dresden, Germany
- Department of Child and Adolescence Psychiatry, Psychosomatics and Psychotherapy, Centre of Mental Health, University Hospital Würzburg, Würzburg, Germany
- German Center of Prevention Research on Mental Health, Julius-Maximilians Universität Würzburg, Würzburg, Germany
| | - Stefan J. Kiebel
- Department of Psychology, Technische Universität Dresden, Dresden, Germany
- Centre for Tactile Internet with Human-in-the-Loop (CeTI), Technische Universität Dresden, Dresden, Germany
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Tsao A, Yousefzadeh SA, Meck WH, Moser MB, Moser EI. The neural bases for timing of durations. Nat Rev Neurosci 2022; 23:646-665. [PMID: 36097049 DOI: 10.1038/s41583-022-00623-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/19/2022] [Indexed: 11/10/2022]
Abstract
Durations are defined by a beginning and an end, and a major distinction is drawn between durations that start in the present and end in the future ('prospective timing') and durations that start in the past and end either in the past or the present ('retrospective timing'). Different psychological processes are thought to be engaged in each of these cases. The former is thought to engage a clock-like mechanism that accurately tracks the continuing passage of time, whereas the latter is thought to engage a reconstructive process that utilizes both temporal and non-temporal information from the memory of past events. We propose that, from a biological perspective, these two forms of duration 'estimation' are supported by computational processes that are both reliant on population state dynamics but are nevertheless distinct. Prospective timing is effectively carried out in a single step where the ongoing dynamics of population activity directly serve as the computation of duration, whereas retrospective timing is carried out in two steps: the initial generation of population state dynamics through the process of event segmentation and the subsequent computation of duration utilizing the memory of those dynamics.
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Affiliation(s)
- Albert Tsao
- Department of Biology, Stanford University, Stanford, CA, USA.
| | | | - Warren H Meck
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - May-Britt Moser
- Centre for Neural Computation, Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
| | - Edvard I Moser
- Centre for Neural Computation, Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway.
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