1
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Selvan RN, Cheng M, Siestrup S, Mecklenbrauck F, Jainta B, Pomp J, Zahedi A, Tamosiunaite M, Wörgötter F, Schubotz RI. Updating predictions in a complex repertoire of actions and its neural representation. Neuroimage 2024; 296:120687. [PMID: 38871038 DOI: 10.1016/j.neuroimage.2024.120687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 05/03/2024] [Accepted: 06/11/2024] [Indexed: 06/15/2024] Open
Abstract
Even though actions we observe in everyday life seem to unfold in a continuous manner, they are automatically divided into meaningful chunks, that are single actions or segments, which provide information for the formation and updating of internal predictive models. Specifically, boundaries between actions constitute a hub for predictive processing since the prediction of the current action comes to an end and calls for updating of predictions for the next action. In the current study, we investigated neural processes which characterize such boundaries using a repertoire of complex action sequences with a predefined probabilistic structure. Action sequences consisted of actions that started with the hand touching an object (T) and ended with the hand releasing the object (U). These action boundaries were determined using an automatic computer vision algorithm. Participants trained all action sequences by imitating demo videos. Subsequently, they returned for an fMRI session during which the original action sequences were presented in addition to slightly modified versions thereof. Participants completed a post-fMRI memory test to assess the retention of original action sequences. The exchange of individual actions, and thus a violation of action prediction, resulted in increased activation of the action observation network and the anterior insula. At U events, marking the end of an action, increased brain activation in supplementary motor area, striatum, and lingual gyrus was indicative of the retrieval of the previously encoded action repertoire. As expected, brain activation at U events also reflected the predefined probabilistic branching structure of the action repertoire. At T events, marking the beginning of the next action, midline and hippocampal regions were recruited, reflecting the selected prediction of the unfolding action segment. In conclusion, our findings contribute to a better understanding of the various cerebral processes characterizing prediction during the observation of complex action repertoires.
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Affiliation(s)
- Rosari Naveena Selvan
- Department of Psychology, University of Münster, Münster, Germany; Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany; Department for Computational Neuroscience, Third Institute of Physics - Biophysics, University of Göttingen, Göttingen, Germany.
| | - Minghao Cheng
- Department for Computational Neuroscience, Third Institute of Physics - Biophysics, University of Göttingen, Göttingen, Germany
| | - Sophie Siestrup
- Department of Psychology, University of Münster, Münster, Germany; Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
| | - Falko Mecklenbrauck
- Department of Psychology, University of Münster, Münster, Germany; Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
| | - Benjamin Jainta
- Department of Psychology, University of Münster, Münster, Germany
| | - Jennifer Pomp
- Department of Psychology, University of Münster, Münster, Germany; Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
| | - Anoushiravan Zahedi
- Department of Psychology, University of Münster, Münster, Germany; Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
| | - Minija Tamosiunaite
- Department for Computational Neuroscience, Third Institute of Physics - Biophysics, University of Göttingen, Göttingen, Germany; Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania
| | - Florentin Wörgötter
- Department for Computational Neuroscience, Third Institute of Physics - Biophysics, University of Göttingen, Göttingen, Germany
| | - Ricarda I Schubotz
- Department of Psychology, University of Münster, Münster, Germany; Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
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2
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Heng JG, Zhang J, Bonetti L, Lim WPH, Vuust P, Agres K, Chen SHA. Understanding music and aging through the lens of Bayesian inference. Neurosci Biobehav Rev 2024; 163:105768. [PMID: 38908730 DOI: 10.1016/j.neubiorev.2024.105768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 06/05/2024] [Accepted: 06/10/2024] [Indexed: 06/24/2024]
Abstract
Bayesian inference has recently gained momentum in explaining music perception and aging. A fundamental mechanism underlying Bayesian inference is the notion of prediction. This framework could explain how predictions pertaining to musical (melodic, rhythmic, harmonic) structures engender action, emotion, and learning, expanding related concepts of music research, such as musical expectancies, groove, pleasure, and tension. Moreover, a Bayesian perspective of music perception may shed new insights on the beneficial effects of music in aging. Aging could be framed as an optimization process of Bayesian inference. As predictive inferences refine over time, the reliance on consolidated priors increases, while the updating of prior models through Bayesian inference attenuates. This may affect the ability of older adults to estimate uncertainties in their environment, limiting their cognitive and behavioral repertoire. With Bayesian inference as an overarching framework, this review synthesizes the literature on predictive inferences in music and aging, and details how music could be a promising tool in preventive and rehabilitative interventions for older adults through the lens of Bayesian inference.
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Affiliation(s)
- Jiamin Gladys Heng
- School of Computer Science and Engineering, Nanyang Technological University, Singapore.
| | - Jiayi Zhang
- Interdisciplinary Graduate Program, Nanyang Technological University, Singapore; School of Social Sciences, Nanyang Technological University, Singapore; Centre for Research and Development in Learning, Nanyang Technological University, Singapore
| | - Leonardo Bonetti
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Aarhus, Aalborg, Denmark; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, United Kingdom; Department of Psychiatry, University of Oxford, United Kingdom; Department of Psychology, University of Bologna, Italy
| | | | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Aarhus, Aalborg, Denmark
| | - Kat Agres
- Centre for Music and Health, National University of Singapore, Singapore; Yong Siew Toh Conservatory of Music, National University of Singapore, Singapore
| | - Shen-Hsing Annabel Chen
- School of Social Sciences, Nanyang Technological University, Singapore; Centre for Research and Development in Learning, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; National Institute of Education, Nanyang Technological University, Singapore.
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3
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Rao RPN. A sensory-motor theory of the neocortex. Nat Neurosci 2024; 27:1221-1235. [PMID: 38937581 DOI: 10.1038/s41593-024-01673-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 04/26/2024] [Indexed: 06/29/2024]
Abstract
Recent neurophysiological and neuroanatomical studies suggest a close interaction between sensory and motor processes across the neocortex. Here, I propose that the neocortex implements active predictive coding (APC): each cortical area estimates both latent sensory states and actions (including potentially abstract actions internal to the cortex), and the cortex as a whole predicts the consequences of actions at multiple hierarchical levels. Feedback from higher areas modulates the dynamics of state and action networks in lower areas. I show how the same APC architecture can explain (1) how we recognize an object and its parts using eye movements, (2) why perception seems stable despite eye movements, (3) how we learn compositional representations, for example, part-whole hierarchies, (4) how complex actions can be planned using simpler actions, and (5) how we form episodic memories of sensory-motor experiences and learn abstract concepts such as a family tree. I postulate a mapping of the APC model to the laminar architecture of the cortex and suggest possible roles for cortico-cortical and cortico-subcortical pathways.
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Affiliation(s)
- Rajesh P N Rao
- Center for Neurotechnology, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
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4
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McGovern HT, Otten M. Priors and prejudice: hierarchical predictive processing in intergroup perception. Front Psychol 2024; 15:1386370. [PMID: 38939217 PMCID: PMC11210009 DOI: 10.3389/fpsyg.2024.1386370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 05/20/2024] [Indexed: 06/29/2024] Open
Abstract
Hierarchical predictive processing provides a framework outlining how prior expectations shape perception and cognition. Here, we highlight hierarchical predictive processing as a framework for explaining how social context and group-based social knowledge can directly shape intergroup perception. More specifically, we argue that hierarchical predictive processing confers a uniquely valuable toolset to explain extant findings and generate novel hypotheses for intergroup perception. We first provide an overview of hierarchical predictive processing, specifying its primary theoretical assumptions. We then review evidence showing how prior knowledge influences intergroup perception. Next, we outline how hierarchical predictive processing can account well for findings in the intergroup perception literature. We then underscore the theoretical strengths of hierarchical predictive processing compared to other frameworks in this space. We finish by outlining future directions and laying out hypotheses that test the implications of hierarchical predictive processing for intergroup perception and intergroup cognition more broadly. Taken together, hierarchical predictive processing provides explanatory value and capacity for novel hypothesis generation for intergroup perception.
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Affiliation(s)
- H. T. McGovern
- School of Psychology, The University of Queensland, Brisbane, QLD, Australia
| | - Marte Otten
- Brain & Cognition Group, Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
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5
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Sáringer S, Fehér Á, Sáry G, Kaposvári P. Perceptual Expectations Are Reflected by Early Alpha Power Reduction. J Cogn Neurosci 2024; 36:1282-1296. [PMID: 38652100 DOI: 10.1162/jocn_a_02169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
The predictability of a stimulus can be characterized by its transitional probability. Perceptual expectations derived from the transitional probability of the stimulus were found to modulate the early alpha oscillations in the sensory regions of the brain when neural responses to expected versus unexpected stimuli were compared. The objective of our study was to find out the extent to which this low-frequency oscillation reflects stimulus predictability. We aimed to detect the alpha-power difference with smaller differences in transitional probabilities by comparing expected stimuli with neutral ones. We studied the effect of expectation on perception by applying an unsupervised visual statistical learning paradigm with expected and neutral stimuli embedded in an image sequence while recording EEG. Time-frequency analysis showed that expected stimuli elicit lower alpha power in the window of 8-12 Hz and 0-400 msec after stimulus presentation, appearing in the centroparietal region. Comparing previous findings of expectancy-based alpha-band modulation with our results suggests that early alpha oscillation shows an inverse relationship with stimulus predictability. Although current data are insufficient to determine the origin of the alpha power reduction, this could be a potential sign of expectation suppression in cortical oscillatory activity.
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6
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Han D, Doya K, Li D, Tani J. Synergizing habits and goals with variational Bayes. Nat Commun 2024; 15:4461. [PMID: 38796491 DOI: 10.1038/s41467-024-48577-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 05/06/2024] [Indexed: 05/28/2024] Open
Abstract
Behaving efficiently and flexibly is crucial for biological and artificial embodied agents. Behavior is generally classified into two types: habitual (fast but inflexible), and goal-directed (flexible but slow). While these two types of behaviors are typically considered to be managed by two distinct systems in the brain, recent studies have revealed a more sophisticated interplay between them. We introduce a theoretical framework using variational Bayesian theory, incorporating a Bayesian intention variable. Habitual behavior depends on the prior distribution of intention, computed from sensory context without goal-specification. In contrast, goal-directed behavior relies on the goal-conditioned posterior distribution of intention, inferred through variational free energy minimization. Assuming that an agent behaves using a synergized intention, our simulations in vision-based sensorimotor tasks explain the key properties of their interaction as observed in experiments. Our work suggests a fresh perspective on the neural mechanisms of habits and goals, shedding light on future research in decision making.
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Affiliation(s)
- Dongqi Han
- Microsoft Research Asia, Shanghai, 200232, China.
| | - Kenji Doya
- Okinawa Institute of Science and Technology, Okinawa, 904-0495, Japan
| | - Dongsheng Li
- Microsoft Research Asia, Shanghai, 200232, China
| | - Jun Tani
- Okinawa Institute of Science and Technology, Okinawa, 904-0495, Japan
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7
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Bortolotti A, Conti A, Romagnoli A, Sacco PL. Imagination vs. routines: festive time, weekly time, and the predictive brain. Front Hum Neurosci 2024; 18:1357354. [PMID: 38736532 PMCID: PMC11082368 DOI: 10.3389/fnhum.2024.1357354] [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: 12/17/2023] [Accepted: 04/05/2024] [Indexed: 05/14/2024] Open
Abstract
This paper examines the relationship between societal structures shaped by traditions, norms, laws, and customs, and creative expressions in arts and media through the lens of the predictive coding framework in cognitive science. The article proposes that both dimensions of culture can be viewed as adaptations designed to enhance and train the brain's predictive abilities in the social domain. Traditions, norms, laws, and customs foster shared predictions and expectations among individuals, thereby reducing uncertainty in social environments. On the other hand, arts and media expose us to simulated experiences that explore alternative social realities, allowing the predictive machinery of the brain to hone its skills through exposure to a wider array of potentially relevant social circumstances and scenarios. We first review key principles of predictive coding and active inference, and then explore the rationale of cultural traditions and artistic culture in this perspective. Finally, we draw parallels between institutionalized normative habits that stabilize social worlds and creative and imaginative acts that temporarily subvert established conventions to inject variability.
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Affiliation(s)
- Alessandro Bortolotti
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio” of Chieti-Pescara, Chieti, Italy
| | - Alice Conti
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio” of Chieti-Pescara, Chieti, Italy
| | | | - Pier Luigi Sacco
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio” of Chieti-Pescara, Chieti, Italy
- metaLAB (at) Harvard, Cambridge, MA, United States
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8
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Zárate-Rochín AM. Contemporary neurocognitive models of memory: A descriptive comparative analysis. Neuropsychologia 2024; 196:108846. [PMID: 38430963 DOI: 10.1016/j.neuropsychologia.2024.108846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024]
Abstract
The great complexity involved in the study of memory has given rise to numerous hypotheses and models associated with various phenomena at different levels of analysis. This has allowed us to delve deeper in our knowledge about memory but has also made it difficult to synthesize and integrate data from different lines of research. In this context, this work presents a descriptive comparative analysis of contemporary models that address the structure and function of multiple memory systems. The main goal is to outline a panoramic view of the key elements that constitute these models in order to visualize both the current state of research and possible future directions. The elements that stand out from different levels of analysis are distributed neural networks, hierarchical organization, predictive coding, homeostasis, and evolutionary perspective.
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Affiliation(s)
- Alba Marcela Zárate-Rochín
- Instituto de Investigaciones Cerebrales, Universidad Veracruzana, Dr. Castelazo Ayala s/n, Industrial Animas, 91190, Xalapa-Enríquez, Veracruz, Mexico.
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9
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Marvan T, Phillips WA. Cellular mechanisms of cooperative context-sensitive predictive inference. CURRENT RESEARCH IN NEUROBIOLOGY 2024; 6:100129. [PMID: 38665363 PMCID: PMC11043869 DOI: 10.1016/j.crneur.2024.100129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 02/14/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024] Open
Abstract
We argue that prediction success maximization is a basic objective of cognition and cortex, that it is compatible with but distinct from prediction error minimization, that neither objective requires subtractive coding, that there is clear neurobiological evidence for the amplification of predicted signals, and that we are unconvinced by evidence proposed in support of subtractive coding. We outline recent discoveries showing that pyramidal cells on which our cognitive capabilities depend usually transmit information about input to their basal dendrites and amplify that transmission when input to their distal apical dendrites provides a context that agrees with the feedforward basal input in that both are depolarizing, i.e., both are excitatory rather than inhibitory. Though these intracellular discoveries require a level of technical expertise that is beyond the current abilities of most neuroscience labs, they are not controversial and acclaimed as groundbreaking. We note that this cellular cooperative context-sensitivity greatly enhances the cognitive capabilities of the mammalian neocortex, and that much remains to be discovered concerning its evolution, development, and pathology.
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Affiliation(s)
- Tomáš Marvan
- Institute of Philosophy, Czech Academy of Sciences (CAS), Czech Republic
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10
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Dercksen TT, Widmann A, Noesselt T, Wetzel N. Somatosensory omissions reveal action-related predictive processing. Hum Brain Mapp 2024; 45:e26550. [PMID: 38050773 PMCID: PMC10915725 DOI: 10.1002/hbm.26550] [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: 07/11/2023] [Revised: 11/07/2023] [Accepted: 11/16/2023] [Indexed: 12/06/2023] Open
Abstract
The intricate relation between action and somatosensory perception has been studied extensively in the past decades. Generally, a forward model is thought to predict the somatosensory consequences of an action. These models propose that when an action is reliably coupled to a tactile stimulus, unexpected absence of the stimulus should elicit prediction error. Although such omission responses have been demonstrated in the auditory modality, it remains unknown whether this mechanism generalizes across modalities. This study therefore aimed to record action-induced somatosensory omission responses using EEG in humans. Self-paced button presses were coupled to somatosensory stimuli in 88% of trials, allowing a prediction, or in 50% of trials, not allowing a prediction. In the 88% condition, stimulus omission resulted in a neural response consisting of multiple components, as revealed by temporal principal component analysis. The oN1 response suggests similar sensory sources as stimulus-evoked activity, but an origin outside primary cortex. Subsequent oN2 and oP3 responses, as previously observed in the auditory domain, likely reflect modality-unspecific higher order processes. Together, findings straightforwardly demonstrate somatosensory predictions during action and provide evidence for a partially amodal mechanism of prediction error generation.
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Affiliation(s)
- Tjerk T. Dercksen
- Research Group Neurocognitive DevelopmentLeibniz Institute for NeurobiologyMagdeburgGermany
- Center for Behavioral Brain SciencesMagdeburgGermany
| | - Andreas Widmann
- Research Group Neurocognitive DevelopmentLeibniz Institute for NeurobiologyMagdeburgGermany
- Wilhelm Wundt Institute for PsychologyLeipzig UniversityLeipzigGermany
| | - Tömme Noesselt
- Center for Behavioral Brain SciencesMagdeburgGermany
- Department of Biological PsychologyOtto‐von‐Guericke‐University MagdeburgMagdeburgGermany
| | - Nicole Wetzel
- Research Group Neurocognitive DevelopmentLeibniz Institute for NeurobiologyMagdeburgGermany
- Center for Behavioral Brain SciencesMagdeburgGermany
- University of Applied Sciences Magdeburg‐StendalStendalGermany
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11
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Sladky R, Kargl D, Haubensak W, Lamm C. An active inference perspective for the amygdala complex. Trends Cogn Sci 2024; 28:223-236. [PMID: 38103984 DOI: 10.1016/j.tics.2023.11.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 11/14/2023] [Accepted: 11/16/2023] [Indexed: 12/19/2023]
Abstract
The amygdala is a heterogeneous network of subcortical nuclei with central importance in cognitive and clinical neuroscience. Various experimental designs in human psychology and animal model research have mapped multiple conceptual frameworks (e.g., valence/salience and decision making) to ever more refined amygdala circuitry. However, these predominantly bottom up-driven accounts often rely on interpretations tailored to a specific phenomenon, thus preventing comprehensive and integrative theories. We argue here that an active inference model of amygdala function could unify these fractionated approaches into an overarching framework for clearer empirical predictions and mechanistic interpretations. This framework embeds top-down predictive models, informed by prior knowledge and belief updating, within a dynamical system distributed across amygdala circuits in which self-regulation is implemented by continuously tracking environmental and homeostatic demands.
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Affiliation(s)
- Ronald Sladky
- Social, Cognitive, and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Liebiggasse 5, 1010 Vienna, Austria; Vienna Cognitive Science Hub, University of Vienna, 1010 Vienna, Austria.
| | - Dominic Kargl
- Department of Neuronal Cell Biology, Center for Brain Research, Medical University of Vienna, Spitalgasse 4, 1090 Vienna, Austria
| | - Wulf Haubensak
- Department of Neuronal Cell Biology, Center for Brain Research, Medical University of Vienna, Spitalgasse 4, 1090 Vienna, Austria; Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus Vienna Biocenter 1, 1030 Vienna, Austria
| | - Claus Lamm
- Social, Cognitive, and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Liebiggasse 5, 1010 Vienna, Austria; Vienna Cognitive Science Hub, University of Vienna, 1010 Vienna, Austria
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12
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Del Popolo Cristaldi F, Buodo G, Gambarota F, Oosterwijk S, Mento G. How previous experience shapes future affective subjective ratings: A follow-up study investigating implicit learning and cue ambiguity. PLoS One 2024; 19:e0297954. [PMID: 38335190 PMCID: PMC10857730 DOI: 10.1371/journal.pone.0297954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 01/11/2024] [Indexed: 02/12/2024] Open
Abstract
People use their previous experience to predict future affective events. Since we live in ever-changing environments, affective predictions must generalize from past contexts (from which they may be implicitly learned) to new, potentially ambiguous contexts. This study investigated how past (un)certain relationships influence subjective experience following new ambiguous cues, and whether past relationships can be learned implicitly. Two S1-S2 paradigms were employed as learning and test phases in two experiments. S1s were colored circles, S2s negative or neutral affective pictures. Participants (Experiment 1 N = 121, Experiment 2 N = 116) were assigned to the certain (CG) or uncertain group (UG), and they were presented with 100% (CG) or 50% (UG) S1-S2 congruency during an uninstructed (Experiment 1) or implicit (Experiment 2) learning phase. During the test phase both groups were presented with a new 75% S1-S2 paradigm, and ambiguous (Experiment 1) or unambiguous (Experiment 2) S1s. Participants were asked to rate the expected valence of upcoming S2s (expectancy ratings), or their experienced valence and arousal (valence and arousal ratings). In Experiment 1 ambiguous cues elicited less negative expectancy ratings, and less unpleasant valence ratings, independently of prior experience. In Experiment 2, both groups showed similar expectancies, predicting upcoming pictures' valence according to the 75% contingencies of the test phase. Overall, we found that in the presence of ambiguous cues subjective affective experience is dampened, and that implicit previous experience does not emerge at the subjective level by significantly shaping reported affective experience.
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Affiliation(s)
| | - Giulia Buodo
- Department of General Psychology, University of Padua, Padua, Italy
| | - Filippo Gambarota
- Department of Developmental Psychology and Socialization, University of Padua, Padua, Italy
| | - Suzanne Oosterwijk
- Department of Social Psychology, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Brain and Cognition Centre (ABC), Amsterdam, The Netherlands
| | - Giovanni Mento
- Department of General Psychology, University of Padua, Padua, Italy
- Scientific Institute, IRCCS E. Medea, Conegliano, Treviso, Italy
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13
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Bergmann J, Petro LS, Abbatecola C, Li MS, Morgan AT, Muckli L. Cortical depth profiles in primary visual cortex for illusory and imaginary experiences. Nat Commun 2024; 15:1002. [PMID: 38307834 PMCID: PMC10837448 DOI: 10.1038/s41467-024-45065-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 01/12/2024] [Indexed: 02/04/2024] Open
Abstract
Visual illusions and mental imagery are non-physical sensory experiences that involve cortical feedback processing in the primary visual cortex. Using laminar functional magnetic resonance imaging (fMRI) in two studies, we investigate if information about these internal experiences is visible in the activation patterns of different layers of primary visual cortex (V1). We find that imagery content is decodable mainly from deep layers of V1, whereas seemingly 'real' illusory content is decodable mainly from superficial layers. Furthermore, illusory content shares information with perceptual content, whilst imagery content does not generalise to illusory or perceptual information. Together, our results suggest that illusions and imagery, which differ immensely in their subjective experiences, also involve partially distinct early visual microcircuits. However, overlapping microcircuit recruitment might emerge based on the nuanced nature of subjective conscious experience.
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Affiliation(s)
- Johanna Bergmann
- Imaging Centre of Excellence (ICE), Queen Elizabeth University Hospital, University of Glasgow, Glasgow, UK.
- Centre for Cognitive Neuroimaging (CCNi), School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.
- Department of Psychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Lucy S Petro
- Imaging Centre of Excellence (ICE), Queen Elizabeth University Hospital, University of Glasgow, Glasgow, UK
- Centre for Cognitive Neuroimaging (CCNi), School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Clement Abbatecola
- Imaging Centre of Excellence (ICE), Queen Elizabeth University Hospital, University of Glasgow, Glasgow, UK
- Centre for Cognitive Neuroimaging (CCNi), School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Min S Li
- Centre for Cognitive Neuroimaging (CCNi), School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Centre for Computational Neuroscience and Cognitive Robotics, School of Psychology, University of Birmingham, Birmingham, UK
| | - A Tyler Morgan
- Imaging Centre of Excellence (ICE), Queen Elizabeth University Hospital, University of Glasgow, Glasgow, UK
- Centre for Cognitive Neuroimaging (CCNi), School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Functional MRI Core Facility, National Institute of Mental Health, NIH, Bethesda, MD, 20817, USA
| | - Lars Muckli
- Imaging Centre of Excellence (ICE), Queen Elizabeth University Hospital, University of Glasgow, Glasgow, UK.
- Centre for Cognitive Neuroimaging (CCNi), School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.
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14
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Hodson R, Mehta M, Smith R. The empirical status of predictive coding and active inference. Neurosci Biobehav Rev 2024; 157:105473. [PMID: 38030100 DOI: 10.1016/j.neubiorev.2023.105473] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/27/2023] [Accepted: 11/16/2023] [Indexed: 12/01/2023]
Abstract
Research on predictive processing models has focused largely on two specific algorithmic theories: Predictive Coding for perception and Active Inference for decision-making. While these interconnected theories possess broad explanatory potential, they have only recently begun to receive direct empirical evaluation. Here, we review recent studies of Predictive Coding and Active Inference with a focus on evaluating the degree to which they are empirically supported. For Predictive Coding, we find that existing empirical evidence offers modest support. However, some positive results can also be explained by alternative feedforward (e.g., feature detection-based) models. For Active Inference, most empirical studies have focused on fitting these models to behavior as a means of identifying and explaining individual or group differences. While Active Inference models tend to explain behavioral data reasonably well, there has not been a focus on testing empirical validity of active inference theory per se, which would require formal comparison to other models (e.g., non-Bayesian or model-free reinforcement learning models). This review suggests that, while promising, a number of specific research directions are still necessary to evaluate the empirical adequacy and explanatory power of these algorithms.
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Affiliation(s)
| | | | - Ryan Smith
- Laureate Institute for Brain Research, USA.
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15
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Pezzulo G, Parr T, Cisek P, Clark A, Friston K. Generating meaning: active inference and the scope and limits of passive AI. Trends Cogn Sci 2024; 28:97-112. [PMID: 37973519 DOI: 10.1016/j.tics.2023.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 10/03/2023] [Accepted: 10/05/2023] [Indexed: 11/19/2023]
Abstract
Prominent accounts of sentient behavior depict brains as generative models of organismic interaction with the world, evincing intriguing similarities with current advances in generative artificial intelligence (AI). However, because they contend with the control of purposive, life-sustaining sensorimotor interactions, the generative models of living organisms are inextricably anchored to the body and world. Unlike the passive models learned by generative AI systems, they must capture and control the sensory consequences of action. This allows embodied agents to intervene upon their worlds in ways that constantly put their best models to the test, thus providing a solid bedrock that is - we argue - essential to the development of genuine understanding. We review the resulting implications and consider future directions for generative AI.
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Affiliation(s)
- Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
| | - Thomas Parr
- Nuffield Department of Clinical Neurosciences, University of Oxford
| | - Paul Cisek
- Department of Neuroscience, University of Montréal, Montréal, Québec, Canada
| | - Andy Clark
- Department of Philosophy, University of Sussex, Brighton, UK; Department of Informatics, University of Sussex, Brighton, UK; Department of Philosophy, Macquarie University, Sydney, New South Wales, Australia
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK; VERSES AI Research Lab, Los Angeles, CA, USA
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16
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Deperrois N, Petrovici MA, Senn W, Jordan J. Learning beyond sensations: How dreams organize neuronal representations. Neurosci Biobehav Rev 2024; 157:105508. [PMID: 38097096 DOI: 10.1016/j.neubiorev.2023.105508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 12/05/2023] [Accepted: 12/09/2023] [Indexed: 12/25/2023]
Abstract
Semantic representations in higher sensory cortices form the basis for robust, yet flexible behavior. These representations are acquired over the course of development in an unsupervised fashion and continuously maintained over an organism's lifespan. Predictive processing theories propose that these representations emerge from predicting or reconstructing sensory inputs. However, brains are known to generate virtual experiences, such as during imagination and dreaming, that go beyond previously experienced inputs. Here, we suggest that virtual experiences may be just as relevant as actual sensory inputs in shaping cortical representations. In particular, we discuss two complementary learning principles that organize representations through the generation of virtual experiences. First, "adversarial dreaming" proposes that creative dreams support a cortical implementation of adversarial learning in which feedback and feedforward pathways engage in a productive game of trying to fool each other. Second, "contrastive dreaming" proposes that the invariance of neuronal representations to irrelevant factors of variation is acquired by trying to map similar virtual experiences together via a contrastive learning process. These principles are compatible with known cortical structure and dynamics and the phenomenology of sleep thus providing promising directions to explain cortical learning beyond the classical predictive processing paradigm.
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Affiliation(s)
| | | | - Walter Senn
- Department of Physiology, University of Bern, Bern, Switzerland
| | - Jakob Jordan
- Department of Physiology, University of Bern, Bern, Switzerland; Electrical Engineering, Yale University, New Haven, CT, United States
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17
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Pérez-González D, Lao-Rodríguez AB, Aedo-Sánchez C, Malmierca MS. Acetylcholine modulates the precision of prediction error in the auditory cortex. eLife 2024; 12:RP91475. [PMID: 38241174 PMCID: PMC10942646 DOI: 10.7554/elife.91475] [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] [Indexed: 01/21/2024] Open
Abstract
A fundamental property of sensory systems is their ability to detect novel stimuli in the ambient environment. The auditory brain contains neurons that decrease their response to repetitive sounds but increase their firing rate to novel or deviant stimuli; the difference between both responses is known as stimulus-specific adaptation or neuronal mismatch (nMM). Here, we tested the effect of microiontophoretic applications of ACh on the neuronal responses in the auditory cortex (AC) of anesthetized rats during an auditory oddball paradigm, including cascade controls. Results indicate that ACh modulates the nMM, affecting prediction error responses but not repetition suppression, and this effect is manifested predominantly in infragranular cortical layers. The differential effect of ACh on responses to standards, relative to deviants (in terms of averages and variances), was consistent with the representational sharpening that accompanies an increase in the precision of prediction errors. These findings suggest that ACh plays an important role in modulating prediction error signaling in the AC and gating the access of these signals to higher cognitive levels.
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Affiliation(s)
- David Pérez-González
- Cognitive and Auditory Neuroscience Laboratory, Institute of Neuroscience of Castilla y León, Calle Pintor Fernando GallegoSalamancaSpain
- Institute for Biomedical Research of Salamanca (IBSAL)SalamancaSpain
- Department of Basic Psychology, Psychobiology and Behavioural Science Methodology, Faculty of Psychology, Campus Ciudad Jardín, University of SalamancaSalamancaSpain
| | - Ana Belén Lao-Rodríguez
- Cognitive and Auditory Neuroscience Laboratory, Institute of Neuroscience of Castilla y León, Calle Pintor Fernando GallegoSalamancaSpain
- Institute for Biomedical Research of Salamanca (IBSAL)SalamancaSpain
| | - Cristian Aedo-Sánchez
- Cognitive and Auditory Neuroscience Laboratory, Institute of Neuroscience of Castilla y León, Calle Pintor Fernando GallegoSalamancaSpain
- Institute for Biomedical Research of Salamanca (IBSAL)SalamancaSpain
| | - Manuel S Malmierca
- Cognitive and Auditory Neuroscience Laboratory, Institute of Neuroscience of Castilla y León, Calle Pintor Fernando GallegoSalamancaSpain
- Institute for Biomedical Research of Salamanca (IBSAL)SalamancaSpain
- Department of Biology and Pathology, Faculty of Medicine, Campus Miguel de Unamuno, University of SalamancaSalamancaSpain
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18
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Luu P, Tucker DM, Friston K. From active affordance to active inference: vertical integration of cognition in the cerebral cortex through dual subcortical control systems. Cereb Cortex 2024; 34:bhad458. [PMID: 38044461 DOI: 10.1093/cercor/bhad458] [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: 07/17/2023] [Revised: 11/07/2023] [Accepted: 11/08/2023] [Indexed: 12/05/2023] Open
Abstract
In previous papers, we proposed that the dorsal attention system's top-down control is regulated by the dorsal division of the limbic system, providing a feedforward or impulsive form of control generating expectancies during active inference. In contrast, we proposed that the ventral attention system is regulated by the ventral limbic division, regulating feedback constraints and error-correction for active inference within the neocortical hierarchy. Here, we propose that these forms of cognitive control reflect vertical integration of subcortical arousal control systems that evolved for specific forms of behavior control. The feedforward impetus to action is regulated by phasic arousal, mediated by lemnothalamic projections from the reticular activating system of the lower brainstem, and then elaborated by the hippocampus and dorsal limbic division. In contrast, feedback constraint-based on environmental requirements-is regulated by the tonic activation furnished by collothalamic projections from the midbrain arousal control centers, and then sustained and elaborated by the amygdala, basal ganglia, and ventral limbic division. In an evolutionary-developmental analysis, understanding these differing forms of active affordance-for arousal and motor control within the subcortical vertebrate neuraxis-may help explain the evolution of active inference regulating the cognition of expectancy and error-correction within the mammalian 6-layered neocortex.
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Affiliation(s)
- Phan Luu
- Brain Electrophysiology Laboratory Company, Riverfront Research Park, 1776 Millrace Dr., Eugene, OR 97403, United States
- Department of Psychology, University of Oregon, Eugene, OR 97403, United States
| | - Don M Tucker
- Brain Electrophysiology Laboratory Company, Riverfront Research Park, 1776 Millrace Dr., Eugene, OR 97403, United States
- Department of Psychology, University of Oregon, Eugene, OR 97403, United States
| | - Karl Friston
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London WC1N 3AR, United Kingdom
- VERSES AI Research Lab, Los Angeles, CA 90016, USA
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19
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Seignette K, Jamann N, Papale P, Terra H, Porneso RO, de Kraker L, van der Togt C, van der Aa M, Neering P, Ruimschotel E, Roelfsema PR, Montijn JS, Self MW, Kole MHP, Levelt CN. Experience shapes chandelier cell function and structure in the visual cortex. eLife 2024; 12:RP91153. [PMID: 38192196 PMCID: PMC10963032 DOI: 10.7554/elife.91153] [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] [Indexed: 01/10/2024] Open
Abstract
Detailed characterization of interneuron types in primary visual cortex (V1) has greatly contributed to understanding visual perception, yet the role of chandelier cells (ChCs) in visual processing remains poorly characterized. Using viral tracing we found that V1 ChCs predominantly receive monosynaptic input from local layer 5 pyramidal cells and higher-order cortical regions. Two-photon calcium imaging and convolutional neural network modeling revealed that ChCs are visually responsive but weakly selective for stimulus content. In mice running in a virtual tunnel, ChCs respond strongly to events known to elicit arousal, including locomotion and visuomotor mismatch. Repeated exposure of the mice to the virtual tunnel was accompanied by reduced visual responses of ChCs and structural plasticity of ChC boutons and axon initial segment length. Finally, ChCs only weakly inhibited pyramidal cells. These findings suggest that ChCs provide an arousal-related signal to layer 2/3 pyramidal cells that may modulate their activity and/or gate plasticity of their axon initial segments during behaviorally relevant events.
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Affiliation(s)
- Koen Seignette
- Department of Molecular Visual Plasticity, Netherlands Institute for NeuroscienceAmsterdamNetherlands
| | - Nora Jamann
- Department of Axonal Signaling, Netherlands Institute for NeuroscienceAmsterdamNetherlands
- Department of Biology Cell Biology, Neurobiology and Biophysics, Faculty of Science, Utrecht UniversityUtrechtNetherlands
| | - Paolo Papale
- Department of Vision & Cognition, Netherlands Institute for NeuroscienceAmsterdamNetherlands
| | - Huub Terra
- Department of Molecular Visual Plasticity, Netherlands Institute for NeuroscienceAmsterdamNetherlands
| | - Ralph O Porneso
- Department of Molecular Visual Plasticity, Netherlands Institute for NeuroscienceAmsterdamNetherlands
| | - Leander de Kraker
- Department of Molecular Visual Plasticity, Netherlands Institute for NeuroscienceAmsterdamNetherlands
| | - Chris van der Togt
- Department of Molecular Visual Plasticity, Netherlands Institute for NeuroscienceAmsterdamNetherlands
- Department of Vision & Cognition, Netherlands Institute for NeuroscienceAmsterdamNetherlands
| | - Maaike van der Aa
- Department of Molecular Visual Plasticity, Netherlands Institute for NeuroscienceAmsterdamNetherlands
| | - Paul Neering
- Department of Molecular Visual Plasticity, Netherlands Institute for NeuroscienceAmsterdamNetherlands
- Department of Vision & Cognition, Netherlands Institute for NeuroscienceAmsterdamNetherlands
| | - Emma Ruimschotel
- Department of Molecular Visual Plasticity, Netherlands Institute for NeuroscienceAmsterdamNetherlands
| | - Pieter R Roelfsema
- Department of Vision & Cognition, Netherlands Institute for NeuroscienceAmsterdamNetherlands
- Laboratory of Visual Brain Therapy, Sorbonne Université, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut de la VisionParisFrance
- Department of Integrative Neurophysiology, Centre for Neurogenomics and Cognitive Research, VU UniversityAmsterdamNetherlands
- Department of Psychiatry, Academic Medical Center, University of AmsterdamAmsterdamNetherlands
| | - Jorrit S Montijn
- Department of Cortical Structure & Function, Netherlands Institute for NeuroscienceAmsterdamNetherlands
| | - Matthew W Self
- Department of Vision & Cognition, Netherlands Institute for NeuroscienceAmsterdamNetherlands
| | - Maarten HP Kole
- Department of Axonal Signaling, Netherlands Institute for NeuroscienceAmsterdamNetherlands
- Department of Biology Cell Biology, Neurobiology and Biophysics, Faculty of Science, Utrecht UniversityUtrechtNetherlands
| | - Christiaan N Levelt
- Department of Molecular Visual Plasticity, Netherlands Institute for NeuroscienceAmsterdamNetherlands
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, VU University AmsterdamAmsterdamNetherlands
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20
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Shipp S. Computational components of visual predictive coding circuitry. Front Neural Circuits 2024; 17:1254009. [PMID: 38259953 PMCID: PMC10800426 DOI: 10.3389/fncir.2023.1254009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 12/13/2023] [Indexed: 01/24/2024] Open
Abstract
If a full visual percept can be said to be a 'hypothesis', so too can a neural 'prediction' - although the latter addresses one particular component of image content (such as 3-dimensional organisation, the interplay between lighting and surface colour, the future trajectory of moving objects, and so on). And, because processing is hierarchical, predictions generated at one level are conveyed in a backward direction to a lower level, seeking to predict, in fact, the neural activity at that prior stage of processing, and learning from errors signalled in the opposite direction. This is the essence of 'predictive coding', at once an algorithm for information processing and a theoretical basis for the nature of operations performed by the cerebral cortex. Neural models for the implementation of predictive coding invoke specific functional classes of neuron for generating, transmitting and receiving predictions, and for producing reciprocal error signals. Also a third general class, 'precision' neurons, tasked with regulating the magnitude of error signals contingent upon the confidence placed upon the prediction, i.e., the reliability and behavioural utility of the sensory data that it predicts. So, what is the ultimate source of a 'prediction'? The answer is multifactorial: knowledge of the current environmental context and the immediate past, allied to memory and lifetime experience of the way of the world, doubtless fine-tuned by evolutionary history too. There are, in consequence, numerous potential avenues for experimenters seeking to manipulate subjects' expectation, and examine the neural signals elicited by surprising, and less surprising visual stimuli. This review focuses upon the predictive physiology of mouse and monkey visual cortex, summarising and commenting on evidence to date, and placing it in the context of the broader field. It is concluded that predictive coding has a firm grounding in basic neuroscience and that, unsurprisingly, there remains much to learn.
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Affiliation(s)
- Stewart Shipp
- Institute of Ophthalmology, University College London, London, United Kingdom
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21
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Gjini K, Casey C, Kunkel D, Her M, Banks MI, Pearce RA, Lennertz R, Sanders RD. Delirium is associated with loss of feedback cortical connectivity. Alzheimers Dement 2024; 20:511-524. [PMID: 37695013 PMCID: PMC10840828 DOI: 10.1002/alz.13471] [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: 06/27/2023] [Revised: 08/10/2023] [Accepted: 08/18/2023] [Indexed: 09/12/2023]
Abstract
INTRODUCTION Post-operative delirium (POD) is associated with increased morbidity and mortality but is bereft of treatments, largely due to our limited understanding of the underlying pathophysiology. We hypothesized that delirium reflects a disturbance in cortical connectivity that leads to altered predictions of the sensory environment. METHODS High-density electroencephalogram recordings during an oddball auditory roving paradigm were collected from 131 patients. Dynamic causal modeling (DCM) analysis facilitated inference about the neuronal connectivity and inhibition-excitation dynamics underlying auditory-evoked responses. RESULTS Mismatch negativity amplitudes were smaller in patients with POD. DCM showed that delirium was associated with decreased left-sided superior temporal gyrus (l-STG) to auditory cortex feedback connectivity. Feedback connectivity also negatively correlated with delirium severity and systemic inflammation. Increased inhibition of l-STG, with consequent decreases in feed-forward and feed-back connectivity, occurred for oddball tones during delirium. DISCUSSION Delirium is associated with decreased feedback cortical connectivity, possibly resulting from increased intrinsic inhibitory tone. HIGHLIGHTS Mismatch negativity amplitude was reduced in patients with delirium. Patients with postoperative delirium had increased feedforward connectivity before surgery. Feedback connectivity was diminished from left-side superior temporal gyrus to left primary auditory sensory area during delirium. Feedback connectivity inversely correlated with inflammation and delirium severity.
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Affiliation(s)
- Klevest Gjini
- Department of NeurologyUniversity of Wisconsin–MadisonMadisonWisconsinUSA
| | - Cameron Casey
- Department of AnesthesiologyUniversity of Wisconsin–MadisonMadisonWisconsinUSA
- Pediatric Neuromodulation Laboratory, Waisman CenterUniversity of Wisconsin–MadisonMadisonWisconsinUSA
| | - David Kunkel
- Department of AnesthesiologyUniversity of Wisconsin–MadisonMadisonWisconsinUSA
| | - Maihlee Her
- Department of AnesthesiologyUniversity of Wisconsin–MadisonMadisonWisconsinUSA
| | - Matthew I. Banks
- Department of AnesthesiologyUniversity of Wisconsin–MadisonMadisonWisconsinUSA
- Department of NeuroscienceUniversity of Wisconsin–MadisonMadisonWisconsinUSA
| | - Robert A. Pearce
- Department of AnesthesiologyUniversity of Wisconsin–MadisonMadisonWisconsinUSA
| | - Richard Lennertz
- Department of AnesthesiologyUniversity of Wisconsin–MadisonMadisonWisconsinUSA
| | - Robert D. Sanders
- Department of Anaesthetics & Institute of Academic SurgeryRoyal Prince Alfred HospitalCamperdownNew South WalesAustralia
- NHMRC Clinical Trials Centre and Central Clinical SchoolUniversity of SydneyCamperdownNew South WalesAustralia
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22
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Schoeller F, Horowitz AH, Jain A, Maes P, Reggente N, Christov-Moore L, Pezzulo G, Barca L, Allen M, Salomon R, Miller M, Di Lernia D, Riva G, Tsakiris M, Chalah MA, Klein A, Zhang B, Garcia T, Pollack U, Trousselard M, Verdonk C, Dumas G, Adrien V, Friston K. Interoceptive technologies for psychiatric interventions: From diagnosis to clinical applications. Neurosci Biobehav Rev 2024; 156:105478. [PMID: 38007168 DOI: 10.1016/j.neubiorev.2023.105478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 11/16/2023] [Accepted: 11/19/2023] [Indexed: 11/27/2023]
Abstract
Interoception-the perception of internal bodily signals-has emerged as an area of interest due to its implications in emotion and the prevalence of dysfunctional interoceptive processes across psychopathological conditions. Despite the importance of interoception in cognitive neuroscience and psychiatry, its experimental manipulation remains technically challenging. This is due to the invasive nature of existing methods, the limitation of self-report and unimodal measures of interoception, and the absence of standardized approaches across disparate fields. This article integrates diverse research efforts from psychology, physiology, psychiatry, and engineering to address this oversight. Following a general introduction to the neurophysiology of interoception as hierarchical predictive processing, we review the existing paradigms for manipulating interoception (e.g., interoceptive modulation), their underlying mechanisms (e.g., interoceptive conditioning), and clinical applications (e.g., interoceptive exposure). We suggest a classification for interoceptive technologies and discuss their potential for diagnosing and treating mental health disorders. Despite promising results, considerable work is still needed to develop standardized, validated measures of interoceptive function across domains and before these technologies can translate safely and effectively to clinical settings.
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Affiliation(s)
- Felix Schoeller
- Fluid Interfaces Group, Media Lab, Massachusetts Institute of Technology, USA; Institute for Advanced Consciousness Studies, Santa Monica, CA, USA; Department Cognitive Sciences, University of Haifa, Israel.
| | - Adam Haar Horowitz
- Fluid Interfaces Group, Media Lab, Massachusetts Institute of Technology, USA; Center for Sleep and Cognition, Beth Israel Deaconess Medical Center, Harvard Medical School, USA
| | - Abhinandan Jain
- Fluid Interfaces Group, Media Lab, Massachusetts Institute of Technology, USA
| | - Pattie Maes
- Fluid Interfaces Group, Media Lab, Massachusetts Institute of Technology, USA
| | - Nicco Reggente
- Institute for Advanced Consciousness Studies, Santa Monica, CA, USA
| | | | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Laura Barca
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Micah Allen
- Center of Functionally Integrative Neuroscience, Aarhus University, Denmark; Cambridge Psychiatry, University of Cambridge, UK
| | - Roy Salomon
- Department Cognitive Sciences, University of Haifa, Israel
| | - Mark Miller
- Center for Human Nature, Artificial Intelligence and Neuroscience, Hokkaido University, Japan
| | - Daniele Di Lernia
- Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy; Applied Technology for Neuro- Psychology Laboratory, IRCCS Istituto Auxologico Italiano, Milan, Italy
| | - Giuseppe Riva
- Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy; Applied Technology for Neuro- Psychology Laboratory, IRCCS Istituto Auxologico Italiano, Milan, Italy
| | - Manos Tsakiris
- The Warburg Institute, School of Advanced Study, University of London, UK; Department of Psychology, Royal Holloway, University of London, UK; Department of Behavioural and Cognitive Sciences, University of Luxembourg, Luxembourg
| | - Moussa A Chalah
- EA 4391, Excitabilité Nerveuse et Thérapeutique, Université Paris-Est Créteil, Créteil, France; Service de Physiologie - Explorations Fonctionnelles, Hôpital Henri Mondor, Créteil, France
| | - Arno Klein
- Child Mind Institute, New York City, USA
| | - Ben Zhang
- Institute for Advanced Consciousness Studies, Santa Monica, CA, USA
| | - Teresa Garcia
- Institute for Advanced Consciousness Studies, Santa Monica, CA, USA
| | - Ursula Pollack
- Institute for Advanced Consciousness Studies, Santa Monica, CA, USA
| | - Marion Trousselard
- Institut de Recherche Biomédicale des Armées, Place Général Valérie André, 91220 Brétigny-sur-Orge, France
| | - Charles Verdonk
- Institut de Recherche Biomédicale des Armées, Place Général Valérie André, 91220 Brétigny-sur-Orge, France
| | | | - Vladimir Adrien
- Infrastructure for Clinical Research in Neurosciences (iCRIN) Psychiatry, Paris Brain Institute, Paris, France; Department of Psychiatry, Hôpital Saint-Antoine, AP-HP, Sorbonne Université, 75012 Paris, France
| | - Karl Friston
- Queen Sq, Institute of Neurology, UCL, London WC1N 3AR, UK
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23
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Castro F, Schenke KC. Augmented action observation: Theory and practical applications in sensorimotor rehabilitation. Neuropsychol Rehabil 2023:1-20. [PMID: 38117228 DOI: 10.1080/09602011.2023.2286012] [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: 05/11/2023] [Accepted: 11/10/2023] [Indexed: 12/21/2023]
Abstract
Sensory feedback is a fundamental aspect of effective motor learning in sport and clinical contexts. One way to provide this is through sensory augmentation, where extrinsic sensory information are associated with, and modulated by, movement. Traditionally, sensory augmentation has been used as an online strategy, where feedback is provided during physical execution of an action. In this article, we argue that action observation can be an additional effective channel to provide augmented feedback, which would be complementary to other, more traditional, motor learning and sensory augmentation strategies. Given these similarities between observing and executing an action, action observation could be used when physical training is difficult or not feasible, for example during immobilization or during the initial stages of a rehabilitation protocol when peripheral fatigue is a common issue. We review the benefits of observational learning and preliminary evidence for the effectiveness of using augmented action observation to improve learning. We also highlight current knowledge gaps which make the transition from laboratory to practical contexts difficult. Finally, we highlight the key areas of focus for future research.
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Affiliation(s)
- Fabio Castro
- Institute of Sport, School of Life and Medical Sciences, University of Hertfordshire, Hatfield, UK
| | - Kimberley C Schenke
- School of Natural, Social and Sports Sciences, University of Gloucestershire, Cheltenham, UK
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24
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Schilling A, Sedley W, Gerum R, Metzner C, Tziridis K, Maier A, Schulze H, Zeng FG, Friston KJ, Krauss P. Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception. Brain 2023; 146:4809-4825. [PMID: 37503725 PMCID: PMC10690027 DOI: 10.1093/brain/awad255] [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: 10/26/2022] [Revised: 06/27/2023] [Accepted: 07/15/2023] [Indexed: 07/29/2023] Open
Abstract
Mechanistic insight is achieved only when experiments are employed to test formal or computational models. Furthermore, in analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying healthy auditory perception. With a special focus on tinnitus-as the prime example of auditory phantom perception-we review recent work at the intersection of artificial intelligence, psychology and neuroscience. In particular, we discuss why everyone with tinnitus suffers from (at least hidden) hearing loss, but not everyone with hearing loss suffers from tinnitus. We argue that intrinsic neural noise is generated and amplified along the auditory pathway as a compensatory mechanism to restore normal hearing based on adaptive stochastic resonance. The neural noise increase can then be misinterpreted as auditory input and perceived as tinnitus. This mechanism can be formalized in the Bayesian brain framework, where the percept (posterior) assimilates a prior prediction (brain's expectations) and likelihood (bottom-up neural signal). A higher mean and lower variance (i.e. enhanced precision) of the likelihood shifts the posterior, evincing a misinterpretation of sensory evidence, which may be further confounded by plastic changes in the brain that underwrite prior predictions. Hence, two fundamental processing principles provide the most explanatory power for the emergence of auditory phantom perceptions: predictive coding as a top-down and adaptive stochastic resonance as a complementary bottom-up mechanism. We conclude that both principles also play a crucial role in healthy auditory perception. Finally, in the context of neuroscience-inspired artificial intelligence, both processing principles may serve to improve contemporary machine learning techniques.
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Affiliation(s)
- Achim Schilling
- Neuroscience Lab, University Hospital Erlangen, 91054 Erlangen, Germany
- Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - William Sedley
- Translational and Clinical Research Institute, Newcastle University Medical School, Newcastle upon Tyne NE2 4HH, UK
| | - Richard Gerum
- Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg, 91058 Erlangen, Germany
- Department of Physics and Astronomy and Center for Vision Research, York University, Toronto, ON M3J 1P3, Canada
| | - Claus Metzner
- Neuroscience Lab, University Hospital Erlangen, 91054 Erlangen, Germany
| | | | - Andreas Maier
- Pattern Recognition Lab, University Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - Holger Schulze
- Neuroscience Lab, University Hospital Erlangen, 91054 Erlangen, Germany
| | - Fan-Gang Zeng
- Center for Hearing Research, Departments of Anatomy and Neurobiology, Biomedical Engineering, Cognitive Sciences, Otolaryngology–Head and Neck Surgery, University of California Irvine, Irvine, CA 92697, USA
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Patrick Krauss
- Neuroscience Lab, University Hospital Erlangen, 91054 Erlangen, Germany
- Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg, 91058 Erlangen, Germany
- Pattern Recognition Lab, University Erlangen-Nürnberg, 91058 Erlangen, Germany
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25
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Rockland KS. Cellular and laminar architecture: A short history and commentary. J Comp Neurol 2023; 531:1926-1933. [PMID: 37941081 DOI: 10.1002/cne.25553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 08/11/2023] [Accepted: 10/13/2023] [Indexed: 11/10/2023]
Abstract
The feedforward/feedback classification, as originally stated in relation to early visual areas in the macaque monkey, has had a significant influence on ideas of laminar interactions, area reciprocity, and cortical hierarchical organization. In some contrast with this macroscale "laminar connectomics," a more cellular approach to cortical connections, as briefly surveyed here, points to a still underappreciated heterogeneity of neuronal subtypes and complex microcircuitries. From the perspective of heterogeneities, the question of how brain regions interact and influence each other quickly leads to discussions about concurrent hierarchical and nonhierarchical cortical features, brain organization as a multiscale system forming nested groups and hierarchies, connectomes annotated by multiple biological attributes, and interleaved and overlapping scales of organization.
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Affiliation(s)
- Kathleen S Rockland
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
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26
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Sajid N, Gajardo-Vidal A, Ekert JO, Lorca-Puls DL, Hope TMH, Green DW, Friston KJ, Price CJ. Degeneracy in the neurological model of auditory speech repetition. Commun Biol 2023; 6:1161. [PMID: 37957231 PMCID: PMC10643365 DOI: 10.1038/s42003-023-05515-5] [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: 05/09/2022] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
Both classic and contemporary models of auditory word repetition involve at least four left hemisphere regions: primary auditory cortex for processing sounds; pSTS (within Wernicke's area) for processing auditory images of speech; pOp (within Broca's area) for processing motor images of speech; and primary motor cortex for overt speech articulation. Previous functional-MRI (fMRI) studies confirm that auditory repetition activates these regions, in addition to many others. Crucially, however, contemporary models do not specify how regions interact and drive each other during auditory repetition. Here, we used dynamic causal modelling, to test the functional interplay among the four core brain regions during single auditory word and pseudoword repetition. Our analysis is grounded in the principle of degeneracy-i.e., many-to-one structure-function relationships-where multiple neural pathways can execute the same function. Contrary to expectation, we found that, for both word and pseudoword repetition, (i) the effective connectivity between pSTS and pOp was predominantly bidirectional and inhibitory; (ii) activity in the motor cortex could be driven by either pSTS or pOp; and (iii) the latter varied both within and between individuals. These results suggest that different neural pathways can support auditory speech repetition. This degeneracy may explain resilience to functional loss after brain damage.
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Affiliation(s)
- Noor Sajid
- Wellcome Centre for Human Neuroimaging, QS Institute of Neurology, University College London, London, UK.
| | - Andrea Gajardo-Vidal
- Wellcome Centre for Human Neuroimaging, QS Institute of Neurology, University College London, London, UK
- Centro de Investigación en Complejidad Social (CICS), Universidad del Desarrollo, Concepción, Chile
| | - Justyna O Ekert
- Wellcome Centre for Human Neuroimaging, QS Institute of Neurology, University College London, London, UK
| | - Diego L Lorca-Puls
- Wellcome Centre for Human Neuroimaging, QS Institute of Neurology, University College London, London, UK
- Sección de Neurología, Departamento de Especialidades, Facultad de Medicina, Universidad de Concepción, Concepción, Chile
| | - Thomas M H Hope
- Wellcome Centre for Human Neuroimaging, QS Institute of Neurology, University College London, London, UK
| | - David W Green
- Experimental Psychology, University College London, London, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, QS Institute of Neurology, University College London, London, UK
| | - Cathy J Price
- Wellcome Centre for Human Neuroimaging, QS Institute of Neurology, University College London, London, UK
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27
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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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28
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Sprevak M, Smith R. An Introduction to Predictive Processing Models of Perception and Decision-Making. Top Cogn Sci 2023. [PMID: 37899002 DOI: 10.1111/tops.12704] [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: 04/03/2023] [Revised: 08/30/2023] [Accepted: 10/06/2023] [Indexed: 10/31/2023]
Abstract
The predictive processing framework includes a broad set of ideas, which might be articulated and developed in a variety of ways, concerning how the brain may leverage predictive models when implementing perception, cognition, decision-making, and motor control. This article provides an up-to-date introduction to the two most influential theories within this framework: predictive coding and active inference. The first half of the paper (Sections 2-5) reviews the evolution of predictive coding, from early ideas about efficient coding in the visual system to a more general model encompassing perception, cognition, and motor control. The theory is characterized in terms of the claims it makes at Marr's computational, algorithmic, and implementation levels of description, and the conceptual and mathematical connections between predictive coding, Bayesian inference, and variational free energy (a quantity jointly evaluating model accuracy and complexity) are explored. The second half of the paper (Sections 6-8) turns to recent theories of active inference. Like predictive coding, active inference models assume that perceptual and learning processes minimize variational free energy as a means of approximating Bayesian inference in a biologically plausible manner. However, these models focus primarily on planning and decision-making processes that predictive coding models were not developed to address. Under active inference, an agent evaluates potential plans (action sequences) based on their expected free energy (a quantity that combines anticipated reward and information gain). The agent is assumed to represent the world as a partially observable Markov decision process with discrete time and discrete states. Current research applications of active inference models are described, including a range of simulation work, as well as studies fitting models to empirical data. The paper concludes by considering future research directions that will be important for further development of both models.
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Affiliation(s)
- Mark Sprevak
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh
| | - Ryan Smith
- Laureate Institute for Brain Research, Tulsa, Oklahoma
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29
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Brouillet D, Friston K. Relative fluency (unfelt vs felt) in active inference. Conscious Cogn 2023; 115:103579. [PMID: 37776599 DOI: 10.1016/j.concog.2023.103579] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 09/07/2023] [Accepted: 09/16/2023] [Indexed: 10/02/2023]
Abstract
For a growing number of researchers, it is now accepted that the brain is a predictive organ that predicts the content of the sensorium and crucially the precision of-or confidence in-its own predictions. In order to predict the precision of its predictions, the brain has to infer the reliability of its own beliefs. This means that our brains have to recognise the precision of their predictions or, at least, their accuracy. In this paper, we argue that fluency is product of this recognition process. In short, to recognise fluency is to infer that we have a precise 'grip' on the unfolding processes that generate our sensations. More specifically, we propose that it is changes in fluency - from unfelt to felt - that are both recognised and realised when updating predictions about precision. Unfelt fluency orients attention to unpredicted sensations, while felt fluency supervenes on-and contextualises-unfelt fluency; thereby rendering certain attentional processes, phenomenologically opaque. As such, fluency underwrites the precision we place in our predictions and therefore acts upon our perceptual inferences. Hence, the causes of conscious subjective inference have unconscious perceptual precursors.
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Affiliation(s)
- Denis Brouillet
- University Paul Valéry-Montpellier-France, EPSYLON, France; University Paris Nanterre, LICAE, France.
| | - Karl Friston
- Queen Square Institute of Neurology, University College, London, United Kingdom; Wellcome Centre for Human Neuroimaging, London, United Kingdom
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30
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Abstract
The QBIT theory is a recently introduced multi-disciplinary approach to the problem of consciousness. One of the main axioms of the theory is that when information-theoretic certainty of an observer about a stimulus goes beyond a certain threshold, the observer becomes conscious of that stimulus. This axiom could provide an explanation for how the brain generates consciousness.In short, the QBIT theory suggests that the brain generates consciousness by reducing the entropy of its internal representations below a critical threshold. This paper explains how the brain gradually minimizes the entropy of its internal representations and consequently generate minimum-entropy representations (also known as conscious representations or qualia). The paper also explores the consequences of this entropy-minimization process in the context of quantum information theory.
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Affiliation(s)
- Majid Beshkar
- Tehran University of Medical Sciences, Tehran, Iran.
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31
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Petro LS, Smith FW, Abbatecola C, Muckli L. The Spatial Precision of Contextual Feedback Signals in Human V1. BIOLOGY 2023; 12:1022. [PMID: 37508451 PMCID: PMC10376409 DOI: 10.3390/biology12071022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/23/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023]
Abstract
Neurons in the primary visual cortex (V1) receive sensory inputs that describe small, local regions of the visual scene and cortical feedback inputs from higher visual areas processing the global scene context. Investigating the spatial precision of this visual contextual modulation will contribute to our understanding of the functional role of cortical feedback inputs in perceptual computations. We used human functional magnetic resonance imaging (fMRI) to test the spatial precision of contextual feedback inputs to V1 during natural scene processing. We measured brain activity patterns in the stimulated regions of V1 and in regions that we blocked from direct feedforward input, receiving information only from non-feedforward (i.e., feedback and lateral) inputs. We measured the spatial precision of contextual feedback signals by generalising brain activity patterns across parametrically spatially displaced versions of identical images using an MVPA cross-classification approach. We found that fMRI activity patterns in cortical feedback signals predicted our scene-specific features in V1 with a precision of approximately 4 degrees. The stimulated regions of V1 carried more precise scene information than non-stimulated regions; however, these regions also contained information patterns that generalised up to 4 degrees. This result shows that contextual signals relating to the global scene are similarly fed back to V1 when feedforward inputs are either present or absent. Our results are in line with contextual feedback signals from extrastriate areas to V1, describing global scene information and contributing to perceptual computations such as the hierarchical representation of feature boundaries within natural scenes.
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Affiliation(s)
- Lucy S Petro
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QB, UK
- Imaging Centre for Excellence (ICE), College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G51 4LB, UK
| | - Fraser W Smith
- School of Psychology, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
| | - Clement Abbatecola
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QB, UK
- Imaging Centre for Excellence (ICE), College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G51 4LB, UK
| | - Lars Muckli
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QB, UK
- Imaging Centre for Excellence (ICE), College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G51 4LB, UK
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32
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Lao-Rodríguez AB, Przewrocki K, Pérez-González D, Alishbayli A, Yilmaz E, Malmierca MS, Englitz B. Neuronal responses to omitted tones in the auditory brain: A neuronal correlate for predictive coding. SCIENCE ADVANCES 2023; 9:eabq8657. [PMID: 37315139 DOI: 10.1126/sciadv.abq8657] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 05/09/2023] [Indexed: 06/16/2023]
Abstract
Prediction provides key advantages for survival, and cognitive studies have demonstrated that the brain computes multilevel predictions. Evidence for predictions remains elusive at the neuronal level because of the complexity of separating neural activity into predictions and stimulus responses. We overcome this challenge by recording from single neurons from cortical and subcortical auditory regions in anesthetized and awake preparations, during unexpected stimulus omissions interspersed in a regular sequence of tones. We find a subset of neurons that responds reliably to omitted tones. In awake animals, omission responses are similar to anesthetized animals, but larger and more frequent, indicating that the arousal and attentional state levels affect the degree to which predictions are neuronally represented. Omission-sensitive neurons also responded to frequency deviants, with their omission responses getting emphasized in the awake state. Because omission responses occur in the absence of sensory input, they provide solid and empirical evidence for the implementation of a predictive process.
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Affiliation(s)
- Ana B Lao-Rodríguez
- Cognitive and Auditory Neuroscience Laboratory (CANELAB), Institute of Neuroscience of Castilla y León, University of Salamanca, Salamanca, Spain
- Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
| | - Karol Przewrocki
- Computational Neuroscience Lab, Department of Neurophysiology, Donders Centre of Neuroscience, Nijmegen, Netherlands
| | - David Pérez-González
- Cognitive and Auditory Neuroscience Laboratory (CANELAB), Institute of Neuroscience of Castilla y León, University of Salamanca, Salamanca, Spain
- Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
- Department of Basic Psychology, Psychobiology and Methodology of Behavioral Sciences, University of Salamanca, Salamanca, Spain
| | - Artoghrul Alishbayli
- Computational Neuroscience Lab, Department of Neurophysiology, Donders Centre of Neuroscience, Nijmegen, Netherlands
| | - Evrim Yilmaz
- Computational Neuroscience Lab, Department of Neurophysiology, Donders Centre of Neuroscience, Nijmegen, Netherlands
| | - Manuel S Malmierca
- Cognitive and Auditory Neuroscience Laboratory (CANELAB), Institute of Neuroscience of Castilla y León, University of Salamanca, Salamanca, Spain
- Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
- Department of Cell Biology and Pathology, University of Salamanca, Salamanca, Spain
| | - Bernhard Englitz
- Computational Neuroscience Lab, Department of Neurophysiology, Donders Centre of Neuroscience, Nijmegen, Netherlands
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33
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Chu Q, Ma O, Hang Y, Tian X. Dual-stream cortical pathways mediate sensory prediction. Cereb Cortex 2023:7169133. [PMID: 37197767 DOI: 10.1093/cercor/bhad168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 04/24/2023] [Accepted: 04/26/2023] [Indexed: 05/19/2023] Open
Abstract
Predictions are constantly generated from diverse sources to optimize cognitive functions in the ever-changing environment. However, the neural origin and generation process of top-down induced prediction remain elusive. We hypothesized that motor-based and memory-based predictions are mediated by distinct descending networks from motor and memory systems to the sensory cortices. Using functional magnetic resonance imaging (fMRI) and a dual imagery paradigm, we found that motor and memory upstream systems activated the auditory cortex in a content-specific manner. Moreover, the inferior and posterior parts of the parietal lobe differentially relayed predictive signals in motor-to-sensory and memory-to-sensory networks. Dynamic causal modeling of directed connectivity revealed selective enabling and modulation of connections that mediate top-down sensory prediction and ground the distinctive neurocognitive basis of predictive processing.
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Affiliation(s)
- Qian Chu
- Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning, Division of Arts and Sciences, New York University Shanghai, Shanghai 200126, China
- NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, Shanghai 200062, China
- Max Planck-University of Toronto Centre for Neural Science and Technology, Toronto, ON M5S 2E4, Canada
| | - Ou Ma
- NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, Shanghai 200062, China
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
| | - Yuqi Hang
- NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, Shanghai 200062, China
- Department of Administration, Leadership, and Technology, Steinhardt School of Culture, Education, and Human Development, New York University, New York, NY 10003, United States
| | - Xing Tian
- Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning, Division of Arts and Sciences, New York University Shanghai, Shanghai 200126, China
- NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, Shanghai 200062, China
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
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34
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De Ridder D, Friston K, Sedley W, Vanneste S. A parahippocampal-sensory Bayesian vicious circle generates pain or tinnitus: a source-localized EEG study. Brain Commun 2023; 5:fcad132. [PMID: 37223127 PMCID: PMC10202557 DOI: 10.1093/braincomms/fcad132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 02/14/2023] [Accepted: 04/19/2023] [Indexed: 05/25/2023] Open
Abstract
Pain and tinnitus share common pathophysiological mechanisms, clinical features, and treatment approaches. A source-localized resting-state EEG study was conducted in 150 participants: 50 healthy controls, 50 pain, and 50 tinnitus patients. Resting-state activity as well as functional and effective connectivity was computed in source space. Pain and tinnitus were characterized by increased theta activity in the pregenual anterior cingulate cortex, extending to the lateral prefrontal cortex and medial anterior temporal lobe. Gamma-band activity was increased in both auditory and somatosensory cortex, irrespective of the pathology, and extended to the dorsal anterior cingulate cortex and parahippocampus. Functional and effective connectivity were largely similar in pain and tinnitus, except for a parahippocampal-sensory loop that distinguished pain from tinnitus. In tinnitus, the effective connectivity between parahippocampus and auditory cortex is bidirectional, whereas the effective connectivity between parahippocampus and somatosensory cortex is unidirectional. In pain, the parahippocampal-somatosensory cortex is bidirectional, but parahippocampal auditory cortex unidirectional. These modality-specific loops exhibited theta-gamma nesting. Applying a Bayesian brain model of brain functioning, these findings suggest that the phenomenological difference between auditory and somatosensory phantom percepts result from a vicious circle of belief updating in the context of missing sensory information. This finding may further our understanding of multisensory integration and speaks to a universal treatment for pain and tinnitus-by selectively disrupting parahippocampal-somatosensory and parahippocampal-auditory theta-gamma activity and connectivity.
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Affiliation(s)
- Dirk De Ridder
- Unit of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand
| | - Karl Friston
- Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3AR, UK
| | - William Sedley
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Sven Vanneste
- Correspondence to: Sven Vanneste Lab for Clinical & Integrative Neuroscience Global Brain Health Institute and Institute of Neuroscience Trinity College Dublin, College Green 2, Dublin D02 PN40, Ireland E-mail:
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35
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Floegel M, Kasper J, Perrier P, Kell CA. How the conception of control influences our understanding of actions. Nat Rev Neurosci 2023; 24:313-329. [PMID: 36997716 DOI: 10.1038/s41583-023-00691-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/28/2023] [Indexed: 04/01/2023]
Abstract
Wilful movement requires neural control. Commonly, neural computations are thought to generate motor commands that bring the musculoskeletal system - that is, the plant - from its current physical state into a desired physical state. The current state can be estimated from past motor commands and from sensory information. Modelling movement on the basis of this concept of plant control strives to explain behaviour by identifying the computational principles for control signals that can reproduce the observed features of movements. From an alternative perspective, movements emerge in a dynamically coupled agent-environment system from the pursuit of subjective perceptual goals. Modelling movement on the basis of this concept of perceptual control aims to identify the controlled percepts and their coupling rules that can give rise to the observed characteristics of behaviour. In this Perspective, we discuss a broad spectrum of approaches to modelling human motor control and their notions of control signals, internal models, handling of sensory feedback delays and learning. We focus on the influence that the plant control and the perceptual control perspective may have on decisions when modelling empirical data, which may in turn shape our understanding of actions.
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Affiliation(s)
- Mareike Floegel
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany
| | - Johannes Kasper
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany
| | - Pascal Perrier
- Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, Grenoble, France
| | - Christian A Kell
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany.
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36
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Expectation violations enhance neuronal encoding of sensory information in mouse primary visual cortex. Nat Commun 2023; 14:1196. [PMID: 36864037 PMCID: PMC9981605 DOI: 10.1038/s41467-023-36608-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 02/08/2023] [Indexed: 03/04/2023] Open
Abstract
The response of cortical neurons to sensory stimuli is shaped both by past events (adaptation) and the expectation of future events (prediction). Here we employed a visual stimulus paradigm with different levels of predictability to characterise how expectation influences orientation selectivity in the primary visual cortex (V1) of male mice. We recorded neuronal activity using two-photon calcium imaging (GCaMP6f) while animals viewed sequences of grating stimuli which either varied randomly in their orientations or rotated predictably with occasional transitions to an unexpected orientation. For single neurons and the population, there was significant enhancement in the gain of orientation-selective responses to unexpected gratings. This gain-enhancement for unexpected stimuli was prominent in both awake and anaesthetised mice. We implemented a computational model to demonstrate how trial-to-trial variability in neuronal responses were best characterised when adaptation and expectation effects were combined.
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37
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Zhou Y, He Y, Jin Y, Zeidman P, Gao L, Rong B, Huang H, Feng Y, Cui J, Zhang S, Wang Y, Wang G, Xiang YT, Wang H. Amygdala connectivity related to subsequent stress responses during the COVID-19 outbreak. Front Psychiatry 2023; 14:999934. [PMID: 36911118 PMCID: PMC9996006 DOI: 10.3389/fpsyt.2023.999934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 02/02/2023] [Indexed: 02/25/2023] Open
Abstract
Introduction The amygdala plays an important role in stress responses and stress-related psychiatric disorders. It is possible that amygdala connectivity may be a neurobiological vulnerability marker for stress responses or stress-related psychiatric disorders and will be useful to precisely identify the vulnerable individuals before stress happens. However, little is known about the relationship between amygdala connectivity and subsequent stress responses. The current study investigated whether amygdala connectivity measured before experiencing stress is a predisposing neural feature of subsequent stress responses while individuals face an emergent and unexpected event like the COVID-19 outbreak. Methods Data collected before the COVID-19 pandemic from an established fMRI cohort who lived in the pandemic center in China (Hubei) during the COVID-19 outbreak were used to investigate the relationship between amygdala connectivity and stress responses during and after the pandemic in 2020. The amygdala connectivity was measured with resting-state functional connectivity (rsFC) and effective connectivity. Results We found the rsFC of the right amygdala with the dorsomedial prefrontal cortex (dmPFC) was negatively correlated with the stress responses at the first survey during the COVID-19 outbreak, and the rsFC between the right amygdala and bilateral superior frontal gyri (partially overlapped with the dmPFC) was correlated with SBSC at the second survey. Dynamic causal modeling suggested that the self-connection of the right amygdala was negatively correlated with stress responses during the pandemic. Discussion Our findings expand our understanding about the role of amygdala in stress responses and stress-related psychiatric disorders and suggest that amygdala connectivity is a predisposing neural feature of subsequent stress responses.
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Affiliation(s)
- Yuan Zhou
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Yuwen He
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Centre for Cognitive and Brain Sciences, University of Macau, Macau, Macao SAR, China
| | - Yuening Jin
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Peter Zeidman
- The Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | - Lianlu Gao
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Bei Rong
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Huan Huang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yuan Feng
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Jian Cui
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Shudong Zhang
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Yun Wang
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Gang Wang
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Yu-Tao Xiang
- Centre for Cognitive and Brain Sciences, University of Macau, Macau, Macao SAR, China
- Unit of Psychiatry, Faculty of Health Sciences, Department of Public Health and Medicinal Administration, Institute of Translational Medicine, University of Macau, Macao, Macao SAR, China
| | - Huiling Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Key Laboratory of Developmentally Originated Disease, Wuhan, China
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Klarlund M, Brattico E, Pearce M, Wu Y, Vuust P, Overgaard M, Du Y. Worlds apart? Testing the cultural distance hypothesis in music perception of Chinese and Western listeners. Cognition 2023; 235:105405. [PMID: 36807031 DOI: 10.1016/j.cognition.2023.105405] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 02/02/2023] [Accepted: 02/08/2023] [Indexed: 02/21/2023]
Abstract
According to the cultural distance hypothesis (CDH), individuals learn culture-specific statistical structures in music as internal stylistic models and use these models in predictive processing of music, with musical structures closer to their home culture being easier to predict. This cultural distance effect may be affected by domain-specific (musical ability) and domain-general individual characteristics (openness, implicit cultural bias). To test the CDH and its modulation by individual characteristics, we recruited Chinese and Western adults to categorize stylistically ambiguous and unambiguous Chinese and Western melodies by cultural origin. Categorization performance was better for unambiguous (low CD) than ambiguous melodies (high CD), and for in-culture melodies regardless of ambiguity for both groups, providing evidence for CDH. Musical ability, but not other traits, correlated positively with melody categorization, suggesting that musical ability refines internal stylistic models. Therefore, both cultures show musical enculturation in their home culture with a modulatory effect of individual musical ability.
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Affiliation(s)
- Mathias Klarlund
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing, China; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark.
| | - Elvira Brattico
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark; Department of Education, Psychology, Communication, University of Bari Aldo Moro, Italy
| | - Marcus Pearce
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark; Music Cognition Lab, Queen Mary University of London, London, England, UK
| | - Yiyang Wu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
| | - Morten Overgaard
- Center for Functionally Integrative Neuroscience, Dept of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Yi Du
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China; Chinese Institute for Brain Research, Beijing, China.
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Kirubeswaran OR, Storrs KR. Inconsistent illusory motion in predictive coding deep neural networks. Vision Res 2023; 206:108195. [PMID: 36801664 DOI: 10.1016/j.visres.2023.108195] [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: 07/29/2022] [Revised: 01/31/2023] [Accepted: 01/31/2023] [Indexed: 02/19/2023]
Abstract
Why do we perceive illusory motion in some static images? Several accounts point to eye movements, response latencies to different image elements, or interactions between image patterns and motion energy detectors. Recently PredNet, a recurrent deep neural network (DNN) based on predictive coding principles, was reported to reproduce the "Rotating Snakes" illusion, suggesting a role for predictive coding. We begin by replicating this finding, then use a series of "in silico" psychophysics and electrophysiology experiments to examine whether PredNet behaves consistently with human observers and non-human primate neural data. A pretrained PredNet predicted illusory motion for all subcomponents of the Rotating Snakes pattern, consistent with human observers. However, we found no simple response delays in internal units, unlike evidence from electrophysiological data. PredNet's detection of motion in gradients seemed dependent on contrast, but depends predominantly on luminance in humans. Finally, we examined the robustness of the illusion across ten PredNets of identical architecture, retrained on the same video data. There was large variation across network instances in whether they reproduced the Rotating Snakes illusion, and what motion, if any, they predicted for simplified variants. Unlike human observers, no network predicted motion for greyscale variants of the Rotating Snakes pattern. Our results sound a cautionary note: even when a DNN successfully reproduces some idiosyncrasy of human vision, more detailed investigation can reveal inconsistencies between humans and the network, and between different instances of the same network. These inconsistencies suggest that predictive coding does not reliably give rise to human-like illusory motion.
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Affiliation(s)
| | - Katherine R Storrs
- Department of Experimental Psychology, Justus Liebig University Giessen, Germany; Centre for Mind, Brain and Behaviour (CMBB), University of Marburg and Justus Liebig University Giessen, Germany; School of Psychology, University of Auckland, New Zealand
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Friston K. Computational psychiatry: from synapses to sentience. Mol Psychiatry 2023; 28:256-268. [PMID: 36056173 PMCID: PMC7614021 DOI: 10.1038/s41380-022-01743-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 08/08/2022] [Accepted: 08/11/2022] [Indexed: 01/09/2023]
Abstract
This review considers computational psychiatry from a particular viewpoint: namely, a commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion of a generative model as underwriting (i) sentient processing in the brain, and (ii) the scientific process in psychiatry. The story starts with a view of the brain-from cognitive and computational neuroscience-as an organ of inference and prediction. This offers a formal description of neuronal message passing, distributed processing and belief propagation in neuronal networks; and how certain kinds of dysconnection lead to aberrant belief updating and false inference. The dysconnections in question can be read as a pernicious synaptopathy that fits comfortably with formal notions of how we-or our brains-encode uncertainty or its complement, precision. It then considers how the ensuing process theories are tested empirically, with an emphasis on the computational modelling of neuronal circuits and synaptic gain control that mediates attentional set, active inference, learning and planning. The opportunities afforded by this sort of modelling are considered in light of in silico experiments; namely, computational neuropsychology, computational phenotyping and the promises of a computational nosology for psychiatry. The resulting survey of computational approaches is not scholarly or exhaustive. Rather, its aim is to review a theoretical narrative that is emerging across subdisciplines within psychiatry and empirical scales of investigation. These range from epilepsy research to neurodegenerative disorders; from post-traumatic stress disorder to the management of chronic pain, from schizophrenia to functional medical symptoms.
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Affiliation(s)
- Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, WC1N 3AR, UK.
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41
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Chen ZS. Hierarchical predictive coding in distributed pain circuits. Front Neural Circuits 2023; 17:1073537. [PMID: 36937818 PMCID: PMC10020379 DOI: 10.3389/fncir.2023.1073537] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/07/2023] [Indexed: 03/06/2023] Open
Abstract
Predictive coding is a computational theory on describing how the brain perceives and acts, which has been widely adopted in sensory processing and motor control. Nociceptive and pain processing involves a large and distributed network of circuits. However, it is still unknown whether this distributed network is completely decentralized or requires networkwide coordination. Multiple lines of evidence from human and animal studies have suggested that the cingulate cortex and insula cortex (cingulate-insula network) are two major hubs in mediating information from sensory afferents and spinothalamic inputs, whereas subregions of cingulate and insula cortices have distinct projections and functional roles. In this mini-review, we propose an updated hierarchical predictive coding framework for pain perception and discuss its related computational, algorithmic, and implementation issues. We suggest active inference as a generalized predictive coding algorithm, and hierarchically organized traveling waves of independent neural oscillations as a plausible brain mechanism to integrate bottom-up and top-down information across distributed pain circuits.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, United States
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY, United States
- Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
- Interdisciplinary Pain Research Program, NYU Langone Health, New York, NY, United States
- *Correspondence: Zhe Sage Chen
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Friston K. The ultimate trick?: Comment on: "The Markov blanket trick: On the scope of the free energy principle and active inference" by Raja et al. Phys Life Rev 2022; 43:10-16. [PMID: 35963034 DOI: 10.1016/j.plrev.2022.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 07/28/2022] [Indexed: 12/15/2022]
Affiliation(s)
- Karl Friston
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, WC1N 3AR, UK.
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43
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Paredes R, Ferri F, Seriès P. Influence of E/I balance and pruning in peri-personal space differences in schizophrenia: A computational approach. Schizophr Res 2022; 248:368-377. [PMID: 34509334 DOI: 10.1016/j.schres.2021.06.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 05/06/2021] [Accepted: 06/22/2021] [Indexed: 11/25/2022]
Abstract
The encoding of the space close to the body, named peri-personal space (PPS), is thought to play a crucial role in the unusual experiences of the self observed in schizophrenia (SCZ). However, it is unclear why SCZ patients and high schizotypal (H-SPQ) individuals present a narrower PPS and why the boundaries of the PPS are more sharply defined in patients. We hypothesise that the unusual PPS representation observed in SCZ is caused by an imbalance of excitation and inhibition (E/I) in recurrent synapses of unisensory neurons or an impairment of bottom-up and top-down connectivity between unisensory and multisensory neurons. These hypotheses were tested computationally by manipulating the effects of E/I imbalance, feedback weights and synaptic density in the network. Using simulations we explored the effects of such impairments in the PPS representation generated by the network and fitted the model to behavioural data. We found that increased excitation of sensory neurons could account for the smaller PPS observed in SCZ and H-SPQ, whereas a decrease of synaptic density caused the sharp definition of the PPS observed in SCZ. We propose a novel conceptual model of PPS representation in the SCZ spectrum that can account for alterations in self-world demarcation, failures in tactile discrimination and symptoms observed in patients.
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Affiliation(s)
- Renato Paredes
- The University of Edinburgh, School of Informatics, 10 Crichton Street, Edinburgh, United Kingdom; Cognitive Science Group, Instituto de Investigaciones Psicológicas, Facultad de Psicología Universidad Nacional de Córdoba - CONICET, Argentina; Department of Psychology, Pontifical Catholic University of Peru, Lima, Peru
| | - Francesca Ferri
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Italy
| | - Peggy Seriès
- The University of Edinburgh, School of Informatics, 10 Crichton Street, Edinburgh, United Kingdom.
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44
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Herzog P, Kube T, Fassbinder E. How childhood maltreatment alters perception and cognition - the predictive processing account of borderline personality disorder. Psychol Med 2022; 52:2899-2916. [PMID: 35979924 PMCID: PMC9693729 DOI: 10.1017/s0033291722002458] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 06/24/2022] [Accepted: 07/18/2022] [Indexed: 01/05/2023]
Abstract
Borderline personality disorder (BPD) is a severe mental disorder, comprised of heterogeneous psychological and neurobiological pathologies. Here, we propose a predictive processing (PP) account of BPD to integrate these seemingly unrelated pathologies. In particular, we argue that the experience of childhood maltreatment, which is highly prevalent in BPD, leaves a developmental legacy with two facets: first, a coarse-grained, alexithymic model of self and others - leading to a rigidity and inflexibility concerning beliefs about self and others. Second, this developmental legacy leads to a loss of confidence or precision afforded beliefs about the consequences of social behavior. This results in an over reliance on sensory evidence and social feedback, with concomitant lability, impulsivity and hypersensitivity. In terms of PP, people with BPD show a distorted belief updating in response to new information with two opposing manifestations: rapid changes in beliefs and a lack of belief updating despite disconfirmatory evidence. This account of distorted information processing has the potential to explain both the instability (of affect, self-image, and interpersonal relationships) and the rigidity (of beliefs about self and others) which is typical of BPD. At the neurobiological level, we propose that enhanced levels of dopamine are associated with the increased integration of negative social feedback, and we also discuss the hypothesis of an impaired inhibitory control of the prefrontal cortex in the processing of negative social information. Our account may provide a new understanding not only of the clinical aspects of BPD, but also a unifying theory of the corresponding neurobiological pathologies. We conclude by outlining some directions for future research on the behavioral, neurobiological, and computational underpinnings of this model, and point to some clinical implications of it.
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Affiliation(s)
- Philipp Herzog
- Department of Psychiatry and Psychotherapy, University of Lübeck, Ratzeburger Allee 160, D-23562 Lübeck, Germany
- Department of Psychiatry and Psychotherapy, Christian-Albrechts-University of Kiel, Niemannsweg 147, D-24105 Kiel, Germany
- Department of Psychology, University of Koblenz-Landau, Ostbahnstr. 10, 76829 Landau, Germany
| | - Tobias Kube
- Department of Psychology, University of Koblenz-Landau, Ostbahnstr. 10, 76829 Landau, Germany
| | - Eva Fassbinder
- Department of Psychiatry and Psychotherapy, Christian-Albrechts-University of Kiel, Niemannsweg 147, D-24105 Kiel, Germany
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45
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Applying the Properties of Neurons in Machine Learning: A Brain-like Neural Model with Interactive Stimulation for Data Classification. Brain Sci 2022; 12:brainsci12091191. [PMID: 36138927 PMCID: PMC9496749 DOI: 10.3390/brainsci12091191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 11/16/2022] Open
Abstract
Some neural models achieve outstanding results in image recognition, semantic segmentation and natural language processing. However, their classification performance on structured and small-scale datasets that do not involve feature extraction is worse than that of traditional algorithms, although they require more time to train. In this paper, we propose a brain-like neural model with interactive stimulation (NMIS) that focuses on data classification. It consists of a primary neural field and a senior neural field that play different cognitive roles. The former is used to correspond to real instances in the feature space, and the latter stores the category pattern. Neurons in the primary field exchange information through interactive stimulation and their activation is transmitted to the senior field via inter-field interaction, simulating the mechanisms of neuronal interaction and synaptic plasticity, respectively. The proposed NMIS is biologically plausible and does not involve complex optimization processes. Therefore, it exhibits better learning ability on small-scale and structured datasets than traditional BP neural networks. For large-scale data classification, a nearest neighbor NMIS (NN_NMIS), an optimized version of NMIS, is proposed to improve computational efficiency. Numerical experiments performed on some UCI datasets show that the proposed NMIS and NN_NMIS are significantly superior to some classification algorithms that are widely used in machine learning.
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46
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Gjini K, Casey C, Tanabe S, Bo A, Parker M, White M, Kunkel D, Lennertz R, Pearce RA, Betthauser T, Christian BT, Johnson SC, Bendlin BB, Sanders RD. Greater tau pathology is associated with altered predictive coding. Brain Commun 2022; 4:fcac209. [PMID: 36226138 PMCID: PMC9547525 DOI: 10.1093/braincomms/fcac209] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 05/04/2022] [Accepted: 08/12/2022] [Indexed: 01/25/2023] Open
Abstract
Altered predictive coding may underlie the reduced auditory mismatch negativity amplitude observed in patients with dementia. We hypothesized that accumulating dementia-associated pathologies, including amyloid and tau, lead to disturbed predictions of our sensory environment. This would manifest as increased reliance on 'observed' sensory information with an associated increase in feedforward, and decrease in feedback, signalling. To test this hypothesis, we studied a cross-sectional cohort of participants who underwent PET imaging and high-density EEG during an oddball paradigm, and used dynamic casual modelling and Bayesian statistics to make inferences about the neuronal architectures (generators) and mechanisms (effective connectivity) underlying the observed auditory-evoked responses. Amyloid-β imaging with [C-11] Pittsburgh Compound-B PET was qualitatively rated using established criteria. Tau-positive PET scans, with [F-18]MK-6240, were defined by an MK-6240 standardized uptake value ratio positivity threshold at 2 standard deviations above the mean of the Amyloid(-) group in the entorhinal cortex (entorhinal MK-6240 standardized uptake value ratio > 1.27). The cross-sectional cohort included a total of 56 participants [9 and 13 participants in the Tau(+) and Amyloid(+) subgroups, respectively: age interquartile range of (73.50-75.34) and (70.5-75.34) years, 56 and 69% females, respectively; 46 and 43 participants in the Tau(-) and Amyloid(-) subgroups, respectively: age interquartile range of (62.72-72.5) and (62.64-72.48) years, 67 and 65% females, respectively]. Mismatch negativity amplitudes were significantly smaller in Tau+ subgroup than Tau- subgroup (cluster statistics corrected for multiple comparisons: P = 0.028). Dynamic causal modelling showed that tau pathology was associated with increased feedforward connectivity and decreased feedback connectivity, with increased excitability of superior temporal gyrus but not inferior frontal regions. This effect on superior temporal gyrus was consistent with the distribution of tau disease on PET in these participants, indicating that the observed differences in mismatch negativity reflect pathological changes evolving in preclinical dementia. Exclusion of participants with diagnosed mild cognitive impairment or dementia did not affect the results. These observational data provide proof of concept that abnormalities in predictive coding may be detected in the preclinical phase of Alzheimer's disease. This framework also provides a construct to understand how progressive impairments lead to loss of orientation to the sensory world in dementia. Based on our modelling results, plus animal models indicating that Alzheimer's disease pathologies produce hyperexcitability of higher cortical regions through local disinhibition, mismatch negativity might be a useful monitor to deploy as strategies that target interneuron dysfunction are developed.
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Affiliation(s)
- Klevest Gjini
- Department of Neurology, University of Wisconsin, Madison, WI, USA
| | - Cameron Casey
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - Sean Tanabe
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - Amber Bo
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - Margaret Parker
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - Marissa White
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - David Kunkel
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - Richard Lennertz
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - Robert A Pearce
- Department of Anesthesiology, University of Wisconsin, Madison, WI, USA
| | - Tobey Betthauser
- Department of Medicine, University of Wisconsin, Madison, WI, USA
| | | | | | | | - Robert D Sanders
- Specialty of Anaesthetics, University of Sydney, Camperdown, Australia
- Department of Anaesthetics, Royal Prince Alfred Hospital, Camperdown, Australia
- Central Clinical School & NHMRC Clinical Trials Centre, Institute of Academic Surgery, Camperdown, Australia
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Del Popolo Cristaldi F, Mento G, Buodo G, Sarlo M. Emotion regulation strategies differentially modulate neural activity across affective prediction stages: An HD-EEG investigation. Front Behav Neurosci 2022; 16:947063. [PMID: 35990725 PMCID: PMC9388773 DOI: 10.3389/fnbeh.2022.947063] [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: 05/18/2022] [Accepted: 07/12/2022] [Indexed: 11/29/2022] Open
Abstract
Emotion regulation (ER) strategies can influence how affective predictions are constructed by the brain (generation stage) to prearrange action (implementation stage) and update internal models according to incoming stimuli (updating stage). However, neurocomputational mechanisms by which this is achieved are unclear. We investigated through high-density EEG if different ER strategies (expressive suppression vs. cognitive reappraisal) predicted event-related potentials (ERPs) and brain source activity across affective prediction stages, as a function of contextual uncertainty. An S1-S2 paradigm with emotional faces and pictures as S1s and S2s was presented to 36 undergraduates. Contextual uncertainty was manipulated across three blocks with 100, 75, or 50% S1-S2 affective congruency. The effects of ER strategies, as assessed through the Emotion Regulation Questionnaire, on ERP and brain source activity were tested for each prediction stage through linear mixed-effects models. No ER strategy affected prediction generation. During implementation, in the 75% block, a higher tendency to suppress emotions predicted higher activity in the left supplementary motor area at 1,500–2,000 ms post-stimulus, and smaller amplitude of the Contingent Negative Variation at 2,000–2,500 ms. During updating, in the 75% block, a higher tendency to cognitively reappraise emotions predicted larger P2, Late Positive Potential, and right orbitofrontal cortex activity. These results suggest that both ER strategies interact with the levels of contextual uncertainty by differently modulating ERPs and source activity, and that different strategies are deployed in a moderately predictive context, supporting the efficient updating of affective predictive models only in the context in which model updating occurs.
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Affiliation(s)
| | - Giovanni Mento
- Department of General Psychology, University of Padua, Padua, Italy
- Padua Neuroscience Center (PNC), University of Padua, Padua, Italy
| | - Giulia Buodo
- Department of General Psychology, University of Padua, Padua, Italy
| | - Michela Sarlo
- Department of Communication Sciences, Humanities and International Studies, University of Urbino Carlo Bo, Urbino, Italy
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48
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Dual counterstream architecture may support separation between vision and predictions. Conscious Cogn 2022; 103:103375. [DOI: 10.1016/j.concog.2022.103375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 12/03/2021] [Accepted: 06/28/2022] [Indexed: 11/24/2022]
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49
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Pooling strategies in V1 can account for the functional and structural diversity across species. PLoS Comput Biol 2022; 18:e1010270. [PMID: 35862423 PMCID: PMC9345491 DOI: 10.1371/journal.pcbi.1010270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 08/02/2022] [Accepted: 06/01/2022] [Indexed: 11/19/2022] Open
Abstract
Neurons in the primary visual cortex are selective to orientation with various degrees of selectivity to the spatial phase, from high selectivity in simple cells to low selectivity in complex cells. Various computational models have suggested a possible link between the presence of phase invariant cells and the existence of orientation maps in higher mammals’ V1. These models, however, do not explain the emergence of complex cells in animals that do not show orientation maps. In this study, we build a theoretical model based on a convolutional network called Sparse Deep Predictive Coding (SDPC) and show that a single computational mechanism, pooling, allows the SDPC model to account for the emergence in V1 of complex cells with or without that of orientation maps, as observed in distinct species of mammals. In particular, we observed that pooling in the feature space is directly related to the orientation map formation while pooling in the retinotopic space is responsible for the emergence of a complex cells population. Introducing different forms of pooling in a predictive model of early visual processing as implemented in SDPC can therefore be viewed as a theoretical framework that explains the diversity of structural and functional phenomena observed in V1.
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50
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Del Popolo Cristaldi F, Buodo G, Duma GM, Sarlo M, Mento G. Unbalanced functional connectivity at rest affects the ERP correlates of affective prediction in high intolerance of uncertainty individuals: A high density EEG investigation. Int J Psychophysiol 2022; 178:22-33. [PMID: 35709946 DOI: 10.1016/j.ijpsycho.2022.06.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 05/28/2022] [Accepted: 06/07/2022] [Indexed: 12/21/2022]
Abstract
In a recent study we outlined the link between Intolerance of Uncertainty (IU) and the neural correlates of affective predictions, as constructed by the brain (generation stage) to prepare to relevant stimuli (implementation stage), and update predictive models according to incoming stimuli (updating stage). In this study we further explored whether the brain's functional organization at rest can modulate neural activity elicited within an emotional S1-S2 paradigm as a function of IU and uncertainty of S1-S2 contingencies. We computed resting state functional connectivity (RS-FC) from a 3-min resting period recorded with high density EEG, and we tested whether RS graph theory nodal measures (i.e., strength, clustering coefficient, betweenness centrality) predicted in-task ERP modulation as a function of IU. We found that RS-FC differently predicted in-task ERPs within the generation and updating stages. Higher IU levels were associated to altered RS-FC patterns within both domain-specific (i.e., right superior temporal sulcus) and domain-general regions (i.e., right orbitofrontal cortex), predictive of a reduced modulation of in-task ERPs in the generation and updating stages. This is presumably ascribable to an unbalancing between synchronization and integration within these regions, which may disrupt the exchange of information between top-down and bottom-up pathways. This altered RS-FC pattern may in turn result in the construction of less efficient affective predictions and a reduced ability to deal with contextual uncertainty in individuals high in IU.
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Affiliation(s)
| | - Giulia Buodo
- Department of General Psychology, University of Padua, Via Venezia 8, 35131 Padova, Italy
| | - Gian Marco Duma
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, 27 Bd Jean Moulin, 13005 Marseille, France
| | - Michela Sarlo
- Department of Communication Sciences, Humanities and International Studies, University of Urbino Carlo Bo, Via Saffi 15, 61029 Urbino, Italy
| | - Giovanni Mento
- Department of General Psychology, University of Padua, Via Venezia 8, 35131 Padova, Italy; Padua Neuroscience Center (PNC), University of Padua, Via Giuseppe Orus 2, 35131 Padova, Italy
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