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Papastamou F, Dumont C, Destrebecqz A, Kissine M. Predictive Processing During Cue-Outcome Associative Learning in Autistic Children. J Autism Dev Disord 2024:10.1007/s10803-024-06448-6. [PMID: 38951312 DOI: 10.1007/s10803-024-06448-6] [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] [Accepted: 06/19/2024] [Indexed: 07/03/2024]
Abstract
PURPOSE Predictive coding theories posit that autism is characterized by an over-adjustment to prediction errors, resulting in frequent updates of prior beliefs. Atypical weighting of prediction errors is generally considered to negatively impact the construction of stable models of the world, but may also yield beneficial effects. In a novel associative learning paradigm, we investigated whether unexpected events trigger faster learning updates in favour of subtle but fully predictive cues in autistic children compared to their non-autistic counterparts. We also explored the relationship between children's language proficiency and their predictive performances. METHODS Anticipatory fixations and explicit predictions were recorded during three associative learning tasks with deterministic or probabilistic contingencies. One of the probabilistic tasks was designed so that a fully predictive but subtle cue was overshadowed by a less predictive salient one. RESULTS Both autistic and non-autistic children based their learning on the salient cue, and, contrary to our predictions, showed no signs of updating in favour of the subtle cue. While both groups demonstrated associative learning, autistic children made less accurate explicit predictions than their non-autistic peers in all tasks. Explicit prediction performances were positively correlated with language proficiency in non-autistic children, but no such correlation was observed in autistic children. CONCLUSION These results suggest no over-adjustment to prediction errors in autistic children and highlight the need to control for general performance in cue-outcome associative learning in predictive processing studies. Further research is needed to explore the nature of the relationship between predictive processing and language development in autism.
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Affiliation(s)
- Fanny Papastamou
- | F.R.S.-FNRS - Fonds de la Recherche Scientifique Fondation d'utilité publique, Rue d'Egmont 5, Brussels, B-1000, Belgium.
- CRCN, Université libre de Bruxelles, 50 avenue F.D. Roosevelt, Brussels, CP 175, 1050, Belgium.
| | - Charlotte Dumont
- | F.R.S.-FNRS - Fonds de la Recherche Scientifique Fondation d'utilité publique, Rue d'Egmont 5, Brussels, B-1000, Belgium
- CRCN, Université libre de Bruxelles, 50 avenue F.D. Roosevelt, Brussels, CP 175, 1050, Belgium
| | - Arnaud Destrebecqz
- CRCN, Université libre de Bruxelles, 50 avenue F.D. Roosevelt, Brussels, CP 175, 1050, Belgium
| | - Mikhail Kissine
- | F.R.S.-FNRS - Fonds de la Recherche Scientifique Fondation d'utilité publique, Rue d'Egmont 5, Brussels, B-1000, Belgium
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Nour Eddine S, Brothers T, Wang L, Spratling M, Kuperberg GR. A predictive coding model of the N400. Cognition 2024; 246:105755. [PMID: 38428168 PMCID: PMC10984641 DOI: 10.1016/j.cognition.2024.105755] [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: 03/22/2023] [Revised: 02/14/2024] [Accepted: 02/19/2024] [Indexed: 03/03/2024]
Abstract
The N400 event-related component has been widely used to investigate the neural mechanisms underlying real-time language comprehension. However, despite decades of research, there is still no unifying theory that can explain both its temporal dynamics and functional properties. In this work, we show that predictive coding - a biologically plausible algorithm for approximating Bayesian inference - offers a promising framework for characterizing the N400. Using an implemented predictive coding computational model, we demonstrate how the N400 can be formalized as the lexico-semantic prediction error produced as the brain infers meaning from the linguistic form of incoming words. We show that the magnitude of lexico-semantic prediction error mirrors the functional sensitivity of the N400 to various lexical variables, priming, contextual effects, as well as their higher-order interactions. We further show that the dynamics of the predictive coding algorithm provides a natural explanation for the temporal dynamics of the N400, and a biologically plausible link to neural activity. Together, these findings directly situate the N400 within the broader context of predictive coding research. More generally, they raise the possibility that the brain may use the same computational mechanism for inference across linguistic and non-linguistic domains.
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Affiliation(s)
- Samer Nour Eddine
- Department of Psychology and Center for Cognitive Science, Tufts University, United States of America.
| | - Trevor Brothers
- Department of Psychology and Center for Cognitive Science, Tufts University, United States of America; Department of Psychology, North Carolina A&T, United States of America
| | - Lin Wang
- Department of Psychology and Center for Cognitive Science, Tufts University, United States of America; Department of Psychiatry and the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, United States of America
| | | | - Gina R Kuperberg
- Department of Psychology and Center for Cognitive Science, Tufts University, United States of America; Department of Psychiatry and the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, United States of America
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Li C, Qiu J, Huang H. Meta predictive learning model of languages in neural circuits. Phys Rev E 2024; 109:044309. [PMID: 38755909 DOI: 10.1103/physreve.109.044309] [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: 11/06/2023] [Accepted: 03/18/2024] [Indexed: 05/18/2024]
Abstract
Large language models based on self-attention mechanisms have achieved astonishing performances, not only in natural language itself, but also in a variety of tasks of different nature. However, regarding processing language, our human brain may not operate using the same principle. Then, a debate is established on the connection between brain computation and artificial self-supervision adopted in large language models. One of most influential hypotheses in brain computation is the predictive coding framework, which proposes to minimize the prediction error by local learning. However, the role of predictive coding and the associated credit assignment in language processing remains unknown. Here, we propose a mean-field learning model within the predictive coding framework, assuming that the synaptic weight of each connection follows a spike and slab distribution, and only the distribution, rather than specific weights, is trained. This meta predictive learning is successfully validated on classifying handwritten digits where pixels are input to the network in sequence, and moreover, on the toy and real language corpus. Our model reveals that most of the connections become deterministic after learning, while the output connections have a higher level of variability. The performance of the resulting network ensemble changes continuously with data load, further improving with more training data, in analogy with the emergent behavior of large language models. Therefore, our model provides a starting point to investigate the connection among brain computation, next-token prediction, and general intelligence.
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Affiliation(s)
- Chan Li
- PMI Laboratory, School of Physics, Sun Yat-sen University, Guangzhou 510275, People's Republic of China
- Department of Physics, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Junbin Qiu
- PMI Laboratory, School of Physics, Sun Yat-sen University, Guangzhou 510275, People's Republic of China
| | - Haiping Huang
- PMI Laboratory, School of Physics, Sun Yat-sen University, Guangzhou 510275, People's Republic of China
- Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, Sun Yat-sen University, Guangzhou 510275, People's Republic of China
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Zhu J, Tian KJ, Carrasco M, Denison RN. Temporal attention recruits fronto-cingulate cortex to amplify stimulus representations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583738. [PMID: 38496610 PMCID: PMC10942468 DOI: 10.1101/2024.03.06.583738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The human brain receives a continuous stream of input, but it faces significant constraints in its ability to process every item in a sequence of stimuli. Voluntary temporal attention can alleviate these constraints by using information about upcoming stimulus timing to selectively prioritize a task-relevant item over others in a sequence. But the neural mechanisms underlying this ability remain unclear. Here, we manipulated temporal attention to successive stimuli in a two-target temporal cueing task, while controlling for temporal expectation by using fully predictable stimulus timing. We recorded magnetoencephalography (MEG) in human observers and measured the effects of temporal attention on orientation representations of each stimulus using time-resolved multivariate decoding in both sensor and source space. Voluntary temporal attention enhanced the orientation representation of the first target 235-300 milliseconds after target onset. Unlike previous studies that did not isolate temporal attention from temporal expectation, we found no evidence that temporal attention enhanced early visual evoked responses. Instead, and unexpectedly, the primary source of enhanced decoding for attended stimuli in the critical time window was a contiguous region spanning left frontal cortex and cingulate cortex. The results suggest that voluntary temporal attention recruits cortical regions beyond the ventral stream at an intermediate processing stage to amplify the representation of a target stimulus, which may serve to protect it from subsequent interference by a temporal competitor.
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Hubbard RJ, Federmeier KD. The Impact of Linguistic Prediction Violations on Downstream Recognition Memory and Sentence Recall. J Cogn Neurosci 2024; 36:1-23. [PMID: 37902591 PMCID: PMC10864033 DOI: 10.1162/jocn_a_02078] [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] [Indexed: 10/31/2023]
Abstract
Predicting upcoming words during language comprehension not only affects processing in the moment but also has consequences for memory, although the source of these memory effects (e.g., whether driven by lingering pre-activations, re-analysis following prediction violations, or other mechanisms) remains underspecified. Here, we investigated downstream impacts of prediction on memory in two experiments. First, we recorded EEG as participants read strongly and weakly constraining sentences with expected, unexpected but plausible, or semantically anomalous endings ("He made a holster for his gun / father / train") and were tested on their recognition memory for the sentence endings. Participants showed similar rates of false alarms for predicted but never presented sentence endings whether the prediction violation was plausible or anomalous, suggesting that these arise from pre-activation of the expected words during reading. During sentence reading, especially in strongly constraining sentences, plausible prediction violations elicited an anterior positivity; anomalous endings instead elicited a posterior positivity, whose amplitude was predictive of later memory for those anomalous words. ERP patterns at the time of recognition differentiated plausible and anomalous sentence endings: Words that had been plausible prediction violations elicited enhanced late positive complex amplitudes, suggesting greater episodic recollection, whereas anomalous sentence endings elicited greater N1 amplitudes, suggesting attentional tagging. In a follow-up behavioral study, a separate group of participants read the same sentence stimuli and were tested for sentence-level recall. We found that recall of full sentences was impaired when sentences ended with a prediction violation. Taken together, the results suggest that prediction violations draw attention and affect encoding of the violating word, in a manner that depends on plausibility, and that this, in turn, may impair future memory of the gist of the sentence.
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Ryskin R, Nieuwland MS. Prediction during language comprehension: what is next? Trends Cogn Sci 2023; 27:1032-1052. [PMID: 37704456 DOI: 10.1016/j.tics.2023.08.003] [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: 10/28/2022] [Revised: 08/03/2023] [Accepted: 08/04/2023] [Indexed: 09/15/2023]
Abstract
Prediction is often regarded as an integral aspect of incremental language comprehension, but little is known about the cognitive architectures and mechanisms that support it. We review studies showing that listeners and readers use all manner of contextual information to generate multifaceted predictions about upcoming input. The nature of these predictions may vary between individuals owing to differences in language experience, among other factors. We then turn to unresolved questions which may guide the search for the underlying mechanisms. (i) Is prediction essential to language processing or an optional strategy? (ii) Are predictions generated from within the language system or by domain-general processes? (iii) What is the relationship between prediction and memory? (iv) Does prediction in comprehension require simulation via the production system? We discuss promising directions for making progress in answering these questions and for developing a mechanistic understanding of prediction in language.
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Affiliation(s)
- Rachel Ryskin
- Department of Cognitive and Information Sciences, University of California Merced, 5200 Lake Road, Merced, CA 95343, USA.
| | - Mante S Nieuwland
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands; Donders Institute for Brain, Cognition, and Behaviour, Nijmegen, The Netherlands
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