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1. Does predictive processing imply predictive coding in models of spoken word recognition?

   Magnuson, J. S.; Li, M.; Luthra, S.; You, H.; Steiner, R. (July 2020, Proceedings of the Cognitive Science Society)

   Pervasive behavioral and neural evidence for predictive processing has led to claims that language processing depends upon predictive coding. In some cases, this may reflect a conflation of terms, but predictive coding formally is a computational mechanism where only deviations from top- down expectations are passed between levels of representation. We evaluate three models’ ability to simulate predictive processing and ask whether they exhibit the putative hallmark of formal predictive coding (reduced signal when input matches expectations). Of crucial interest, TRACE, an interactive activation model that does not explicitly implement prediction, exhibits both predictive processing and model- internal signal reduction. This may indicate that interactive activation is functionally equivalent or approximant to predictive coding, or that caution is warranted in interpreting neural signal reduction as diagnostic of predictive coding. 

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2. Cortical encoding of melodic expectations in human temporal cortex

   https://doi.org/10.7554/eLife.51784  

   Di Liberto, Giovanni M; Pelofi, Claire; Bianco, Roberta; Patel, Prachi; Mehta, Ashesh D; Herrero, Jose L; de Cheveigné, Alain; Shamma, Shihab; Mesgarani, Nima (March 2020, eLife)

   Humans engagement in music rests on underlying elements such as the listeners’ cultural background and interest in music. These factors modulate how listeners anticipate musical events, a process inducing instantaneous neural responses as the music confronts these expectations. Measuring such neural correlates would represent a direct window into high-level brain processing. Here we recorded cortical signals as participants listened to Bach melodies. We assessed the relative contributions of acoustic versus melodic components of the music to the neural signal. Melodic features included information on pitch progressions and their tempo, which were extracted from a predictive model of musical structure based on Markov chains. We related the music to brain activity with temporal response functions demonstrating, for the first time, distinct cortical encoding of pitch and note-onset expectations during naturalistic music listening. This encoding was most pronounced at response latencies up to 350 ms, and in both planum temporale and Heschl’s gyrus. 

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3. DiseaseNet: a transfer learning approach to noncommunicable disease classification

   https://doi.org/10.1186/s12859-024-05734-5  

   Gore, Steven; Meche, Bailey; Shao, Danyang; Ginnett, Benjamin; Zhou, Kelly; Azad, Rajeev K (December 2024, BMC Bioinformatics)

   Abstract As noncommunicable diseases (NCDs) pose a significant global health burden, identifying effective diagnostic and predictive markers for these diseases is of paramount importance. Epigenetic modifications, such as DNA methylation, have emerged as potential indicators for NCDs. These have previously been exploited in other contexts within the framework of neural network models that capture complex relationships within the data. Applications of neural networks have led to significant breakthroughs in various biological or biomedical fields but these have not yet been effectively applied to NCD modeling. This is, in part, due to limited datasets that are not amenable to building of robust neural network models. In this work, we leveraged a neural network trained on one class of NCDs, cancer, as the basis for a transfer learning approach to non-cancer NCD modeling. Our results demonstrate promising performance of the model in predicting three NCDs, namely, arthritis, asthma, and schizophrenia, for the respective blood samples, with an overall accuracy (f-measure) of 94.5%. Furthermore, a concept based explanation method called Testing with Concept Activation Vectors (TCAV) was used to investigate the importance of the sample sources and understand how future training datasets for multiple NCD models may be improved. Our findings highlight the effectiveness of transfer learning in developing accurate diagnostic and predictive models for NCDs. 

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4. Intonational Cues to Segmental Contrasts in the Native Language Facilitate the Processing of Intonational Cues to Lexical Stress in the Second Language

   https://doi.org/10.3389/fcomm.2022.845430  

   Kim, Hyoju; Tremblay, Annie (April 2022, Frontiers in Communication)

   This study examines whether second language (L2) learners' processing of an intonationally cued lexical contrast is facilitated when intonational cues signal a segmental contrast in the native language (L1). It does so by investigating Seoul Korean and French listeners' processing of intonationally cued lexical-stress contrasts in English. Neither Seoul Korean nor French has lexical stress; instead, the two languages have similar intonational systems where prominence is realized at the level of the Accentual Phrase. A critical difference between the two systems is that French has only one tonal pattern underlying the realization of the Accentual Phrase, whereas Korean has two underlying tonal patterns that depend on the laryngeal feature of the phrase-initial segment. The L and H tonal cues thus serve to distinguish segments at the lexical level in Korean but not in French; Seoul Korean listeners are thus hypothesized to outperform French listeners when processing English lexical stress realized only with (only) tonal cues (H * on the stressed syllable). Seoul Korean and French listeners completed a sequence-recall task with four-item sequences of English words that differed in intonationally cued lexical stress (experimental condition) or in word-initial segment (control condition). The results showed higher accuracy for Seoul Korean listeners than for French listeners only when processing English lexical stress, suggesting that the processing of an intonationally cued lexical contrast in the L2 is facilitated when intonational cues signal a segmental contrast in the L1. These results are interpreted within the scope of the cue-based transfer approach to L2 prosodic processing. 

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5. On the relationship between predictive coding and backpropagation

   https://doi.org/10.1371/journal.pone.0266102  

   Rosenbaum, Robert (March 2022, PLOS ONE)

   Cymbalyuk, Gennady S. (Ed.)

   Artificial neural networks are often interpreted as abstract models of biological neuronal networks, but they are typically trained using the biologically unrealistic backpropagation algorithm and its variants. Predictive coding has been proposed as a potentially more biologically realistic alternative to backpropagation for training neural networks. This manuscript reviews and extends recent work on the mathematical relationship between predictive coding and backpropagation for training feedforward artificial neural networks on supervised learning tasks. Implications of these results for the interpretation of predictive coding and deep neural networks as models of biological learning are discussed along with a repository of functions, Torch2PC, for performing predictive coding with PyTorch neural network models. 

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