Hierarchical temporal prediction captures motion processing along the visual pathway
Abstract
Visual neurons respond selectively to features that become increasingly complex from the eyes to the cortex. Retinal neurons prefer flashing spots of light, primary visual cortical (V1) neurons prefer moving bars, and those in higher cortical areas favor complex features like moving textures. Previously, we showed that V1 simple cell tuning can be accounted for by a basic model implementing temporal prediction - representing features that predict future sensory input from past input (Singer et al., 2018). Here we show that hierarchical application of temporal prediction can capture how tuning properties change across at least two levels of the visual system. This suggests that the brain does not efficiently represent all incoming information; instead, it selectively represents sensory inputs that help in predicting the future. When applied hierarchically, temporal prediction extracts time-varying features that depend on increasingly high-level statistics of the sensory input.
Data availability
All custom code used in this study was implemented in Python. The code for the models and analyses shown in Figures 1-8 and associated sections can be found at https://bitbucket.org/ox-ang/hierarchical_temporal_prediction/src/master/. The V1 neural response data (Ringach et al., 2002) used for comparison with the temporal prediction model in Figure 6 came from http://ringachlab.net/ ("Data & Code", "Orientation tuning in Macaque V1"). The V1 image response data used to test the models included in Figure 9 were downloaded with permission from https://github.com/sacadena/Cadena2019PlosCB (Cadena et al., 2019). The V1 movie response data used to test these models were collected in the Laboratory of Dario Ringach at UCLA and downloaded from https://crcns.org/data-sets/vc/pvc-1 (Nahaus and Ringach, 2007; Ringach and Nahaus, 2009). The code for the models and analyses shown in Figure 9 and the associated section can be found at https://github.com/webstorms/StackTP and https://github.com/webstorms/NeuralPred. The movies used for training the models in Figure 9 are available at https://figshare.com/articles/dataset/Natural_movies/24265498.
Article and author information
Author details
Funding
Wellcome Trust (WT108369/Z/2015/Z)
- Ben DB Willmore
- Andrew J King
- Nicol S Harper
University of Oxford Clarendon Fund
- Yosef Singer
- Luke CL Taylor
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2023, Singer et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Further reading
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Perceptual systems heavily rely on prior knowledge and predictions to make sense of the environment. Predictions can originate from multiple sources of information, including contextual short-term priors, based on isolated temporal situations, and context-independent long-term priors, arising from extended exposure to statistical regularities. While the effects of short-term predictions on auditory perception have been well-documented, how long-term predictions shape early auditory processing is poorly understood. To address this, we recorded magnetoencephalography data from native speakers of two languages with different word orders (Spanish: functor-initial vs Basque: functor-final) listening to simple sequences of binary sounds alternating in duration with occasional omissions. We hypothesized that, together with contextual transition probabilities, the auditory system uses the characteristic prosodic cues (duration) associated with the native language’s word order as an internal model to generate long-term predictions about incoming non-linguistic sounds. Consistent with our hypothesis, we found that the amplitude of the mismatch negativity elicited by sound omissions varied orthogonally depending on the speaker’s linguistic background and was most pronounced in the left auditory cortex. Importantly, listening to binary sounds alternating in pitch instead of duration did not yield group differences, confirming that the above results were driven by the hypothesized long-term ‘duration’ prior. These findings show that experience with a given language can shape a fundamental aspect of human perception – the neural processing of rhythmic sounds – and provides direct evidence for a long-term predictive coding system in the auditory cortex that uses auditory schemes learned over a lifetime to process incoming sound sequences.
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