Motion Processing: How the brain stays in sync with the real world
The brain can predict the location of a moving object to compensate for the delays caused by the processing of neural signals.
- Experimental Psychology, Helmholtz Institute, Utrecht University, Netherlands
In professional baseball the batter has to hit a ball that can be travelling as fast as 170 kilometers per hour. Part of the challenge is that the batter only has access to outdated information: it takes the brain about 80–100 milliseconds to process visual information, during which time the baseball will have moved about 4.5 meters closer to the batter (Allison et al., 1994; Thorpe et al., 1996). This should make it virtually impossible to consistently hit the baseball, but the batters in Major League Baseball manage to do so about 90% of the time. How is this possible?
Fortunately, baseballs and other objects in our world are governed by the laws of physics, so it is usually possible to predict their trajectories. It has been proposed that the brain can work out where a moving object is in almost real time by exploiting this predictability to compensate for the delays caused by processing (Hogendoorn and Burkitt, 2019; Kiebel et al., 2008; Nijhawan, 1994). However, it has not been clear how the brain might be able to do this.
Since predictions must be made within a matter of milliseconds, highly time-sensitive methods are needed to study this process. Previous experiments were unsuccessful in determining the exact timing of brain activity (Wang et al., 2014). Now, in eLife, Philippa Anne Johnson and colleagues at the University of Melbourne and the University of Amsterdam report new insights into motion processing (Johnson et al., 2023).
Johnson et al. used a combination of electroencephalogram (EEG) recordings and pattern recognition algorithms to investigate how long it took participants to process the location of objects that either flashed in one place (static objects) or moved in a straight line (moving objects). Using machine learning techniques, Johnson et al. first identified how the brain represents a non-moving object (Grootswagers et al., 2017). They accurately mapped patterns of neural activity, which corresponded to the location of the static object during the experiment. Participants took about 80 milliseconds to process this information (Figure 1).
Figure 1
Motion processing in the human brain.
Johnson et al. compared how long it takes the brain to process visual information about static objects and moving objects. The static objects (top) did not move but were briefly shown in unpredictable locations on the screen: the delay between the appearance of the object and the representation of its location in the brain was about 80 milliseconds. However, when the object moved in a predictable manner (bottom), the delay was much smaller.
Strikingly, Johnson et al. discovered that the brain represented the moving object at location different to where one would expect it to be (i.e., not at the location from 80ms ago). Instead, the internal representation of the moving object was aligned to its actual current location so that the brain was able to track moving objects in real time. The visual system must therefore be able to correct the position by at least 80 milliseconds worth of movement, indicating that the brain can effectively compensate for temporal processing delays by predicting (or extrapolating) where a moving object will be located in the future.
To fully grasp how motion prediction processes compensate for the lag between the external world and the brain, it is important to know where in the visual system this compensatory mechanism occurs. Johnson et al. showed that the delay was already fully compensated for in the visual cortex, indicating that the compensation happens early during visual processing. There is evidence to suggest that some degree of motion prediction occurs in the retina, but Johnson et al. argue that this on its own is not enough to fully compensate for the delays caused by neural processing (Berry et al., 1999).
Another possibility is that a brain area involved in a later stage of motion perception, called the middle temporal area, may also play a role in predicting the location of an object (Maus et al., 2013). This region is thought to provide predictive feedback signals that help to compensate for the neural processing delay between the real world and the brain (Hogendoorn and Burkitt, 2019). More research is needed to test this theory, for example, by directly recording neurons in the middle temporal area in primates and rodents using intracranial electrodes. Gaining access to such accurate spatial and temporal neural information might be key to identifying where predictions are made and what they foresee exactly.
The work of Johnson et al. confirms that motion prediction of around 80–100 milliseconds can almost completely compensate for the lag between events in the real world and their internal representation in the brain. As such, humans are able to react to incredibly fast events – if they are predictable, like a baseball thrown at a batter. Neural delays need to be accounted for in all types of information processing within the brain, including the planning and execution of movements. A deeper understanding of such compensatory processes will ultimately help us to understand how the human brain can cope with a fast world, while the speed of its internal signaling is limited. The evidence here seems to suggest that we overcome these neural delays during motion perception by living in our brain’s prediction of the present.
References
-
Decoding dynamic brain patterns from evoked responses: a tutorial on multivariate pattern analysis applied to time series neuroimaging dataJournal of Cognitive Neuroscience 29:677–697.https://doi.org/10.1162/jocn_a_01068
-
A hierarchy of time-scales and the brainPLOS Computational Biology 4:e1000209.https://doi.org/10.1371/journal.pcbi.1000209
-
Motion direction biases and decoding in human visual cortexThe Journal of Neuroscience 34:12601–12615.https://doi.org/10.1523/JNEUROSCI.1034-14.2014
Article and author information
Author details
Publication history
Copyright
© 2023, Koevoet, Sahakian et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,979
- views
-
- 129
- downloads
-
- 0
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Motion Processing: How the brain stays in sync with the real world
eLife 12:e85301.
https://doi.org/10.7554/eLife.85301
Further reading
-
- Neuroscience
- Physics of Living Systems
Neurons generate and propagate electrical pulses called action potentials which annihilate on arrival at the axon terminal. We measure the extracellular electric field generated by propagating and annihilating action potentials and find that on annihilation, action potentials expel a local discharge. The discharge at the axon terminal generates an inhomogeneous electric field that immediately influences target neurons and thus provokes ephaptic coupling. Our measurements are quantitatively verified by a powerful analytical model which reveals excitation and inhibition in target neurons, depending on position and morphology of the source-target arrangement. Our model is in full agreement with experimental findings on ephaptic coupling at the well-studied Basket cell-Purkinje cell synapse. It is able to predict ephaptic coupling for any other synaptic geometry as illustrated by a few examples.
-
- Neuroscience
The classical diagnosis of Parkinsonism is based on motor symptoms that are the consequence of nigrostriatal pathway dysfunction and reduced dopaminergic output. However, a decade prior to the emergence of motor issues, patients frequently experience non-motor symptoms, such as a reduced sense of smell (hyposmia). The cellular and molecular bases for these early defects remain enigmatic. To explore this, we developed a new collection of five fruit fly models of familial Parkinsonism and conducted single-cell RNA sequencing on young brains of these models. Interestingly, cholinergic projection neurons are the most vulnerable cells, and genes associated with presynaptic function are the most deregulated. Additional single nucleus sequencing of three specific brain regions of Parkinson’s disease patients confirms these findings. Indeed, the disturbances lead to early synaptic dysfunction, notably affecting cholinergic olfactory projection neurons crucial for olfactory function in flies. Correcting these defects specifically in olfactory cholinergic interneurons in flies or inducing cholinergic signaling in Parkinson mutant human induced dopaminergic neurons in vitro using nicotine, both rescue age-dependent dopaminergic neuron decline. Hence, our research uncovers that one of the earliest indicators of disease in five different models of familial Parkinsonism is synaptic dysfunction in higher-order cholinergic projection neurons and this contributes to the development of hyposmia. Furthermore, the shared pathways of synaptic failure in these cholinergic neurons ultimately contribute to dopaminergic dysfunction later in life.
