Poster No:
2073
Submission Type:
Abstract Submission
Authors:
Reuben Rideaux1, Kacie Lee1
Institutions:
1The University of Sydney, Sydney, NSW
First Author:
Co-Author:
Kacie Lee
The University of Sydney
Sydney, NSW
Introduction:
The capacity to adapt to patterns in the environment supports biological function from sensory processing to motor action. The temporal context in which sensory events are embedded can be leveraged to more effectively process and respond to this information. Influential theories of normative brain function such as predictive coding posit that temporal context serves to improve representational fidelity and reduce neurometabolic expenditure (Barlow, 1961; Rao & Ballard, 1999).
Humans are sensitive to the temporal context of events across multiple time scales. At the shortest scale, each event influences processing of the next event (micro). For example, stimulus reproductions are biased towards previous stimuli, i.e., serial dependency (Fischer & Whitney, 2014). At intermediate scales, short sequences of events form patterns that uniquely influence responses to subsequent events (meso). For instance, events that satisfy regular patterns produce attenuated neural responses (Squires et al., 1975) and are responded to more quickly and accurately than events that violate them (Kirby, 1976). At longer time scales, the relative frequency of past events can alter how those in the present are processed (macro). These effects are often referred to as statistical learning and are thought to reflect adaptation to regularities in the environment (Simoncelli & Olshousen, 2001).
Previous work has developed a variety of experimental designs to isolate the influence of temporal context at each scale and examine its behavioural and neural consequences. This approach has propagated multiple fields of research and implicitly asserts qualitive differences, however, it limits comparison between temporal context at different scales. It is possible that temporal context serves perception differently at each time scale and is thus associated with unique adaptive influences on sensory processing. Alternatively, a common rule may be applied at all scales of temporal context, signalling a unifying adaptive mechanism.
Methods:
To measure changes in visual perception associated with different scales of temporal context, we tasked participants with indicating the location of serially presented visual stimuli (Gaussian blobs randomly positioned at a fixed distance around a central fixation point). To assess response time and accuracy, participants performed a speeded binary judgement (e.g., left or right of fixation) on each trial. On 10% of trials, participants additionally reproduced the location of the stimulus, providing a measure of recall precision. To test the influence of attention, trials were sorted according to two spatial reference planes, based on the location of the stimulus: task-related and task-unrelated. The task-related plane corresponded to participants' binary judgement and the task-unrelated plane was orthogonal to this.
To test for neural correlates of micro temporal context, we re-analyzed a previously published EEG dataset in which human participants viewed visual (arc) stimuli presented at random angles around fixation, while monitoring for targets (Rideaux, 2024). We used multivariate linear-discriminant analysis to classify the location of stimuli according to task-related (< or > |90| from target angle) and unrelated planes (orthogonal to task-related plane), from parietal and occipital sensors.
Results:
We identify two distinct mechanisms that operate across all scales. The first is moderated by attention and supports rapid motor responses to expected events. The second is independent of task-demands and dampens the feedforward neural responses to expected events, leading to unexpected events eliciting earlier and more precise neural representations.
Conclusions:
While the neural mechanisms that implement the influence of temporal context across different scales may vary, they appear to promote two unifying outcomes: 1) rapid motor responses to expected events, and 2) prioritized encoding speed and fidelity of unexpected events..
Novel Imaging Acquisition Methods:
EEG 2
Perception, Attention and Motor Behavior:
Perception: Visual 1
Keywords:
Electroencephaolography (EEG)
Perception
Vision
1|2Indicates the priority used for review
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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pupillometry
Provide references using APA citation style.
Barlow, H. B. (1961). Possible Principles Underlying the Transformations of Sensory Messages. In W. A. Rosenblith (Ed.), Sensory Communication (pp. 216–234). The MIT Press. https://doi.org/10.7551/mitpress/9780262518420.003.0013
Fischer, J., & Whitney, D. (2014). Serial dependence in visual perception. Nature Neuroscience, 17(5), Article 5. https://doi.org/10.1038/nn.3689
Kirby, N. H. (1976). Sequential effects in two-choice reaction time: Automatic facilitation or subjective expectancy? Journal of Experimental Psychology: Human Perception and Performance, 2(4), 567–577. https://doi.org/10.1037/0096-1523.2.4.567
Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), Article 1. https://doi.org/10.1038/4580
Rideaux, R. (2024). Task-related modulation of event-related potentials does not reflect changes to sensory representations. Imaging Neuroscience, 2, 1–13. https://doi.org/10.1162/imag_a_00266
Simoncelli, E. P., & Olshausen, B. A. (2001). Natural Image Statistics and Neural Representation. Annual Review of Neuroscience, 24(1), 1193–1216. https://doi.org/10.1146/annurev.neuro.24.1.1193
Squires, N. K., Squires, K. C., & Hillyard, S. A. (1975). Two varieties of long-latency positive waves evoked by unpredictable auditory stimuli in man. Electroencephalography and Clinical Neurophysiology, 38(4), 387–401. https://doi.org/10.1016/0013-4694(75)90263-1
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