Biology subjects

Wyble, B.

Publications and source records attributed to Wyble, B..

6 recordsLinked to original sources

bioRxiv2026

The 2-feature Simultaneous Type/ Serial Token Model of temporal feature conjunctions: Successes are merely fortunate conjunctions.

Resolving the binding problem, i.e., how we integrate perceptual features in space and time to perceive objects, people and events as coherent wholes instead of masses of free-floating features, has been a central goal of cognitive scientists for decades. Conjunction errors occur whenever features are mis-bound leading to illusory percepts that were not actually presented. A specific kind of Rapid Serial Visual Presentation experiment induces temporal illusory conjunctions, called distractor intrusion errors. Despite these errors being well-established, current theories and models provide limited explanations. We present the 2-feature Simultaneous Type/Serial Token (2f-ST2) Model, the latest addition to the family of ST2-models, which simulates the most important behavioural and electrophysiological distractor intrusion findings. These include response distributions, reaction time patterns, and evoked electrophysiology responses. Importantly, we simulate these results across diverse empirical paradigms, implementing a range of stimulus sets, temporal dynamics, and involving multiple responses after each trial, without needing to modify our model's architecture or core parameters. Our findings are discussed in relation to theories of selective attention and working-memory binding. We argue that the 2f-ST2 model is more parsimonious than the leading theory of distractor-intrusions (the focussed-attention/sophisticated-guessing model), since it uses only a single binding mechanism, implying that correct bindings are simply fortunate conjunctions. The 2f-ST2 model further makes predictions for stimulus-competition, subjective-visibility and human electrophysiology. In summary, the ST2-model is a general framework for understanding how perception, temporal attention and working-memory interact, and the 2f-ST2 model extends the framework to distractor intrusions, thus explaining temporal feature binding.

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bioRxiv2022

What the flip? What the P-N flip can tell us about proactive suppression

It has been debated whether salient distractors in visual search can be proactively suppressed to completely prevent attentional capture, as the occurrence of proactive suppression implies that the initial shift of attention is not entirely driven by physical salience. While the presence of a Pd component in the EEG (associated with suppression) without a preceding N2pc component (associated with selection) has been used as evidence for proactive suppression, the link between these ERPs and the underlying mechanisms is not always clear. This is exemplified in two recent papers that observed the same waveform pattern, where an early Pd-like component flipped to a N2pc-like component, but provided very different interpretations (Drisdelle & Eimer, Psychophysiology, 58(9), 1-12, 2021; Kerzel & Burra, Journal of Cognitive Neuroscience, 32(6), 1170-1183, 2020). Using RAGNAROC (Wyble et al., Psychological Review, 127(6), 1163-1198, 2020), a computational model of reflexive attention, we successfully simulated this ERP pattern with minimal changes to its existing architecture, providing a parsimonious and mechanistic explanation for this flip in the EEG that is unique from both of the previous interpretations. Our account supports the occurrence of proactive suppression and demonstrates the benefits of incorporating computational modelling into theory building.

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bioRxiv2021

The Memory for Latent Representations: An Account of Working Memory that Builds on Visual Knowledge for Efficient and Detailed Visual Representations

Visual knowledge obtained from our lifelong experience of the world plays a critical role in our ability to build short-term memories. We propose a mechanistic explanation of how working memories are built from the latent representations of visual knowledge and can then be reconstructed. The proposed model, Memory for Latent Representations (MLR), features a variational autoencoder with an architecture that corresponds broadly to the human visual system and an activation-based binding pool of neurons that binds items attributes to tokenized representations. The simulation results revealed that the shapes of familiar items can be encoded and retrieved efficiently from latents in higher levels of the visual hierarchy. On the other hand, novel patterns that are completely outside the training set can be stored from a single exposure using only latents from early layers of the visual system. Moreover, a given stimulus in working memory can have multiple codes, representing specific visual features such as shape or color, in addition to categorical information. Finally, we validated our model by testing a series of predictions against behavioral results obtained from WM tasks. The model provides a compelling demonstration of how visual knowledge yields compact visual representation for efficient memory encoding.

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bioRxiv2019

A delay in sampling information from temporally autocorrelated visual stimuli

Much of our world changes smoothly in time, yet the allocation of attention is typically studied with sudden changes - transients. When stimuli change gradually there is a sizeable lag between when a cue is presented and when an object is sampled (Carlson, Hogendoorn, & Verstraten, 2006; Sheth, Nijhawan & Shimojo, 2000). Yet this lag is not seen with rapid serial visual presentation (RSVP) stimuli where temporally uncorrelated stimuli are presented (Vul, Kanwisher & Nieuwenstein 2008; Goodbourn & Holcombe, 2015). These findings collectively suggest that temporal autocorrelation of a feature paradoxically increases the latency at which information is sampled. This hypothesis was tested by comparing stimuli changing smoothly in time (autocorrelated) to stimuli that change randomly. Participants attempted to report the color coincident with a visual cue. The result was a smaller selection lag for the randomly varying condition relative to the condition with a smooth color trajectory. Our third experiment finds that the increase in selection latency is due to the smoothness of the color change after the cue rather than extrapolated predictions based on the color changes presented before the cue. Together, these results support a theory of attentional drag, whereby attention remains engaged at a location longer when features are changing smoothly. A computational model provides insights into neural mechanisms that might underlie the effect.

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bioRxiv2018

Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition

A quintessential challenge for any perceptual system is the need to focus on task-relevant information without being blindsided by unexpected, yet important information. The human visual system incorporates several solutions to this challenge, one of which is a reflexive covert attention system that is rapidly responsive to both the physical salience and the task-relevance of new information. This paper presents a model that simulates behavioral and neural correlates of reflexive attention as the product of brief neural attractor states that are formed across the visual hierarchy when attention is engaged. Such attractors emerge from an attentional gradient distributed over a population of topographically organized neurons and serve to focus processing at one or more locations in the visual field, while inhibiting the processing of lower priority information. The model moves towards a resolution of key debates about the nature of reflexive attention, such as whether it is parallel or serial, and whether suppression effects are distributed in a spatial surround, or selectively at the location of distractors. Most importantly, the model develops a framework for understanding the neural mechanisms of visual attention as a spatiotopic decision process within a hierarchy and links them to observable correlates such as accuracy, reaction time, and the N2pc and PD components of the EEG. This last contribution is the most crucial for repairing the disconnect that exists between our understanding of behavioral and neural correlates of attention.

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bioRxiv2018

Conceptual content in images triggers rapid shifts of covert attention

The visual system can use conceptual information to search for targets even in the absence of clear featural signifiers1, and visual saccades are often directed at target objects defined by conceptual content2. These abilities are a core component of our facility with the visual world. Here, we evaluate whether contingent mechanisms of visual attention, known to trigger in response to target features such as motion, color or luminance3, are also triggered by visual patterns that match conceptually specified categories. These pre-registered experiments provide convergent behavioral and electrophysiological support that covert spatial attention is rapidly triggered by natural image exemplars from superordinate conceptually described target sets such as dinner food or four-legged animal, even when each target was viewed only once. In the behavioral experiment when two targets were presented with onsets separated by only 167ms, subjects reported the second target more often when it was in the same spatial location as the first. In the EEG experiment, images elicited clear N2pc and P3 components only when they matched the conceptually specified target set. The latency of the N2pc peaked at roughly 250ms, which is comparable to that commonly found in other N2pc studies for simpler stimulus types. These results suggest that vision quickly decodes conceptual information from natural images and selectively deploys spatial attention to locations containing information that matches current search goals.

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