Biology subjects

Alink, A.

Publications and source records attributed to Alink, A..

5 recordsLinked to original sources

bioRxiv2026

Expectation Consecutively boosts Neural Processing of Expected and Unexpected Visual Information

This electroencephalography (EEG) study provides evidence that expectations dynamically shape visually evoked brain responses through a biphasic temporal mechanism: an earlier enhancement of expected image components, followed by a later prioritization of unexpected components. Across six sessions each, six participants were presented with a deterministic sequence of four images with intermixed composite trials that contained both the expected and the unexpected image. To quantify expected and unexpected identity information over time, we applied Linear Discriminant Analysis. The observed temporal dissociation suggests that the brain initially integrates sensory input with prior expectations in a Bayesian manner before shifting to emphasize surprising information, thereby supporting both efficient recognition and adaptive model updating. Furthermore, expectations affected superordinate image processing (animal vs. non-animal), showing the same pattern as identity processing, with earlier enhancement of expected information followed by a prioritization of surprising information.

neuroscience↗

bioRxiv2020

Can expectation suppression be explained by reduced attention to predictable stimuli?

The expectation-suppression effect - reduced stimulus-evoked responses to expected stimuli - is widely considered to be an empirical hallmark of reduced prediction errors in the framework of predictive coding. Here we challenge this notion by proposing that that expectation suppression could be explained by a reduced attention effect. Specifically, we argue that reduced responses to predictable stimuli can also be explained by a reduced saliency-driven allocation of attention. We base our discussion mainly on findings in the visual cortex and propose that resolving this controversy requires the assessment of qualitative differences between the ways in which attention and surprise enhance brain responses.

neuroscience↗

bioRxiv2018

Individuals with clinically relevant autistic traits tend to have an eye for detail

Individuals with an autism spectrum disorder (ASD) diagnosis are often described as having an eye for detail1. This observation, and the finding that individuals with ASD tend to see the trees before the forest when performing the Navon task2, has led to the proposal that ASD is characterized by a bias towards processing local image details3. However, it remains to be shown that natural image recognition in individuals with autism depends more on fine image detail. Here, we resolve this issue by showing that natural image recognition relies more on details in individuals with an above-median number of autistic traits. Furthermore, we found that reliance on details was best predicted by the presence of the most clinically relevant autistic traits. Therefore, our findings raise the possibility that a wide range of real-life abilities and difficulties associated with ASD are related to an enhanced reliance on visual details.

neuroscience↗

bioRxiv2017

From neurons to voxels - repetition suppression is best modelled by local neural scaling

Inferring neural mechanisms from functional magnetic resonance imaging (fMRI) is challenging because the fMRI signal integrates over millions of neurons. One approach is to compare computational models that map neural activity to fMRI responses, to see which best predicts fMRI data. We used this approach to compare four possible neural mechanisms of fMRI adaptation to repeated stimuli (scaling, sharpening, repulsive shifting and attractive shifting), acting across three domains (global, local and remote). Six features of fMRI repetition effects were identified, both univariate and multivariate, from two independent fMRI experiments. After searching over parameter values, only the local scaling model could simultaneously fit all data features from both experiments. Thus fMRI stimulus repetition effects are best captured by down-scaling neuronal tuning curves in proportion to the difference between the stimulus and neuronal preference. These results emphasize the importance of formal modelling for bridging neuronal and fMRI levels of investigation.

neuroscience↗

bioRxiv2017

Aversive Learning Changes Face-Viewing Strategies, as Revealed by Model-Based Fixation-Pattern Similarity Analysis

Animals can effortlessly adapt their behavior by generalizing from past experiences, and avoid harm in novel aversive situations. In our current understanding, the perceptual similarity between learning and generalization samples is viewed as one major factor driving aversive generalization. Alternatively, the threat-prediction account proposes that perceptual similarity should lead to generalization to the extent it predicts harmful outcomes. We tested these views using a two-dimensional perceptual continuum of faces. During learning, one face is conditioned to predict a harmful event, whereas the most dissimilar face stays neutral; introducing an adversity gradient defined only along one dimension. Learning changed the way how humans sampled information during viewing of faces. These occurred specifically along the adversity gradient leading to an increased dissimilarity of eye-movement patterns along the threat-related dimension. This provides evidence for the threat-prediction account of generalization, which conceives perceptual factors to be relevant to the extent they predict harmful outcomes.

neuroscience↗