Biology subjects

Sörensen, L. K. A.

Publications and source records attributed to Sörensen, L. K. A..

3 recordsLinked to original sources

bioRxiv2023

Human visual cortex and deep convolutional neural network care deeply about object background

Deep convolutional neural networks (DCNNs) are able to predict brain activity during object categorization tasks, but factors contributing to this predictive power are not fully understood. Our study aimed to investigate the factors contributing to the predictive power of DCNNs in object categorization tasks. We compared the activity of four DCNN architectures with electroencephalography (EEG) recordings obtained from 62 human subjects during an object categorization task. Previous physiological studies on object categorization have highlighted the importance of figure-ground segregation - the ability to distinguish objects from their backgrounds. Therefore, we set out to investigate if figure-ground segregation could explain DCNNs predictive power. Using a stimuli set consisting of identical target objects embedded in different backgrounds, we examined the influence of object background versus object category on both EEG and DCNN activity. Crucially, the recombination of naturalistic objects and experimentally-controlled backgrounds creates a sufficiently challenging and naturalistic task, while allowing us to retain experimental control. Our results showed that early EEG activity (<100ms) and early DCNN layers represent object background rather than object category. We also found that the predictive power of DCNNs on EEG activity is related to processing of object backgrounds, rather than categories. We provided evidence from both trained and untrained (i.e. random weights) DCNNs, showing figure-ground segregation to be a crucial step prior to the learning of object features. These findings suggest that both human visual cortex and DCNNs rely on the segregation of object backgrounds and target objects in order to perform object categorization. Altogether, our study provides new insights into the mechanisms underlying object categorization as we demonstrated that both human visual cortex and DCNNs care deeply about object background. Author summaryOur study aimed to investigate the factors contributing to the predictive power of deep convolutional neural networks (DCNNs) on EEG activity in object recognition tasks. We compared the activity of four DCNN architectures with human neural recordings during an object categorization task. We used a stimuli set consisting of identical target objects embedded in different phase-scrambled backgrounds. The distinction between object backgrounds and object categories allows us to investigate the influence of either factor for human subjects and DCNNs. Surprisingly, we found that both human visual processing and early DCNNs layers dedicate a large proportion of activity to processing object backgrounds instead of object category. Furthermore, this shared ability to make object backgrounds (and not just object category) invariant is largely the reason why DCNNs are predictive of brain dynamics in our experiment. We posit this shared ability to be an important solution for object categorization. Finally, we conclude that DCNNs, like humans, care deeply about object backgrounds.

neuroscience↗

bioRxiv2022

Mechanisms of human dynamic object recognition revealed by sequential deep neural networks

Humans can rapidly recognize objects in a dynamically changing world. This ability is showcased by the fact that observers succeed at recognizing objects in rapidly changing image sequences, at up to 13 ms/image. To date, the mechanisms that govern dynamic object recognition remain poorly understood. Here, we developed deep learning models for dynamic recognition and compared different computational mechanisms, contrasting feedforward and recurrent, single-image and sequential processing as well as different forms of adaptation. We found that only models that integrate images sequentially via lateral recurrence mirrored human performance (N=36) and were predictive of trial-by-trial responses across image durations (13-80 ms/image) while also displaying a temporal correspondence. Augmenting this model with adaptation markedly improved dynamic recognition and accelerated its representational dynamics, thereby predicting human trial-by-trial responses using fewer processing resources. These findings provide new insights into the mechanisms rendering object recognition so fast and effective in a dynamic visual world.

neuroscience↗

bioRxiv2021

Deep hierarchical sensory processing accounts for effects of arousal state on perceptual decision-making

Arousal levels strongly affect task performance. Yet, what arousal level is optimal for a task depends on its difficulty. Easy task performance peaks at higher arousal levels, whereas performance on difficult tasks displays an inverted U-shape relationship with arousal, peaking at medium arousal levels, an observation first made by Yerkes and Dodson in 1908. It is commonly proposed that the noradrenergic locus coeruleus system regulates these effects on performance through a widespread release of noradrenaline resulting in changes of cortical gain. This account, however, does not explain why performance decays with high arousal levels only in difficult, but not in simple tasks. Here, we present a mechanistic model that revisits the Yerkes-Dodson effect from a sensory perspective: a deep convolutional neural network augmented with a global gain mechanism reproduced the same interaction between arousal state and task difficulty in its performance. Investigating this model revealed that global gain states differentially modulated sensory information encoding across the processing hierarchy, which explained their differential effects on performance on simple versus difficult tasks. These findings offer a novel hierarchical sensory processing account of how, and why, arousal state affects task performance. Authors summaryOver a hundred years ago, it was first observed that the effect of arousal on performance depends on task difficulty: the Yerkes-Dodson effect. Difficult tasks are best solved at intermediate arousal levels, whereas easy tasks benefit from a high arousal state. Current theories on how arousal affects neural processing cannot explain this effect of task difficulty. Here, we implement a key effect of arousal on cortical processing, a change in neuronal gain, in a computational model of visual processing capable of object recognition. Across a series of experiments, we find that our model can reproduce the Yerkes-Dodson effect behaviorally and that this effect can be explained by where in the processing hierarchy different arousal states optimize sensory information encoding.

neuroscience↗