Disentangling five dimensions of animacy in human brain and behaviour

Jozwik, Kamila M.; Najarro, Elias; Bosch, Jasper J.F. van den; Charest, Ian; Cichy, Radoslaw Martin; Kriegeskorte, Nikolaus

doi:10.1038/s42003-022-04194-y

Cited by 6 publications

(6 citation statements)

References 48 publications

(93 reference statements)

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“…For animacy classification, we also found significant information for both fixation crosses (Fig. 4b, turquoise and red curve) with peaks at 200 ms [170 280] (standard fixation cross) and 170 ms [160 280] (bullseye fixation cross), consistent with previous results 45–47,50 . However, the difference curve (Fig.…”

Section: Resultssupporting

confidence: 91%

The influence of the bullseye versus standard fixation cross on eye movements and classifying natural images from EEG

Häberle

Çelikkol

Cichy

2023

Preprint

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Eye movements are a ubiquitous and natural behavior, but in many tightly controlled experimental visual paradigms, eye movements are undesirable. Their occurrence can pose challenges to the interpretation of behavioral and neuroscientific data, in particular for magneto- and electroencephalography (M/EEG), which is sensitive to signals created by eye muscle movement. Here we compared the effect of two different fixation symbols - the standard fixation cross and the bullseye fixation cross - in the context of a visual paradigm with centrally presented naturalistic object images. We investigated eye movements and EEG data recorded simultaneously using behavioral and multivariate analysis techniques. Our findings comparing the bullseye to the standard fixation cross are threefold. First, the bullseye fixation cross reduces the number of saccades and amplitude size of microsaccades. Second, the bullseye subtly reduces classification accuracy in both eye tracking and EEG data for the classification of single object images, but not for the superlevel category animacy. Third, using representational similarity analysis, we found a systematic relationship between eye tracking and EEG data at the level of single images for the standard, but not for the bullseye fixation cross. In conclusion, we recommend the bullseye fixation cross in experimental paradigms with fixation when particularly tight control of fixation is beneficial.

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Section: Resultssupporting

confidence: 91%

The influence of the bullseye versus standard fixation cross on eye movements and classifying natural images from EEG

Häberle

Çelikkol

Cichy

2023

Preprint

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“…Our findings raise the question of what features the categorical template consists of, and whether categorical templates are specific to the categories used here. Animate and inanimate objects differ in terms of mid-, and high-level visual features (e.g., Proklova et al, 2016 ; Long et al, 2018 ; Thorat et al, 2019 ; Jozwik et al, 2022 ), and it has been proposed that the human visual system is particularly sensitive to these category-diagnostic features ( New et al, 2007 ), as also reflected in the animate–inanimate organization of the ventral visual cortex ( Chao et al, 1999 ; Kriegeskorte et al, 2008 ; Grill-Spector and Weiner, 2014 ; Thorat et al, 2019 ). This raises the possibility that categorical attention in memory search, as revealed here, is specific to the distinction between animate and inanimate objects.…”

Section: Discussionmentioning

confidence: 99%

Category-Based Attention Facilitates Memory Search

Shang,

Yeh,

Zhao

et al. 2024

eNeuro

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We often need to decide whether the object we look at is also the object we look for. When we look for one specific object, this process can be facilitated by feature-based attention. However, when we look for many objects at the same time (e.g., the products on our shopping list) such a strategy may no longer be possible, as research has shown that we can actively prepare to detect only one or two objects at a time. Therefore, looking for multiple objects additionally requires long-term memory search, slowing down decision making. Interestingly, however, previous research has shown that distractor objects can be efficiently rejected during memory search when they are from a different category than the items in the memory set. Here, using EEG, we show that this efficiency is supported by top-down attention at the category level. In Experiment 1, human participants (both sexes) performed a memory search task on individually presented objects from different categories, most of which were distractors. We observed category-level attentional modulation of distractor processing from ∼150 ms after stimulus onset, expressed both as an evoked response modulation and as an increase in decoding accuracy of same-category distractors. In Experiment 2, memory search was performed on two concurrently presented objects. When both objects were distractors, spatial attention (indexed by the N2pc component) was directed to the object that was of the same category as the objects in the memory set. Together, these results demonstrate how top-down attention can facilitate memory search.Significance statementWhen we are in the supermarket, we repeatedly decide whether a product we look at (e.g., a banana) is on our memorized shopping list (e.g., apples, oranges, kiwis). This requires searching our memory, which takes time. However, when the product is of an entirely different category (e.g., dairy instead of fruit), the decision can be made quickly. Here, we used EEG to show that this between-category advantage in memory search tasks is supported by top-down attentional modulation of visual processing: The visual response evoked by distractor objects was modulated by category membership, and spatial attention was quickly directed to the location of within-category (vs. between-category) distractors. These results demonstrate a close link between attention and memory.

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“…To determine whether CNNs and humans have different representational similarities depending on the supralevel category as previously observed for visual objects (Jozwik et al, 2022 ; Bracci, Ritchie, Kalfas, & Op de Beeck, 2019 ), we performed the analysis on all, natural, and man-made scenes separately, using the parts of the RDM for the respective scenes. For each of the three R/FCNN time courses, we collected the correlation peak latency, which represents the time point when EEG and network representations are most similar.…”

Section: Methodsmentioning

confidence: 99%

Empirically Identifying and Computationally Modeling the Brain–Behavior Relationship for Human Scene Categorization

Karapetian,

Boyanova,

Pandaram

et al. 2023

Journal of Cognitive Neuroscience

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Humans effortlessly make quick and accurate perceptual decisions about the nature of their immediate visual environment, such as the category of the scene they face. Previous research has revealed a rich set of cortical representations potentially underlying this feat. However, it remains unknown which of these representations are suitably formatted for decision-making. Here, we approached this question empirically and computationally, using neuroimaging and computational modelling. For the empirical part, we collected EEG data and RTs from human participants during a scene categorization task (natural vs. man-made). We then related neural representations to behavior using a multivariate extension of signal detection theory. We observed a correlation specifically between ∼100 msec and ∼200 msec after stimulus onset, suggesting that the neural scene representations in this time period are suitably formatted for decision-making. For the computational part, we evaluated a recurrent convolutional neural network as a model of brain and behavior. Unifying our previous observations in an image-computable model, recurrent convolutional neural network predicted well the neural representations, the behavioral scene categorization data, as well as the relationship between them. Our results identify and computationally characterize the neural and behavioral correlates of scene categorization in humans.

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Disentangling five dimensions of animacy in human brain and behaviour

Cited by 6 publications

References 48 publications

The influence of the bullseye versus standard fixation cross on eye movements and classifying natural images from EEG

The influence of the bullseye versus standard fixation cross on eye movements and classifying natural images from EEG

Category-Based Attention Facilitates Memory Search

Empirically Identifying and Computationally Modeling the Brain–Behavior Relationship for Human Scene Categorization

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