The human brain is specialized for face processing, yet we sometimes perceive illusory faces in objects. It is unknown whether these natural errors of face detection originate from a rapid process based on visual features or from a slower, cognitive re-interpretation. Here we use a multifaceted approach to understand both the spatial distribution and temporal dynamics of illusory face representation in the brain by combining functional magnetic resonance imaging and magnetoencephalography neuroimaging data with model-based analysis. We find that the representation of illusory faces is confined to occipital-temporal face-selective visual cortex. The temporal dynamics reveal a striking evolution in how illusory faces are represented relative to human faces and matched objects. Illusory faces are initially represented more similarly to real faces than matched objects are, but within ~250 ms, the representation transforms, and they become equivalent to ordinary objects. This is consistent with the initial recruitment of a broadly-tuned face detection mechanism which privileges sensitivity over selectivity.

Understanding object representations requires a broad, comprehensive sampling of the objects in our visual world with dense measurements of brain activity and behavior. Here we present THINGS-data, a multimodal collection of large-scale neuroimaging and behavioral datasets in humans, comprising densely-sampled functional MRI and magnetoencephalographic recordings, as well as 4.70 million similarity judgments in response to thousands of photographic images for up to 1,854 object concepts. THINGS-data is unique in its breadth of richly-annotated objects, allowing for testing countless hypotheses at scale while assessing the reproducibility of previous findings. Beyond the unique insights promised by each individual dataset, the multimodality of THINGS-data allows combining datasets for a much broader view into object processing than previously possible. Our analyses demonstrate the high quality of the datasets and provide five examples of hypothesis-driven and data-driven applications. THINGS-data constitutes the core public release of the THINGS initiative (https://things-initiative.org) for bridging the gap between disciplines and the advancement of cognitive neuroscience.

Numerical format describes the way magnitude is conveyed, for example, as a digit ("3") or Roman numeral ("III"). In the field of numerical cognition, there is an ongoing debate of whether magnitude representation is independent of numerical format. Here, we examine the time course of magnitude processing when using different symbolic formats. We presented participants with a series of digits and dice patterns corresponding to the magnitudes of 1 to 6 while performing a 1-back task on magnitude. Magnetoencephalography offers an opportunity to record brain activity with high temporal resolution. Multivariate pattern analysis applied to magnetoencephalographic data allows us to draw conclusions about brain activation patterns underlying information processing over time. The results show that we can cross-decode magnitude when training the classifier on magnitude presented in one symbolic format and testing the classifier on the other symbolic format. This suggests a similar representation of these numerical symbols. In addition, results from a time generalization analysis show that digits were accessed slightly earlier than dice, demonstrating temporal asynchronies in their shared representation of magnitude. Together, our methods allow a distinction between format-specific signals and format-independent representations of magnitude showing evidence that there is a shared representation of magnitude accessed via different symbols.

1Colour is a defining feature of many objects, playing a crucial role in our ability to 2 rapidly recognise things in the world around us and make categorical distinctions. For example, 3 colour is a useful cue when distinguishing lemons from limes or blackberries from raspberries. 4That means our representation of many objects includes key colour-related information. The 5 question addressed here is whether the neural representation activated by knowing that 6 something is red is the same as that activated when we actually see something red, particularly 7 in regard to timing. We addressed this question using neural timeseries 8 (magnetoencephalography, MEG) data to contrast real colour perception and implied object 9 colour activation. We applied multivariate pattern analysis (MVPA) to analyse the brain 10 activation patterns evoked by colour accessed via real colour perception and implied colour 11 activation. Applying MVPA to MEG data allows us here to focus on the temporal dynamics of 12 these processes. Male and female human participants (N=18) viewed isoluminant red and green 13 shapes and grey-scale, luminance-matched pictures of fruits and vegetables that are red (e.g., 14 tomato) or green (e.g., kiwifruit) in nature. We show that the brain activation pattern evoked 15 by real colour perception is similar to implied colour activation, but that this pattern is 16 instantiated at a later time. These results suggest that a common colour representation can be 17 triggered by activating object representations from memory and perceiving colours.

Colour is a defining feature of many objects, playing a crucial role in our ability to rapidly recognise things in the world around us and make categorical distinctions. For example, colour is a useful cue when distinguishing lemons from limes or blackberries from raspberries.That means our representation of many objects includes key colour-related information. The question addressed here is whether the neural representation activated by knowing that something is red is the same as that activated when we actually see something red, particularly in regard to timing. We addressed this question using neural timeseries (magnetoencephalography, MEG) data to contrast real colour perception and implied object colour activation. We applied multivariate pattern analysis (MVPA) to analyse the brain activation patterns evoked by colour accessed via real colour perception and implied colour activation. Applying MVPA to MEG data allows us here to focus on the temporal dynamics of these processes. Male and female human participants (N=18) viewed isoluminant red and green shapes and grey-scale, luminance-matched pictures of fruits and vegetables that are red (e.g., tomato) or green (e.g., kiwifruit) in nature. We show that the brain activation pattern evoked by real colour perception is similar to implied colour activation, but that this pattern is instantiated at a later time. These results suggest that a common colour representation can be triggered by activating object representations from memory and perceiving colours.

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Bayes Factors can be used to provide quantifiable evidence for contrasting hypotheses and have thus become increasingly popular in cognitive science. However, Bayes Factors are rarely used to statistically assess the results of neuroimaging experiments. Here, we provide an empirically-driven guide on implementing Bayes Factors for time-series neural decoding results. Using real and simulated Magnetoencephalography (MEG) data, we examine how parameters such as the shape of the prior and data size affect Bayes Factors. Additionally, we discuss benefits Bayes Factors bring to analysing multivariate pattern analysis data and show how using Bayes Factors can be used instead or in addition to traditional frequentist approaches.

The ability to rapidly and accurately recognize complex objects is a crucial function of the human visual system. To recognize an object, we need to bind incoming visual features, such as color and form, together into cohesive neural representations and integrate these with our preexisting knowledge about the world. For some objects, typical color is a central feature for recognition; for example, a banana is typically yellow. Here, we applied multivariate pattern analysis on time-resolved neuroimaging (MEG) data to examine how object-color knowledge affects emerging object representations over time. Our results from 20 participants (11 female) show that the typicality of object-color combinations influences object representations, although not at the initial stages of object and color processing. We find evidence that color decoding peaks later for atypical object-color combinations compared with typical object-color combinations, illustrating the interplay between processing incoming object features and stored object knowledge. Together, these results provide new insights into the integration of incoming visual information with existing conceptual object knowledge.