Biology subjects

Tiedemann, H.

Publications and source records attributed to Tiedemann, H..

4 recordsLinked to original sources

bioRxiv2026

How we draw and recognize things that don't exist

How do we make sense of something weve never seen before? Classifying objects into superordinate classes like animal is a key step in interpreting novel experiences, but is challenging because radically different items (e.g., octopus, rabbit) must somehow be grouped together. In general, no single feature is shared by all members. We reasoned that to classify or imagine novel items from outside the distribution of previous experiences, observers parse objects into meaningful component features that they can mentally recombine ( compositionality). To test this, we asked participants to draw familiar and novel members of nine superordinate object classes. We then asked other participants to classify the drawings, and mark and label their defining parts. We find that human classification performance is well predicted by a Bayesian classifier that optimally combines the part labels, suggesting humans can create and classify out-of-distribution experiences through a compositional generative representation of object features.

neuroscience↗

bioRxiv2023

The statistics of natural shapes predict high-level aftereffects in human vision

Shape perception is essential for numerous everyday behaviors from object recognition to grasping and handling objects. Yet how the brain encodes shape remains poorly understood. Here, we probed shape representations using visual aftereffects--perceptual distortions that occur following extended exposure to a stimulus--to resolve a long-standing debate about shape encoding. We implemented contrasting low-level and high-level computational models of neural adaptation, which made precise and distinct predictions about the illusory shape distortions the observers experience following adaptation. Directly pitting the predictions of the two models against one another revealed that the perceptual distortions are driven by high-level shape attributes derived from the statistics of natural shapes. Our findings suggest that the diverse shape attributes thought to underlie shape encoding (e.g., curvature distributions, skeletons, aspect ratio) are the result of a visual system that learns to encode natural shape geometries based on observing many objects.

neuroscience↗

bioRxiv2021

One shot generalization in humans revealed through a drawing task

Humans have the striking ability to learn and generalize new visual concepts from just a single exemplar. We suggest that when presented with a novel object, observers identify its significant features and infer a generative model of its shape, allowing them to mentally synthesize plausible variants. To test this, we showed participants abstract 2D shapes ( Exemplars) and asked them to draw new objects ( Variations) belonging to the same class. We show that this procedure created genuine novel categories. In line with our hypothesis, particular features of each Exemplar were preserved in its Variations and there was striking agreement between participants about which shape features were most distinctive. Also, we show that strategies to create Variations were strongly driven by part structure: new objects typically modified individual parts (e.g., limbs) of the Exemplar, often preserving part order, sometimes altering it. Together, our findings suggest that sophisticated internal generative models are key to how humans analyze and generalize from single exemplars.

neuroscience↗

bioRxiv2020

An image-computable model of human visual shape similarity

Shape is a defining feature of objects. Yet, no image-computable model accurately predicts how similar or different shapes appear to human observers. To address this, we developed a model ( ShapeComp), based on over 100 shape features (e.g., area, compactness, Fourier descriptors). When trained to capture the variance in a database of >25,000 animal silhouettes, ShapeComp predicts human shape similarity judgments almost perfectly (r2>0.99) without fitting any parameters to human data. To test the model, we created carefully selected arrays of complex novel shapes using a Generative Adversarial Network trained on the animal silhouettes, which we presented to observers in a wide range of tasks. Our findings show that human shape perception is inherently multidimensional and optimized for comparing natural shapes. ShapeComp outperforms conventional metrics, and can also be used to generate perceptually uniform stimulus sets, making it a powerful tool for investigating shape and object representations in the human brain.

neuroscience↗