Home > Meeting Topics and Material, News Stories > Weekly QuEST Discussion Topics and News, 4 April

Weekly QuEST Discussion Topics and News, 4 April

• Visual Recognition
As Soon as You Know It Is There, You Know What It Is
Kalanit Grill-Spector1 and Nancy Kanwisher2
1Department of Psychology, Stanford University, and 2Department of Brain and Cognitive Sciences, Massachusetts
Institute of Technology – an article that attempts to advance a Theory of Object Recognition – ABSTRACT—What is the sequence of processing steps involved in visual object recognition?

We varied the exposure duration of natural images and measured subjects’ performance on three different tasks, each designed to tap a different candidate component process of object recognition.
For each exposure duration,
– accuracy was lower and reaction time longer on a within-category identification task (e.g., distinguishing pigeons from other birds)

– than on a perceptual categorization task (e.g., birds vs. cars).

However, strikingly, at each exposure duration, subjects performed just as quickly and accurately on the categorization task as they did on a task requiring only object detection:
– By the time subjects knew an image contained an object at all, they already knew its category.

These findings place powerful constraints on theories of object recognition.

Second Article – a preprint related to a recent news story:

Neural portraits of perception: Reconstructing face images from evoked brain activity
Q13Q3 Alan S. Cowen a, MarvinM. Chunb, Brice A. Kuhl c,d
Q2 a Department of Psychology, University of California Berkeley, USA
b Department of Psychology, Yale University, USA
Q4 c Department of Psychology, New York University, USA
d Center for Neural Science, New York University, US
• Recent neuroimaging advances have allowed visual experience to be reconstructed from patterns of brain activity.

• While neural reconstructions have ranged in complexity, they have relied almost exclusively on retinotopic mappings between visual input and activity in early visual cortex.

• However, subjective perceptual information is tied more closely to higher-level cortical regions that have not yet been used as the primary basis for neural reconstructions.

• Furthermore, no reconstruction studies to date have reported reconstructions of face images, which activate a highly distributed cortical network.

• Thus, we investigated

• (a) whether individual face images could be accurately reconstructed from distributed patterns of neural activity, and

• (b) whether this could be achieved even when excluding activity within occipital cortex.

• Our approach involved four steps.

• (1) Principal component analysis (PCA) was used to identify components that efficiently represented a set of training faces.

• (2) The identified components were then mapped, using a machine learning algorithm, to fMRI activity collected during viewing of the training faces.

• (3) Based on activity elicited by a new set of test faces, the algorithm predicted associated component scores.

• (4) Finally, these scores were transformed into reconstructed images. Using both objective and subjective validation measures, we show that our methods yield strikingly accurate neural reconstructions of faces even when excluding occipital cortex.


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