Showing papers by "Greg S. Corrado published in 2009"

The Trouble with Choice: Studying Decision Variables in the Brain

Abstract: Publisher Summary This chapter focuses on challenge of studying the neurobiology of decision-making. Establishing causal links between neural responses and perceptual or cognitive phenomena is a fundamental challenge faced by researchers not only in neuroeconomics, but also in all of cognitive neuroscience. Historically, support for links between anatomy and function has come from patients or experimental animals with lesions restricted to the anatomic area of interest. Indeed, lesion studies first implicated ventromedial prefrontal cortex in value-based decision-making by demonstrating that damage to this region impaired performance on reward-cued reversal learning tasks and other tasks in which the best choice on each trial had to be inferred from the outcomes of earlier choices. Demonstrating neural correlates of a decision variable is, in principle, straightforward; it is substantially more challenging to prove that the correlated neural activity plays a causal role in the brain's decision-making process in the manner suggested by the proposed decision variable.

Recursive Sparse, Spatiotemporal Coding

Abstract: We present a new approach to learning sparse, spatiotemporal codes in which the number of basis vectors, their orientations, velocities and the size of their receptive fields change over the duration of unsupervised training. The algorithm starts with a relatively small, initial basis with minimal temporal extent. This initial basis is obtained through conventional sparse coding techniques and is expanded over time by recursively constructing a new basis consisting of basis vectors with larger temporal extent that proportionally conserve regions of previously trained weights. These proportionally conserved weights are combined with the result of adjusting newly added weights to represent a greater range of primitive motion features. The size of the current basis is determined probabilistically by sampling from existing basis vectors according to their activation on the training set. The resulting algorithm produces bases consisting of filters that are bandpass, spatially oriented and temporally diverse in terms of their transformations and velocities. The basic methodology borrows inspiration from the layer-by-layer learning of multiple-layer restricted Boltzmann machines developed by Geoff Hinton and his students. Indeed, we can learn multiple-layer sparse codes by training a stack of denoising autoencoders, but we have had greater success using L1 regularized regression in a variation on Olshausen and Field’s original SPARSENET. To accelerate learning and focus attention, we apply a space-time interest-point operator that selects for periodic motion. This attentional mechanism enables us to efficiently compute and compactly represent a broad range of interesting motion. We demonstrate the utility of our approach by using it to recognize human activity in video. Our algorithm meets or exceeds the performance of state-of-the-art activity-recognition methods.

Toward Online Measurement of Decision State

Abstract: In traditional perceptual decision-making experiments, two pieces of data recollected on each trial: response time and accuracy. But how confident were participants and how did their decision state evolve over time? We asked participants to provide a continuous readout of their decision state by moving a cursor along a sliding scale between a 100% certain left response and a 100% certain right response. Subjects did not terminate the trials; rather, trials were timed out at random and subjects were scored based on the cursor position at the time. Higher rewards for correct responses and higher penalties for errors were associated with extreme responses so that the response with the highest ex[pected value was that which accurately reflected the participant's odds of being correct. This procedure encourages participants to expose the time-course of their evolving decision state. Evidence on how well they can do this will be presented.