Recently in lab meeting, I presented
Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT
Klaus Wimmer, Albert Compte, Alex Roxin, Diogo Peixoto, Alfonso Renart & Jaime de la Rocha. Nature Communications, 2015
Wimmer et al. reanalyze and reinterpret a classic dataset of neural recordings from MT while monkeys perform a motion discrimination task. The classic result shows that the firing rates of neurons in MT are correlated with the monkey’s choice, even when the stimulus is the same. This covariation of neural activity and choice, termed choice probability, could indicate sensory variability causing behavioral variability or it could result from top-down signals that reflect the monkey’s choice.
Today we discussed a paper on sequential effects in psychophysics by Fründ et al. Although inter-trial dependencies are known to exist, psychophysical responses are typically modeled as independent Bernoulli observations. One way to account for the effect of previous trial outcomes is to use logistic regression with terms that represent previous stimuli or responses (Busse et al). Fründ and colleagues extend this approach by including a lapse rate which captures responses where the subject was not doing the task.
We discussed a recent paper that uses ocular following responses in humans to demonstrate a dissociation between the oculomotor system and the perceptual system. Using stimuli generated by filtering noise in the Fourier domain the authors could construct naturalistic random-phase textures that have identical speeds, but differ in their frequency content. Interestingly, as the stimuli became more broadband, the oculomotor system could take advantage of the richness of the stimulus and became more rapid and precise whereas the humans’ psychophysical performance decreased.
Rather than conclude that there are two distinct visual systems, the authors hypothesize that both the oculomotor and perceptual systems rely on the same encoding and decoding mechanism and instead have different gain control . Using likelihood base methods with an added gain control step, they fit the dissociated response of the two systems by using Naka-Rushton gain control for the eye movements and everybody’s friend, divisive normalization, for the perceptual system.
Last Thursday we discussed how to fit psychophysical reverse correlation kernels using logistic regression, regularized by using an L1 prior over a basis vectors defined by a Laplacian pyramid (Mineault et al 2009). In psychophysical reverse correlation, a signal is embedded in noise and the observer’s choices are correlated with the fluctuations in the noise, revealing the underlying template the observer is using to do the task. Traditionally this is done by sorting the choices — as hits, misses, false alarms correct rejects — and averaging across the noise frames for each set of choices, then subtracting the average noise frame for the misses and correct rejects from the hits and false alarms. The resulting kernel is the size (space x space x time) of the stimulus, which becomes high-dimensional fast and therefore requires a lot of trials to get enough data. As an alternative, one can use maximum likelihood to do logistic regression and apply priors to reduce the number of trials required:
maximize , where Y is the observer’s responses, x is a matrix of the stimulus (trials x stimulus vector) augmented by a column of ones (for the observer’s bias), and w is the observer’s kernel (size = [1 x(1,:)]). Using a sparse prior (L1 norm) over a set of smooth basis (defined by a laplacian pyramid) reduces the number of trials required to fit the kernel while adding only one hyperparameter. The authors use simulations and real psychophysical data to fit an observer’s psychophysical kernel and their code is available here.