Predicting perceptual decision-making from human brain activity.
Functional MRI provides neurobiological measurements on the neuronal, synaptic and neuromodulatory mechanisms evolving over fast time scales – seconds, minutes and days. Fast information processing is important for goal-oriented behavior and decision-making. Healthy brains implement several mechanisms to quickly and reliably associate sensory inputs with internal goals and behavioral choices. Attention is an umbrella-term we use to address experimentally the set of brain mechanisms that deal with the selection of information. There are several types of attention. For example, primates can attend to a location in space, an object or feature – such as a color. During graduate school I studied the behavioral changes in contrast and visual acuity thresholds elicited by exogenous attention. This is the type of attention car drivers use to detect approaching danger. Contrast and acuity are fundamental processes that happen early on in the visual analysis. They are necessary for most subsequent perceptual processes; stimuli below acuity- or contrast-threshold are invisible to us. In a series of articles I showed that when attention is attracted to a location in the sensory scene, visibility improves. Concurrently to improved vision at attended locations, both contrast sensitivity and acuity are impaired at locations away from attended ones (Pestilli & Carrasco Vision Research 2005; Pestilli, Viera & Carrasco Journal of Vision 2007; Montagna, Pestilli & Carrasco Vision Research 2009; Pestilli, Ling & Carrasco Vision Research 2009). These behavioral trade-offs have matched effects on cortical activity, whereby the fMRI response in early visual cortex increases at attended and decreases at unattended locations (Liu, Pestilli & Carrasco Neuron 2005).
Attention and visual selection.
A fundamental endeavor of neuroscience is to identify the neural mechanisms supporting healthy human decision-making. A successful way to embark in such an effort is by using computational methods to predict decision-making from brain activity. The challenge of such an approach is that it requires concurrent measurements of cortical activity and behavior in tasks for which models can be formulated to quantitatively predict the two measurements. When Gustav Fechner initiated what we call Psychophysics, he had specifically this problem in mind. Fechner had no access to measurements of brain activity, which he replaced with the psychological quantity just-noticeable-difference (JND). Recently, I used the very idea of JND in combination with measurements of brain activity (fMRI) and computational modeling to test the predictions of three alternative models of attentional enhancement. We demonstrated that improvements in behavioral sensitivity with attention are fully accounted for by a model of visual selection (a gating mechanism that pools sensory signals across the visual scene) that does not requires a change in quality of the sensory representation (Pestilli et al., Neuron 2011; Gardner, Hara and Pestilli, Frontiers in Computational Neuroscience in review).
Mechanisms of value processing and motivated behavior.
Many situations require selecting one among several options of different value. In these situations valuation and preference drive motivation and behavior. Although attention allows for selecting task-relevant information out of the sensory scene, many decisions depend on the subjective value assigned to alternative options. About one hundred years before Fechner, Daniel Bernoulli (1700-1782) suggested that valuation conforms to a psychological rule akin to the JND proposed by Fechner for sensory Psychophysics. Bernoulli proposed that humans decide among alternatives by using internal utility not extrinsic value. Just like for the JND, utility is equal for alternatives with values of equal ratio. Bernoulli’s Utility Theory held still for more than two-hundred years until Amos Tversky and Daniel Kahneman in the 1980‘s showed that the theory has flaws. They proposed the Prospect Theory, suggesting that utility is not absolute, but depends on the prospective of the decision-maker. Critically, they demonstrated that humans are loss averse; utility is higher for losses than gains of identical absolute value . We investigated the effects of loss aversion, expecting gains versus losses, on perceptual decision-making and cortical activity in early visual cortex (Pestilli, Khan & Ferrera, Society for Neuroscience 2011). We measured fMRI response in early visual cortex concurrently with contrast discrimination thresholds for stimuli of different expected value. We used a computational model to manipulate task difficulty and expected value on a trial-by-trial base. The model determined that on half the trials observers had to perform a perceptual judgment expecting to win. On the other half of the trials subjects performed an identical judgment but expecting to lose. Both perceptual judgments, stimuli and absolute values were identical for gain- and loss-trials. We found that behavioral sensitivity improved and cortical response increased with value in early visual cortex (V1, V2, V3, hV4). The increase followed the log-expected absolute value but was, independent on whether the expected outcome was a loss or a gain. This is predicted by the Utility Theory. Most importantly, cortical responses in early visual cortex were higher for loss predicting stimuli than for gain-predicting stimuli of equal value: an effect predicted only by the Prospect Theory (Pestilli, Khan and Ferrera in preparation).