Leigh E Nystrom’s research while affiliated with Princeton University and other places

Background: We assessed whether evidence accumulation could be observed in the BOLD signal during perceptual decision making. This presents a challenge since the hemodynamic response is slow, while perceptual decisions are typically fast. New method: Guided by theoretical predictions of the drift diffusion model, we slowed down decisions by penalizing participants for incorrect responses. Second, we distinguished BOLD activity related to stimulus detection (modeled using a boxcar) from activity related to integration (modeled using a ramp) by minimizing the collinearity of GLM regressors. This was achieved by dissecting a boxcar into its two most orthogonal components: an "up-ramp" and a "down-ramp." Third, we used a control condition in which stimuli and responses were similar to the experimental condition, but that did not engage evidence accumulation of the stimuli. Results: The results revealed an absence of areas in parietal cortex that have been proposed to drive perceptual decision making but have recently come into question; and newly identified regions that are candidates for involvement in evidence accumulation. Comparison with existing methods: Previous fMRI studies have either used fast perceptual decision making, which precludes the measurement of evidence accumulation, or slowed down responses by gradually revealing stimuli. The latter approach confounds perceptual detection with evidence accumulation because accumulation is constrained by perceptual input. Conclusions: We slowed down the decision making process itself while leaving perceptual information intact. This provided a more sensitive and selective observation of brain regions associated with the evidence accumulation processes underlying perceptual decision making than previous.

Studies of subjective well-being have conventionally relied upon self-report, which directs subjects' attention to their emotional experiences. This method presumes that attention itself does not influence emotional processes, which could bias sampling. We tested whether attention influences experienced utility (the moment-by-moment experience of pleasure) by using functional magnetic resonance imaging (fMRI) to measure the activity of brain systems thought to represent hedonic value while manipulating attentional load. Subjects received appetitive or aversive solutions orally while alternatively executing a low or high attentional load task. Brain regions associated with hedonic processing, including the ventral striatum, showed a response to both juice and quinine. This response decreased during the high-load task relative to the low-load task. Thus, attentional allocation may influence experienced utility by modulating (either directly or indirectly) the activity of brain mechanisms thought to represent hedonic value.

Many perceptual decision making models posit that participants accumulate noisy evidence over time to improve the accuracy of their decisions, and that in free response tasks, participants respond when the accumulated evidence reaches a decision threshold. Research on the neural correlates of these models' components focuses primarily on evidence accumulation. Far less attention has been paid to the neural correlates of decision thresholds, reflecting the final commitment to a decision. Inspired by a model of bistable neural activity that implements a decision threshold, we reinterpret human lateralized readiness potentials (LRPs) as reflecting the crossing of a decision threshold. Interestingly, this threshold crossing preserves signatures of a drift-diffusion process of evidence accumulation that feeds in to the threshold mechanism. We show that, as our model predicts, LRP amplitudes and growth rates recorded while participants performed a motion discrimination task correlate with individual differences in behaviorally-estimated prior beliefs, decision thresholds and evidence accumulation rates. As such LRPs provide a useful measure to test dynamical models of both evidence accumulation and decision commitment processes non-invasively.

We examined whether older adults differ from younger adults in how they learn from rewarding and aversive outcomes. Human participants were asked to either learn to choose actions that lead to monetary reward or learn to avoid actions that lead to monetary losses. To examine age differences in the neurophysiological mechanisms of learning, we applied a combination of computational modeling and fMRI. Behavioral results showed age-related impairments in learning from reward but not in learning from monetary losses. Consistent with these results, we observed age-related reductions in BOLD activity during learning from reward in the ventromedial PFC. Furthermore, the model-based fMRI analysis revealed a reduced responsivity of the ventral striatum to reward prediction errors during learning in older than younger adults. This age-related reduction in striatal sensitivity to reward prediction errors may result from a decline in phasic dopaminergic learning signals in the elderly.

Multivariate pattern analysis (MVPA) is a relatively recent innovation in functional magnetic resonance imaging (fMRI) methods. MVPA is increasingly widely used, as it is apparently more effective than classical general linear model analysis (GLMA) for detecting response patterns or representations that are distributed at a fine spatial scale. However, we demonstrate that widely used approaches to MVPA can systematically admit certain confounds that are appropriately eliminated by GLMA. Thus confounds rather than distributed representations may explain some cases in which MVPA produced positive results but GLMA did not. The issue is that it is common practice in MVPA to conduct group tests on single-subject summary statistics that discard the sign or direction of underlying effects, whereas GLMA group tests are conducted directly on single-subject effects themselves. We describe how this common MVPA practice undermines standard experiment design logic that is intended to control at the group level for certain types of confounds, such as time on task and individual differences. Furthermore, we note that a simple application of linear regression can restore experimental control when using MVPA in many situations. Finally, we present a case study with novel fMRI data in the domain of rule representations, or flexible stimulus-response mappings, which has seen several recent MVPA publications. In our new dataset, as with recent reports, standard MVPA appears to reveal rule representations in prefrontal cortex regions, whereas GLMA produces null results. However, controlling for a variable that is confounded with rule at the individual-subject level but not the group level (reaction time differences across rules) eliminates the MVPA results. This raises the question of whether recently reported results truly reflect rule representations, or rather the effects of confounds such as reaction time, difficulty, or other variables of no interest.

Humans are adept at switching between goal-directed behaviors quickly and effectively. The prefrontal cortex (PFC) is thought to play a critical role by encoding, updating, and maintaining internal representations of task context in working memory. It has also been hypothesized that the encoding of context representations in PFC is regulated by phasic dopamine gating signals. Here we use multimodal methods to test these hypotheses. First we used functional MRI (fMRI) to identify regions of PFC associated with the representation of context in a working memory task. Next we used single-pulse transcranial magnetic stimulation (TMS), guided spatially by our fMRI findings and temporally by previous event-related EEG recordings, to disrupt context encoding while participants performed the same working memory task. We found that TMS pulses to the right dorsolateral PFC (DLPFC) immediately after context presentation, and well in advance of the response, adversely impacted context-dependent relative to context-independent responses. This finding causally implicates right DLPFC function in context encoding. Finally, using the same paradigm, we conducted high-resolution fMRI measurements in brainstem dopaminergic nuclei (ventral tegmental area and substantia nigra) and found phasic responses after presentation of context stimuli relative to other stimuli, consistent with the timing of a gating signal that regulates the encoding of representations in PFC. Furthermore, these responses were positively correlated with behavior, as well as with responses in the same region of right DLPFC targeted in the TMS experiment, lending support to the hypothesis that dopamine phasic signals regulate encoding, and thereby the updating, of context representations in PFC.