Neuroscience II

In many real world situations, sensory events of interest might appear at any moment and require us to act. Perceptual decisions like these, especially when stimuli are ambiguous or noisy, are thought to be facilitated by the accumulation of perceptual evidence over time until reaching a decision bound - a criterion amount of evidence. Temporal uncertainty requires a fine balance between accumulating evidence for better accuracy while avoiding false-alarms based on noise alone, as well as accounting for the cost of continuously maintaining a state of readiness. The decision-maker can, however, use cues in the environment to make top-down dynamic adjustments of the decision-making process. Previous research has shown that cues can have multiple modulatory effects spanning the sensorimotor pathway, but how such effects translate to computational adjustments in the decision-making process remains unknown Here, we examine the effect of external auditory warning cues, while participants indicated the direction (left/right) of intermittent 1.4-second periods of coherent motion targets within otherwise incoherent, continuously moving dots. Behavioural modelling showed that participants respond to the cue by quickly lowering their decision bound, which is supported by a shifting of electroencephalogram signatures of motor preparation (β power) towards an action-triggering bound as response to the warning cue. However, this is unlikely to be the only adjustment. We further explore additional adjustments looking at neural signatures tracing 1) basic sensory encoding of evidence strength (steady state visual motion evoked potentials; SSMEPs), and 2) evidence accumulation (centro-parietal positivity; CPP). In ongoing work, these signals allow us to map out the multiple effects of the cue on the decision-making processes, using neurally-informed modelling.

This is an in-person presentation on July 21, 2023 (09:20 ~ 09:40 UTC).

Perceptual decision-making evolves from interactions between cortical neurons which decode sensory information, and the basal ganglia which integrate across sources of information to prepare for action. The subthalamic nucleus (STN) dynamically controls the decision threshold, determining the necessary amount of corticostriatal evidence for response initiation. Past research focused on evidence accumulation models with fixed decision bounds, while neural data and biophysical simulations suggest that STN activity is highly dynamic. These dynamics may be mechanistically reflected by theta- and beta-band oscillations. In this study, we aimed to use these e-phys biosignatures to determine if dynamic activity in the STN is mostly modulated by active conflict, task difficulty, or both. To do so, we recorded electrophysiological activity in the STN and globus pallidus (GP) of 17 patients with Parkinson’s disease (n=14) or dystonia (n=3) during a direction discrimination task. Stimuli involved moving random-dot patterns that independently varied in motion strength (coherence), and motion direction (angular trajectory). These conditions separately manipulated task difficulty (easy, hard) and active conflict (low, high). Leveraging recent advances in likelihood-free inference, we tested the aforementioned aims by using a wide variety of sequential sampling models (SSMs). First, we found that models with Weibull-informed collapsing boundaries outperformed classical Diffusion Decision models, Ornstein-Uhlenbeck models, and models with linear collapsing boundaries. Second, motion strength coherence (difficulty) affected evidence accumulation (drift rate), while angular trajectory (active conflict) affected response caution (decision threshold). Finally, preliminary modeling results based on neural regressors suggest that within-trial dynamics of STN and GP theta and beta activity influence decision dynamics. This study advances our understanding of how neural dynamics in the basal ganglia influence decision dynamics as a function of task difficulty and perceptual conflict. It has the potential to resolve some discrepancies in previous studies assuming a fixed decision boundary that is impacted by a single measure of neural activity.

This is an in-person presentation on July 21, 2023 (09:40 ~ 10:00 UTC).

Formal models of behavior express or explain an expected relationship between stimuli and responses in a controlled (repeatable) experiment. Formal models in the behavioral neurosciences (more often, "computational models") describe, at one or more levels, such as neurotransmitter uptake, single neuron spike trains, or regions of the brain, the neurochemical events that lead to an activity downstream in the network, which is sometimes but not always the observable response. In both fields, the question why a given response is observed on a given trial is answered by reproducing the behavior or activity (statistically) in the model. At least with respect to cross-communication and mutual benefits, there are two well-known problems with this shared paradigm. First, behavioral models are specified at a level of abstraction far beyond the level of neurological models. It is not obvious, therefore, whether there should be any relationship at all between the two constructions, or whether an apparent similarity is meaningful or constitutes 'evidence' to support the two theories. Second, neurological activity, even at the level of single neurons, is exceedingly complex, and this complexity is observable in minute detail. The idea of limiting the number of free parameters so that goodness-of-fit statistics can be compared is self-defeating from the outset. In this paper/proposal, I will briefly explain how a relatively new statistical methodology in the neurosciences, called Bayesian decoding, can connect the two sciences in a rigorous manner. Basically, instead of asking what events give rise to intelligent behavior, the modeler asks what information about the stimulus and the possible responses is contained in the events that lead to the response, that is, what properties of these events can be predicted and to what degree.

This is an in-person presentation on July 21, 2023 (10:00 ~ 10:20 UTC).

The Penn Electrophysiology of Encoding and Retrieval Study (PEERS) aimed to characterize the behavioral and electrophysiological (EEG) correlates of memory encoding and retrieval in highly practiced individuals. Across five PEERS experiments, 300+ subjects contributed more than 7,000 ninety-minute memory testing sessions with recorded EEG data. Here we tell the story of PEERS: it’s genesis, evolution, major findings, and the lessons it taught us about taking a big science approach to the study of memory and the human brain. In particular, we focus on the role of big data in combining computational modeling approaches to cognitive with the study of individual differences.

This is an in-person presentation on July 21, 2023 (10:20 ~ 10:40 UTC).