Computational models of eye movements and their application to schizophrenia

Prospective and retrospective representations of saccadic movements in primate prefrontal cortex

The dorso-lateral prefrontal cortex (dlPFC) contributes to flexible, goal-directed behaviors. However, a coherent picture of dlPFC function is lacking, as its activity is often studied only in relation to a few events within a fully learned behavioral task. Here we obtain a comprehensive description of dlPFC activity across different task epochs, saccade types, tasks, and learning stages. We consistently observe the strongest modulation of neural activity in relation to a retrospective representation of the most recent saccade. Prospective, planning-like activity is limited to task-related, delayed saccades directly eligible for a reward. The link between prospective and retrospective representations is highly structured, potentially reflecting a hard-wired feature of saccade responses. Only prospective representations are modulated by the recent behavioral history, but neither representation is modulated by day-to-day behavioral improvements. The dlPFC thus combines tightly linked flexible and rigid representations with a dominant contribution from retrospective signals maintaining the memory of past actions.

TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

Computational Dissociation of Dopaminergic and Cholinergic Effects on Action Selection and Inhibitory Control

BACKGROUND

Patients with schizophrenia make more errors than healthy subjects in the antisaccade task. In this paradigm, participants are required to inhibit a reflexive saccade to a target and to select the correct action (a saccade in the opposite direction). While the precise origin of this deficit is not clear, it has been connected to aberrant dopaminergic and cholinergic neuromodulation.

METHODS

To study the impact of dopamine and acetylcholine on inhibitory control and action selection, we administered two selective drugs (levodopa 200 mg/galantamine 8 mg) to healthy volunteers (N = 100) performing the antisaccade task. The computational model SERIA (stochastic early reaction, inhibition, and late action) was employed to separate the contribution of inhibitory control and action selection to empirical reaction times and error rates.

RESULTS

Modeling suggested that levodopa improved action selection (at the cost of increased reaction times) but did not have a significant effect on inhibitory control. By contrast, according to our model, galantamine affected inhibitory control in a dose-dependent fashion, reducing inhibition failures at low doses and increasing them at higher levels. These effects were sufficiently specific that the computational analysis allowed for identifying the drug administered to an individual with 70% accuracy.

CONCLUSIONS

Our results do not support the hypothesis that elevated tonic dopamine strongly impairs inhibitory control. Rather, levodopa improved the ability to select correct actions. However, inhibitory control was modulated by cholinergic drugs. This approach may provide a starting point for future computational assays that differentiate neuromodulatory abnormalities in heterogeneous diseases like schizophrenia.

A generative learning model for saccade adaptation

Plasticity in the oculomotor system ensures that saccadic eye movements reliably meet their visual goals-to bring regions of interest into foveal, high-acuity vision. Here, we present a comprehensive description of sensorimotor learning in saccades. We induced continuous adaptation of saccade amplitudes using a double-step paradigm, in which participants saccade to a peripheral target stimulus, which then undergoes a surreptitious, intra-saccadic shift (ISS) as the eyes are in flight. In our experiments, the ISS followed a systematic variation, increasing or decreasing from one saccade to the next as a sinusoidal function of the trial number. Over a large range of frequencies, we confirm that adaptation gain shows (1) a periodic response, reflecting the frequency of the ISS with a delay of a number of trials, and (2) a simultaneous drift towards lower saccade gains. We then show that state-space-based linear time-invariant systems (LTIS) represent suitable generative models for this evolution of saccade gain over time. This state-equation algorithm computes the prediction of an internal (or hidden state-) variable by learning from recent feedback errors, and it can be compared to experimentally observed adaptation gain. The algorithm also includes a forgetting rate that quantifies per-trial leaks in the adaptation gain, as well as a systematic, non-error-based bias. Finally, we study how the parameters of the generative models depend on features of the ISS. Driven by a sinusoidal disturbance, the state-equation admits an exact analytical solution that expresses the parameters of the phenomenological description as functions of those of the generative model. Together with statistical model selection criteria, we use these correspondences to characterize and refine the structure of compatible state-equation models. We discuss the relation of these findings to established results and suggest that they may guide further design of experimental research across domains of sensorimotor adaptation.

Switch costs in inhibitory control and voluntary behaviour: A computational study of the antisaccade task

An integral aspect of human cognition is the ability to inhibit stimulus-driven, habitual responses, in favour of complex, voluntary actions. In addition, humans can also alternate between different tasks. This comes at the cost of degraded performance when compared to repeating the same task, a phenomenon called the "task-switch cost." While task switching and inhibitory control have been studied extensively, the interaction between them has received relatively little attention. Here, we used the SERIA model, a computational model of antisaccade behaviour, to draw a bridge between them. We investigated task switching in two versions of the mixed antisaccade task, in which participants are cued to saccade either in the same or in the opposite direction to a peripheral stimulus. SERIA revealed that stopping a habitual action leads to increased inhibitory control that persists onto the next trial, independently of the upcoming trial type. Moreover, switching between tasks induces slower and less accurate voluntary responses compared to repeat trials. However, this only occurs when participants lack the time to prepare the correct response. Altogether, SERIA demonstrates that there is a reconfiguration cost associated with switching between voluntary actions. In addition, the enhanced inhibition that follows antisaccade but not prosaccade trials explains asymmetric switch costs. In conclusion, SERIA offers a novel model of task switching that unifies previous theoretical accounts by distinguishing between inhibitory control and voluntary action generation and could help explain similar phenomena in paradigms beyond the antisaccade task.

Visual Attention Deficits in Schizophrenia Can Arise From Inhibitory Dysfunction in Thalamus or Cortex

Schizophrenia is associated with diverse cognitive deficits, including disorders of attention-related oculomotor behavior. At the structural level, schizophrenia is associated with abnormal inhibitory control in the circuit linking cortex and thalamus. We developed a spiking neural network model that demonstrates how dysfunctional inhibition can degrade attentive gaze control. Our model revealed that perturbations of two functionally distinct classes of cortical inhibitory neurons, or of the inhibitory thalamic reticular nucleus, disrupted processing vital for sustained attention to a stimulus, leading to distractibility. Because perturbation at each circuit node led to comparable but qualitatively distinct disruptions in attentive tracking or fixation, our findings support the search for new eye movement metrics that may index distinct underlying neural defects. Moreover, because the cortico-thalamic circuit is a common motif across sensory, association, and motor systems, the model and extensions can be broadly applied to study normal function and the neural bases of other cognitive deficits in schizophrenia.

Inhibition failures and late errors in the antisaccade task: influence of cue delay

In the antisaccade task participants are required to saccade in the opposite direction of a peripheral visual cue (PVC). This paradigm is often used to investigate inhibition of reflexive responses as well as voluntary response generation. However, it is not clear to what extent different versions of this task probe the same underlying processes. Here, we explored with the Stochastic Early Reaction, Inhibition, and late Action (SERIA) model how the delay between task cue and PVC affects reaction time (RT) and error rate (ER) when pro- and antisaccade trials are randomly interleaved. Specifically, we contrasted a condition in which the task cue was presented before the PVC with a condition in which the PVC served also as task cue. Summary statistics indicate that ERs and RTs are reduced and contextual effects largely removed when the task is signaled before the PVC appears. The SERIA model accounts for RT and ER in both conditions and better so than other candidate models. Modeling demonstrates that voluntary pro- and antisaccades are frequent in both conditions. Moreover, early task cue presentation results in better control of reflexive saccades, leading to fewer fast antisaccade errors and more rapid correct prosaccades. Finally, high-latency errors are shown to be prevalent in both conditions. In summary, SERIA provides an explanation for the differences in the delayed and nondelayed antisaccade task. NEW & NOTEWORTHY In this article, we use a computational model to study the mixed antisaccade task. We contrast two conditions in which the task cue is presented either before or concurrently with the saccadic target. Modeling provides a highly accurate account of participants' behavior and demonstrates that a significant number of prosaccades are voluntary actions. Moreover, we provide a detailed quantitative analysis of the types of error that occur in pro- and antisaccade trials.

The function and failure of sensory predictions

Humans and other primates are equipped with neural mechanisms that allow them to automatically make predictions about future events, facilitating processing of expected sensations and actions. Prediction-driven control and monitoring of perceptual and motor acts are vital to normal cognitive functioning. This review provides an overview of corollary discharge mechanisms involved in predictions across sensory modalities and discusses consequences of predictive coding for cognition and behavior. Converging evidence now links impairments in corollary discharge mechanisms to neuropsychiatric symptoms such as hallucinations and delusions. We review studies supporting a prediction-failure hypothesis of perceptual and cognitive disturbances. We also outline neural correlates underlying prediction function and failure, highlighting similarities across the visual, auditory, and somatosensory systems. In linking basic psychophysical and psychophysiological evidence of visual, auditory, and somatosensory prediction failures to neuropsychiatric symptoms, our review furthers our understanding of disease mechanisms.

The Stochastic Early Reaction, Inhibition, and late Action (SERIA) model for antisaccades

The antisaccade task is a classic paradigm used to study the voluntary control of eye movements. It requires participants to suppress a reactive eye movement to a visual target and to concurrently initiate a saccade in the opposite direction. Although several models have been proposed to explain error rates and reaction times in this task, no formal model comparison has yet been performed. Here, we describe a Bayesian modeling approach to the antisaccade task that allows us to formally compare different models on the basis of their evidence. First, we provide a formal likelihood function of actions (pro- and antisaccades) and reaction times based on previously published models. Second, we introduce the Stochastic Early Reaction, Inhibition, and late Action model (SERIA), a novel model postulating two different mechanisms that interact in the antisaccade task: an early GO/NO-GO race decision process and a late GO/GO decision process. Third, we apply these models to a data set from an experiment with three mixed blocks of pro- and antisaccade trials. Bayesian model comparison demonstrates that the SERIA model explains the data better than competing models that do not incorporate a late decision process. Moreover, we show that the early decision process postulated by the SERIA model is, to a large extent, insensitive to the cue presented in a single trial. Finally, we use parameter estimates to demonstrate that changes in reaction time and error rate due to the probability of a trial type (pro- or antisaccade) are best explained by faster or slower inhibition and the probability of generating late voluntary prosaccades.

Conflict Resolution as Near-Threshold Decision-Making: A Spiking Neural Circuit Model with Two-Stage Competition for Antisaccadic Task

Automatic responses enable us to react quickly and effortlessly, but they often need to be inhibited so that an alternative, voluntary action can take place. To investigate the brain mechanism of controlled behavior, we investigated a biologically-based network model of spiking neurons for inhibitory control. In contrast to a simple race between pro- versus anti-response, our model incorporates a sensorimotor remapping module, and an action-selection module endowed with a “Stop” process through tonic inhibition. Both are under the modulation of rule-dependent control. We tested the model by applying it to the well known antisaccade task in which one must suppress the urge to look toward a visual target that suddenly appears, and shift the gaze diametrically away from the target instead. We found that the two-stage competition is crucial for reproducing the complex behavior and neuronal activity observed in the antisaccade task across multiple brain regions. Notably, our model demonstrates two types of errors: fast and slow. Fast errors result from failing to inhibit the quick automatic responses and therefore exhibit very short response times. Slow errors, in contrast, are due to incorrect decisions in the remapping process and exhibit long response times comparable to those of correct antisaccade responses. The model thus reveals a circuit mechanism for the empirically observed slow errors and broad distributions of erroneous response times in antisaccade. Our work suggests that selecting between competing automatic and voluntary actions in behavioral control can be understood in terms of near-threshold decision-making, sharing a common recurrent (attractor) neural circuit mechanism with discrimination in perception.

We propose a novel neural circuit mechanism and construct a spiking neural network model for resolving conflict between an automatic response and a volitional one. In this mechanism the two types of responses compete against each other under the modulation of top-down control via multiple neural pathways. The model is able to reproduce a wide range of neuronal and behavioral features observed in various studies and provides insights into not just how subjects make correct responses and fast errors, but also why they make slow errors, a type of error often overlooked by previous modeling studies. The model suggests critical roles of tonic (non-racing) top-down inhibition and near-threshold decision-making in neural competition.