Cholinergic stimulation enhances Bayesian belief updating in the deployment of spatial attention

Abstract

The exact mechanisms whereby the cholinergic neurotransmitter system contributes to attentional processing remain poorly understood. Here, we applied computational modeling to psychophysical data (obtained from a spatial attention task) under a psychopharmacological challenge with the cholinesterase inhibitor galantamine (Reminyl). This allowed us to characterize the cholinergic modulation of selective attention formally, in terms of hierarchical Bayesian inference. In a placebo-controlled, within-subject, crossover design, 16 healthy human subjects performed a modified version of Posner's location-cueing task in which the proportion of validly and invalidly cued targets (percentage of cue validity, % CV) changed over time. Saccadic response speeds were used to estimate the parameters of a hierarchical Bayesian model to test whether cholinergic stimulation affected the trial-wise updating of probabilistic beliefs that underlie the allocation of attention or whether galantamine changed the mapping from those beliefs to subsequent eye movements. Behaviorally, galantamine led to a greater influence of probabilistic context (% CV) on response speed than placebo. Crucially, computational modeling suggested this effect was due to an increase in the rate of belief updating about cue validity (as opposed to the increased sensitivity of behavioral responses to those beliefs). We discuss these findings with respect to cholinergic effects on hierarchical cortical processing and in relation to the encoding of expected uncertainty or precision.

Keywords: Bayesian inference; acetylcholine; saccades; spatial attention.

Figure 1.

A, Illustration of the experimental task in which the location of a saccade target (circular grating) was precued by arrows. B, The predictive value of the cue (i.e., the proportions of valid and invalid trials) changed over the time of the experiment.

Figure 2.

Illustration of the perceptual and response models. The perceptual model comprises three hierarchical states (x₁, x₂, and x₃). x₂ and x₃ evolve in time as hierarchically coupled Gaussian random walks (see equations on the left), and x₂ determines the probability that the target appears at the cued location (x₁ = 1). The session-specific parameters ω and ϑ affect the updating of the beliefs about the states x and are estimated from individual RS data on the basis of the attentional weight α(π̂₁) that depends on the precision on the first level of the (inverted) perceptual model. Here, RS is supposed to vary linearly with α(π̂₁) in valid trials and with 1 − α(π̂₁) in invalid trials (right). Whereas ζ_1ν and ζ_1i determine the intercepts (i.e., the absolute level of RS) on valid and invalid trials, respectively, ζ₂ governs the slope of the linear function and hence the strength of the association between RS and the attentional weight α(π̂₁) as derived from the perceptual model. Ellipses represent constants; diamonds represent quantities that change with time (i.e., that carry a time index). Hexagons, like diamonds, represent quantities that change with time but additionally depend on their previous state in time in a Markovian fashion.

Figure 3.

Illustration of the three-way interaction between cue, % CV, and drug for both halves of the experiment. For simplicity, relative RS differences between valid and invalid trials are shown. RS costs were related to the overall speed of responding and are expressed in percentage: (RS valid − RS invalid)/overall RS × 100. Error bars indicate SEM.

Figure 4.

Illustration of the time course of the posterior expectations at the third level of the Bayesian model (μ₃, top) and attentional gain allocated to the cued location α(π̂₁) (bottom), based upon the group average values for ω and ϑ. Because of the dependency of the galantamine-induced increase of ω, we here exemplarily show the results for a normalized weight of 68 kg (placebo session: ω = −6.2, ϑ = 0.60; galantamine session: ω = −5.4, ϑ = 0.61). It can be seen that, under galantamine (red line, with a higher ω), the changes in α(π̂₁) in the different true % CV conditions are more pronounced than under placebo (blue line). The same sequence of valid and invalid trials was presented each subject and in each experimental session because the parameters of the learning process depend on the exact sequence of trials used.

Figure 5.

Illustration of observed and predicted RS in the placebo and galantamine session, respectively. Trials were grouped in 0.1 bins according to the attentional weights α(π̂₁) as derived from the perceptual model with the group average model parameters. Mean observed RS was calculated for these binned trial categories (diamonds). Predicted RS in valid and invalid trials was calculated on the basis of the mean response model parameters (solid lines).