Spatial Attention, Precision, and Bayesian Inference: A Study of Saccadic Response Speed

Vossel, Simone; Mathys, Christoph; Daunizeau, Jean; Bauer, Markus; Driver, Jon; Friston, Karl J.; Stephan, Klaas Ε.

doi:10.1093/cercor/bhs418

Cited by 135 publications

(140 citation statements)

References 68 publications

Supporting

Mentioning

129

Contrasting

Unclassified

Order By: Relevance

“…This allowed us to determine the surprise (I S ) associated with each stimulus and the degree of updating (D KL ) on each trial. In addition, we modeled the strength of participants' prior expectations on each trial as the Shannon entropy of the prior distribution (H S ) because behavioral work indicates that saccadic RTs may depend on this factor (21). The H S depended on both the variance of the generative targets' distribution, which was experimentally manipulated, and on learning.…”

Section: Resultsmentioning

confidence: 99%

Dissociable effects of surprise and model update in parietal and anterior cingulate cortex

O’Reilly

Schüffelgen

Cuell

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

317

401

View full text Add to dashboard Cite

Brains use predictive models to facilitate the processing of expected stimuli or planned actions. Under a predictive model, surprising (low probability) stimuli or actions necessitate the immediate reallocation of processing resources, but they can also signal the need to update the underlying predictive model to reflect changes in the environment. Surprise and updating are often correlated in experimental paradigms but are, in fact, distinct constructs that can be formally defined as the Shannon information (I S ) and KullbackLeibler divergence (D KL ) associated with an observation. In a saccadic planning task, we observed that distinct behaviors and brain regions are associated with surprise/I S and updating/D KL . Although surprise/I S was associated with behavioral reprogramming as indexed by slower reaction times, as well as with activity in the posterior parietal cortex [human lateral intraparietal area (LIP)], the anterior cingulate cortex (ACC) was specifically activated during updating of the predictive model (D KL ). A second saccade-sensitive region in the inferior posterior parietal cortex (human 7a), which has connections to both LIP and ACC, was activated by surprise and modulated by updating. Pupillometry revealed a further dissociation between surprise and updating with an early positive effect of surprise and late negative effect of updating on pupil area. These results give a computational account of the roles of the ACC and two parietal saccade regions, LIP and 7a, by which their involvement in diverse tasks can be understood mechanistically. The dissociation of functional roles between regions within the reorienting/reprogramming network may also inform models of neurological phenomena, such as extinction and Balint syndrome, and neglect.eye movement | prediction | attention | learning | Bayes I n a nonrandom environment, brains can and should make use of past experience to facilitate the processing of incoming sensory information and the selection of actions, through prediction (1, 2). An important aspect of brain function is therefore the construction and tuning of internal models to represent statistics of the environment that are relevant for future behavior.The use of predictive internal models implies that not only are some events well predicted (high probability under the model) but, conversely, some events (which have a low probability under the model) are surprising (3). Surprising events may be associated with behavioral costs; for example, although valid attentional cues speed reaction times (RTs), invalid cues lengthen them (3, 4). However, surprising events can have a further significance for the observer in that they sometimes provide evidence for a change in the environment, which would imply a need to update the brain's internal models to predict future events accurately.Here, we explore the possibility that the brain carries out at least two distinct operations when a surprising event occurs: (i) within trial reorienting processes evoked by surprise, including reallocation o...

show abstract

Section: Resultsmentioning

confidence: 99%

Dissociable effects of surprise and model update in parietal and anterior cingulate cortex

O’Reilly

Schüffelgen

Cuell

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

317

401

View full text Add to dashboard Cite

show abstract

“…Recently, Bayesian models with graphical representation have demonstrated great potential in modeling cognitive functions using behavioral data (Mozer et al, 2002; Reynolds and Mozer, 2009a; Shenoy et al, 2010; Shenoy and Yu, 2011; Tenenbaum et al, 2006; Tenenbaum and Xu, 2000; Vossel et al, 2013; Yu et al, 2009) and brain imaging data (Behrens et al, 2007; den Ouden et al, 2010; Ide et al, 2013). In the following, we review recent studies using Bayesian models with graphical representation to model various aspects of cognitive control, including speed-accuracy trade-off (section 2.2), conflict effects in the Eriksen flanker task (section 2.3), and response inhibition (section 2.4).…”

Section: Modeling Cognitive Control Using Bayesian Modelsmentioning

confidence: 99%

“…Given the high flexibility required of cognitive control, it is unclear that those fixed parameters are globally optimal across various experimental configurations. In fact, it has been shown that fixed learning rates are suboptimal in non-stationary environments (Behrens et al, 2007; den Ouden et al, 2010; Vessel et al, 2013). In the next section, we therefore propose a Bayesian model that resolves these two concerns.…”

Section: Modeling Cognitive Control Using Bayesian Modelsmentioning

confidence: 99%

Bayesian modeling of flexible cognitive control

Jiang

Heller

Egner

2014

Neuroscience & Biobehavioral Reviews

115

View full text Add to dashboard Cite

“Cognitive control” describes endogenous guidance of behavior in situations where routine stimulus-response associations are suboptimal for achieving a desired goal. The computational and neural mechanisms underlying this capacity remain poorly understood. We examine recent advances stemming from the application of a Bayesian learner perspective that provides optimal prediction for control processes. In reviewing the application of Bayesian models to cognitive control, we note that an important limitation in current models is a lack of a plausible mechanism for the flexible adjustment of control over conflict levels changing at varying temporal scales. We then show that flexible cognitive control can be achieved by a Bayesian model with a volatility-driven learning mechanism that modulates dynamically the relative dependence on recent and remote experiences in its prediction of future control demand. We conclude that the emergent Bayesian perspective on computational mechanisms of cognitive control holds considerable promise, especially if future studies can identify neural substrates of the variables encoded by these models, and determine the nature (Bayesian or otherwise) of their neural implementation.

show abstract

“…Learning the environment’s underlying statistical regularities means that responses to explicitly and predictably cued events are typically faster than to those believed improbable [31–34]. This effect is modulated by pharmacological [35,36], surgical [37,38], and neurodegenerative [39] manipulations of ACh.…”

Section: Introductionmentioning

confidence: 99%

Pharmacological Fingerprints of Contextual Uncertainty

et al. 2016

Self Cite

View full text Add to dashboard Cite

Successful interaction with the environment requires flexible updating of our beliefs about the world. By estimating the likelihood of future events, it is possible to prepare appropriate actions in advance and execute fast, accurate motor responses. According to theoretical proposals, agents track the variability arising from changing environments by computing various forms of uncertainty. Several neuromodulators have been linked to uncertainty signalling, but comprehensive empirical characterisation of their relative contributions to perceptual belief updating, and to the selection of motor responses, is lacking. Here we assess the roles of noradrenaline, acetylcholine, and dopamine within a single, unified computational framework of uncertainty. Using pharmacological interventions in a sample of 128 healthy human volunteers and a hierarchical Bayesian learning model, we characterise the influences of noradrenergic, cholinergic, and dopaminergic receptor antagonism on individual computations of uncertainty during a probabilistic serial reaction time task. We propose that noradrenaline influences learning of uncertain events arising from unexpected changes in the environment. In contrast, acetylcholine balances attribution of uncertainty to chance fluctuations within an environmental context, defined by a stable set of probabilistic associations, or to gross environmental violations following a contextual switch. Dopamine supports the use of uncertainty representations to engender fast, adaptive responses.

show abstract

Spatial Attention, Precision, and Bayesian Inference: A Study of Saccadic Response Speed

Cited by 135 publications

References 68 publications

Dissociable effects of surprise and model update in parietal and anterior cingulate cortex

Dissociable effects of surprise and model update in parietal and anterior cingulate cortex

Bayesian modeling of flexible cognitive control

Pharmacological Fingerprints of Contextual Uncertainty

Contact Info

Product

Resources

About