Sequential memory-guided saccades and target selection: a neural model of the frontal eye fields

Memory-Guided Saccades and Non-Motor Symptoms Improve after Botulinum Toxin Therapy in Cervical Dystonia

Background/Objectives: Cervical dystonia (CD) is a condition characterized by involuntary activity of cervical muscles, which is often accompanied by various non-motor symptoms. Recent studies indicate impaired saccadic eye movements in CD. Local administration of botulinum toxin type A (BoNT/A), which causes temporary paralysis of the injected muscle, is the first-line treatment of focal dystonia, including CD. To our knowledge, concurrent observation of the effect of BoNT/A on smooth eye movements, voluntary saccades, memory-guided saccades, and antisaccades in CD has not yet been explored. The aim of this study was to assess the effect of BoNT/A on eye movements and non-motor symptoms in patients with CD, which, when altered, could imply a central effect of BoNT/A. Methods: Thirty patients with CD performed smooth pursuit, prosaccadic expression, memory-guided saccades, and antisaccade tasks; eye movements were recorded by an eye tracker. Motor and non-motor symptoms, including depression, anxiety, pain, disability, and cognitive changes prior to and after BoNT/A administration, were also evaluated. Results: The number of correct onward counts (p < 0.001), overall correct memory-guided saccades count (p = 0.005), motor symptoms (p = 0.001), and non-motor symptoms, i.e., anxiety (p = 0.04), depression (p = 0.02), and cognition (p < 0.001) markedly improved after BoNT/A administration. Conclusions: Memory-guided saccades, depression, and anxiety improve after BoNT/A in CD.

The intersection between the oculomotor and hippocampal memory systems: empirical developments and clinical implications

Decades of cognitive neuroscience research has shown that where we look is intimately connected to what we remember. In this article, we review findings from human and nonhuman animals, using behavioral, neuropsychological, neuroimaging, and computational modeling methods, to show that the oculomotor and hippocampal memory systems interact in a reciprocal manner, on a moment-to-moment basis, mediated by a vast structural and functional network. Visual exploration serves to efficiently gather information from the environment for the purpose of creating new memories, updating existing memories, and reconstructing the rich, vivid details from memory. Conversely, memory increases the efficiency of visual exploration. We call for models of oculomotor control to consider the influence of the hippocampal memory system on the cognitive control of eye movements, and for models of hippocampal and broader medial temporal lobe function to consider the influence of the oculomotor system on the development and expression of memory. We describe eye movement-based applications for the detection of neurodegeneration and delivery of therapeutic interventions for mental health disorders for which the hippocampus is implicated and memory dysfunctions are at the forefront.

Accumulators, Neurons, and Response Time

The marriage of cognitive neurophysiology and mathematical psychology to understand decision-making has been exceptionally productive. This interdisciplinary area is based on the proposition that particular neurons or circuits instantiate the accumulation of evidence specified by mathematical models of sequential sampling and stochastic accumulation. This linking proposition has earned widespread endorsement. Here, a brief survey of the history of the proposition precedes a review of multiple conundrums and paradoxes concerning the accuracy, precision, and transparency of that linking proposition. Correctly establishing how abstract models of decision-making are instantiated by particular neural circuits would represent a remarkable accomplishment in mapping mind to brain. Failing would reveal challenging limits for cognitive neuroscience. This is such a vigorous area of research because so much is at stake.

Functional Categories of Visuomotor Neurons in Macaque Frontal Eye Field

Frontal eye field (FEF) in macaque monkeys contributes to visual attention, visual-motor transformations and production of eye movements. Traditionally, neurons in FEF have been classified by the magnitude of increased discharge rates following visual stimulus presentation, during a waiting period, and associated with eye movement production. However, considerable heterogeneity remains within the traditional visual, visuomovement, and movement categories. Cluster analysis is a data-driven method of identifying self-segregating groups within a dataset. Because many cluster analysis techniques exist and outcomes vary with analysis assumptions, consensus clustering aggregates over multiple analyses, identifying robust groups. To describe more comprehensively the neuronal composition of FEF, we applied a consensus clustering technique for unsupervised categorization of patterns of spike rate modulation measured during a memory-guided saccade task. We report 10 functional categories, expanding on the traditional 3 categories. Categories were distinguished by latency, magnitude, and sign of visual response; the presence of sustained activity; and the dynamics, magnitude and sign of saccade-related modulation. Consensus clustering can include other metrics and can be applied to datasets from other brain regions to provide better information guiding microcircuit models of cortical function.

A neural model of the frontal eye fields with reward-based learning

Decision-making is a flexible process dependent on the accumulation of various kinds of information; however, the corresponding neural mechanisms are far from clear. We extended a layered model of the frontal eye field to a learning-based model, using computational simulations to explain the cognitive process of choice tasks. The core of this extended model has three aspects: direction-preferred populations that cluster together the neurons with the same orientation preference, rule modules that control different rule-dependent activities, and reward-based synaptic plasticity that modulates connections to flexibly change the decision according to task demands. After repeated attempts in a number of trials, the network successfully simulated three decision choice tasks: an anti-saccade task, a no-go task, and an associative task. We found that synaptic plasticity could modulate the competition of choices by suppressing erroneous choices while enhancing the correct (rewarding) choice. In addition, the trained model captured some properties exhibited in animal and human experiments, such as the latency of the reaction time distribution of anti-saccades, the stop signal mechanism for canceling a reflexive saccade, and the variation of latency to half-max selectivity. Furthermore, the trained model was capable of reproducing the re-learning procedures when switching tasks and reversing the cue-saccade association.

Mojibake - The rehearsal of word fragments in verbal recall

Theories of verbal rehearsal usually assume that whole words are being rehearsed. However, words consist of letter sequences, or syllables, or word onset-vowel-coda, amongst many other conceptualizations of word structure. A more general term is the 'grain size' of word units (Ziegler and Goswami, 2005). In the current study, a new method measured the quantitative percentage of correctly remembered word structure. The amount of letters in the correct letter sequence as per cent of word length was calculated, disregarding missing or added letters. A forced rehearsal was tested by repeating each memory list four times. We tested low frequency (LF) English words versus geographical (UK) town names to control for content. We also tested unfamiliar international (INT) non-words and names of international (INT) European towns to control for familiarity. An immediate versus distributed repetition was tested with a between-subject design. Participants responded with word fragments in their written recall especially when they had to remember unfamiliar words. While memory of whole words was sensitive to content, presentation distribution and individual sex and language differences, recall of word fragments was not. There was no trade-off between memory of word fragments with whole word recall during the repetition, instead also word fragments significantly increased. Moreover, while whole word responses correlated with each other during repetition, and word fragment responses correlated with each other during repetition, these two types of word recall responses were not correlated with each other. Thus there may be a lower layer consisting of free, sparse word fragments and an upper layer that consists of language-specific, orthographically and semantically constrained words.

Where's Waldo? How perceptual, cognitive, and emotional brain processes cooperate during learning to categorize and find desired objects in a cluttered scene

The Where's Waldo problem concerns how individuals can rapidly learn to search a scene to detect, attend, recognize, and look at a valued target object in it. This article develops the ARTSCAN Search neural model to clarify how brain mechanisms across the What and Where cortical streams are coordinated to solve the Where's Waldo problem. The What stream learns positionally-invariant object representations, whereas the Where stream controls positionally-selective spatial and action representations. The model overcomes deficiencies of these computationally complementary properties through What and Where stream interactions. Where stream processes of spatial attention and predictive eye movement control modulate What stream processes whereby multiple view- and positionally-specific object categories are learned and associatively linked to view- and positionally-invariant object categories through bottom-up and attentive top-down interactions. Gain fields control the coordinate transformations that enable spatial attention and predictive eye movements to carry out this role. What stream cognitive-emotional learning processes enable the focusing of motivated attention upon the invariant object categories of desired objects. What stream cognitive names or motivational drives can prime a view- and positionally-invariant object category of a desired target object. A volitional signal can convert these primes into top-down activations that can, in turn, prime What stream view- and positionally-specific categories. When it also receives bottom-up activation from a target, such a positionally-specific category can cause an attentional shift in the Where stream to the positional representation of the target, and an eye movement can then be elicited to foveate it. These processes describe interactions among brain regions that include visual cortex, parietal cortex, inferotemporal cortex, prefrontal cortex (PFC), amygdala, basal ganglia (BG), and superior colliculus (SC).

A neural model of sequential movement planning and control of eye movements: Item-Order-Rank working memory and saccade selection by the supplementary eye fields

How does working memory store multiple spatial positions to control sequences of eye movements, particularly when the same items repeat at multiple list positions, or ranks, during the sequence? An Item-Order-Rank model of working memory shows how rank-selective representations enable storage and recall of items that repeat at arbitrary list positions. Rank-related activity has been observed in many areas including the posterior parietal cortices (PPC), prefrontal cortices (PFC) and supplementary eye fields (SEF). The model shows how rank information, originating in PPC, may support rank-sensitive PFC working memory representations and how SEF may select saccades stored in working memory. It also proposes how SEF may interact with downstream regions such as the frontal eye fields (FEF) during memory-guided sequential saccade tasks, and how the basal ganglia (BG) may control the flow of information. Model simulations reproduce behavioral, anatomical and electrophysiological data under multiple experimental paradigms, including visually- and memory-guided single and sequential saccade tasks. Simulations reproduce behavioral data during two SEF microstimulation paradigms, showing that their seemingly inconsistent findings about saccade latency can be reconciled.

Neurally constrained modeling of perceptual decision making

Stochastic accumulator models account for response time in perceptual decision-making tasks by assuming that perceptual evidence accumulates to a threshold. The present investigation mapped the firing rate of frontal eye field (FEF) visual neurons onto perceptual evidence and the firing rate of FEF movement neurons onto evidence accumulation to test alternative models of how evidence is combined in the accumulation process. The models were evaluated on their ability to predict both response time distributions and movement neuron activity observed in monkeys performing a visual search task. Models that assume gating of perceptual evidence to the accumulating units provide the best account of both behavioral and neural data. These results identify discrete stages of processing with anatomically distinct neural populations and rule out several alternative architectures. The results also illustrate the use of neurophysiological data as a model selection tool and establish a novel framework to bridge computational and neural levels of explanation.

Predictive activity in macaque frontal eye field neurons during natural scene searching

Generating sequences of multiple saccadic eye movements allows us to search our environment quickly and efficiently. Although the frontal eye field cortex (FEF) has been linked to target selection and making saccades, little is known about its role in the control and performance of the sequences of saccades made during self-guided visual search. We recorded from FEF cells while monkeys searched for a target embedded in natural scenes and examined the degree to which cells with visual and visuo-movement activity showed evidence of target selection for future saccades. We found that for about half of these cells, activity during the fixation period between saccades predicted the next saccade in a sequence at an early time that precluded selection based on current visual input to a cell's response field. In addition to predicting the next saccade, activity during the fixation prior to two successive saccades also predicted the direction and goal of the second saccade in the sequence. We refer to this as advanced predictive activity. Unlike activity indicating the upcoming saccade, advanced predictive activity occurred later in the fixation period, mirroring the order of the saccade sequence itself. The remaining cells without advanced predictive activity did not predict future saccades but reintroduced the signal for the upcoming saccade at an intermediate time in the fixation period. Together these findings suggest that during natural visual search the timing of FEF cell activity is consistent with a role in specifying targets for one or more future saccades in a search sequence.

Visual and motor connectivity and the distribution of calcium-binding proteins in macaque frontal eye field: implications for saccade target selection

The frontal eye field (FEF) contributes to directing visual attention and saccadic eye movement through intrinsic processing, interactions with extrastriate visual cortical areas (e.g., V4), and projections to subcortical structures (e.g., superior colliculus, SC). Several models have been proposed to describe the relationship between the allocation of visual attention and the production of saccades. We obtained anatomical information that might provide useful constraints on these models by evaluating two characteristics of FEF. First, we investigated the laminar distribution of efferent connections from FEF to visual areas V4 + TEO and to SC. Second, we examined the laminar distribution of different populations of GABAergic neurons in FEF. We found that the neurons in FEF that project to V4 + TEO are located predominantly in the supragranular layers, colocalized with the highest density of calbindin- and calretinin-immunoreactive inhibitory interneurons. In contrast, the cell bodies of neurons that project to SC are found only in layer 5 of FEF, colocalized primarily with parvalbumin inhibitory interneurons. None of the neurons in layer 5 that project to V4 + TEO also project to SC. These results provide useful constraints for cognitive models of visual attention and saccade production by indicating that different populations of neurons project to extrastriate visual cortical areas and to SC. This finding also suggests that FEF neurons projecting to visual cortex and SC are embedded in different patterns of intracortical circuitry.

Bayesian models of eye movement selection with retinotopic maps

Among the various possible criteria guiding eye movement selection, we investigate the role of position uncertainty in the peripheral visual field. In particular, we suggest that, in everyday life situations of object tracking, eye movement selection probably includes a principle of reduction of uncertainty. To evaluate this hypothesis, we confront the movement predictions of computational models with human results from a psychophysical task. This task is a freely moving eye version of the multiple object tracking task, where the eye movements may be used to compensate for low peripheral resolution. We design several Bayesian models of eye movement selection with increasing complexity, whose layered structures are inspired by the neurobiology of the brain areas implied in this process. Finally, we compare the relative performances of these models with regard to the prediction of the recorded human movements, and show the advantage of taking explicitly into account uncertainty for the prediction of eye movements.

Visual remapping by vector subtraction: analysis of multiplicative gain field models

Saccadic eye movements remain spatially accurate even when the target becomes invisible and the initial eye position is perturbed. The brain accomplishes this in part by remapping the remembered target location in retinal coordinates. The computation that underlies this visual remapping is approximated by vector subtraction: the original saccade vector is updated by subtracting the vector corresponding to the intervening eye movement. The neural mechanism by which vector subtraction is implemented is not fully understood. Here, we investigate vector subtraction within a framework in which eye position and retinal target position signals interact multiplicatively (gain field). When the eyes move, they induce a spatial modulation of the firing rates across a retinotopic map of neurons. The updated saccade metric can be read from the shift of the peak of the population activity across the map. This model uses a quasi-linear (half-rectified) dependence on the eye position and requires the slope of the eye position input to be negatively proportional to the preferred retinal position of each neuron. We derive analytically this constraint and study its range of validity. We discuss how this mechanism relates to experimental results reported in the frontal eye fields of macaque monkeys.

A microcircuit model of the frontal eye fields

The cortical control of eye movements is highly sophisticated. Not only can eye movements be made to the most salient target in a visual scene, but they can also be controlled by top-down rules as is required for visual search or reading. The cortical area called frontal eye fields (FEF) has been shown to play a key role in the visual to oculomotor transformations in tasks requiring an eye movement pattern that is not completely reactive, but follows a previously learned rule. The layered, local cortical circuit, which provides the anatomical substrate for all cortical computation, has been studied extensively in primary sensory cortex. These studies led to the concept of a "canonical circuit" for neocortex (Douglas et al., 1989; Douglas and Martin, 1991), which proposes that all areas of neocortex share a common basic circuit. However, it has not ever been explored whether in principle the detailed canonical circuit derived from cat area 17 (Binzegger et al., 2004) could implement the quite different functions of prefrontal cortex. Here, we show that the canonical circuit can, with a few modifications, model the primate FEF. The spike-based network of integrate-and-fire neurons was tested in tasks that were used in electrophysiological experiments in behaving macaque monkeys. The dynamics of the model matched those of neurons observed in the FEF, and the behavioral results matched those observed in psychophysical experiments. The close relationship between the model and the cortical architecture allows a detailed comparison of the simulation results with physiological data and predicts details of the anatomical circuit of the FEF.

From brainstem to cortex: computational models of saccade generation circuitry

The brain circuitry of saccadic eye movements, from brainstem to cortex, has been extensively studied during the last 30 years. The wealth of data gathered allowed the conception of numerous computational models. These models proposed descriptions of the putative mechanisms generating this data, and, in turn, made predictions and helped to plan new experiments. In this article, we review the computational models of the five main brain regions involved in saccade generation: reticular formation saccadic burst generators, superior colliculus, cerebellum, basal ganglia and premotor cortical areas. We present the various topics these models are concerned with: location of the feedback loop, multimodal saccades, long-term adaptation, on the fly trajectory correction, strategy and metrics selection, short-term spatial memory, transformations between retinocentric and craniocentric reference frames, sequence learning, to name the principle ones. Our objective is to provide a global view of the whole system. Indeed, narrowing too much the modelled areas while trying to explain too much data is a recurrent problem that should be avoided. Moreover, beyond the multiple research topics remaining to be solved locally, questions regarding the operation of the whole structure can now be addressed by building on the existing models.