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Background: Transitive inference has a long history of study in developmental contexts. However, trial-and-error transitive inference learning (in which participants infer item relations, rather than evaluate them explicitly from verbal descriptions) has been the subject of minimal study in childhood samples with reported clinical diagnoses.Methods: Children aged 8 to 10 underwent a battery of clinical assessments and received a range of diagnoses, potentially including autism spectrum disorder (ASD), attention-deficit hyperactive disorder (ADHD), anxiety disorders (AD), specific learning disorders (SLD), and/or communication disorders (CD). Participants also performed a simple trial-and-error learning task that tested for transitive inference. Response accuracy and reaction time were assessed in a statistical model that controlled for diagnostic comorbidity at the group level.Results: Participants in all diagnostic categories showed evidence of transitive inference, and thus succeeded at the task. However, a model comparison analysis suggested that those diagnosed with ASD succeeded in a qualitatively different way, responding more slowly to each choice and improving faster across trials than their non-ASD counterparts. Additionally, transitive inference performance was not associated with IQ. Overall, our data suggest that superficially similar performance levels between ASD and non-ASD participants may have resulted from a difference in the speed-accuracy tradeoff made by each group.Conclusions: This provides a preliminary profile of the impact of various clinical diagnoses on TI performance in young children. Of these, an ASD diagnosis resulted in the largest difference in task strategy.

Knowledge of transitive relationships between items can contribute to learning the order of a set of stimuli from pairwise comparisons. However, cognitive mechanisms of transitive inferences based on rank order remain unclear, as are relative contributions of reward associations and rule-based inference. To explore these issues, we created a conflict between rule- and reward-based learning during a serial ordering task. Rhesus macaques learned two lists, each containing five stimuli that were trained exclusively with adjacent pairs. Selection of the higher-ranked item resulted in rewards. “Small reward” lists yielded two drops of fluid reward, whereas “large reward” lists yielded five drops. Following training of adjacent pairs, monkeys were tested on novels pairs. One item was selected from each list, such that a ranking rule could conflict with preferences for large rewards. Differences between the corresponding reward magnitudes had a strong influence on accuracy, but we also observed a symbolic distance effect. That provided evidence of a rule-based influence on decisions. RT comparisons suggested a conflict between rule- and reward-based processes. We conclude that performance reflects the contributions of two strategies and that a model-based strategy is employed in the face of a strong countervailing reward incentive.

Relational cognition — the ability to infer relationships that generalize to novel combinations of objects — is fundamental to human and animal intelligence. Despite this importance, it remains unclear how relational cognition is implemented in the brain due in part to a lack of hypotheses and predictions at the levels of collective neural activity and behavior. Here we discovered, analyzed, and experimentally tested neural networks (NNs) that perform transitive inference (TI), a classic relational task (if A > B and B > C, then A > C). We found NNs that (i) generalized perfectly, despite lacking overt transitive structure prior to training, (ii) generalized when the task required working memory (WM), a capacity thought essential to inference in the brain, (iii) emergently expressed behaviors long observed in living subjects, in addition to a novel order-dependent behavior, and (iv) adopted different task solutions yielding alternative behavioral and neural predictions. Further, in a large-scale experiment, we found that human subjects performing WM-based TI showed behavior that matched an unorthodox subset of the NNs. These findings provide neural insights into a classical relational ability, with wider implications for how the brain realizes relational cognition.

An emerging approach with potential in improving the treatment of neurodegenerative diseases and brain tumors is the use of focused ultrasound (FUS) to bypass the blood–brain barrier (BBB) in a non-invasive and localized manner. A large body of pre-clinical work has paved the way for the gradual clinical implementation of FUS-induced BBB opening. Even though the safety profile of FUS treatments in rodents has been extensively studied, the histological and behavioral effects of clinically relevant BBB opening in large animals are relatively understudied. Here, we examine the histological and behavioral safety profile following localized BBB opening in non-human primates (NHPs), using a neuronavigation-guided clinical system prototype. We show that FUS treatment triggers a short-lived immune response within the targeted region without exacerbating the touch accuracy or reaction time in visual-motor cognitive tasks. Our experiments were designed using a multiple-case-study approach, in order to maximize the acquired data and support translation of the FUS system into human studies. Four NHPs underwent a single session of FUS-mediated BBB opening in the prefrontal cortex. Two NHPs were treated bilaterally at different pressures, sacrificed on day 2 and 18 post-FUS, respectively, and their brains were histologically processed. In separate experiments, two NHPs that were earlier trained in a behavioral task were exposed to FUS unilaterally, and their performance was tracked for at least 3 weeks after BBB opening. An increased microglia density around blood vessels was detected on day 2, but was resolved by day 18. We also detected signs of enhanced immature neuron presence within areas that underwent BBB opening, compared to regions with an intact BBB, confirming previous rodent studies. Logistic regression analysis showed that the NHP cognitive performance did not deteriorate following BBB opening. These preliminary results demonstrate that neuronavigation-guided FUS with a single-element transducer is a non-invasive method capable of reversibly opening the BBB, without substantial histological or behavioral impact in an animal model closely resembling humans. Future work should confirm the observations of this multiple-case-study work across animals, species and tasks.

Does serial learning result in specific associations between pairs of items, or does it result in a cognitive map based on relations of all items? In 2 experiments, we trained human participants to learn various lists of photographic images. We then tested the participants on new lists of photographic images. These new lists were constructed by selecting only 1 image from each list learned during training. In Experiment 1, participants were trained to choose the earlier (experimenter defined) item when presented with adjacent pairs of items on each of 5 different 5-item lists. Participants were then tested on derived lists, in which each item retained its original ordinal position, even though each of the presented pairs was novel. Participants performed above chance on all of the derived lists. In Experiment 2, a different group of participants received the same training as those of Experiment 1, but the ordinal positions of items were systematically changed on each derived list. The response accuracy for Experiment 2 varied inversely with the degree to which an item's original ordinal position was changed. These results can be explained by a model in which participants learned to make both positional inferences about the absolute rank of each stimulus, and transitive inferences about the relative ranks of pairs of stimuli. These inferences enhanced response accuracy when ordinal position was maintained, but not when it was changed. Our results demonstrate quantitatively that, in addition to item-item associations that participants acquire while learning a list of arbitrary items, they form a cognitive map that represents both experienced and inferred relationships. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Monkeys can learn the implied ranking of pairs of images drawn from an ordered set, despite never seeing all of the images simultaneously and without explicit spatial or temporal cues. We recorded the activity of posterior parietal cortex (including lateral intraparietal area LIP) neurons while monkeys learned 7-item transitive inference (TI) lists with 2 items presented on each trial. Behavior and neuronal activity were significantly influenced by the ordinal relationship of the stimulus pairs, specifically symbolic distance (the difference in rank) and joint rank (the sum of the ranks). Symbolic distance strongly predicted decision accuracy and learning rate. An effect of joint rank on performance was found nested within the symbolic distance effect. Across the population of neurons, there was significant modulation of firing correlated with the relative ranks of the two stimuli presented on each trial. Neurons exhibited selectivity for stimulus rank during learning, but not before or after. The observed behavior is poorly explained by associative or reward mechanisms, and appears more consistent with a mental workspace model in which implied serial order is mapped within a spatial framework. The neural data suggest that posterior parietal cortex supports serial learning by representing information about the ordinal relationship of the stimuli presented during a given trial.

Brain perturbation studies allow detailed causal inferences of behavioral and neural processes. Because the combination of brain perturbation methods and neural measurement techniques is inherently challenging, research in humans has predominantly focused on non-invasive, indirect brain perturbations, or neurological lesion studies. Non-human primates have been indispensable as a neurobiological system that is highly similar to humans while simultaneously being more experimentally tractable, allowing visualization of the functional and structural impact of systematic brain perturbation. This review considers the state of the art in non-human primate brain perturbation with a focus on approaches that can be combined with neuroimaging. We consider both non-reversible (lesions) and reversible or temporary perturbations such as electrical, pharmacological, optical, optogenetic, chemogenetic, pathway-selective, and ultrasound based interference methods. Method-specific considerations from the research and development community are offered to facilitate research in this field and support further innovations. We conclude by identifying novel avenues for further research and innovation and by highlighting the clinical translational potential of the methods. ispartof: NEUROIMAGE vol:235 ispartof: location:United States status: published

Understanding how organisms make transitive inferences is critical to understanding their general ability to learn serial relationships. In this context, transitive inference (TI) can be understood as a specific heuristic that applies broadly to many different serial learning tasks, which have been the focus of hundreds of studies involving dozens of species. In the present study, monkeys learned the order of 7-item lists of photographic stimuli by trial and error, and were then tested on "derived" lists. These derived test lists combined stimuli from multiple training lists in ambiguous ways, sometimes changing their order relative to training. We found that subjects displayed strong preferences when presented with novel test pairs, even when those pairs were drawn from different training lists. These preferences were helpful when test pairs had an ordering congruent with their ranks during training, but yielded consistently below-chance performance when pairs had an incongruent order relative to training. This behavior can be explained by the joint contributions of transitive inference and another heuristic that we refer to as "positional inference." Positional inferences play a complementary role to transitive inferences in facilitating choices between novel pairs of stimuli. The theoretical framework that best explains both transitive and positional inferences is a spatial model that represents both the position of each stimulus and its uncertainty. A computational implementation of this framework yields accurate predictions about both correct responses and errors on derived lists.

The implied order of a ranked set of visual images can be learned by transitive inference, without reliance on stimulus features that explicitly signal their order. Such learning is difficult to explain by associative mechanisms but can be accounted for by cognitive representations and processes such as transitive inference. Our study seeks to determine if those representations may be applied to categories of images without explicit verbal instruction. Specifically, we asked whether participants can (a) infer that images being presented belonged to familiar categories, even when every image presented during every trial is unique, and (b) perform transitive inferences about the ordering of those categories. To address these questions, we compared the performance of humans during a standard TI task, which used the same set of images throughout the session, to performance in a category TI tasks, which drew images from a set of categories. Each of the images used in the category TI task was only presented once, limiting the extent to which stimulus-outcome associations could be learned. Participants were able to learn the order of the categories based on transitive inference. However, participants in the category TI condition did not produce a symbolic distance effect. These findings collectively suggest that differing cognitive processes may underpin serial learning when learning about specific stimuli versus stimulus categories.

Transitive inference (TI) is a classic learning paradigm for which the relative contributions of experienced rewards and representation-based inference have been debated vigorously, particularly regarding the notion that animals are capable of logic and reasoning. Rhesus macaque subjects and human participants performed a TI task in which, prior to learning a 7-item list (ABCDEFG), a block of trials presented exclusively the pair FG. Contrary to the expectation of associative models, the high prior rate of reward for F did not disrupt subsequent learning of the entire list. Monkeys (who each completed many sessions with novel stimuli) learned to anticipate that novel stimuli should be preferred over F. We interpret this as evidence of a task representation of TI that generalizes beyond learning about specific stimuli. Humans (who were task-naive) showed a transitory bias to F when it was paired with novel stimuli, but very rapidly unlearned that bias. Performance with respect to the remaining stimuli was consistent with past reports of TI in both species. These results are difficult to reconcile with any account that assigns the strength of association between individual stimuli and rewards. Instead, they support sophisticated cognitive processes in both species, albeit with some species differences. (PsycINFO Database Record

Monkeys are able to learn the implied ordering of pairs of images drawn from an ordered set, without ever seeing all of the images simultaneously and without explicit spatial or temporal cues. The learning of implied order differs from learning of explicit visual or motor sequences. We recorded the activity of parietal neurons in rhesus macaques while they learned 7-item TI lists when only 2 items were presented on each trial. Behavior and ensemble neuronal activity were significantly influenced by the ordinal relationship of the stimulus pairs, specifically symbolic distance (the difference in rank) and joint ranks (the sum of the ranks). Symbolic distance strongly predicted decision accuracy, and learning was consistently faster as symbolic distance increased. An effect of joint rank on performance was also found nested within the symbolic distance effect. Across the population of neurons, there was significant modulation of firing correlated with the relative ranks of the two stimuli presented on each trial. Neurons exhibited selectivity for stimulus rank during learning, but not before or after. The observed behavior during learning is best explained by a virtual workspace model, not by associative or reward mechanisms. The neural data support a role for posterior parietal cortex in representing several variables that contribute to serial learning, particularly information about the ordinal ranks of the stimuli presented during a given trial. Thus, parietal cortex appears to belong to a neural substrate for learning and representing abstract relationships in a virtual workspace.

PURPOSE OF REVIEW: The past decade has seen rapid growth in the application of focused ultrasound (FUS) as a tool for basic neuroscience research and potential treatment of brain disorders. Here, we review recent developments in our understanding of how FUS can alter brain activity, perception and behavior when applied to the central nervous system, either alone or in combination with circulating agents. RECENT FINDINGS: Focused ultrasound in the central nervous system can directly excite or inhibit neuronal activity, as well as affect perception and behavior. Combining FUS with intravenous microbubbles to open the blood-brain barrier also affects neural activity and behavior, and the effects may be more sustained than FUS alone. Opening the BBB also allows delivery of drugs that do not cross the intact BBB including viral vectors for gene delivery. SUMMARY: While further research is needed to elucidate the biophysical mechanisms, focused ultrasound, alone or in combination with other factors, is rapidly maturing as an effective technology for altering brain activity. Future challenges include refining control over targeting specificity, the volume of affected tissue, cell-type specificity (excitatory or inhibitory), and the duration of neural and behavioral effects.

Brain diseases including neurological disorders and tumors remain under treated due to the challenge to access the brain, and blood-brain barrier (BBB) restricting drug delivery which, also profoundly limits the development of pharmacological treatment. Focused ultrasound (FUS) with microbubbles is the sole method to open the BBB noninvasively, locally, and transiently and facilitate drug delivery, while translation to the clinic is challenging due to long procedure, targeting limitations, or invasiveness of current systems. In order to provide rapid, flexible yet precise applications, we have designed a noninvasive FUS and monitoring system with the protocol tested in monkeys (from in silico preplanning and simulation, real-time targeting and acoustic mapping, to post-treatment assessment). With a short procedure (30 min) similar to current clinical imaging duration or radiation therapy, the achieved targeting (both cerebral cortex and subcortical structures) and monitoring accuracy was close to the predicted 2-mm lower limit. This system would enable rapid clinical transcranial FUS applications outside of the MRI system without a stereotactic frame, thereby benefiting patients especially in the elderly population.

For more than 100 years, psychologists have struggled to determine what is learned during serial learning. The method of derived lists is a powerful tool for studying this question. In two experiments, we trained human participants to learn implicit lists by the Transitive Inference (TI) method. We then tested their knowledge of ordinal position of those items. In Experiment 1, participants were presented with pairs of photographic stimuli from five different 5-item training lists by presenting adjacent pairs of items from one list on every trial. Participants were then tested on pairs of items drawn from different lists, in which each item maintained its original ordinal position as it had during training. In Experiment 2, a different group of participants was trained on the same five 5-item lists as that of Experiment 1. However, the order of the items in the derived lists of Experiment 2 was changed systematically. In this latter experiment, the acquisition rate for the derived lists varied inversely with the degree of change of ordinal position. We explain these results by using a model in which participants learn to make positional, as well as transitive inferences, allowing them to infer the relative and absolute position of each item during testing on derived lists.

Macaques are often used as a model system for invasive investigations of the neural substrates of cognition. However, 25 million years of evolution separate humans and macaques from their last common ancestor, and this has likely substantially impacted the function of the cortical networks underlying cognitive processes, such as attention. We examined the homology of frontoparietal networks underlying attention by comparing functional MRI data from macaques and humans performing the same visual search task. Although there are broad similarities, we found fundamental differences between the species. First, humans have more dorsal attention network areas than macaques, indicating that in the course of evolution the human attention system has expanded compared with macaques. Second, potentially homologous areas in the dorsal attention network have markedly different biases toward representing the contralateral hemifield, indicating that the underlying neural architecture of these areas may differ in the most basic of properties, such as receptive field distribution. Third, despite clear evidence of the temporoparietal junction node of the ventral attention network in humans as elicited by this visual search task, we did not find functional evidence of a temporoparietal junction in macaques. None of these differences were the result of differences in training, experimental power, or anatomical variability between the two species. The results of this study indicate that macaque data should be applied to human models of cognition cautiously, and demonstrate how evolution may shape cortical networks.

Primates are able to track small moving visual targets using smooth pursuit eye movements. Target motion for smooth pursuit is signaled by neurons in visual cortical areas MT and MST. In this study, we trained monkeys to either initiate or withhold smooth pursuit in the presence of a moving target to test whether this decision was reflected in the relative strength of “go” and “no-go” processes. We found that the gain of the motor response depended strongly on whether monkeys were instructed to initiate or withhold pursuit, thus demonstrating voluntary control of pursuit initiation. We found that the amplitude of the neuronal response to moving targets in areas MT and MST was also significantly lower on no-go trials (by 2.1 spikes/s on average). The magnitude of the neural response reduction was small compared with the behavioral gain reduction. There were no significant differences in neuronal direction selectivity, spatial selectivity, or response reliability related to pursuit initiation or the absence thereof. Variability in eye speed was negatively correlated with firing rate variability after target motion onset during go trials but not during no-go trials, suggesting that MT and MST activity represents an error signal for a negative feedback controller. We speculate that modulation of the visual motion signals in areas MT and MST may be one of the first visual cortical events in the initiation of smooth pursuit and that the small early response modulation may be amplified to produce an all-or-none motor response by downstream areas.

Drug delivery to subcortical regions is susceptible to the blood–brain barrier (BBB) impeding the molecular exchange between the blood stream and the brain parenchyma. Focused ultrasound (FUS) coupled with the administration of microbubbles has been proved to open the BBB locally, transiently, and noninvasively both in rodents and in nonhuman-primates (NHPs). The development of this disruption technique independent of MRI monitoring is of primordial importance yet restrained to the targeting optimization. This paper establishes the linear relationship of the incidence angle with the volume of BBB opening ( $V_{\mathrm{BBB}}$ ) and the peak negative pressure when sonicating the caudate nucleus and the putamen region of five NHPs. In addition, the effect of central nervous system structures on the opening morphology is evaluated by identification of the gray-to-white-matter ratio at the opening site. Finally, the targeting accuracy is assessed through the estimation of the geometric and angle shift of the opening from the targeted region. Interestingly, results prove a monotonic increase of the opening volume with close to normal incidence angles. Moreover, 80.35% of the opening lies on gray-matter regions compared with only 19.41% attributed to the white matter. The opening was found to be shifted axially, toward the transducer, and laterally with an average angle shift of 4.5°. Finally, we were capable of showing reproducibility of targeting accuracy with the same stereotactic and ultrasonic parameters. This paper documents the a priori prediction of the opening volume through manipulation of the angle and pressure as well as establishing the predictability, accuracy, and safety of FUS-induced BBB opening in NHPs.

Category learning in animals is typically trained explicitly, in most instances by varying the exemplars of a single category in a matching-to-sample task. Here, we show that male rhesus macaques can learn categories by a transitive inference paradigm in which novel exemplars of five categories were presented throughout training. Instead of requiring decisions about a constant set of repetitively presented stimuli, we studied the macaque's ability to determine the relative order of multiple exemplars of particular stimuli that were rarely repeated. Ordinal decisions generalized both to novel stimuli and, as a consequence, to novel pairings. Thus, we showed that rhesus monkeys could learn to categorize on the basis of implied ordinal position, without prior matching-to-sample training, and that they could then make inferences about category order. Our results challenge the plausibility of association models of category learning and broaden the scope of the transitive inference paradigm.

Category learning in animals is typically trained explicitly, in most instances by varying the exemplars of a single category in a matching-to-sample task. Here, we show that rhesus macaques can learn categories by a transitive inference paradigm in which novel exemplars of five categories were presented throughout each training session. Instead of requiring decisions about a constant set of repetitively presented stimuli, we studied the macaque’s ability to determine the relative order of multiple exemplars of particular stimuli that were rarely repeated. Ordinal decisions generalized both to novel stimuli and, as a consequence, to novel pairings. Thus, we showed that rhesus monkeys could learn to categorize on the basis of implied ordinal position, and that they could then make inferences about category order. Our results challenge the plausibility of association models of category learning and broaden the scope of the transitive inference paradigm.Significance StatementThe cognitive abilities of non-human animals are of enduring interest to scientists and the general public because they blur the dividing line between human and non-human intelligence. Categorization and sequence learning are highly abstract cognitive abilities each in their own right. This study is the first to provide evidence that visual categories can be ordered serially by macaque monkeys using a behavioral paradigm that provides no explicit feedback about category or serial order. These results strongly challenge accounts of learning based on stimulus-outcome associations.

A fundamental goal of cognitive neuroscience is to understand how brain activity generates complex mental states and behaviors. While neuronal activity may predict or correlate with behavioral responses in a cognitive task, the use of electrical microstimulation presents the possibility to augment such correlational findings with direct evidence for causal relationships. Although microstimulation has been used for many years as a tool for mapping sensory and motor function, its role in learning, memory and decision-making has emerged only recently. Focal microstimulation of higher cortical areas can produce complex mental states and sequences of action. However, the relationship between the locus of stimulation and the percepts or actions evoked is often stereotyped and inflexible. The challenge is to develop stimulation systems that do not have fixed output but can flexibly contribute to complex cognitive and behavioral tasks. We discuss how microstimulation has been instrumental in manipulating a wide spectrum of cognitive functions including working memory, perceptual decisions and executive control by enhancing attention, re-ordering temporal sequence of saccades, improving associative learning or cognitive performance. For example, stimulation in prefrontal, parietal and sensory cortices may establish causal effects on decision-making, while microstimulation of inferotemporal cortex or caudate nucleus enhances associative learning. Building cognitive prosthetics based on the insights gleaned from such studies may depend on the development of multiple-input, multiple-output (MIMO) devices that allow subjects to control stimulation with their own thoughts in a closed-loop system.

Focused ultrasound with microbubbles has been used to noninvasively and selectively deliver pharmacological agents across the blood-brain barrier (BBB) for treating brain diseases. Acoustic cavitation monitoring could serve as an on-line tool to assess and control the treatment. While it demonstrated a strong correlation in small animals, its translation to primates remains in question due to the anatomically different and highly heterogeneous brain structures with gray and white matteras well as dense vasculature. In addition, the drug delivery efficiency and the BBB opening volume have never been shown to be predictable through cavitation monitoring in primates. This study aimed at determining how cavitation activity is correlated with the amount and concentration of gadolinium delivered through the BBB and its associated delivery efficiency as well as the BBB opening volume in non-human primates. Another important finding entails the effect of heterogeneous brain anatomy and vasculature of a primate brain, i.e., presence of large cerebral vessels, gray and white matter that will also affect the cavitation activity associated with variation of BBB opening in different tissue types, which is not typically observed in small animals. Both these new findings are critical in the primate brain and provide essential information for clinical applications.

Transitive inference (TI) is a classic learning paradigm for which the relative contributions of experienced rewards and representation-driven inference have been vigorously debated, particularly with regard to the notion that animals are capable of logic and reasoning. Rhesus macaque subjects and human participants performed a TI task in which, prior to learning a seven-item list ABCDEFG, a block of trials presented exclusively the pair FG. Contrary to the expectation of associative models, the high prior rate of reward for F did not disrupt learning of the entire list. Monkeys (who each completed many sessions) learned to anticipate that novel stimuli should be preferred over F. We interpret this as evidence of a general task representation of TI that generalizes beyond learning about specific stimuli. Humans (who were task-naïve) showed a transitory bias to F when it was paired with novel stimuli, but very rapidly unlearned that bias. Performance with respect to the remaining stimuli was consistent with past reports of TI in both species. These results are difficult to reconcile with any account that seeks to assign the strength of association between individual stimuli and rewards. Instead, they support both sophisticated cognitive processes in both species, albeit with some species differences.

The electrophysiological study of learning is hampered by modern procedures for estimating firing rates: Such procedures usually require large datasets, and also require that included trials be functionally identical. Unless a method can track the real-time dynamics of how firing rates evolve, learning can only be examined in the past tense. We propose a quantitative procedure, called ARRIS, that can uncover trial-by-trial firing dynamics. ARRIS provides reliable estimates of firing rates based on small samples using the reversible-jump Markov chain Monte Carlo algorithm. Using weighted interpolation, ARRIS can also provide estimates that evolve over time. As a result, both real-time estimates of changing activity, and of task-dependent tuning, can be obtained during the initial stages of learning.

Noninvasive brain stimulation using focused ultrasound has many potential applications as a research and clinical tool. Here, we investigated the effect of focused ultrasound (FUS) combined with systemically administered microbubbles on visual-motor decision-making behavior in monkeys. We applied FUS to the putamen in one hemisphere to open the blood-brain barrier, and then tested behavioral performance 3-4 hours later. On days when the monkeys were treated with FUS, their decisions were faster and more accurate than days without sonication. The performance improvement suggested both a shift in the decision criterion and an enhancement of the use of sensory evidence in the decision process. FUS also interacted with the effect of a low dose of haloperidol. The results suggest that a two-minute application of FUS can have a sustained impact on performance of complex cognitive tasks, and may increase the efficacy of psychoactive medications. The results lend further support to the idea that the dorsal striatum plays an integral role in evidence- and reward-based decision-making.

This introductory chapter describes and explains the focus of the book which is to present attention modelling in a multidisciplinary perspective. We first address the preliminary question of why one should care about modelling attention. First we show that it is a gate of conscious awareness at the interface between inner and outer and it is the key to survival. In the second time, we show that attentive computers using models of attention could be able to find regularities and irregularities in the input signal and thus be able to detect and react to unexpected or abnormal events.

The biological basis of human attention has been greatly advanced through the use of monkeys as a model organism. The visual system of macaque monkeys is highly similar to that of humans. Monkeys can be trained to attend to visual stimuli while holding their eyes still. This makes them ideal subjects for studying neurophysiological correlates of attention. Over the past few decades, such studies have revealed the computational mechanisms through which attention modulates visual responses. At the level of individual cortical neurons, attention can act to increase the gain and reduce the variability of responses to attended visual stimuli. These results set the stage for the development of a biologically plausible standard model of sensory attention. By applying the principles of signal detection theory, this model can link physiological effects of attention to psychophysical performance. The general approach of attempting to explain behavior based on neuronal activity can be used to refine and elaborate the standard model.

This chapter summarizes the main direct and indirect objective ways to measure attention at a macroscale. We focus on quantitative techniques that provide fine-grain spatial and temporal information about attentive responses. The attentive response can be either measured directly in the brain or indirectly through participants’ behavior. Only one of the techniques that are described here is based on participant active feedback: mouse tracking. This is because the mouse-tracking feedback is very close to the one of the eye tracking, and this is an emerging approach of interest for the future: it requires less time and less money and provides more data than classical eye tracking.

This book contributes to the crucial endeavor of understanding and modeling human attention. It gives an overview of physiological and computer science models, an extensive approach to model validation, as well as new trends and applications of attention models. It also paves the way for further investigations. Some directions for future research are discussed in the next section, in relation to the major contributions summarized above. In the second section, a perspective on issues beyond attention, such as higher level processing and consciousness, is provided. We propose that human attention can be viewed as a suite of computational strategies that are essential for autonomous behavior by agents both natural and artificial. The study of attention should go beyond filtering of sensory data to develop an understanding of how relevant and valuable information is actively gathered by agents who possess an integrated awareness of both their internal goals, needs and abilities and external sources of sustenance or danger. This kind of awareness implies an ability to model both the environment and the self that acts within that environment. Understanding the computational mechanisms underlying active, goal-oriented attention may be a first step toward artificial consciousness.

Attention plays many roles in perception and behavior, one of the most important being the enhancement of responses to subtle stimuli that might otherwise go unnoticed. The computational mechanisms that underlie attention’s role in stimulus detectability can be understood through the lens of signal detection theory (SDT). Attention can improve detectability by modifying signal and noise characteristics, optimizing decision criteria, and pooling across multiple detectors. SDT provides an integrated account of the effects of stimulus prevalence, and the effect of rewards and punishments. By computing detectability across the visual field, one can construct salience maps that take into account stimulus strength, likelihood, and value.

The dorsal medial frontal cortex (dMFC) is highly active during choice behavior. Though many models have been proposed to explain dMFC function, the conflict monitoring model is the most influential. It posits that dMFC is primarily involved in detecting interference between competing responses thus signaling the need for control. It accurately predicts increased neural activity and response time (RT) for incompatible (high-interference) vs. compatible (low-interference) decisions. However, it has been shown that neural activity can increase with time on task, even when no decisions are made. Thus, the greater dMFC activity on incompatible trials may stem from longer RTs rather than response conflict. This study shows that (1) the conflict monitoring model fails to predict the relationship between error likelihood and RT, and (2) the dMFC activity is not sensitive to congruency, error likelihood, or response conflict, but is monotonically related to time on task.

In neuroimaging research on attention, cognitive control, decision-making, and other areas where response time (RT) is a critical variable, the temporal variability associated with the decision is often assumed to be inconsequential to the hemodynamic response (HDR) in rapid event-related designs. On this basis, the majority of published studies model brain activity lasting less than four seconds with brief impulses representing the onset of neural or cognitive events, which are then convolved with the hemodynamic impulse response function (HRF). However, electrophysiological studies have shown that decision-related neuronal activity is not instantaneous, but in fact, often lasts until the motor response. It is therefore possible that small differences in neural processing durations, similar to human RTs, will produce noticeable changes in the HDR, and therefore in the results of regression analyses. In this study we compare the effectiveness of traditional models that assume no temporal variance with a model that explicitly accounts for the duration of very brief epochs of neural activity. Using both simulations and fMRI data, we show that brief differences in duration are detectable, making it possible to dissociate of the effects of stimulus intensity from stimulus duration, and that optimizing the model for the type of activity being detected improves the statistical power, consistency, and interpretability of results.

Due to delays in visuomotor processing, eye movements directed toward moving targets must integrate both target position and velocity to be accurate. It is unknown where and how target velocity information is incorporated into the planning of rapid (saccadic) eye movements. We recorded the activity of neurons in frontal eye fields (FEFs) while monkeys made saccades to stationary and moving targets. A substantial fraction of FEF neurons was found to encode not only the initial position of a moving target, but the metrics (amplitude and direction) of the saccade needed to intercept the target. Many neurons also encoded target velocity in a nearly linear manner. The quasi-linear dependence of firing rate on target velocity means that the neuronal response can be directly read out to compute the future position of a target moving with constant velocity. This is demonstrated using a quantitative model in which saccade amplitude is encoded in the population response of neurons tuned to retinal target position and modulated by target velocity.

The coordination of saccadic and smooth pursuit eye movements in macaque monkeys was investigated using a target selection paradigm with two moving targets crossing at a center fixation point. A task in which monkeys selected a target based on its color was used to test the hypothesis that common neural signals underlie target selection for pursuit and saccades, as well as testing whether target selection signals are available to the saccade and pursuit systems simultaneously or sequentially. Several combinations of target color, speed, and direction were used. In all cases, smooth pursuit was highly selective for the rewarded target before any saccade occurred. On >80% of the trials, the saccade was directed toward the same target as both pre- and postsaccadic pursuit. The results favor a model in which a shared target selection signal is simultaneously available to both the saccade and pursuit systems, rather than a sequential model.

Signals related to eye position are essential for visual perception and eye movements, and are powerful modulators of sensory responses in many regions of the visual and oculomotor systems. We show that visual and pre-saccadic responses of frontal eye field (FEF) neurons are modulated by initial eye position in a way suggestive of a multiplicative mechanism (gain field). Furthermore the slope of the eye position sensitivity tends to be negatively correlated with preferred retinal position across the population. A model with Gaussian visual receptive fields and linear-rectified eye position gain fields accounts for a large portion of the variance in the recorded data. Using physiologically derived parameters, this model is able to subtract the gaze shift from the vector representing the retinal location of the target. This computation might be used to maintain a memory of target location in space during ongoing eye movements. This updated spatial memory can be read directly from the locus of the peak of activity across the retinotopic map of FEF and it is the result of a vector subtraction between retinal target location when flashed and subsequent eye displacement in the dark.

Over the past fifteen years, focused ultrasound coupled with intravenously administered microbubbles (FUS) has been proven an effective, non-invasive technique to open the blood-brain barrier (BBB) in vivo. Here we show that FUS can safely and effectively open the BBB at the basal ganglia and thalamus in alert non-human primates (NHP) while they perform a behavioral task. The BBB was successfully opened in 89% of cases at the targeted brain regions of alert NHP with an average volume of opening 28% larger than prior anesthetized FUS procedures. Safety (lack of edema or microhemorrhage) of FUS was also improved during alert compared to anesthetized procedures. No physiological effects (change in heart rate, motor evoked potentials) were observed during any of the procedures. Furthermore, the application of FUS did not disrupt reaching behavior, but in fact improved performance by decreasing reaction times by 23 ms and significantly decreasing touch error by 0.76 mm on average.

Drug delivery to subcortical regions is susceptible to the blood-brain barrier (BBB) hindering the molecular exchange between the blood stream and the brain parenchyma. Focused ultrasound coupled with the administration of microbubbles has been proven to open the BBB locally, transiently and non-invasively both in rodents and in Non-Human-Primates (NHPs). The development of this disruption technique independent of MRI monitoring is of outstanding importance yet restrained to the targeting optimization. The current paper establishes the linear relationship of the incidence angle with the volume of BBB opening (V BBB ) and the applied pressure when sonicating the Caudate Nucleus and the Putamen region of five NHPs. In addition, the effect of central nervous system structures on the opening morphology is evaluated by identification of the gray-to-white-matter ratio at the opening site. Finally, the targeting accuracy is assessed by estimation of the geometric and angle shift of the opening from the targeted region. Interestingly, results prove a monotonic increase of the opening volume with close to normal incidence angles. Moreover, 80.35% of the opening lies on gray matter regions compared to only 19.41% attributed to the white matter. The BBB opening was found to be shifted axially, towards the transducer, and laterally with an average angle shift at 4.5°. Finally, we were able to reproduce our findings by employing the same stereotactic and ultrasonic parameters in different experiments and animals. This study documents the a priori prediction of the opening volume through manipulation of the angle and pressure establishing the predictability, accuracy and safety of the current technique on NHPs.

Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species. Despite these robust behavioral effects, reinforcement learning models reliant on reward prediction error or associative strength routinely fail to perform these inferences. We propose an algorithm called betasort, inspired by cognitive processes, which performs transitive inference at low computational cost. This is accomplished by (1) representing stimulus positions along a unit span using beta distributions, (2) treating positive and negative feedback asymmetrically, and (3) updating the position of every stimulus during every trial, whether that stimulus was visible or not. Performance was compared for rhesus macaques, humans, and the betasort algorithm, as well as Q-learning, an established reward-prediction error (RPE) model. Of these, only Q-learning failed to respond above chance during critical test trials. Betasort’s success (when compared to RPE models) and its computational efficiency (when compared to full Markov decision process implementations) suggests that the study of reinforcement learning in organisms will be best served by a feature-driven approach to comparing formal models., Author Summary Although machine learning systems can solve a wide variety of problems, they remain limited in their ability to make logical inferences. We developed a new computational model, called betasort, which addresses these limitations for a certain class of problems: Those in which the algorithm must infer the order of a set of items by trial and error. Unlike extant machine learning systems (but like children and many non-human animals), betasort is able to perform “transitive inferences” about the ordering of a set of images. The patterns of error made by betasort resemble those made by children and non-human animals, and the resulting learning achieved at low computational cost. Additionally, betasort is difficult to classify as either “model-free” or “model-based” according to the formal specifications of those classifications in the machine learning literature. One of the broader implications of these results is that achieving a more comprehensive understanding of how the brain learns will require analysts to entertain other candidate learning models.

Inhibitory control is an important component of executive function that allows organisms to abort emerging behavioral plans or ongoing actions on the fly as new sensory information becomes available. Current models treat inhibitory control as a race between a Go- and a Stop process that may be mediated by partially distinct neural substrates, i.e., the direct and the hyper-direct pathway of the basal ganglia. The fact that finishing times of the Stop process (Stop-Signal Reaction Time, SSRT) cannot be observed directly has precluded a precise comparison of the functional properties that govern the initiation (GoRT) and inhibition (SSRT) of a motor response. To solve this problem, we modified an existing inhibitory paradigm and developed a non-parametric framework to measure the trial-by-trial variability of SSRT. A series of simulations verified that the non-parametric approach is on par with a parametric approach and yields accurate estimates of the entire SSRT distribution from as few as ~750 trials. Our results show that in identical settings, the distribution of SSRT is very similar to the distribution of GoRT albeit somewhat shorter, wider and significantly less right-skewed. The ability to measure the precise shapes of SSRT distributions opens new avenues for research into the functional properties of the hyper-direct pathway that is believed to mediate inhibitory control.