Learning where to look for a hidden target

Eye movements reflect active statistical learning

What is the link between eye movements and sensory learning? Although some theories have argued for an automatic interaction between what we know and where we look that continuously modulates human information gathering behavior during both implicit and explicit learning, there exists limited experimental evidence supporting such an ongoing interplay. To address this issue, we used a visual statistical learning paradigm combined with a gaze-contingent stimulus presentation and manipulated the explicitness of the task to explore how learning and eye movements interact. During both implicit exploration and explicit visual learning of unknown composite visual scenes, spatial eye movement patterns systematically and gradually changed in accordance with the underlying statistical structure of the scenes. Moreover, the degree of change was directly correlated with the amount and type of knowledge the observers acquired. This suggests that eye movements are potential indicators of active learning, a process where long-term knowledge, current visual stimuli and an inherent tendency to reduce uncertainty about the visual environment jointly determine where we look.

Multistability, perceptual value, and internal foraging

Substantial experimental, theoretical, and computational insights into sensory processing have been derived from the phenomena of perceptual multistability-when two or more percepts alternate or switch in response to a single sensory input. Here, we review a range of findings suggesting that alternations can be seen as internal choices by the brain responding to values. We discuss how elements of external, experimenter-controlled values and internal, uncertainty- and aesthetics-dependent values influence multistability. We then consider the implications for the involvement in switching of regions, such as the anterior cingulate cortex, which are more conventionally tied to value-dependent operations such as cognitive control and foraging.

Long-range cortical synchronization supports abrupt visual learning

Visual plasticity declines sharply after the critical period, yet we easily learn to recognize new faces and places, even as adults. Such learning is often characterized by a "moment of insight," an abrupt and dramatic improvement in recognition. The mechanisms that support abrupt learning are unknown, but one hypothesis is that they involve changes in synchronization between brain regions. To test this hypothesis, we used a behavioral task in which non-human primates rapidly learned to recognize novel images and to associate them with specific responses. Simultaneous recordings from inferotemporal and prefrontal cortices revealed a transient synchronization of neural activity between these areas that peaked around the moment of insight. Synchronization was strongest between inferotemporal sites that encoded images and reward-sensitive prefrontal sites. Moreover, its magnitude intensified gradually over image exposures, suggesting that abrupt learning is the culmination of a search for informative signals within a circuit linking sensory information to task demands.

Previous Motor Actions Outweigh Sensory Information in Sensorimotor Statistical Learning

Humans can use their previous experience in form of statistical priors to improve decisions. It is, however, unclear how such priors are learned and represented. Importantly, it has remained elusive whether prior learning is independent of the sensorimotor system involved in the learning process or not, as both modality-specific and modality-general learning have been reported in the past. Here, we used a saccadic eye movement task to probe the learning and representation of a spatial prior across a few trials. In this task, learning occurs in an unsupervised manner and through encountering trial-by-trial visual hints drawn from a distribution centered on the target location. Using a model-comparison approach, we found that participants’ prior knowledge is largely represented in the form of their previous motor actions, with minimal influence from the previously seen visual hints. By using two different motor contexts for response (looking either at the estimated target location, or exactly opposite to it), we could further compare whether prior experience obtained in one motor context can be transferred to the other. Although learning curves were highly similar, and participants seemed to use the same strategy for both response types, they could not fully transfer their knowledge between contexts, as performance and confidence ratings dropped after a switch of the required response. Together, our results suggest that humans preferably use the internal representations of their previous motor actions, rather than past incoming sensory information, to form statistical sensorimotor priors on the timescale of a few trials.

Visual instrumental learning in blindsight monkeys

Blindsight is the residual visuo-motor ability without subjective awareness observed after lesions of the primary visual cortex (V1). Various visual functions are retained, however, instrumental visual associative learning remains to be investigated. Here we examined the secondary reinforcing properties of visual cues presented to the hemianopic field of macaque monkeys with unilateral V1 lesions. Our aim was to test the potential role of visual pathways bypassing V1 in reinforcing visual instrumental learning. When learning the location of a hidden area in an oculomotor search task, conditioned visual cues presented to the lesion-affected hemifield operated as an effective secondary reinforcer. We noted that not only the hidden area location, but also the vector of the saccade entering the target area was reinforced. Importantly, when the visual reinforcement signal was presented in the lesion-affected field, the monkeys continued searching, as opposed to stopping when the cue was presented in the intact field. This suggests the monkeys were less confident that the target location had been discovered when the reinforcement cue was presented in the affected field. These results indicate that the visual signals mediated by the residual visual pathways after V1 lesions can access fundamental reinforcement mechanisms but with impaired visual awareness.

Optimal policy for attention-modulated decisions explains human fixation behavior

Traditional accumulation-to-bound decision-making models assume that all choice options are processed with equal attention. In real life decisions, however, humans alternate their visual fixation between individual items to efficiently gather relevant information (Yang et al., 2016). These fixations also causally affect one's choices, biasing them toward the longer-fixated item (Krajbich et al., 2010). We derive a normative decision-making model in which attention enhances the reliability of information, consistent with neurophysiological findings (Cohen and Maunsell, 2009). Furthermore, our model actively controls fixation changes to optimize information gathering. We show that the optimal model reproduces fixation-related choice biases seen in humans and provides a Bayesian computational rationale for this phenomenon. This insight led to additional predictions that we could confirm in human data. Finally, by varying the relative cognitive advantage conferred by attention, we show that decision performance is benefited by a balanced spread of resources between the attended and unattended items.

A World Unto Itself: Human Communication as Active Inference

Recent theoretical work in developmental psychology suggests that humans are predisposed to align their mental states with those of other individuals. One way this manifests is in cooperative communication; that is, intentional communication aimed at aligning individuals' mental states with respect to events in their shared environment. This idea has received strong empirical support. The purpose of this paper is to extend this account by proposing an integrative model of the biobehavioral dynamics of cooperative communication. Our formulation is based on active inference. Active inference suggests that action-perception cycles operate to minimize uncertainty and optimize an individual's internal model of the world. We propose that humans are characterized by an evolved adaptive prior belief that their mental states are aligned with, or similar to, those of conspecifics (i.e., that 'we are the same sort of creature, inhabiting the same sort of niche'). The use of cooperative communication emerges as the principal means to gather evidence for this belief, allowing for the development of a shared narrative that is used to disambiguate interactants' (hidden and inferred) mental states. Thus, by using cooperative communication, individuals effectively attune to a hermeneutic niche composed, in part, of others' mental states; and, reciprocally, attune the niche to their own ends via epistemic niche construction. This means that niche construction enables features of the niche to encode precise, reliable cues about the deontic or shared value of certain action policies (e.g., the utility of using communicative constructions to disambiguate mental states, given expectations about shared prior beliefs). In turn, the alignment of mental states (prior beliefs) enables the emergence of a novel, contextualizing scale of cultural dynamics that encompasses the actions and mental states of the ensemble of interactants and their shared environment. The dynamics of this contextualizing layer of cultural organization feedback, across scales, to constrain the variability of the prior expectations of the individuals who constitute it. Our theory additionally builds upon the active inference literature by introducing a new set of neurobiologically plausible computational hypotheses for cooperative communication. We conclude with directions for future research.

Attentional avoidance of threatening stimuli

Aversive conditioning has been shown to influence the control of attention, such that aversively conditioned stimuli receive elevated priority. Although aversively conditioned but task-irrelevant distractors are known to capture attention during speeded search in rapid orienting tasks, it is unclear whether this bias extends to situations where orienting can be more deliberate. We demonstrate that punishment, via electric shock, does not give rise to oculomotor capture by shock-associated stimuli during a foraging task; rather, such aversively conditioned stimuli are actively avoided when searching through a display. On the other hand, even during a foraging task, we found some evidence for a covert attentional bias to threat. Our findings indicate that the previously described effects of aversive conditioning on visual search may not generalize beyond the initial glance and can be suppressed when conditions allow for more deliberate search strategies. More generally, our findings reveal that sustained attentional avoidance of aversively conditioned stimuli is possible during active search.

Correlation of neural activity with behavioral kinematics reveals distinct sensory encoding and evidence accumulation processes during active tactile sensing

Many real-world decisions rely on active sensing, a dynamic process for directing our sensors (e.g. eyes or fingers) across a stimulus to maximize information gain. Though ecologically pervasive, limited work has focused on identifying neural correlates of the active sensing process. In tactile perception, we often make decisions about an object/surface by actively exploring its shape/texture. Here we investigate the neural correlates of active tactile decision-making by simultaneously measuring electroencephalography (EEG) and finger kinematics while subjects interrogated a haptic surface to make perceptual judgments. Since sensorimotor behavior underlies decision formation in active sensing tasks, we hypothesized that the neural correlates of decision-related processes would be detectable by relating active sensing to neural activity. Novel brain-behavior correlation analysis revealed that three distinct EEG components, localizing to right-lateralized occipital cortex (LOC), middle frontal gyrus (MFG), and supplementary motor area (SMA), respectively, were coupled with active sensing as their activity significantly correlated with finger kinematics. To probe the functional role of these components, we fit their single-trial-couplings to decision-making performance using a hierarchical-drift-diffusion-model (HDDM), revealing that the LOC modulated the encoding of the tactile stimulus whereas the MFG predicted the rate of information integration towards a choice. Interestingly, the MFG disappeared from components uncovered from control subjects performing active sensing but not required to make perceptual decisions. By uncovering the neural correlates of distinct stimulus encoding and evidence accumulation processes, this study delineated, for the first time, the functional role of cortical areas in active tactile decision-making.

Humans quickly learn to blink strategically in response to environmental task demands

Eye blinking is one of the most frequent human actions. The control of blinking is thought to reflect complex interactions between maintaining clear and healthy vision and influences tied to central dopaminergic functions including cognitive states, psychological factors, and medical conditions. The most imminent consequence of blinking is a temporary loss of vision. Minimizing this loss of information is a prominent explanation for changes in blink rates and temporarily suppressed blinks, but quantifying this loss is difficult, as environmental regularities are usually complex and unknown. Here we used a controlled detection experiment with parametrically generated event statistics to investigate human blinking control. Subjects were able to learn environmental regularities and adapted their blinking behavior strategically to better detect future events. Crucially, our design enabled us to develop a computational model that allows quantifying the consequence of blinking in terms of task performance. The model formalizes ideas from active perception by describing blinking in terms of optimal control in trading off intrinsic costs for blink suppression with task-related costs for missing an event under perceptual uncertainty. Remarkably, this model not only is sufficient to reproduce key characteristics of the observed blinking behavior such as blink suppression and blink compensation but also predicts without further assumptions the well-known and diverse distributions of time intervals between blinks, for which an explanation has long been elusive.

Transcranial Direct Current Stimulation Facilitates Associative Learning and Alters Functional Connectivity in the Primate Brain

There has been growing interest in transcranial direct current stimulation (tDCS), a non-invasive technique purported to modulate neural activity via weak, externally applied electric fields. Although some promising preliminary data have been reported for applications ranging from stroke rehabilitation to cognitive enhancement, little is known about how tDCS affects the human brain, and some studies have concluded that it may have no effect at all. Here, we describe a macaque model of tDCS that allows us to simultaneously examine the effects of tDCS on brain activity and behavior. We find that applying tDCS to right prefrontal cortex improves monkeys' performance on an associative learning task. While firing rates do not change within the targeted area, tDCS does induce large low-frequency oscillations in the underlying tissue. These oscillations alter functional connectivity, both locally and between distant brain areas, and these long-range changes correlate with tDCS's effects on behavior. Together, these results are consistent with the idea that tDCS leads to widespread changes in brain activity and suggest that it may be a valuable method for cheaply and non-invasively altering functional connectivity in humans.

Benefits of fading in perceptual learning are driven by more than dimensional attention

Individuals learn to classify percepts effectively when the task is initially easy and then gradually increases in difficulty. Some suggest that this is because easy-to-discriminate events help learners focus attention on discrimination-relevant dimensions. Here, we tested whether such attentional-spotlighting accounts are sufficient to explain easy-to-hard effects in auditory perceptual learning. In two experiments, participants were trained to discriminate periodic, frequency-modulated (FM) tones in two separate frequency ranges (300–600 Hz or 3000–6000 Hz). In one frequency range, sounds gradually increased in similarity as training progressed. In the other, stimulus similarity was constant throughout training. After training, participants showed better performance in their progressively trained frequency range, even though the discrimination-relevant dimension across ranges was the same. Learning theories that posit experience-dependent changes in stimulus representations and/or the strengthening of associations with differential responses, predict the observed specificity of easy-to-hard effects, whereas attentional-spotlighting theories do not. Calibrating the difficulty and temporal sequencing of training experiences to support more incremental representation-based learning can enhance the effectiveness of practice beyond any benefits gained from explicitly highlighting relevant dimensions.

Learning rational temporal eye movement strategies

During active behavior humans redirect their gaze several times every second within the visual environment. Where we look within static images is highly efficient, as quantified by computational models of human gaze shifts in visual search and face recognition tasks. However, when we shift gaze is mostly unknown despite its fundamental importance for survival in a dynamic world. It has been suggested that during naturalistic visuomotor behavior gaze deployment is coordinated with task-relevant events, often predictive of future events, and studies in sportsmen suggest that timing of eye movements is learned. Here we establish that humans efficiently learn to adjust the timing of eye movements in response to environmental regularities when monitoring locations in the visual scene to detect probabilistically occurring events. To detect the events humans adopt strategies that can be understood through a computational model that includes perceptual and acting uncertainties, a minimal processing time, and, crucially, the intrinsic costs of gaze behavior. Thus, subjects traded off event detection rate with behavioral costs of carrying out eye movements. Remarkably, based on this rational bounded actor model the time course of learning the gaze strategies is fully explained by an optimal Bayesian learner with humans' characteristic uncertainty in time estimation, the well-known scalar law of biological timing. Taken together, these findings establish that the human visual system is highly efficient in learning temporal regularities in the environment and that it can use these regularities to control the timing of eye movements to detect behaviorally relevant events.

Active sensing in the categorization of visual patterns

Interpreting visual scenes typically requires us to accumulate information from multiple locations in a scene. Using a novel gaze-contingent paradigm in a visual categorization task, we show that participants' scan paths follow an active sensing strategy that incorporates information already acquired about the scene and knowledge of the statistical structure of patterns. Intriguingly, categorization performance was markedly improved when locations were revealed to participants by an optimal Bayesian active sensor algorithm. By using a combination of a Bayesian ideal observer and the active sensor algorithm, we estimate that a major portion of this apparent suboptimality of fixation locations arises from prior biases, perceptual noise and inaccuracies in eye movements, and the central process of selecting fixation locations is around 70% efficient in our task. Our results suggest that participants select eye movements with the goal of maximizing information about abstract categories that require the integration of information from multiple locations.

DOI:http://dx.doi.org/10.7554/eLife.12215.001

To interact with the world around us, we need to decide how best to direct our eyes and other senses to extract relevant information. When viewing a scene, people fixate on a sequence of locations by making fast eye movements to shift their gaze between locations. Previous studies have shown that these fixations are not random, but are actively chosen so that they depend on both the scene and the task. For example, in order to determine the gender or emotion from a face, we fixate around the eyes or the nose, respectively.

Previous studies have only analyzed whether humans choose the optimal fixation locations in very simple situations, such as searching for a square among a set of circles. Therefore, it is not known how efficient we are at optimizing our rapid eye movements to extract high-level information from visual scenes, such as determining whether an image of fur belongs to a cheetah or a zebra.

Yang, Lengyel and Wolpert developed a mathematical model that determines the amount of information that can be extracted from an image by any set of fixation locations. The model could also work out the next best fixation location that would maximize the amount of information that could be collected. This model shows that humans are about 70% efficient in planning each eye movement. Furthermore, it suggests that the inefficiencies are largely caused by imperfect vision and inaccurate eye movements.

Yang, Lengyel and Wolpert’s findings indicate that we combine information from multiple locations to direct our eye movements so that we can maximize the information we collect from our surroundings. The next challenge is to extend this mathematical model and experimental approach to even more complex visual tasks, such as judging an individual’s intentions, or working out the relationships between people in real-life settings.

DOI:http://dx.doi.org/10.7554/eLife.12215.002

Using Highlighting to Train Attentional Expertise

Acquiring expertise in complex visual tasks is time consuming. To facilitate the efficient training of novices on where to look in these tasks, we propose an attentional highlighting paradigm. Highlighting involves dynamically modulating the saliency of a visual image to guide attention along the fixation path of a domain expert who had previously viewed the same image. In Experiment 1, we trained naive subjects via attentional highlighting on a fingerprint-matching task. Before and after training, we asked subjects to freely inspect images containing pairs of prints and determine whether the prints matched. Fixation sequences were automatically scored for the degree of expertise exhibited using a Bayesian discriminative model of novice and expert gaze behavior. Highlighted training causes gaze behavior to become more expert-like not only on the trained images but also on transfer images, indicating generalization of learning. In Experiment 2, to control for the possibility that the increase in expertise is due to mere exposure, we trained subjects via highlighting of fixation sequences from novices, not experts, and observed no transition toward expertise. In Experiment 3, to determine the specificity of the training effect, we trained subjects with expert fixation sequences from images other than the one being viewed, which preserves coarse-scale statistics of expert gaze but provides no information about fine-grain features. Observing at least a partial transition toward expertise, we obtain only weak evidence that the highlighting procedure facilitates the learning of critical local features. We discuss possible improvements to the highlighting procedure.

Habit Learning by Naive Macaques Is Marked by Response Sharpening of Striatal Neurons Representing the Cost and Outcome of Acquired Action Sequences

Over a century of scientific work has focused on defining the factors motivating behavioral learning. Observations in animals and humans trained on a wide range of tasks support reinforcement learning (RL) algorithms as accounting for the learning. Still unknown, however, are the signals that drive learning in naive, untrained subjects. Here, we capitalized on a sequential saccade task in which macaque monkeys acquired repetitive scanning sequences without instruction. We found that spike activity in the caudate nucleus after each trial corresponded to an integrated cost-benefit signal that was highly correlated with the degree of naturalistic untutored learning by the monkeys. Across learning, neurons encoding both cost and outcome gradually acquired increasingly sharp phasic trial-end responses that paralleled the development of the habit-like, repetitive saccade sequences. Our findings demonstrate an integrated cost-benefit signal by which RL and its neural correlates could drive naturalistic behaviors in freely behaving primates.

Prospective Optimization with Limited Resources

The future is uncertain because some forthcoming events are unpredictable and also because our ability to foresee the myriad consequences of our own actions is limited. Here we studied how humans select actions under such extrinsic and intrinsic uncertainty, in view of an exponentially expanding number of prospects on a branching multivalued visual stimulus. A triangular grid of disks of different sizes scrolled down a touchscreen at a variable speed. The larger disks represented larger rewards. The task was to maximize the cumulative reward by touching one disk at a time in a rapid sequence, forming an upward path across the grid, while every step along the path constrained the part of the grid accessible in the future. This task captured some of the complexity of natural behavior in the risky and dynamic world, where ongoing decisions alter the landscape of future rewards. By comparing human behavior with behavior of ideal actors, we identified the strategies used by humans in terms of how far into the future they looked (their “depth of computation”) and how often they attempted to incorporate new information about the future rewards (their “recalculation period”). We found that, for a given task difficulty, humans traded off their depth of computation for the recalculation period. The form of this tradeoff was consistent with a complete, brute-force exploration of all possible paths up to a resource-limited finite depth. A step-by-step analysis of the human behavior revealed that participants took into account very fine distinctions between the future rewards and that they abstained from some simple heuristics in assessment of the alternative paths, such as seeking only the largest disks or avoiding the smaller disks. The participants preferred to reduce their depth of computation or increase the recalculation period rather than sacrifice the precision of computation.

We investigated the human ability to organize behavior prospectively, for multiple future steps in risky, dynamic environments. In a setting that resembled a video game, participants selected the most rewarding paths traversing a triangular lattice of disks of different sizes, while the lattice scrolled down a touchscreen at a constant speed. Disk sizes represented the rewards; missing a disk incurred a penalty. Every choice excluded a number of the disks accessible in the future, encouraging subjects to examine prospective paths as far into the future as they could. In contrast to previous evidence that humans tend to reduce the computational difficulty of decision making by means of simplifying heuristics, our participants appeared to perform an exhaustive computation of all possible future scenarios within a horizon limited by a fixed number of computations. Under increasing time pressure, participants either reduced the computational horizon or recalculated the expected rewards less frequently, revealing a resource-limited ability for rapid detailed computation of prospective actions. To perform such intensive computations, participants could take advantage of the massively parallel neural architecture of the visual system allowing one to concurrently process information from multiple retinal locations.

Statistical learning modulates the direction of the first head movement in a large-scale search task

Foraging and search tasks in everyday activities are often performed in large, open spaces, necessitating head and body movements. Such activities are rarely studied in the laboratory, leaving important questions unanswered regarding the role of attention in large-scale tasks. Here we examined the guidance of visual attention by statistical learning in a large-scale, outdoor environment. We used the orientation of the first head movement as a proxy for spatial attention and examined its correspondence with reaction time (RT). Participants wore a lightweight camera on a baseball cap while searching for a coin on the concrete floor of a 64-m(2) outdoor space. We coded the direction of the first head movement at the start of a trial. The results showed that the first head movement was highly sensitive to the location probability of the coin and demonstrated more rapid adjustment to changes in environmental statistics than RTs did. Because the first head movement occurred ten times faster than the search RT, these results show that visual statistical learning affected attentional orienting early in large-scale tasks.

Efficient saccade planning requires time and clear choices

We use eye movements constantly to gather information. Saccades are efficient when they maximize the information required for the task, however there is controversy regarding the efficiency of eye movement planning. For example, saccades are efficient when searching for a single target (Nature, 434 (2005) 387-391), but are inefficient when searching for an unknown number of targets in noise, particularly under time pressure (Vision Research 74 (2012), 61-71). In this study, we used a multiple-target search paradigm and explored whether altering the noise level or increasing saccadic latency improved efficiency. Experiments used stimuli with two levels of discriminability such that saccades to the less discriminable stimuli provided more information. When these two noise levels corresponded to low and moderate visibility, most observers did not preferentially select informative locations, but looked at uncertain and probable target locations equally often. We then examined whether eye movements could be made more efficient by increasing the discriminability of the two stimulus levels and by delaying the first saccade so that there was more time for decision processes to influence the saccade choices. Some observers did indeed increase the proportion of their saccades to informative locations under these conditions. Others, however, made as many saccades as they could during the limited time and were unselective about the saccade goal. A clear trend that emerges across all experiments is that conditions with a greater proportion of efficient saccades are associated with a longer latency to initiate saccades, suggesting that the choice of informative locations requires deliberate planning.

Spatial scale, rather than nature of task or locomotion, modulates the spatial reference frame of attention

Visuospatial attention is strongly biased to locations that had frequently contained a search target before. However, the function of this bias depends on the reference frame in which attended locations are coded. Previous research has shown a striking difference between tasks administered on a computer monitor and those administered in a large environment, with the former inducing viewer-centered learning and the latter environment-centered learning. Why does environment-centered learning fail on a computer? Here, we tested 3 possibilities: differences in spatial scale, the nature of task, and locomotion may each influence the reference frame of attention. Participants searched for a target on a monitor placed flat on a stand. On each trial, they stood at a different location around the monitor. The target was frequently located in a fixed area of the monitor, but changes in participants' perspective rendered this area random relative to the participants. Under incidental learning conditions, participants failed to acquire environment-centered learning even when (a) the task and display resembled those of a large-scale task and (b) the search task required locomotion. The difficulty in inducing environment-centered learning on a computer underscores the egocentric nature of visual attention. It supports the idea that spatial scale modulates the reference frame of attention.

Consciousness

No one did more to draw neuroscientists' attention to the problem of consciousness in the twentieth century than Francis Crick, who may be better known as the co-discoverer (with James Watson) of the structure of DNA. Crick focused his research on visual awareness and based his analysis on the progress made over the last fifty years in uncovering the neural mechanisms underlying visual perception. Because much of what happens in our brains occurs below the level of consciousness and many of our intuitions about unconscious processing are misleading, consciousness remains an elusive problem. In the end, when all of the brain mechanisms that underlie consciousness have been identified, will we still be asking: "What is consciousness?" Or will the question shift, just as the question "What is life?" is no longer the same as it was before Francis Crick?

Changing viewer perspectives reveals constraints to implicit visual statistical learning

Statistical learning-learning environmental regularities to guide behavior-likely plays an important role in natural human behavior. One potential use is in search for valuable items. Because visual statistical learning can be acquired quickly and without intention or awareness, it could optimize search and thereby conserve energy. For this to be true, however, visual statistical learning needs to be viewpoint invariant, facilitating search even when people walk around. To test whether implicit visual statistical learning of spatial information is viewpoint independent, we asked participants to perform a visual search task from variable locations around a monitor placed flat on a stand. Unbeknownst to participants, the target was more often in some locations than others. In contrast to previous research on stationary observers, visual statistical learning failed to produce a search advantage for targets in high-probable regions that were stable within the environment but variable relative to the viewer. This failure was observed even when conditions for spatial updating were optimized. However, learning was successful when the rich locations were referenced relative to the viewer. We conclude that changing viewer perspective disrupts implicit learning of the target's location probability. This form of learning shows limited integration with spatial updating or spatiotopic representations.

Prospective Optimization

Human performance approaches that of an ideal observer and optimal actor in some perceptual and motor tasks. These optimal abilities depend on the capacity of the cerebral cortex to store an immense amount of information and to flexibly make rapid decisions. However, behavior only approaches these limits after a long period of learning while the cerebral cortex interacts with the basal ganglia, an ancient part of the vertebrate brain that is responsible for learning sequences of actions directed toward achieving goals. Progress has been made in understanding the algorithms used by the brain during reinforcement learning, which is an online approximation of dynamic programming. Humans also make plans that depend on past experience by simulating different scenarios, which is called prospective optimization. The same brain structures in the cortex and basal ganglia that are active online during optimal behavior are also active offline during prospective optimization. The emergence of general principles and algorithms for goal-directed behavior has consequences for the development of autonomous devices in engineering applications.

Learning optimal eye movements to unusual faces

Eye movements, which guide the fovea's high resolution and computational power to relevant areas of the visual scene, are integral to efficient, successful completion of many visual tasks. How humans modify their eye movements through experience with their perceptual environments, and its functional role in learning new tasks, has not been fully investigated. Here, we used a face identification task where only the mouth discriminated exemplars to assess if, how, and when eye movement modulation may mediate learning. By interleaving trials of unconstrained eye movements with trials of forced fixation, we attempted to separate the contributions of eye movements and covert mechanisms to performance improvements. Without instruction, a majority of observers substantially increased accuracy and learned to direct their initial eye movements towards the optimal fixation point. The proximity of an observer's default face identification eye movement behavior to the new optimal fixation point and the observer's peripheral processing ability were predictive of performance gains and eye movement learning. After practice in a subsequent condition in which observers were directed to fixate different locations along the face, including the relevant mouth region, all observers learned to make eye movements to the optimal fixation point. In this fully learned state, augmented fixation strategy accounted for 43% of total efficiency improvements while covert mechanisms accounted for the remaining 57%. The findings suggest a critical role for eye movement planning to perceptual learning, and elucidate factors that can predict when and how well an observer can learn a new task with unusual exemplars.

Saccades during visual exploration align hippocampal 3-8 Hz rhythms in human and non-human primates

Visual exploration in primates depends on saccadic eye movements (SEMs) that cause alternations of neural suppression and enhancement. This modulation extends beyond retinotopic areas, and is thought to facilitate perception; yet saccades may also influence brain regions critical for forming memories of these exploratory episodes. The hippocampus, for example, shows oscillatory activity that is generally associated with encoding of information. Whether or how hippocampal oscillations are influenced by eye movements is unknown. We recorded the neural activity in the human and macaque hippocampus during visual scene search. Across species, SEMs were associated with a time-limited alignment of a low-frequency (3–8 Hz) rhythm. The phase alignment depended on the task and not only on eye movements per se, and the frequency band was not a direct consequence of saccade rate. Hippocampal theta-frequency oscillations are produced by other mammals during repetitive exploratory behaviors, including whisking, sniffing, echolocation, and locomotion. The present results may reflect a similar yet distinct primate homologue supporting active perception during exploration.