Bayesian and Discriminative Models for Active Visual Perception across Saccades.

The brain interprets sensory inputs to guide behavior, but behavior itself disrupts sensory inputs. Perceiving a coherent world while acting in it constitutes active perception. For example, saccadic eye movements displace visual images on the retina and yet the brain perceives visual stability. Because this percept of visual stability has been shown to be influenced by prior expectations, we tested the hypothesis that it is Bayesian. The key prediction was that priors would be used more as sensory uncertainty increases. Humans and rhesus macaques reported whether an image moved during saccades. We manipulated both prior expectations and levels of sensory uncertainty. All psychophysical data were compared with the predictions of Bayesian ideal observer models. We found that humans were Bayesian for continuous judgments. For categorical judgments, however, they were anti-Bayesian: they used their priors less with greater uncertainty. We studied this categorical result further in macaques. The animals' judgments were similarly anti-Bayesian for sensory uncertainty caused by external, image noise, but Bayesian for uncertainty due to internal, motor-driven noise. A discriminative learning model explained the anti-Bayesian effects. We conclude that active vision uses both Bayesian and discriminative models depending on task requirements (continuous vs categorical) and the source of uncertainty (image noise vs motor-driven noise). In the context of previous knowledge about the saccadic system, our results provide an example of how the comparative analysis of Bayesian versus non-Bayesian models of perception offers novel insights into underlying neural organization.

Neuronal mechanisms of dynamic strategic competition.

Competitive social interactions, as in chess or poker, often involve multiple moves and countermoves deployed tactically within a broader strategic plan. Such maneuvers are supported by mentalizing or theory-of-mind-reasoning about the beliefs, plans, and goals of an opponent. The neuronal mechanisms underlying strategic competition remain largely unknown. To address this gap, we studied humans and monkeys playing a virtual soccer game featuring continuous competitive interactions. Humans and monkeys deployed similar tactics within broadly identical strategies, which featured unpredictable trajectories and precise timing for kickers, and responsiveness to opponents for goalies. We used Gaussian Process (GP) classification to decompose continuous gameplay into a series of discrete decisions predicated on the evolving states of self and opponent. We extracted relevant model parameters as regressors for neuronal activity in macaque mid-superior temporal sulcus (mSTS), the putative homolog of human temporo-parietal junction (TPJ), an area selectively engaged during strategic social interactions. We discovered two spatially-segregated populations of mSTS neurons that signaled actions of self and opponent, sensitivities to state changes, and previous and current trial outcomes. Inactivating mSTS reduced kicker unpredictability and impaired goalie responsiveness. These findings demonstrate mSTS neurons multiplex information about the current states of self and opponent as well as history of previous interactions to support ongoing strategic competition, consistent with hemodynamic activity found in human TPJ.

Social cognitive processes explain bias in juror decisions.

Jury decisions are among the most consequential social decisions in which bias plays a notable role. While courts take measures to reduce the influence of non-evidentiary factors, jurors may still incorporate biases into their decisions. One common bias, crime-type bias, is the extent to which the perceived strength of a prosecutor's case depends on the severity of the crime. Moral judgment, affect and social cognition have been proposed as core processes underlying this and other biases. Behavioral evidence alone has been insufficient to distinguish these explanations. To identify the mechanism underlying crime-type bias, we collected functional magnetic resonance imaging patterns of brain activation from mock jurors reading criminal scenarios. Brain patterns from crime-type bias were most similar to those associated with social cognition (mentalizing and racial bias) but not affect or moral judgment. Our results support a central role for social cognition in juror decisions and suggest that crime-type bias and cultural bias may arise from similar mechanisms.

Efficient coding, channel capacity, and the emergence of retinal mosaics.

Among the most striking features of retinal organization is the grouping of its output neurons, the retinal ganglion cells (RGCs), into a diversity of functional types. Each of these types exhibits a mosaic-like organization of receptive fields (RFs) that tiles the retina and visual space. Previous work has shown that many features of RGC organization, including the existence of ON and OFF cell types, the structure of spatial RFs, and their relative arrangement, can be predicted on the basis of efficient coding theory. This theory posits that the nervous system is organized to maximize information in its encoding of stimuli while minimizing metabolic costs. Here, we use efficient coding theory to present a comprehensive account of mosaic organization in the case of natural videos as the retinal channel capacity-the number of simulated RGCs available for encoding-is varied. We show that mosaic density increases with channel capacity up to a series of critical points at which, surprisingly, new cell types emerge. Each successive cell type focuses on increasingly high temporal frequencies and integrates signals over larger spatial areas. In addition, we show theoretically and in simulation that a transition from mosaic alignment to anti-alignment across pairs of cell types is observed with increasing output noise and decreasing input noise. Together, these results offer a unified perspective on the relationship between retinal mosaics, efficient coding, and channel capacity that can help to explain the stunning functional diversity of retinal cell types.

Neural Support for Contributions of Utility and Narrative Processing of Evidence in Juror Decision Making.

Efforts to explain complex human decisions have focused on competing theories emphasizing utility and narrative mechanisms. These are difficult to distinguish using behavior alone. Both narrative and utility theories have been proposed to explain juror decisions, which are among the most consequential complex decisions made in a modern society. Here, we asked jury-eligible male and female subjects to rate the strength of a series of criminal cases while recording the resulting patterns of brain activation. We compared patterns of brain activation associated with evidence accumulation to patterns of brain activation derived from a large neuroimaging database to look for signatures of the cognitive processes associated with different models of juror decision-making. Evidence accumulation correlated with multiple narrative processes, including reading and recall. Of the cognitive processes traditionally viewed as components of utility, activation patterns associated with uncertainty, but not value, were more active with stronger evidence. Independent of utility and narrative, activations linked to reasoning and relational logic also correlated with increasing evidence. Hierarchical modeling of cognitive processes associated with evidence accumulation supported a more prominent role for narrative in weighing evidence in complex decisions. However, utility processes were also associated with evidence accumulation. These complementary findings support an emerging view that integrates utility and narrative processes in complex decisions.SIGNIFICANCE STATEMENT The last decade has seen a sharply increased interest in narrative as a central cognitive process in human decision-making and as an important factor in the evolution of human societies. However, the roles of narrative versus utility models of decision-making remain hotly debated. While available models frequently produce similar behavioral predictions, they rely on different cognitive processes and so their roles can be separated using the right neural tests. Here, we use brain imaging during mock juror decisions to show that cognitive processes associated with narrative, and to a lesser extent utility, were engaged while subjects evaluated evidence. These results are consistent with interactions between narrative and utility processes during complex decision-making.

Machine learning prediction of neurocognitive impairment among people with HIV using clinical and multimodal magnetic resonance imaging data.

Diagnosis of HIV-associated neurocognitive impairment (NCI) continues to be a clinical challenge. The purpose of this study was to develop a prediction model for NCI among people with HIV using clinical- and magnetic resonance imaging (MRI)-derived features. The sample included 101 adults with chronic HIV disease. NCI was determined using a standardized neuropsychological testing battery comprised of seven domains. MRI features included gray matter volume from high-resolution anatomical scans and white matter integrity from diffusion-weighted imaging. Clinical features included demographics, substance use, and routine laboratory tests. Least Absolute Shrinkage and Selection Operator Logistic regression was used to perform variable selection on MRI features. These features were subsequently used to train a support vector machine (SVM) to predict NCI. Three different classification tasks were performed: one used only clinical features; a second used only selected MRI features; a third used both clinical and selected MRI features. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity with a tenfold cross-validation. The SVM classifier that combined selected MRI with clinical features outperformed the model using clinical features or MRI features alone (AUC: 0.83 vs. 0.62 vs. 0.79; accuracy: 0.80 vs. 0.65 vs. 0.72; sensitivity: 0.86 vs. 0.85 vs. 0.86; specificity: 0.71 vs. 0.37 vs. 0.52). Our results provide preliminary evidence that combining clinical and MRI features can increase accuracy in predicting NCI and could be developed as a potential tool for NCI diagnosis in HIV clinical practice.

Neurons in primate prefrontal cortex signal valuable social information during natural viewing.

Information about social partners is innately valuable to primates. Decisions about which sources of information to consume are highly naturalistic but also complex and place unusually strong demands on the brain's decision network. In particular, both the orbitofrontal cortex (OFC) and lateral prefrontal cortex (LPFC) play key roles in decision making and social behaviour, suggesting a likely role in social information-seeking as well. To test this idea, we developed a 'channel surfing' task in which monkeys were shown a series of 5 s video clips of conspecifics engaged in natural behaviours at a field site. Videos were annotated frame-by-frame using an ethogram of species-typical behaviours, an important source of social information. Between each clip, monkeys were presented with a choice between targets that determined which clip would be seen next. Monkeys' gaze during playback indicated differential engagement depending on what behaviours were presented. Neurons in both OFC and LPFC responded to choice targets and to video, and discriminated a subset of the behaviours in the ethogram during video viewing. These findings suggest that both OFC and LPFC are engaged in processing social information that is used to guide dynamic information-seeking decisions. This article is part of the theme issue 'Existence and prevalence of economic behaviours among non-human primates'.

Continuous decisions.

Humans and other animals evolved to make decisions that extend over time with continuous and ever-changing options. Nonetheless, the academic study of decision-making is mostly limited to the simple case of choice between two options. Here, we advocate that the study of choice should expand to include continuous decisions. Continuous decisions, by our definition, involve a continuum of possible responses and take place over an extended period of time during which the response is continuously subject to modification. In most continuous decisions, the range of options can fluctuate and is affected by recent responses, making consideration of reciprocal feedback between choices and the environment essential. The study of continuous decisions raises new questions, such as how abstract processes of valuation and comparison are co-implemented with action planning and execution, how we simulate the large number of possible futures our choices lead to, and how our brains employ hierarchical structure to make choices more efficiently. While microeconomic theory has proven invaluable for discrete decisions, we propose that engineering control theory may serve as a better foundation for continuous ones. And while the concept of value has proven foundational for discrete decisions, goal states and policies may prove more useful for continuous ones. This article is part of the theme issue 'Existence and prevalence of economic behaviours among non-human primates'.

Attention-deficit/hyperactivity disorder and the explore/exploit trade-off.

The ability to maximize rewards and minimize the costs of obtaining them is vital to making advantageous explore/exploit decisions. Exploratory decisions are theorized to be greater among individuals with attention-deficit/hyperactivity disorder (ADHD), potentially due to deficient catecholamine transmission. Here, we examined the effects of ADHD status and methylphenidate, a common ADHD medication, on explore/exploit decisions using a 6-armed bandit task. We hypothesized that ADHD participants would make more exploratory decisions than controls, and that MPH would reduce group differences. On separate study days, adults with (n = 26) and without (n = 23) ADHD completed the bandit task at baseline, and after methylphenidate or placebo in counter-balanced order. Explore/exploit decisions were modeled using reinforcement learning algorithms. ADHD participants made more exploratory decisions (i.e., chose options without the highest expected reward value) and earned fewer points than controls in all three study days, and methylphenidate did not affect these outcomes. Baseline exploratory choices were positively associated with hyperactive ADHD symptoms across all participants. These results support several theoretical models of increased exploratory choices in ADHD and suggest the unexplained variance in ADHD decisions may be due to less value tracking. The inability to suppress actions with little to no reward value may be a key feature of hyperactive ADHD symptoms.

Monkeys and humans implement causal inference to simultaneously localize auditory and visual stimuli.

The environment is sampled by multiple senses, which are woven together to produce a unified perceptual state. However, optimally unifying such signals requires assigning particular signals to the same or different underlying objects or events. Many prior studies (especially in animals) have assumed fusion of cross-modal information, whereas recent work in humans has begun to probe the appropriateness of this assumption. Here we present results from a novel behavioral task in which both monkeys (Macaca mulatta) and humans localized visual and auditory stimuli and reported their perceived sources through saccadic eye movements. When the locations of visual and auditory stimuli were widely separated, subjects made two saccades, while when the two stimuli were presented at the same location they made only a single saccade. Intermediate levels of separation produced mixed response patterns: a single saccade to an intermediate position on some trials or separate saccades to both locations on others. The distribution of responses was well described by a hierarchical causal inference model that accurately predicted both the explicit "same vs. different" source judgments as well as biases in localization of the source(s) under each of these conditions. The results from this task are broadly consistent with prior work in humans across a wide variety of analogous tasks, extending the study of multisensory causal inference to nonhuman primates and to a natural behavioral task with both a categorical assay of the number of perceived sources and a continuous report of the perceived position of the stimuli.NEW & NOTEWORTHY We developed a novel behavioral paradigm for the study of multisensory causal inference in both humans and monkeys and found that both species make causal judgments in the same Bayes-optimal fashion. To our knowledge, this is the first demonstration of behavioral causal inference in animals, and this cross-species comparison lays the groundwork for future experiments using neuronal recording techniques that are impractical or impossible in human subjects.

Dorsolateral and dorsomedial prefrontal cortex track distinct properties of dynamic social behavior.

Understanding how humans make competitive decisions in complex environments is a key goal of decision neuroscience. Typical experimental paradigms constrain behavioral complexity (e.g. choices in discrete-play games), and thus, the underlying neural mechanisms of dynamic social interactions remain incompletely understood. Here, we collected fMRI data while humans played a competitive real-time video game against both human and computer opponents, and then, we used Bayesian non-parametric methods to link behavior to neural mechanisms. Two key cognitive processes characterized behavior in our task: (i) the coupling of one's actions to another's actions (i.e. opponent sensitivity) and (ii) the advantageous timing of a given strategic action. We found that the dorsolateral prefrontal cortex displayed selective activation when the subject's actions were highly sensitive to the opponent's actions, whereas activation in the dorsomedial prefrontal cortex increased proportionally to the advantageous timing of actions to defeat one's opponent. Moreover, the temporoparietal junction tracked both of these behavioral quantities as well as opponent social identity, indicating a more general role in monitoring other social agents. These results suggest that brain regions that are frequently implicated in social cognition and value-based decision-making also contribute to the strategic tracking of the value of social actions in dynamic, multi-agent contexts.

Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game.

Previous studies of strategic social interaction in game theory have predominantly used games with clearly-defined turns and limited choices. Yet, most real-world social behaviors involve dynamic, coevolving decisions by interacting agents, which poses challenges for creating tractable models of behavior. Here, using a game in which humans competed against both real and artificial opponents, we show that it is possible to quantify the instantaneous dynamic coupling between agents. Adopting a reinforcement learning approach, we use Gaussian Processes to model the policy and value functions of participants as a function of both game state and opponent identity. We found that higher-scoring participants timed their final change in direction to moments when the opponent's counter-strategy was weaker, while lower-scoring participants less precisely timed their final moves. This approach offers a natural set of metrics for facilitating analysis at multiple timescales and suggests new classes of experimental paradigms for assessing behavior.

Latent goal models for dynamic strategic interaction.

Understanding the principles by which agents interact with both complex environments and each other is a key goal of decision neuroscience. However, most previous studies have used experimental paradigms in which choices are discrete (and few), play is static, and optimal solutions are known. Yet in natural environments, interactions between agents typically involve continuous action spaces, ongoing dynamics, and no known optimal solution. Here, we seek to bridge this divide by using a "penalty shot" task in which pairs of monkeys competed against each other in a competitive, real-time video game. We modeled monkeys' strategies as driven by stochastically evolving goals, onscreen positions that served as set points for a control model that produced observed joystick movements. We fit this goal-based dynamical system model using approximate Bayesian inference methods, using neural networks to parameterize players' goals as a dynamic mixture of Gaussian components. Our model is conceptually simple, constructed of interpretable components, and capable of generating synthetic data that capture the complexity of real player dynamics. We further characterized players' strategies using the number of change points on each trial. We found that this complexity varied more across sessions than within sessions, and that more complex strategies benefited offensive players but not defensive players. Together, our experimental paradigm and model offer a powerful combination of tools for the study of realistic social dynamics in the laboratory setting.

Feedback-Based Learning in Aging: Contributions and Trajectories of Change in Striatal and Hippocampal Systems.

The striatum supports learning from immediate feedback by coding prediction errors (PEs), whereas the hippocampus (HC) plays a parallel role in learning from delayed feedback. Both regions show evidence of decline in human aging, but behavioral research suggests greater decline in HC versus striatal functions. The present study included male and female humans and used fMRI to examine younger and older adults' brain activation patterns during a learning task with choice feedback presented immediately or after a brief delay. Participants then completed a surprise memory task that tested their recognition of trial-unique feedback stimuli, followed by assessments of postlearning cue preference, outcome probability awareness, and willingness to pay. The study yielded three main findings. First, behavioral measures indicated similar rates of learning in younger and older adults across conditions, but postlearning measures indicated impairment in older adults' ability to subsequently apply learning to discriminate between cues. Second, PE signals in the striatum were greater for immediate versus delayed feedback in both age groups, but PE signals in the HC were greater for delayed versus immediate feedback only in younger adults. Third, unlike younger adults, older adults failed to exhibit enhanced episodic memory for outcome stimuli in the delayed-feedback condition. Together, these findings indicate that HC circuits supporting learning and memory decline more than striatal circuits in healthy aging, which suggests that declines in HC learning signals may be an important predictor of deficits in learning-dependent economic decisions among older adults.SIGNIFICANCE STATEMENT The hippocampus (HC) and striatum play distinct and critical roles in learning. Substantial research suggests that age-related decline in learning supported by the HC outpaces decline in learning supported by the striatum; however, such inferences have been drawn by comparing performance in tasks with fundamentally different structures. The present study overcomes this obstacle by implementing a single fMRI-learning paradigm with a subtle variation in feedback timing to examine differential age effects on memory supported by the HC and striatum. Our results provide converging behavioral and brain-imaging evidence showing that HC circuits supporting learning and memory decline more than striatal circuits in healthy aging and that declines in HC learning signals may predict early deficits in learning-dependent decisions among older adults.

Modelling the effects of crime type and evidence on judgments about guilt.

Concerns over wrongful convictions have spurred an increased focus on understanding criminal justice decision-making. This study describes an experimental approach that complements conventional mock-juror experiments and case studies by providing a rapid, high-throughput screen for identifying preconceptions and biases that can influence how jurors and lawyers evaluate evidence in criminal cases. The approach combines an experimental decision task derived from marketing research with statistical modeling to explore how subjects evaluate the strength of the case against a defendant. The results show that, in the absence of explicit information about potential error rates or objective reliability, subjects tend to overweight widely used types of forensic evidence, but give much less weight than expected to a defendant's criminal history. Notably, for mock jurors, the type of crime also biases their confidence in guilt independent of the evidence. This bias is positively correlated with the seriousness of the crime. For practicing prosecutors and other lawyers, the crime-type bias is much smaller, yet still correlates with the seriousness of the crime.

Local Fields in Human Subthalamic Nucleus Track the Lead-up to Impulsive Choices.

The ability to adaptively minimize not only motor but cognitive symptoms of neurological diseases, such as Parkinson's Disease (PD) and obsessive-compulsive disorder (OCD), is a primary goal of next-generation deep brain stimulation (DBS) devices. On the basis of studies demonstrating a link between beta-band synchronization and severity of motor symptoms in PD, the minimization of beta band activity has been proposed as a potential training target for closed-loop DBS. At present, no comparable signal is known for the impulsive side effects of PD, though multiple studies have implicated theta band activity within the subthalamic nucleus (STN), the site of DBS treatment, in processes of conflict monitoring and countermanding. Here, we address this challenge by recording from multiple independent channels within the STN in a self-paced decision task to test whether these signals carry information sufficient to predict stopping behavior on a trial-by-trial basis. As in previous studies, we found that local field potentials (LFPs) exhibited modulations preceding self-initiated movements, with power ramping across multiple frequencies during the deliberation period. In addition, signals showed phasic changes in power around the time of decision. However, a prospective model that attempted to use these signals to predict decision times showed effects of risk level did not improve with the addition of LFPs as regressors. These findings suggest information tracking the lead-up to impulsive choices is distributed across multiple frequency scales in STN, though current techniques may not possess sufficient signal-to-noise ratios to predict-and thus curb-impulsive behavior on a moment-to-moment basis.

Neuron's eye view: Inferring features of complex stimuli from neural responses.

Experiments that study neural encoding of stimuli at the level of individual neurons typically choose a small set of features present in the world-contrast and luminance for vision, pitch and intensity for sound-and assemble a stimulus set that systematically varies along these dimensions. Subsequent analysis of neural responses to these stimuli typically focuses on regression models, with experimenter-controlled features as predictors and spike counts or firing rates as responses. Unfortunately, this approach requires knowledge in advance about the relevant features coded by a given population of neurons. For domains as complex as social interaction or natural movement, however, the relevant feature space is poorly understood, and an arbitrary a priori choice of features may give rise to confirmation bias. Here, we present a Bayesian model for exploratory data analysis that is capable of automatically identifying the features present in unstructured stimuli based solely on neuronal responses. Our approach is unique within the class of latent state space models of neural activity in that it assumes that firing rates of neurons are sensitive to multiple discrete time-varying features tied to the stimulus, each of which has Markov (or semi-Markov) dynamics. That is, we are modeling neural activity as driven by multiple simultaneous stimulus features rather than intrinsic neural dynamics. We derive a fast variational Bayesian inference algorithm and show that it correctly recovers hidden features in synthetic data, as well as ground-truth stimulus features in a prototypical neural dataset. To demonstrate the utility of the algorithm, we also apply it to cluster neural responses and demonstrate successful recovery of features corresponding to monkeys and faces in the image set.

Decoding working memory content from attentional biases.

What we are currently thinking influences where we attend. The finding that active maintenance of visual items in working memory (WM) biases attention toward memory-matching objects-even when WM content is irrelevant for attentional goals-suggests a tight link between WM and attention. To test whether this link is reliable enough to infer specific WM content from measures of attentional bias, we applied multivariate pattern classification techniques to response times from an unrelated visual search task during a WM delay. Single-trial WM content was successfully decoded from incidental attentional bias within an individual, highlighting the specificity and reliability of the WM-attention link. Furthermore, classifiers trained on a group of individuals predicted WM content in another, completely independent individual-implying a shared cognitive mechanism of memory-driven attentional bias. The existence of such classifiers demonstrates that memory-based attentional bias is both a robust and generalizable probe of WM.

Altruistic traits are predicted by neural responses to monetary outcomes for self vs charity.

Human altruism is often expressed through charitable donation-supporting a cause that benefits others in society, at cost to oneself. The underlying mechanisms of this other-regarding behavior remain imperfectly understood. By recording event-related-potential (ERP) measures of brain activity from human participants during a social gambling task, we identified markers of differential responses to receipt of monetary outcomes for oneself vs for a charitable cause. We focused our ERP analyses on the frontocentral feedback-related negativity (FRN) and three subcomponents of the attention-related P300 (P3) brain wave: the frontocentral P2 and P3a and the parietal P3b. The FRN distinguished between gains and losses for both self and charity outcomes. Importantly, this effect of outcome valence was greater for self than charity for both groups and was independent of two altruism-related measures: participants' pre-declared intended donations and the actual donations resulting from their choices. In contrast, differences in P3 subcomponents for outcomes for self vs charity strongly predicted both of our laboratory measures of altruism-as well as self-reported engagement in real-life altruistic behaviors. These results indicate that individual differences in altruism are linked to individual differences in the relative deployment of attention (as indexed by the P3) toward outcomes affecting other people.