Recurrent Neural Circuits Overcome Partial Inactivation by Compensation and Re-learning

Technical advances in artificial manipulation of neural activity have precipitated a surge in studying the causal contribution of brain circuits to cognition and behavior. However, complexities of neural circuits challenge interpretation of experimental results, necessitating new theoretical frameworks for reasoning about causal effects. Here, we take a step in this direction, through the lens of recurrent neural networks trained to perform perceptual decisions. We show that understanding the dynamical system structure that underlies network solutions provides a precise account for the magnitude of behavioral effects due to perturbations. Our framework explains past empirical observations by clarifying the most sensitive features of behavior, and how complex circuits compensate and adapt to perturbations. In the process, we also identify strategies that can improve the interpretability of inactivation experiments.

Neural Mechanisms That Make Perceptual Decisions Flexible

Neural mechanisms of perceptual decision making have been extensively studied in experimental settings that mimic stable environments with repeating stimuli, fixed rules, and payoffs. In contrast, we live in an ever-changing environment and have varying goals and behavioral demands. To accommodate variability, our brain flexibly adjusts decision-making processes depending on context. Here, we review a growing body of research that explores the neural mechanisms underlying this flexibility. We highlight diverse forms of context dependency in decision making implemented through a variety of neural computations. Context-dependent neural activity is observed in a distributed network of brain structures, including posterior parietal, sensory, motor, and subcortical regions, as well as the prefrontal areas classically implicated in cognitive control. We propose that investigating the distributed network underlying flexible decisions is key to advancing our understanding and discuss a path forward for experimental and theoretical investigations.

Differences in memory for what, where, and when components of recently formed episodes

An integral feature of human memory is the ability to recall past events. What distinguishes such episodic memory from semantic or associative memory is the joint encoding and retrieval of "what," "where," and "when" (WWW) for such events. Surprisingly, little work has addressed whether all three components of WWW are retrieved with equal fidelity when remembering episodes. To study this question, we created a novel task where human participants identified matched or mismatched still images sampled from recently viewed synthetic movies. The mismatch images only probe one of the three WWW components at a time, allowing us to separately test accuracies for each component of the episodes. Crucially, each WWW component in the movies is easily distinguishable in isolation, thereby making any differences in accuracy between components due to how they are joined in memory. We find that memory for "when" has the lowest accuracy, with it being the component most influenced by primacy and recency. Furthermore, the memory of "when" is most susceptible to interference due to changes in task load. These findings suggest that episodes are not stored and retrieved as a coherent whole but instead their components are either stored or retrieved differentially as part of an active reconstruction process. NEW & NOTEWORTHY When we store and subsequently retrieve episodes, does the brain encode them holistically or in separate parts that are later reconstructed? Using a task where participants study abstract episodes and on any given trial are probed on the what, where, and when components, we find mnemonic differences between them. Accuracy for "when" memory is the lowest, as it is most influenced by primacy, recency, and interference, suggesting that episodes are not treated holistically by the brain.

Bayesian inference with incomplete knowledge explains perceptual confidence and its deviations from accuracy

In perceptual decisions, subjects infer hidden states of the environment based on noisy sensory information. Here we show that both choice and its associated confidence are explained by a Bayesian framework based on partially observable Markov decision processes (POMDPs). We test our model on monkeys performing a direction-discrimination task with post-decision wagering, demonstrating that the model explains objective accuracy and predicts subjective confidence. Further, we show that the model replicates well-known discrepancies of confidence and accuracy, including the hard-easy effect, opposing effects of stimulus variability on confidence and accuracy, dependence of confidence ratings on simultaneous or sequential reports of choice and confidence, apparent difference between choice and confidence sensitivity, and seemingly disproportionate influence of choice-congruent evidence on confidence. These effects may not be signatures of sub-optimal inference or discrepant computational processes for choice and confidence. Rather, they arise in Bayesian inference with incomplete knowledge of the environment.

Distinct population code for movement kinematics and changes of ongoing movements in human subthalamic nucleus

The subthalamic nucleus (STN) is theorized to globally suppress movement through connections with downstream basal ganglia structures. Current theories are supported by increased STN activity when subjects withhold an uninitiated action plan, but a critical test of these theories requires studying STN responses when an ongoing action is replaced with an alternative. We perform this test in subjects with Parkinson’s disease using an extended reaching task where the movement trajectory changes mid-action. We show that STN activity decreases during action switches, contrary to prevalent theories. Furthermore, beta oscillations in the STN local field potential, which are associated with movement inhibition, do not show increased power or spiking entrainment during switches. We report an inhomogeneous population neural code in STN, with one sub-population encoding movement kinematics and direction and another encoding unexpected action switches. We suggest an elaborate neural code in STN that contributes to planning actions and changing the plans.

Representational geometry of perceptual decisions in the monkey parietal cortex

Lateral intraparietal (LIP) neurons represent formation of perceptual decisions involving eye movements. In circuit models for these decisions, neural ensembles that encode actions compete to form decisions. Consequently, representation and readout of the decision variables (DVs) are implemented similarly for decisions with identical competing actions, irrespective of input and task context differences. Further, DVs are encoded as partially potentiated action plans through balance of activity of action-selective ensembles. Here, we test those core principles. We show that in a novel face-discrimination task, LIP firing rates decrease with supporting evidence, contrary to conventional motion-discrimination tasks. These opposite response patterns arise from similar mechanisms in which decisions form along curved population-response manifolds misaligned with action representations. These manifolds rotate in state space based on context, indicating distinct optimal readouts for different tasks. We show similar manifolds in lateral and medial prefrontal cortices, suggesting similar representational geometry across decision-making circuits.

Anomalous sensitivity enhancement of nano-graphitic electrochemical micro-sensors with reducing the operating voltage

Microscopic interactions between electrochemical sensors and biomolecules critically influence the sensitivity. Here, we report an unexpected dependence of the sensitivity on the upper potential limit (UPL) in voltammetry experiments. In particular, we find that the sensitivity of substrate-supported nano-graphitic micro-sensors in response to dopamine increases almost linearly with the inverse of UPL in voltammetry experiments with rapid potential sweeps. Our experiments and multi-physics simulations reveal that the main cause behind this phenomenon is the UPL-induced electrostatic force that influences the steady-state number of dopamine molecules on the sensor surface. Our findings illustrate a new strategy for enhancing the performance of planar electrochemical micro-sensors.

Prefrontal cortex represents heuristics that shape choice bias and its integration into future behavior

Goal-directed behavior requires integrating sensory information with prior knowledge about the environment. Behavioral biases that arise from these priors could increase positive outcomes when the priors match the true structure of the environment, but mismatches also happen frequently and could cause unfavorable outcomes. Biases that reduce gains and fail to vanish with training indicate fundamental suboptimalities arising from ingrained heuristics of the brain. Here, we report systematic, gain-reducing choice biases in highly trained monkeys performing a motion direction discrimination task where only the current stimulus is behaviorally relevant. The monkey's bias fluctuated at two distinct time scales: slow, spanning tens to hundreds of trials, and fast, arising from choices and outcomes of the most recent trials. Our findings enabled single trial prediction of biases, which influenced the choice especially on trials with weak stimuli. The pre-stimulus activity of neuronal ensembles in the monkey prearcuate gyrus represented these biases as an offset along the decision axis in the state space. This offset persisted throughout the stimulus viewing period, when sensory information was integrated, leading to a biased choice. The pre-stimulus representation of history-dependent bias was functionally indistinguishable from the neural representation of upcoming choice before stimulus onset, validating our model of single-trial biases and suggesting that pre-stimulus representation of choice could be fully defined by biases inferred from behavioral history. Our results indicate that the prearcuate gyrus reflects intrinsic heuristics that compute bias signals, as well as the mechanisms that integrate them into the oculomotor decision-making process.

Decoding and perturbing decision states in real time

In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment¹. The process of deliberation can be described by a time-varying decision variable (DV), decoded from neural population activity, that predicts a subject's upcoming decision². Within single trials, however, there are large moment-to-moment fluctuations in the DV, the behavioural significance of which is unclear. Here, using real-time, neural feedback control of stimulus duration, we show that within-trial DV fluctuations, decoded from motor cortex, are tightly linked to decision state in macaques, predicting behavioural choices substantially better than the condition-averaged DV or the visual stimulus alone. Furthermore, robust changes in DV sign have the statistical regularities expected from behavioural studies of changes of mind³. Probing the decision process on single trials with weak stimulus pulses, we find evidence for time-varying absorbing decision bounds, enabling us to distinguish between specific models of decision making.

Designing and Interpreting Psychophysical Investigations of Cognition

Scientific experimentation depends on the artificial control of natural phenomena. The inaccessibility of cognitive processes to direct manipulation can make such control difficult to realize. Here, we discuss approaches for overcoming this challenge. We advocate the incorporation of experimental techniques from sensory psychophysics into the study of cognitive processes such as decision making and executive control. These techniques include the use of simple parameterized stimuli to precisely manipulate available information and computational models to jointly quantify behavior and neural responses. We illustrate the potential for such techniques to drive theoretical development, and we examine important practical details of how to conduct controlled experiments when using them. Finally, we highlight principles guiding the use of computational models in studying the neural basis of cognition.

Quantitative Principles for Precise Engineering of Sensitivity in Graphene Electrochemical Sensors

A major difficulty in implementing carbon-based electrode arrays with high device-packing density is to ensure homogeneous and high sensitivities across the array. Overcoming this obstacle requires quantitative microscopic models that can accurately predict electrode sensitivity from its material structure. Such models are currently lacking. Here, it is shown that the sensitivity of graphene electrodes to dopamine and serotonin neurochemicals in fast-scan cyclic voltammetry measurements is strongly linked to point defects, whereas it is unaffected by line defects. Using the physics of point defects in graphene, a microscopic model is introduced that explains how point defects determine sensitivity. The predictions of this model match the empirical observation that sensitivity linearly increases with the density of point defects. This model is used to guide the nanoengineering of graphene structures for optimum sensitivity. This approach achieves reproducible fabrication of miniaturized sensors with extraordinarily higher sensitivity than conventional materials. These results lay the foundation for new integrated electrochemical sensor arrays based on nanoengineered graphene.

Decision Making through Integration of Sensory Evidence at Prolonged Timescales

When multiple pieces of information bear on a decision, the best approach is to combine the evidence provided by each one. Evidence integration models formalize the computations underlying this process [1-3], explain human perceptual discrimination behavior [4-9], and correspond to neuronal responses elicited by discrimination tasks [10-14]. These findings suggest that evidence integration is key to understanding the neural basis of decision making [15-18]. But while evidence integration has most often been studied with simple tasks that limit deliberation to relatively brief periods, many natural decisions unfold over much longer durations. Neural network models imply acute limitations on the timescale of evidence integration [19-23], and it is currently unknown whether existing computational insights can generalize beyond rapid judgments. Here, we introduce a new psychophysical task and report model-based analyses of human behavior that demonstrate evidence integration at long timescales. Our task requires probabilistic inference using brief samples of visual evidence that are separated in time by long and unpredictable gaps. We show through several quantitative assays how decision making can approximate a normative integration process that extends over tens of seconds without accruing significant memory leak or noise. These results support the generalization of evidence integration models to a broader class of behaviors while posing new challenges for models of how these computations are implemented in biological networks.

Sensory and decision-making processes underlying perceptual adaptation

Perceptual systems adapt to their inputs. As a result, prolonged exposure to particular stimuli alters judgments about subsequent stimuli. This phenomenon is commonly assumed to be sensory in origin. Changes in the decision-making process, however, may also be a component of adaptation. Here, we quantify sensory and decision-making contributions to adaptation in a facial expression paradigm. As expected, exposure to happy or sad expressions shifts the psychometric function toward the adaptor. More surprisingly, response times show both an overall decline and an asymmetry, with faster responses opposite the adapting category, implicating a substantial change in the decision-making process. Specifically, we infer that sensory changes from adaptation are accompanied by changes in how much sensory information is accumulated for the two choices. We speculate that adaptation influences implicit expectations about the stimuli one will encounter, causing modifications in the decision-making process as part of a normative response to a change in context.

Psychophysical reverse correlation reflects both sensory and decision-making processes

Goal-directed behavior depends on both sensory mechanisms that gather information from the outside world and decision-making mechanisms that select appropriate behavior based on that sensory information. Psychophysical reverse correlation is commonly used to quantify how fluctuations of sensory stimuli influence behavior and is generally believed to uncover the spatiotemporal weighting functions of sensory processes. Here we show that reverse correlations also reflect decision-making processes and can deviate significantly from the true sensory filters. Specifically, changes of decision bound and mechanisms of evidence integration systematically alter psychophysical reverse correlations. Similarly, trial-to-trial variability of sensory and motor delays and decision times causes systematic distortions in psychophysical kernels that should not be attributed to sensory mechanisms. We show that ignoring details of the decision-making process results in misinterpretation of reverse correlations, but proper use of these details turns reverse correlation into a powerful method for studying both sensory and decision-making mechanisms.

Perceptual insensitivity to higher-order statistical moments of coherent random dot motion

When the visual system analyzes distributed patterns of sensory inputs, what features of those distributions does it use? It has been previously demonstrated that higher-order statistical moments of luminance distributions influence perception of static surfaces and textures. Here, we tested whether the brain also represents higher-order moments of dynamic stimuli. We constructed random dot kinematograms, where dots moved according to probability distributions that selectively differed in terms of their mean, variance, skewness, or kurtosis. When viewing these stimuli, human observers were sensitive to the mean direction of coherent motion and to the variance of dot displacement angles, but they were insensitive to skewness and kurtosis. Observer behavior accorded with a model of directional motion energy, suggesting that information about higher-order moments is discarded early in the visual processing hierarchy. These results demonstrate that use of higher-order moments is not a general property of visual perception.

Hybrid CMOS-Graphene Sensor Array for Subsecond Dopamine Detection

We introduce a hybrid CMOS-graphene sensor array for subsecond measurement of dopamine via fast-scan cyclic voltammetry (FSCV). The prototype chip has four independent CMOS readout channels, fabricated in a 65-nm process. Using planar multilayer graphene as biologically compatible sensing material enables integration of miniaturized sensing electrodes directly above the readout channels. Taking advantage of the chemical specificity of FSCV, we introduce a region of interest technique, which subtracts a large portion of the background current using a programmable low-noise constant current at about the redox potentials. We demonstrate the utility of this feature for enhancing the sensitivity by measuring the sensor response to a known dopamine concentration in vitro at three different scan rates. This strategy further allows us to significantly reduce the dynamic range requirements of the analog-to-digital converter (ADC) without compromising the measurement accuracy. We show that an integrating dual-slope ADC is adequate for digitizing the background-subtracted current. The ADC operates at a sampling frequency of 5-10 kHz and has an effective resolution of about 60 pA, which corresponds to a theoretical dopamine detection limit of about 6 nM. Our hybrid sensing platform offers an effective solution for implementing next-generation FSCV devices that can enable precise recording of dopamine signaling in vivo on a large scale.

Confidence Is the Bridge between Multi-stage Decisions

Demanding tasks often require a series of decisions to reach a goal. Recent progress in perceptual decision-making has served to unite decision accuracy, speed, and confidence in a common framework of bounded evidence accumulation, furnishing a platform for the study of such multi-stage decisions. In many instances, the strategy applied to each decision, such as the speed-accuracy trade-off, ought to depend on the accuracy of the previous decisions. However, as the accuracy of each decision is often unknown to the decision maker, we hypothesized that subjects may carry forward a level of confidence in previous decisions to affect subsequent decisions. Subjects made two perceptual decisions sequentially and were rewarded only if they made both correctly. The speed and accuracy of individual decisions were explained by noisy evidence accumulation to a terminating bound. We found that subjects adjusted their speed-accuracy setting by elevating the termination bound on the second decision in proportion to their confidence in the first. The findings reveal a novel role for confidence and a degree of flexibility, hitherto unknown, in the brain's ability to rapidly and precisely modify the mechanisms that control the termination of a decision.

Three challenges for connecting model to mechanism in decision-making

Recent years have seen a growing interest in understanding the neural mechanisms that support decision-making. The advent of new tools for measuring and manipulating neurons, alongside the inclusion of multiple new animal models and sensory systems has led to the generation of many novel datasets. The potential for these new approaches to constrain decision-making models is unprecedented. Here, we argue that to fully leverage these new approaches, three challenges must be met. First, experimenters must design well-controlled behavioral experiments that make it possible to distinguish competing behavioral strategies. Second, analyses of neural responses should think beyond single neurons, taking into account tradeoffs of single-trial versus trial-averaged approaches. Finally, quantitative model comparisons should be used, but must consider common obstacles.

Temporal dynamics of visual category representation in the macaque inferior temporal cortex

Object categories are recognized at multiple levels of hierarchical abstractions. Psychophysical studies have shown a more rapid perceptual access to the mid-level category information (e.g., human faces) than the higher (superordinate; e.g., animal) or the lower (subordinate; e.g., face identity) level. Mid-level category members share many features, whereas few features are shared among members of different mid-level categories. To understand better the neural basis of expedited access to mid-level category information, we examined neural responses of the inferior temporal (IT) cortex of macaque monkeys viewing a large number of object images. We found an earlier representation of mid-level categories in the IT population and single-unit responses compared with superordinate- and subordinate-level categories. The short-latency representation of mid-level category information shows that visual cortex first divides the category shape space at its sharpest boundaries, defined by high/low within/between-group similarity. This short-latency, mid-level category boundary map may be a prerequisite for representation of other categories at more global and finer scales.

Comment on "Single-trial spike trains in parietal cortex reveal discrete steps during decision-making"

Latimeret al (Reports, 10 July 2015, p. 184) claim that during perceptual decision formation, parietal neurons undergo one-time, discrete steps in firing rate instead of gradual changes that represent the accumulation of evidence. However, that conclusion rests on unsubstantiated assumptions about the time window of evidence accumulation, and their stepping model cannot explain existing data as effectively as evidence-accumulation models.

Neural Mechanisms of Post-error Adjustments of Decision Policy in Parietal Cortex

Humans often slow down after mistakes (post-error slowing [PES]), but the neural mechanism and adaptive role of PES remain controversial. We studied changes in the neural mechanisms of decision making after errors in humans and monkeys that performed a motion direction discrimination task. We found that PES is mediated by two factors: a reduction in sensitivity to sensory information and an increase in the decision bound. Both effects are implemented through dynamic changes in the decision-making process. Neuronal responses in the monkey lateral intraparietal area revealed that bound changes are implemented by decreasing an evidence-independent urgency signal. They also revealed a reduction in the rate of evidence accumulation, reflecting reduced sensitivity. These changes in the bound and sensitivity provide a quantitative account of choices and response times. We suggest that PES reflects an adaptive increase of decision bound in anticipation of maladaptive reductions in sensitivity to incoming evidence.

A common mechanism underlies changes of mind about decisions and confidence

Decisions are accompanied by a degree of confidence that a selected option is correct. A sequential sampling framework explains the speed and accuracy of decisions and extends naturally to the confidence that the decision rendered is likely to be correct. However, discrepancies between confidence and accuracy suggest that confidence might be supported by mechanisms dissociated from the decision process. Here we show that this discrepancy can arise naturally because of simple processing delays. When participants were asked to report choice and confidence simultaneously, their confidence, reaction time and a perceptual decision about motion were explained by bounded evidence accumulation. However, we also observed revisions of the initial choice and/or confidence. These changes of mind were explained by a continuation of the mechanism that led to the initial choice. Our findings extend the sequential sampling framework to vacillation about confidence and invites caution in interpreting dissociations between confidence and accuracy.

DOI:http://dx.doi.org/10.7554/eLife.12192.001

To understand how the brain makes decisions is to understand how we think – how we deal with information, interpret it and agree with a particular interpretation of the information. Neuroscience has begun to uncover the mechanisms that underlie these processes by linking the activity of nerve cells in the brain to different aspects of making decisions. These include how long it takes to reach a decision, why we make errors and how confident we feel about a decision.

Sometimes when we make a decision and have committed to an answer, we then change our minds. Now, van den Berg et al. have asked whether the brain mechanisms that support a change of mind also support a change in confidence. To investigate this problem, human volunteers were asked to perform a difficult task where they had to decide whether a field of randomly moving dots had a tendency to drift to the left or to the right.

During the experiment, van den Berg et al. recorded how long the volunteers took to make their decision, how confident the volunteers felt about their choice, and whether they were correct. Analyzing this data revealed that all of these measures could be explained by a mechanism where the brain accumulates evidence only until there appears to be enough evidence to favor one choice over the other. This process specifies how confident an individual should be based on the quality of the sensory evidence and how long it takes to make a decision.

In addition, van den Berg et al. found that occasionally a volunteer changed their mind about how confident they were about a decision after they’d made it, as if they had continued to think about it. This was despite the volunteers receiving no more information about the task or how well they had done once they had made their decision. Therefore, it appears that the brain processed additional information that had already been detected but did not have time to affect the initial choice.

The activity of the nerve cells in the brain was not recorded as the volunteers made their decisions. Future experiments that incorporate these measurements could help reveal how the brain performs the necessary computations and account for the time delay seen in processing some of the data. Where is this delayed information processed in the brain, and how does it lead to a change of mind?

DOI:http://dx.doi.org/10.7554/eLife.12192.002

Predicting the Accuracy of a Decision: A Neural Mechanism of Confidence

The quantitative study of decision-making has traditionally rested on three key behavioral measures: accuracy, response time, and confidence. Of these, confidence--defined as the degree of belief, prior to feedback, that a decision is correct-is least well understood at the level of neural mechanism, although recent years have seen a surge in interest in the topic among theoretical and systems neuroscientists. Here we review some of these developments and highlight a particular candidate mechanism for assigning confidence in a perceptual decision. The mechanism is appealing because it is rooted in the same decision-making framework--bounded accumulation of evidence--that successfully explains accuracy and reaction time in many tasks, and it is validated by neurophysiology and microstimulation experiments.

Effects of cortical microstimulation on confidence in a perceptual decision

Decisions are often associated with a degree of certainty, or confidence--an estimate of the probability that the chosen option will be correct. Recent neurophysiological results suggest that the central processing of evidence leading to a perceptual decision also establishes a level of confidence. Here we provide a causal test of this hypothesis by electrically stimulating areas of the visual cortex involved in motion perception. Monkeys discriminated the direction of motion in a noisy display and were sometimes allowed to opt out of the direction choice if their confidence was low. Microstimulation did not reduce overall confidence in the decision but instead altered confidence in a manner that mimicked a change in visual motion, plus a small increase in sensory noise. The results suggest that the same sensory neural signals support choice, reaction time, and confidence in a decision and that artificial manipulation of these signals preserves the quantitative relationship between accumulated evidence and confidence.

Dimensionality of object representations in monkey inferotemporal cortex

We have calculated the intrinsic dimensionality of visual object representations in anterior inferotemporal (AIT) cortex, based on responses of a large sample of cells stimulated with photographs of diverse objects. Because dimensionality was dependent on data set size, we determined asymptotic dimensionality as both the number of neurons and number of stimulus image approached infinity. Our final dimensionality estimate was 93 (SD: ± 11), indicating that there is basis set of approximately 100 independent features that characterize the dimensions of neural object space. We believe this is the first estimate of the dimensionality of neural visual representations based on single-cell neurophysiological data. The dimensionality of AIT object representations was much lower than the dimensionality of the stimuli. We suggest that there may be a gradual reduction in the dimensionality of object representations in neural populations going from retina to inferotemporal cortex as receptive fields become increasingly complex.

Dynamics of neural population responses in prefrontal cortex indicate changes of mind on single trials

Decision making is a complex process in which different sources of information are combined into a decision variable (DV) that guides action [1, 2]. Neurophysiological studies have typically sought insight into the dynamics of the decision-making process and its neural mechanisms through statistical analysis of large numbers of trials from sequentially recorded single neurons or small groups of neurons [3-6]. However, detecting and analyzing the DV on individual trials has been challenging [7]. Here we show that by recording simultaneously from hundreds of units in prearcuate gyrus of macaque monkeys performing a direction discrimination task, we can predict the monkey's choices with high accuracy and decode DV dynamically as the decision unfolds on individual trials. This advance enabled us to study changes of mind (CoMs) that occasionally happen before the final commitment to a decision [8-10]. On individual trials, the decoded DV varied significantly over time and occasionally changed its sign, identifying a potential CoM. Interrogating the system by random stopping of the decision-making process during the delay period after stimulus presentation confirmed the validity of identified CoMs. Importantly, the properties of the candidate CoMs also conformed to expectations based on prior theoretical and behavioral studies [8]: they were more likely to go from an incorrect to a correct choice, they were more likely for weak and intermediate stimuli than for strong stimuli, and they were more likely earlier in the trial. We suggest that simultaneous recording of large neural populations provides a good estimate of DV and explains idiosyncratic aspects of the decision-making process that were inaccessible before.

Mortality from sudden unexpected death in epilepsy (SUDEP) in a cohort of adults with intellectual disability

BACKGROUND

People with intellectual disability (ID) and epilepsy are more likely to die prematurely than the general population. A significant number of deaths in people with epilepsy may be potentially preventable through better seizure control, regular monitoring and raising awareness among patients and carers. The aim of this project was to study mortality from sudden unexpected death in epilepsy (SUDEP) in adults with ID.

METHODS

All adults (≥20 years old) living in Leicester city, Leicestershire and Rutland, UK, with ID between 1993 and 2010 were identified using the Leicestershire Intellectual Disability Register database. People with and without ID who died during the same period were identified using death certificate data from the Office for National Statistics (ONS). Deaths from probable and definite SUDEP were identified. Additional information on adults with ID who had died from probable or definite SUDEP was obtained from case notes and post-mortem reports, where available. Cases of probable and definite SUDEP in adults with ID were compared with the general population using standardised mortality ratios (SMRs).

RESULTS

A total of 898 adults with ID had died over the 18-year study period. Of these, 244 deaths (27%) occurred in people with ID who had a diagnosis of epilepsy. Twenty-six people with ID died from probable or definite SUDEP, which was the second most common cause of death among adults with ID and epilepsy. All-cause specific SMRs were 2.2 [95% confidence interval (CI): 2.0-2.4] and 2.8 (95% CI: 2.5-3.1) for men and women with ID respectively. SMRs were 3.2 (95% CI: 2.7-3.8) and 5.6 (95% CI: 4.6-6.7) for men and women with epilepsy and ID respectively. During the same study period, 83 adults without ID had died of probable or definite SUDEP. The SMRs for SUDEP in patients with ID were 37.6 for men (95% CI: 21.9-60.2) and 52.0 for women (95% CI: 23.8-98.8). We found that in the majority of ID cases there was little detailed documentation on the circumstances surrounding deaths, no communication with patients/carers about risk of SUDEP and an absence of post-mortem reports or carers' referral for bereavement counselling.

CONCLUSION

The authors believe that a comprehensive risk management under a multiagency/multidisciplinary framework should be undertaken for all adults with ID and epilepsy in day-to-day clinical practice to reduce mortality in people with ID.

A neural mechanism of speed-accuracy tradeoff in macaque area LIP

Decision making often involves a tradeoff between speed and accuracy. Previous studies indicate that neural activity in the lateral intraparietal area (LIP) represents the gradual accumulation of evidence toward a threshold level, or evidence bound, which terminates the decision process. The level of this bound is hypothesized to mediate the speed-accuracy tradeoff. To test this, we recorded from LIP while monkeys performed a motion discrimination task in two speed-accuracy regimes. Surprisingly, the terminating threshold levels of neural activity were similar in both regimes. However, neurons recorded in the faster regime exhibited stronger evidence-independent activation from the beginning of decision formation, effectively reducing the evidence-dependent neural modulation needed for choice commitment. Our results suggest that control of speed vs accuracy may be exerted through changes in decision-related neural activity itself rather than through changes in the threshold applied to such neural activity to terminate a decision.

DOI:http://dx.doi.org/10.7554/eLife.02260.001

Many actions involve a trade-off between speed and accuracy, with typing being a good example: the faster you try to type a sentence, the more mistakes you are likely to make. Mathematical models have successfully reproduced the speed-accuracy trade-off, but it is not clear how the brain represents and weighs up these two factors. Now, Hanks et al. have shown how single neurons in a region of the brain called the lateral intraparietal cortex vary their firing rate to optimize the balance between speed and accuracy.

Two macaque monkeys were trained to fixate on a single dot on a screen and then move their eyes in one of two directions in response to movies of random dots on a video screen. Initially, the monkeys received a reward immediately after every correct response, whereas incorrect responses were punished with a very short time-out. Under these conditions, the optimal strategy is to respond quickly at the expense of accuracy. In a separate block of trials, the monkeys were again rewarded for correct responses, but this time their reward was delayed if they responded too quickly. The most effective strategy now is to respond accurately, but more slowly.

In both the ‘high speed’ and ‘high accuracy’ conditions, the firing of neurons in lateral intraparietal cortex increased while the dots were on the screen. As soon as the firing rate reached a threshold—representing the point at which the monkey had accumulated enough evidence to make a decision about the direction of movement—the monkey moved its eyes. Previous theories had suggested that when speed was the priority, the level of activity required to trigger a decision would be lower than when accuracy was emphasized. Surprisingly, however, the threshold did not differ between the ‘high speed’ and ‘high accuracy’ conditions. Instead, neurons displayed a higher initial firing rate whenever speed was prioritized, enabling the monkey to make a decision on the basis of less evidence.

This finding is consistent with human brain imaging studies that have shown increased baseline activity in decision-making circuitry when speed is prioritized over accuracy. Studying these mechanisms could help to reveal why some individuals are more impulsive decision-makers than others.

DOI:http://dx.doi.org/10.7554/eLife.02260.002

Decision making as a window on cognition

A decision is a commitment to a proposition or plan of action based on information and values associated with the possible outcomes. The process operates in a flexible timeframe that is free from the immediacy of evidence acquisition and the real time demands of action itself. Thus, it involves deliberation, planning, and strategizing. This Perspective focuses on perceptual decision making in nonhuman primates and the discovery of neural mechanisms that support accuracy, speed, and confidence in a decision. We suggest that these mechanisms expose principles of cognitive function in general, and we speculate about the challenges and directions before the field.

Cortical reinstatement mediates the relationship between content-specific encoding activity and subsequent recollection decisions

Episodic recollection entails the conscious remembrance of event details associated with previously encountered stimuli. Recollection depends on both the establishment of cortical representations of event features during stimulus encoding and the cortical reinstatement of these representations at retrieval. Here, we used multivoxel pattern analyses of functional magnetic resonance imaging data to examine how cortical and hippocampal activity at encoding and retrieval drive recollective memory decisions. During encoding, words were associated with face or scene source contexts. At retrieval, subjects were cued to recollect the source associate of each presented word. Neurally derived estimates of encoding strength and pattern reinstatement in occipitotemporal cortex were computed for each encoding and retrieval trial, respectively. Analyses demonstrated that (1) cortical encoding strength predicted subsequent memory accuracy and reaction time, (2) encoding strength predicted encoding-phase hippocampal activity, and (3) encoding strength and retrieval-phase hippocampal activity predicted the magnitude of cortical reinstatement. Path analyses further indicated that cortical reinstatement partially mediated both the effect of cortical encoding strength and the effect of retrieval-phase hippocampal activity on subsequent source memory performance. Taken together, these results indicate that memory-guided decisions are driven in part by a pathway leading from hippocampally linked cortical encoding of event attributes to hippocampally linked cortical reinstatement at retrieval.

Urban-rural differences in the nature and prevalence of mental ill-health in adults with intellectual disabilities

BACKGROUND

In the general population there are statistically significant urban-rural differences in the rate of common mental disorders. In people with intellectual disability (ID) no study has attempted to address this issue.

AIMS

To compare the prevalence of mental illness, autism spectrum disorder (ASD) and behaviour disorder in people with ID living in urban areas with those living in rural areas.

METHODS

Cross-sectional study of 2713 individuals registered with an ID service. Participants were assigned to urban or rural groups using the Department for Environment Food and Rural Affairs rural/urban local authority classification for their district. The main outcome variable was a clinical diagnosis of mental illness, ASD and behaviour disorder. Differences between diagnoses of mental illness in urban and rural areas were evaluated using the chi-squared test for the difference in two independent proportions.

RESULTS

No differences were observed between gender, age and level of ID of service users based on their place of residence. But more people from an ethnic minority background were living in urban areas than rural areas. No differences were observed in the overall prevalence of mental illness by place of residence. However, the results showed that ASD was more common in people living in rural areas.

CONCLUSIONS

We found these results surprising and at odds with the majority of studies carried out in the general population and propose several reasons for the differences found. We believe that the results and further studies in this area will help inform health service provision for those with ID who live in different geographical areas.

Statistics of visual responses in primate inferotemporal cortex to object stimuli

We have characterized selectivity and sparseness in anterior inferotemporal cortex, using a large data set. Responses were collected from 674 monkey inferotemporal cells, each stimulated by 806 object photographs. This 806 × 674 matrix was examined in two ways: columnwise, looking at responses of a single neuron to all images (single-neuron selectivity), and rowwise, looking at the responses of all neurons caused by a single image (population sparseness). Selectivity and sparseness were measured as kurtosis of probability distributions. Population sparseness exceeded single-neuron selectivity, with specific values dependent on the size of the data sample. This difference was principally caused by inclusion, within the population, of neurons with a variety of dynamic ranges (standard deviations of responses over all images). Statistics of large responses were examined by quantifying how quickly the upper tail of the probability distribution decreased (tail heaviness). This analysis demonstrated that population responses had heavier tails than single-neuron responses, consistent with the difference between sparseness and selectivity measurements. Population responses with spontaneous activity subtracted had the heaviest tails, following a power law. The very light tails of single-neuron responses indicate that the critical feature for each neuron is simple enough to have a high probability of occurring within a limited stimulus set. Heavy tails of population responses indicate that there are a large number of different critical features to which different neurons are tuned. These results are inconsistent with some structural models of object recognition that posit that objects are decomposed into a small number of standard features.

Variance as a signature of neural computations during decision making

Traditionally, insights into neural computation have been furnished by averaged firing rates from many stimulus repetitions or trials. We pursue an analysis of neural response variance to unveil neural computations that cannot be discerned from measures of average firing rate. We analyzed single-neuron recordings from the lateral intraparietal area (LIP), during a perceptual decision-making task. Spike count variance was divided into two components using the law of total variance for doubly stochastic processes: (1) variance of counts that would be produced by a stochastic point process with a given rate, and loosely (2) the variance of the rates that would produce those counts (i.e., "conditional expectation"). The variance and correlation of the conditional expectation exposed several neural mechanisms: mixtures of firing rate states preceding the decision, accumulation of stochastic "evidence" during decision formation, and a stereotyped response at decision end. These analyses help to differentiate among several alternative decision-making models.

Representation of confidence associated with a decision by neurons in the parietal cortex

The degree of confidence in a decision provides a graded and probabilistic assessment of expected outcome. Although neural mechanisms of perceptual decisions have been studied extensively in primates, little is known about the mechanisms underlying choice certainty. We have shown that the same neurons that represent formation of a decision encode certainty about the decision. Rhesus monkeys made decisions about the direction of moving random dots, spanning a range of difficulties. They were rewarded for correct decisions. On some trials, after viewing the stimulus, the monkeys could opt out of the direction decision for a small but certain reward. Monkeys exercised this option in a manner that revealed their degree of certainty. Neurons in parietal cortex represented formation of the direction decision and the degree of certainty underlying the decision to opt out.

Probabilistic population codes for Bayesian decision making

When making a decision, one must first accumulate evidence, often over time, and then select the appropriate action. Here, we present a neural model of decision making that can perform both evidence accumulation and action selection optimally. More specifically, we show that, given a Poisson-like distribution of spike counts, biological neural networks can accumulate evidence without loss of information through linear integration of neural activity and can select the most likely action through attractor dynamics. This holds for arbitrary correlations, any tuning curves, continuous and discrete variables, and sensory evidence whose reliability varies over time. Our model predicts that the neurons in the lateral intraparietal cortex involved in evidence accumulation encode, on every trial, a probability distribution which predicts the animal's performance. We present experimental evidence consistent with this prediction and discuss other predictions applicable to more general settings.

Matching categorical object representations in inferior temporal cortex of man and monkey

Inferior temporal (IT) object representations have been intensively studied in monkeys and humans, but representations of the same particular objects have never been compared between the species. Moreover, IT's role in categorization is not well understood. Here, we presented monkeys and humans with the same images of real-world objects and measured the IT response pattern elicited by each image. In order to relate the representations between the species and to computational models, we compare response-pattern dissimilarity matrices. IT response patterns form category clusters, which match between man and monkey. The clusters correspond to animate and inanimate objects; within the animate objects, faces and bodies form subclusters. Within each category, IT distinguishes individual exemplars, and the within-category exemplar similarities also match between the species. Our findings suggest that primate IT across species may host a common code, which combines a categorical and a continuous representation of objects.

Brief report: Aggressive challenging behaviour in adults with intellectual disability following community resettlement

BACKGROUND

Aggressive challenging behaviour is common in adults with intellectual disability (ID) in long-term care facilities. The government's commitment to the closure of all facilities in England has led to concerns over how to manage this behaviour in the community. The aim of this study was to assess changes in aggressive challenging behaviour and psychotropic drug use in adults with ID following resettlement using a person-centred approach.

METHOD

The Modified Overt Aggression Scale was administered to carers of 49 adults with ID prior to discharge from a long-stay hospital and 6 months and 1 year after community resettlement.

RESULTS

All areas of aggressive challenging behaviour reduced significantly between baseline and 6 months following resettlement (P < 0.001). This reduction remained (but did not decrease further) at 1-year follow-up.

CONCLUSIONS

Further work is needed to evaluate the role of environmental setting on aggressive challenging behaviour in adults with ID.

Object category structure in response patterns of neuronal population in monkey inferior temporal cortex

Our mental representation of object categories is hierarchically organized, and our rapid and seemingly effortless categorization ability is crucial for our daily behavior. Here, we examine responses of a large number (>600) of neurons in monkey inferior temporal (IT) cortex with a large number (>1,000) of natural and artificial object images. During the recordings, the monkeys performed a passive fixation task. We found that the categorical structure of objects is represented by the pattern of activity distributed over the cell population. Animate and inanimate objects created distinguishable clusters in the population code. The global category of animate objects was divided into bodies, hands, and faces. Faces were divided into primate and nonprimate faces, and the primate-face group was divided into human and monkey faces. Bodies of human, birds, and four-limb animals clustered together, whereas lower animals such as fish, reptile, and insects made another cluster. Thus the cluster analysis showed that IT population responses reconstruct a large part of our intuitive category structure, including the global division into animate and inanimate objects, and further hierarchical subdivisions of animate objects. The representation of categories was distributed in several respects, e.g., the similarity of response patterns to stimuli within a category was maintained by both the cells that maximally responded to the category and the cells that responded weakly to the category. These results advance our understanding of the nature of the IT neural code, suggesting an inherently categorical representation that comprises a range of categories including the amply investigated face category.

Microstimulation of inferotemporal cortex influences face categorization

The inferior temporal cortex (IT) of primates is thought to be the final visual area in the ventral stream of cortical areas responsible for object recognition. Consistent with this hypothesis, single IT neurons respond selectively to highly complex visual stimuli such as faces. However, a direct causal link between the activity of face-selective neurons and face perception has not been demonstrated. In the present study of macaque monkeys, we artificially activated small clusters of IT neurons by means of electrical microstimulation while the monkeys performed a categorization task, judging whether noisy visual images belonged to 'face' or 'non-face' categories. Here we show that microstimulation of face-selective sites, but not other sites, strongly biased the monkeys' decisions towards the face category. The magnitude of the effect depended upon the degree of face selectivity of the stimulation site, the size of the stimulated cluster of face-selective neurons, and the exact timing of microstimulation. Our results establish a causal relationship between the activity of face-selective neurons and face perception.

Differences in onset latency of macaque inferotemporal neural responses to primate and non-primate faces

Neurons in the visual system respond to different visual stimuli with different onset latencies. However, it has remained unknown which stimulus features, aside from stimulus contrast, determine the onset latencies of responses. To examine the possibility that response onset latencies carry information about complex object images, we recorded single-cell responses in the inferior temporal cortex of alert monkeys, while they viewed >1,000 object stimuli. Many cells responded to human and non-primate animal faces with comparable magnitudes but responded significantly more quickly to human faces than to non-primate animal faces. Differences in onset latency may be used to increase the coding capacity or enhance or suppress information about particular object groups by time-dependent modulation.