Second Guessing in Perceptual Decision-Making.

Human subjects of both sexes were asked to make a perceptual decision between multiple directions of visual motion. In addition to reporting a primary choice, they also had to report a second guess, indicating which of the remaining options they would rather bet on, assuming that they got their primary choice wrong. The second guess was clearly informed by the amounts of sensory evidence that were provided for the different options. A single computational integration-to-threshold model, based on the assumption that the second guess is determined by the rank ordering of accumulated evidence at or shortly after the time of the decision, was able to explain the distribution of primary choices, associated response times, and the distribution of second guesses. This suggests that the decision-maker has access to how well supported unchosen options are by the sensory evidence.SIGNIFICANCE STATEMENT Perceptual decisions require conversion of sensory evidence into a discrete choice. Computational models based on the accumulation of evidence to a decision threshold can explain the distribution of choices and associated decision times. Subjects are also able to report the level of confidence in their decision. Here we show that, when making decisions between more than two alternatives, the decision-maker can even report a second guess that is clearly informed by the sensory evidence. These second guesses show a distribution that is consistent with subjects having access to how much sensory evidence was accumulated for the unchosen options. The decision-maker therefore has knowledge about the outcome of the decision process that goes beyond just the choice and an associated confidence.

Neural dynamics of choice: single-trial analysis of decision-related activity in parietal cortex.

Previous neurophysiological studies of perceptual decision-making have focused on single-unit activity, providing insufficient information about how individual decisions are accomplished. For the first time, we recorded simultaneously from multiple decision-related neurons in parietal cortex of monkeys performing a perceptual decision task and used these recordings to analyze the neural dynamics during single trials. We demonstrate that decision-related lateral intraparietal area neurons typically undergo gradual changes in firing rate during individual decisions, as predicted by mechanisms based on continuous integration of sensory evidence. Furthermore, we identify individual decisions that can be described as a change of mind: the decision circuitry was transiently in a state associated with a different choice before transitioning into a state associated with the final choice. These changes of mind reflected in monkey neural activity share similarities with previously reported changes of mind reflected in human behavior.

Local computation of decision-relevant net sensory evidence in parietal cortex.

To investigate the contribution of parietal cortex to perceptual decisions, we trained monkeys on a perceptual decision task that allowed simultaneous experimental control over how much sensory evidence was provided for each of 3 possible alternative choices and recorded single unit activity and local field potentials (LFPs) from the lateral intraparietal area (LIP). While both the behavior and the spiking activity were largely determined by the difference between how much supporting sensory evidence was provided for a particular choice (pro evidence) and how much sensory evidence was provided for the other alternatives (anti evidence), the LFP reflected roughly the sum of these 2 components. Furthermore, the firing rates showed an earlier influence of the anti evidence than the pro evidence. These observations indicate that LIP does not simply receive already precomputed decision signals but that it plays an active role in computing the decision-relevant net sensory evidence and that this local computation is reflected in the LFP. The results further demonstrate that the competition between the different alternatives cannot solely be mediated by lateral or feedback inhibition, as proposed by a major class of decision models but that feedforward inhibition makes an important contribution.

Neural ensemble dynamics in trunk and hindlimb sensorimotor cortex encode for the control of postural stability.

The cortex has a disputed role in monitoring postural equilibrium and intervening in cases of major postural disturbances. Here, we investigate the patterns of neural activity in the cortex that underlie neural dynamics during unexpected perturbations. In both the primary sensory (S1) and motor (M1) cortices of the rat, unique neuronal classes differentially covary their responses to distinguish different characteristics of applied postural perturbations; however, there is substantial information gain in M1, demonstrating a role for higher-order computations in motor control. A dynamical systems model of M1 activity and forces generated by the limbs reveals that these neuronal classes contribute to a low-dimensional manifold comprised of separate subspaces enabled by congruent and incongruent neural firing patterns that define different computations depending on the postural responses. These results inform how the cortex engages in postural control, directing work aiming to understand postural instability after neurological disease.

A user-friendly algorithm for adaptive closed-loop phase-locked stimulation.

BACKGROUND: Closed-loop phase-locked stimulation experiments are rare due to the unavailability of user-friendly algorithms and devices. Our goal is to provide an algorithm for the detection of oscillatory activity in local field potentials (LFPs) and phase prediction, which is user-friendly and robust to non-stationarities in LFPs of behaving animals. NEW METHOD: We propose an algorithm that only requires specification of the frequency range within which oscillatory episodes are tracked. Frequency-specific detection thresholds and filter parameters are adjusted automatically based on the short-time LFP power spectrum. Estimates of instantaneous frequency and instantaneous phase are used for phase extrapolation, taking advantage of Bayesian estimation. We used real LFP signals, recorded from a variety of different species and different brain areas, as well as artificial LFP signals with known properties to assess the detection and prediction performance of our algorithm and three previously published reference algorithms under various conditions. RESULTS AND COMPARISON WITH EXISTING METHODS: Our algorithm, while significantly more user-friendly than previous approaches, provides a solid detection and prediction performance over a wide range of realistic conditions and, in many cases, has a longer prediction horizon than the reference algorithms. Due to its ability to adjust to changes in the signal, the algorithm is well-prepared to deal with non-stationarities in oscillation frequency, even in the presence of multiple oscillation components. CONCLUSIONS: We have created a universal algorithm for oscillation detection and phase prediction, which performs well and is user-friendly at the same time, making closed-loop phase-locked stimulation experiments easier to accomplish.

Microstimulation of macaque area LIP affects decision-making in a motion discrimination task.

A central goal of cognitive neuroscience is to elucidate the neural mechanisms underlying decision-making. Recent physiological studies suggest that neurons in association areas may be involved in this process. To test this, we measured the effects of electrical microstimulation in the lateral intraparietal area (LIP) while monkeys performed a reaction-time motion discrimination task with a saccadic response. In each experiment, we identified a cluster of LIP cells with overlapping response fields (RFs) and sustained activity during memory-guided saccades. Microstimulation of this cluster caused an increase in the proportion of choices toward the RF of the stimulated neurons. Choices toward the stimulated RF were faster with microstimulation, while choices in the opposite direction were slower. Microstimulation never directly evoked saccades, nor did it change reaction times in a simple saccade task. These results demonstrate that the discharge of LIP neurons is causally related to decision formation in the discrimination task.

Differentiating between integration and non-integration strategies in perceptual decision making.

Many tasks used to study decision-making encourage subjects to integrate evidence over time. Such tasks are useful to understand how the brain operates on multiple samples of information over prolonged timescales, but only if subjects actually integrate evidence to form their decisions. We explored the behavioral observations that corroborate evidence-integration in a number of task-designs. Several commonly accepted signs of integration were also predicted by non-integration strategies. Furthermore, an integration model could fit data generated by non-integration models. We identified the features of non-integration models that allowed them to mimic integration and used these insights to design a motion discrimination task that disentangled the models. In human subjects performing the task, we falsified a non-integration strategy in each and confirmed prolonged integration in all but one subject. The findings illustrate the difficulty of identifying a decision-maker's strategy and support solutions to achieve this goal.

Perceptual decisions between multiple directions of visual motion.

Previous studies and models of perceptual decision making have largely focused on binary choices. However, we often have to choose from multiple alternatives. To study the neural mechanisms underlying multialternative decision making, we have asked human subjects to make perceptual decisions between multiple possible directions of visual motion. Using a multicomponent version of the random-dot stimulus, we were able to control experimentally how much sensory evidence we wanted to provide for each of the possible alternatives. We demonstrate that this task provides a rich quantitative dataset for multialternative decision making, spanning a wide range of accuracy levels and mean response times. We further present a computational model that can explain the structure of our behavioral dataset. It is based on the idea of a race between multiple integrators to a decision threshold. Each of these integrators accumulates net sensory evidence for a particular choice, provided by linear combinations of the activities of decision-relevant pools of sensory neurons.

Stimulus and response conflict processing during perceptual decision making.

Encoding and dealing with conflicting information is essential for successful decision making in a complex environment. In the present fMRI study, stimulus conflict and response conflict are contrasted in the context of a perceptual decision-making dot-motion discrimination task. Stimulus conflict was manipulated by varying dot-motion coherence along task-relevant and task-irrelevant dimensions. Response conflict was manipulated by varying whether or not competing stimulus dimensions provided evidence for the same or different responses. The right inferior frontal gyrus was involved specifically in the resolution of stimulus conflict, whereas the dorsal anterior cingulate cortex was shown to be sensitive to response conflict. Additionally, two regions that have been linked to perceptual decision making with dot-motion stimuli in monkey physiology studies were differentially engaged by stimulus conflict and response conflict. The middle temporal area, previously linked to processing of motion, was strongly affected by the presence of stimulus conflict. On the other hand, the superior parietal lobe, previously associated with accumulation of evidence for a response, was affected by the presence of response conflict. These results shed light on the neural mechanisms that support decision making in the presence of conflict, a cognitive operation fundamental to both basic survival and high-level cognition.

Mind Over Matter: Cognitive Neuroengineering.

Brain-machine interface-once the stuff of science fiction novels-is coming to a computer near you. The only question is: How soon? While the technology is in its infancy, it is already helping people with spinal cord injuries. Our authors examine its potential to be the ultimate game changer for any number of neurodegenerative diseases, as well as behavior, learning, and memory. They take the temperature of where the technology is, where it is going, and the inevitable ethical and regulatory implications.

Microstimulation of visual cortex affects the speed of perceptual decisions.

Direction-selective neurons in the middle temporal visual area (MT) are crucially involved in motion perception, although it is not known exactly how the activity of these neurons is interpreted by the rest of the brain. Here we report that in a two-alternative task, the activity of MT neurons is interpreted as evidence for one direction and against the other. We measured the speed and accuracy of decisions as rhesus monkeys performed a direction-discrimination task. On half of the trials, we stimulated direction-selective neurons in area MT, thereby causing the monkeys to choose the neurons' preferred direction more often. Microstimulation quickened decisions in favor of the preferred direction and slowed decisions in favor of the opposite direction. Even on trials in which microstimulation did not induce a preferred direction choice, it still affected response times. Our findings suggest that during the formation of a decision, sensory evidence for competing propositions is compared and accumulates to a decision-making threshold.

Toward a Neuroscience of Adult Cognitive Developmental Theory.

Piaget's genetic epistemology has provided the constructivist approach upon which child developmental theories were founded, in that infants are thought to progress through distinct cognitive stages until they reach maturity in their early 20's. However, it is now well established that cognition continues to develop after early adulthood, and several "neo-Piagetian" theories have emerged in an attempt to better characterize adult cognitive development. For example, Kegan's Constructive Developmental Theory (CDT) argues that the thought processes used by adults to construct their reality change over time, and reaching higher stages of cognitive development entails becoming objectively aware of emotions and beliefs that were previously in the realm of the subconscious. In recent years, neuroscience has shown a growing interest in the biological substrates and neural mechanisms encompassing adult cognitive development, because psychological and psychiatric disorders can arise from deficiencies therein. In this article, we will use Kegan's CDT as a framework to discuss adult cognitive development in relation to closely correlated existing constructs underlying social processing, such as the perception of self and others. We will review the functional imaging and electrophysiologic evidence behind two key concepts relating to these posited developmental changes. These include self-related processing, a field that distinguishes between having conscious experiences ("being a self") and being aware of oneself having conscious experiences ("being aware of being a self"); and theory of mind, which is the objective awareness of possessing mental states such as beliefs and desires (i.e., having a "mind") and the understanding that others possess mental states that can be different from one's own. We shall see that cortical midline structures, including the medial prefrontal cortex and cingulate gyrus, as well as the temporal lobe, are associated with psychological tasks that test these models. In addition, we will review computational modeling approaches to cognitive development, and show how mathematical modeling can provide insights into how sometimes continuous changes in the neural processing substrate can give rise to relatively discrete developmental stages. Because deficiencies in adult cognitive development can result in disorders such as autism and depression, bridging the gaps between developmental psychology, neuroscience, and modeling has potential implications for clinical practice. As neuromodulation techniques such as deep brain and transcranial stimulation continue to advance, interfacing with these systems may lead to the emergence of novel investigational methods and therapeutic strategies in adults suffering from developmental disorders.

Paradoxical Decision-Making: A Framework for Understanding Cognition in Parkinson's Disease.

People with Parkinson's disease (PD) show impaired decision-making when sensory and memory information must be combined. This recently identified impairment results from an inability to accumulate the proper amount of information needed to make a decision and appears to be independent of dopamine tone and reinforcement learning mechanisms. Although considerable work focuses on PD and decisions involving risk and reward, in this Opinion article we propose that the emerging findings in perceptual decision-making highlight the multisystem nature of PD, and that unraveling the neuronal circuits underlying perceptual decision-making impairment may help in understanding other cognitive impairments in people with PD. We also discuss how a decision-making framework may be extended to gain insights into mechanisms of motor impairments in PD.

Perceptual Decisions in the Presence of Relevant and Irrelevant Sensory Evidence.

Perceptual decisions in the presence of decision-irrelevant sensory information require a selection of decision-relevant sensory evidence. To characterize the mechanism that is responsible for separating decision-relevant from irrelevant sensory information we asked human subjects to make judgments about one of two simultaneously present motion components in a random dot stimulus. Subjects were able to ignore the decision-irrelevant component to a large degree, but their decisions were still influenced by the irrelevant sensory information. Computational modeling revealed that this influence was not simply the consequence of subjects forgetting at times which stimulus component they had been instructed to base their decision on. Instead, residual irrelevant information always seems to be leaking through, and the decision process is captured by a net sensory evidence signal being accumulated to a decision threshold. This net sensory evidence is a linear combination of decision-relevant and irrelevant sensory information. The selection process is therefore well-described by a strong linear gain modulation, which, in our experiment, resulted in the relevant sensory evidence having at least 10 times more impact on the decision than the irrelevant evidence.

Oxidized plasma high-density lipoprotein is decreased in Alzheimer's disease.

Oxidative stress is implicated in the pathogenesis of Alzheimer's disease (AD), and the enzyme myeloperoxidase (MPO) has been identified as one source of reactive oxidants. MPO-mediated oxidation of high-density lipoprotein (HDL) plays an important role in the pathogenesis of atherosclerosis and although several links between cardiovascular disease and AD have been reported, surprisingly little is known about the role of HDL oxidation in AD. We show that MPO binding to isolated HDL depends on the lipidation state of apolipoprotein A-I (apo A-I), the major protein constituent of HDL. When quantifying apo A-I and oxidized HDL in plasma of AD patients and cognitive healthy, age- and gender matched controls, we observed similar apo A-I levels in AD patients (263 +/- 70 mg/dl) and controls (268 +/- 70 mg/dl, p = 0.83). In striking contrast, oxidized HDL was significantly reduced in AD patients (4.72 +/- 1.91 U/dl) compared to controls (6.98 +/- 3.32 U/dl, p = 0.012). The marked decrease of oxidized HDL in AD patients is surprising considering the current oxidation hypothesis. We suggest that additional mechanisms, including increased antioxidant production and/or altered lipoprotein metabolism, might be involved in AD pathology.

Computational approaches to visual decision making.

Computational models based on diffusion processes have been proposed to account for human decision making behaviour in a variety of tasks. This study explores whether such models account for the speed and accuracy of perceptual decisions in a reaction-time random dot motion direction-discrimination task and whether they explain the decision-related activity of neurons recorded from the parietal cortex (area LIP) of monkeys performing the task. While a relatively simple diffusion model can explain the psychometric function and the mean response times, it fails to account for the response time distributions. By adding an 'urgency mechanism' to the diffusion model the psychometric function, the mean response times, and the shape of the response time distributions can be explained. Such an urgency mechanism could be implemented in different ways, but the best match between the physiological data and model predictions is provided by a diffusion process with a time-variant gain of the sensory signals. It can be shown that such a time-variant decision process allows the monkey to perform optimally (in the sense of maximizing reward rate) given the risk of aborting a trial by breaking fixation before a choice can be reported.

Stochastic models of decisions about motion direction: behavior and physiology.

Roitman and Shadlen [Roitman J. D., & Shadlen M. N. (2002). Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. Journal of Neuroscience, 22, 9475-9489] have published a non-human primate study on visual decision making. They collected both behavioral and neurophysiological data and provided evidence that the data are qualitatively consistent with a mechanism based on accumulating sensory evidence up to a decision threshold. I have previously demonstrated that a time-variant diffusion model can account quite well quantitatively for both the behavioral and the neural data. In this manuscript I discuss how well the data constrains different components and parameters of the computational process. I also discuss the biological plausibility of the model parameters. I will demonstrate that a relatively large class of models, both with and without temporal integration and both stationary and time-variant could account for the behavioral data. Both the single cell recordings from the parietal cortex and previously published data from the extrastriate visual cortex provide additional constraints. Overall, the data favor a diffusion model with time-variant gain and leaky integrators. The integration time constant, however, turns out not to be well-constrained by the data.

Patients with Parkinson's Disease Show Impaired Use of Priors in Conditions of Sensory Uncertainty.

Perceptual decisions arise after considering the available sensory evidence [1]. When sensory information is unreliable, a good strategy is to rely on previous experience in similar situations to guide decisions [2-6]. It is well known that patients with Parkinson's disease (PD) are impaired at value-based decision-making [7-11]. How patients combine past experience and sensory information to make perceptual decisions is unknown. We developed a novel, perceptual decision-making task and manipulated the statistics of the sensory stimuli presented to patients with PD and healthy participants to determine the influence of past experience on decision-making. We show that patients with PD are impaired at combining previously learned information with current sensory information to guide decisions. We modeled the results using the drift-diffusion model (DDM) and found that the impairment corresponds to a failure in adjusting the amount of sensory evidence needed to make a decision. Our modeling results also show that two complementary mechanisms operate to implement a bias when two sets of priors are learned concurrently. Asymmetric decision threshold adjustments, as reflected by changes in the starting point of evidence accumulation, are responsible for a general choice bias, whereas the adjustment of a dynamic bias that develops over the course of a trial, as reflected by a drift-rate offset, provides the stimulus-specific component of the prior. A proper interplay between these two processes is required to implement a bias based on concurrent, stimulus-specific priors in decision-making. We show here that patients with PD are impaired in these across-trial decision threshold adjustments.

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.

Haptic texture affects the kinematics of pointing movements, but not of eye movements.

Discrepant findings on the degree of eye-hand coupling suggest its dependence on the task. One task characteristic modulating this coupling may be the relevance of certain target attributes for each motor system. We tested this assumption by comparing eye and hand movements towards targets of different haptic texture, a target attribute which is behaviourally relevant only to the hand, not the eye. Pointing to a slippery target (fur) resulted in longer hand movement time than to a rougher target (sandpaper). This effect was due to an increased ratio of time spent in deceleration. In contrast, eye movement time was invariant across different haptic target textures. Thus, information about target texture is used differently by eye and hand.