Optimal decision-making theories: linking neurobiology with behaviour

Controlled variations in stimulus similarity during learning determine visual discrimination capacity in freely moving mice

The mouse is receiving growing interest as a model organism for studying visual perception. However, little is known about how discrimination and learning interact to produce visual conditioned responses. Here, we adapted a two-alternative forced-choice visual discrimination task for mice and examined how training with equiprobable stimuli of varying similarity influenced conditioned response and discrimination performance as a function of learning. Our results indicate that the slope of the gradients in similarity during training determined the learning rate, the maximum performance and the threshold for successful discrimination. Moreover, the learning process obeyed an inverse relationship between discrimination performance and discriminative resolution, implying that sensitivity within a similarity range cannot be improved without sacrificing performance in another. Our study demonstrates how the interplay between discrimination and learning controls visual discrimination capacity and introduces a new training protocol with quantitative measures to study perceptual learning and visually-guided behavior in freely moving mice.

Decision making by urgency gating: theory and experimental support

It is often suggested that decisions are made when accumulated sensory information reaches a fixed accuracy criterion. This is supported by many studies showing a gradual build up of neural activity to a threshold. However, the proposal that this build up is caused by sensory accumulation is challenged by findings that decisions are based on information from a time window much shorter than the build-up process. Here, we propose that in natural conditions where the environment can suddenly change, the policy that maximizes reward rate is to estimate evidence by accumulating only novel information and then compare the result to a decreasing accuracy criterion. We suggest that the brain approximates this policy by multiplying an estimate of sensory evidence with a motor-related urgency signal and that the latter is primarily responsible for neural activity build up. We support this hypothesis using human behavioral data from a modified random-dot motion task in which motion coherence changes during each trial.

Unreliable gut feelings can lead to correct decisions: the somatic marker hypothesis in non-linear decision chains

Dual-process approaches of decision-making examine the interaction between affective/intuitive and deliberative processes underlying value judgment. From this perspective, decisions are supported by a combination of relatively explicit capabilities for abstract reasoning and relatively implicit evolved domain-general as well as learned domain-specific affective responses. One such approach, the somatic markers hypothesis (SMH), expresses these implicit processes as a system of evolved primary emotions supplemented by associations between affect and experience that accrue over lifetime, or somatic markers. In this view, somatic markers are useful only if their local capability to predict the value of an action is above a baseline equal to the predictive capability of the combined rational and primary emotional subsystems. We argue that decision-making has often been conceived of as a linear process: the effect of decision sequences is additive, local utility is cumulative, and there is no strong environmental feedback. This widespread assumption can have consequences for answering questions regarding the relative weight between the systems and their interaction within a cognitive architecture. We introduce a mathematical formalization of the SMH and study it in situations of dynamic, non-linear decision chains using a discrete-time stochastic model. We find, contrary to expectations, that decision-making events can interact non-additively with the environment in apparently paradoxical ways. We find that in non-lethal situations, primary emotions are represented globally over and above their local weight, showing a tendency for overcautiousness in situated decision chains. We also show that because they tend to counteract this trend, poorly attuned somatic markers that by themselves do not locally enhance decision-making, can still produce an overall positive effect. This result has developmental and evolutionary implications since, by promoting exploratory behavior, somatic markers would seem to be beneficial even at early stages when experiential attunement is poor. Although the model is formulated in terms of the SMH, the implications apply to dual systems theories in general since it makes minimal assumptions about the nature of the processes involved.

Is saccade averaging determined by visual processing or movement planning?

Saccadic averaging that causes subjects' gaze to land between the location of two targets when faced with simultaneously or sequentially presented stimuli has been often used as a probe to investigate the nature of computations that transform sensory representations into an oculomotor plan. Since saccadic movements involve at least two processing stages-a visual stage that selects a target and a movement stage that prepares the response-saccade averaging can either occur due to interference in visual processing or movement planning. By having human subjects perform two versions of a saccadic double-step task, in which the stimuli remained the same, but different instructions were provided (REDIRECT gaze to the later-appearing target vs. FOLLOW the sequence of targets in their order of appearance), we tested two alternative hypotheses. If saccade averaging were due to visual processing alone, the pattern of saccade averaging is expected to remain the same across task conditions. However, whereas subjects produced averaged saccades between two targets in the FOLLOW condition, they produced hypometric saccades in the direction of the initial target in the REDIRECT condition, suggesting that the interaction between competing movement plans produces saccade averaging.

The construction of confidence in a perceptual decision

Decision-making involves the selection of one out of many possible courses of action. A decision may bear on other decisions, as when humans seek a second medical opinion before undergoing a risky surgical intervention. These “meta-decisions” are mediated by confidence judgments—the degree to which decision-makers consider that a choice is likely to be correct. We studied how subjective confidence is constructed from noisy sensory evidence. The psychophysical kernels used to convert sensory information into choice and confidence decisions were precisely reconstructed measuring the impact of small fluctuations in sensory input. This is shown in two independent experiments in which human participants made a decision about the direction of motion of a set of randomly moving dots, or compared the brightness of a group of fluctuating bars, followed by a confidence report. The results of both experiments converged to show that: (1) confidence was influenced by evidence during a short window of time at the initial moments of the decision, and (2) confidence was influenced by evidence for the selected choice but was virtually blind to evidence for the non-selected choice. Our findings challenge classical models of subjective confidence—which posit that the difference of evidence in favor of each choice is the seed of the confidence signal.

The effects of evidence bounds on decision-making: theoretical and empirical developments

Converging findings from behavioral, neurophysiological, and neuroimaging studies suggest an integration-to-boundary mechanism governing decision formation and choice selection. This mechanism is supported by sequential sampling models of choice decisions, which can implement statistically optimal decision strategies for selecting between multiple alternative options on the basis of sensory evidence. This review focuses on recent developments in understanding the evidence boundary, an important component of decision-making raised by experimental findings and models. The article starts by reviewing the neurobiology of perceptual decisions and several influential sequential sampling models, in particular the drift-diffusion model, the Ornstein–Uhlenbeck model and the leaky-competing-accumulator model. In the second part, the article examines how the boundary may affect a model’s dynamics and performance and to what extent it may improve a model’s fits to experimental data. In the third part, the article examines recent findings that support the presence and site of boundaries in the brain. The article considers two questions: (1) whether the boundary is a spontaneous property of neural integrators, or is controlled by dedicated neural circuits; (2) if the boundary is variable, what could be the driving factors behind boundary changes? The review brings together studies using different experimental methods in seeking answers to these questions, highlights psychological and physiological factors that may be associated with the boundary and its changes, and further considers the evidence boundary as a generic mechanism to guide complex behavior.

Single-trial analysis of neuroimaging data: inferring neural networks underlying perceptual decision-making in the human brain

Advances in neural signal and image acquisition as well as in multivariate signal processing and machine learning are enabling a richer and more rigorous understanding of the neural basis of human decision-making. Decision-making is essentially characterized behaviorally by the variability of the decision across individual trials--e.g., error and response time distributions. To infer the neural processes that govern decision-making requires identifying neural correlates of such trial-to-trial behavioral variability. In this paper, we review efforts that utilize signal processing and machine learning to enable single-trial analysis of neural signals acquired while subjects perform simple decision-making tasks. Our focus is on neuroimaging data collected noninvasively via electroencephalograpy (EEG) and functional magnetic resonance imaging (fMRI). We review the specific framework for extracting decision-relevant neural components from the neuroimaging data, the goal being to analyze the trial-to-trial variability of the neural signal along these component directions and to relate them to elements of the decision-making process. We review results for perceptual decision-making and discrimination tasks, including paradigms in which EEG variability is used to inform an fMRI analysis. We discuss how single-trial analysis reveals aspects of the underlying decision-making networks that are unobservable using traditional trial-averaging methods.

Knowing how much you don't know: a neural organization of uncertainty estimates

How we estimate uncertainty is important in decision neuroscience and has wide-ranging implications in basic and clinical neuroscience, from computational models of optimality to ideas on psychopathological disorders including anxiety, depression and schizophrenia. Empirical research in neuroscience, which has been based on divergent theoretical assumptions, has focused on the fundamental question of how uncertainty is encoded in the brain and how it influences behaviour. Here, we integrate several theoretical concepts about uncertainty into a decision-making framework. We conclude that the currently available evidence indicates that distinct neural encoding (including summary statistic-type representations) of uncertainty occurs in distinct neural systems.

The attentional drift-diffusion model extends to simple purchasing decisions

How do we make simple purchasing decisions (e.g., whether or not to buy a product at a given price)? Previous work has shown that the attentional drift-diffusion model (aDDM) can provide accurate quantitative descriptions of the psychometric data for binary and trinary value-based choices, and of how the choice process is guided by visual attention. Here we extend the aDDM to the case of purchasing decisions, and test it using an eye-tracking experiment. We find that the model also provides a reasonably accurate quantitative description of the relationship between choice, reaction time, and visual fixations using parameters that are very similar to those that best fit the previous data. The only critical difference is that the choice biases induced by the fixations are about half as big in purchasing decisions as in binary choices. This suggests that a similar computational process is used to make binary choices, trinary choices, and simple purchasing decisions.

Building Bridges between Perceptual and Economic Decision-Making: Neural and Computational Mechanisms

Investigation into the neural and computational bases of decision-making has proceeded in two parallel but distinct streams. Perceptual decision-making (PDM) is concerned with how observers detect, discriminate, and categorize noisy sensory information. Economic decision-making (EDM) explores how options are selected on the basis of their reinforcement history. Traditionally, the sub-fields of PDM and EDM have employed different paradigms, proposed different mechanistic models, explored different brain regions, disagreed about whether decisions approach optimality. Nevertheless, we argue that there is a common framework for understanding decisions made in both tasks, under which an agent has to combine sensory information (what is the stimulus) with value information (what is it worth). We review computational models of the decision process typically used in PDM, based around the idea that decisions involve a serial integration of evidence, and assess their applicability to decisions between good and gambles. Subsequently, we consider the contribution of three key brain regions – the parietal cortex, the basal ganglia, and the orbitofrontal cortex (OFC) – to perceptual and EDM, with a focus on the mechanisms by which sensory and reward information are integrated during choice. We find that although the parietal cortex is often implicated in the integration of sensory evidence, there is evidence for its role in encoding the expected value of a decision. Similarly, although much research has emphasized the role of the striatum and OFC in value-guided choices, they may play an important role in categorization of perceptual information. In conclusion, we consider how findings from the two fields might be brought together, in order to move toward a general framework for understanding decision-making in humans and other primates.

Computational models of decision making: integration, stability, and noise

Decision making demands the accumulation of sensory evidence over time. Questions remain about how this occurs, but recent years have seen progress on several fronts. The first concerns when optimal accumulation of evidence coincides with the simplest method of accumulating neural activity: summation over time. The second involves what computations the brain might perform when summation is difficult due to imprecision in neural circuits or is suboptimal due to uncertainty or variability in how evidence arrives. Finally, the third concerns sources of noise in decision circuits. Empirical studies have better constrained the extent of this noise, and modeling work is helping to clarify its possible origins.

Knowing When to Move On

We investigated people’s ability to decide how much time to spend on the task at hand. To make such decisions well, one must take into account, among other things, the cost of failing and how one’s task performance changes as a function of time. We first investigated timing decisions when the underlying task was perceptual. Decisions were highly efficient and suggested that people can make good use of perceptual knowledge and abstract reward information. Previous studies have found that perceptual decisions are generally optimal, but that cognitive decisions are generally suboptimal—a perception-cognition gap. Does a similar gap exist for timing decisions? We compared timing decisions for a perceptual task with timing decisions for more cognitive tasks. Performance was highly similar across the tasks, which suggests that knowledge can be acquired, and used to make timing decisions, in an equally efficient way regardless of whether that knowledge is derived through perceptual or cognitive experience.

Neuronal activity in the human subthalamic nucleus encodes decision conflict during action selection

The subthalamic nucleus (STN), which receives excitatory inputs from the cortex and has direct connections with the inhibitory pathways of the basal ganglia, is well positioned to efficiently mediate action selection. Here, we use microelectrode recordings captured during deep brain stimulation surgery as participants engage in a decision task to examine the role of the human STN in action selection. We demonstrate that spiking activity in the STN increases when participants engage in a decision and that the level of spiking activity increases with the degree of decision conflict. These data implicate the STN as an important mediator of action selection during decision processes.

Bias in the brain: a diffusion model analysis of prior probability and potential payoff

In perceptual decision-making, advance knowledge biases people toward choice alternatives that are more likely to be correct and more likely to be profitable. Accumulation-to-bound models provide two possible explanations for these effects: prior knowledge about the relative attractiveness of the alternatives at hand changes either the starting point of the decision process, or the rate of evidence accumulation. Here, we used model-based functional MRI to investigate whether these effects are similar for different types of prior knowledge, and whether there is a common neural substrate underlying bias in simple perceptual choices. We used two versions of the random-dot motion paradigm in which we manipulated bias by: (1) changing the prior likelihood of occurrence for two alternatives ("prior probability") and (2) assigning a larger reward to one of two alternatives ("potential payoff"). Human subjects performed the task inside and outside a 3T MRI scanner. For each manipulation, bias was quantified by fitting the drift diffusion model to the behavioral data. Individual measurements of bias were then used in the imaging analyses to identify regions involved in biasing choice behavior. Behavioral results showed that subjects tended to make more and faster choices toward the alternative that was most probable or had the largest payoff. This effect was primarily due to a change in the starting point of the accumulation process. Imaging results showed that, at cue level, regions of the frontoparietal network are involved in changing the starting points in both manipulations, suggesting a common mechanism underlying the biasing effects of prior knowledge.

Meditate to create: the impact of focused-attention and open-monitoring training on convergent and divergent thinking

The practice of meditation has seen a tremendous increase in the western world since the 60s. Scientific interest in meditation has also significantly grown in the past years; however, so far, it has neglected the idea that different type of meditations may drive specific cognitive-control states. In this study we investigate the possible impact of meditation based on focused-attention (FA) and meditation based on open-monitoring (OM) on creativity tasks tapping into convergent and divergent thinking. We show that FA meditation and OM meditation exert specific effect on creativity. First, OM meditation induces a control state that promotes divergent thinking, a style of thinking that allows many new ideas of being generated. Second, FA meditation does not sustain convergent thinking, the process of generating one possible solution to a particular problem. We suggest that the enhancement of positive mood induced by meditating has boosted the effect in the first case and counteracted in the second case.

Entendeu ou quer que eu desenhe?: transições familiares através da visão sistêmica

Este ensaio teórico tem como objetivo apresentar a teoria sistêmica como um meio de filtrar o caos em que ocorrem as transições familiares, através do recurso de imagens que sintetizam o emaranhado das suas interações. Examinou-se, para tal, os alicerces da teoria sistêmica, destrinchou-se as transformações básicas quando da interação entre células individuais e contextos coletivos e, por fim, explanou-se as transições familiares mais comuns. Entende-se que os prejuízos do possível reducionismo acarretado pelas representações gráficas são plenamente compensados pelo ganho em clareza e objetividade que as ilustrações oferecem para cada processo, facilitando a sua compreensão, manipulação e associação.

The role of eye movements in decision making and the prospect of exposure effects

The aim of the current study was to follow on from previous findings that eye movements can have a causal influence on preference formation. Shimojo et al. (2003) previously found that faces that were presented for a longer duration in a two alternative forced choice task were more likely to be judged as more attractive. This effect only occurred when an eye movement was made towards the faces (with no effect when faces were centrally presented). The current study replicated Shimojo et al.'s (2003) design, whilst controlling for potential inter-stimuli interference in central presentations. As per previous findings, when eye movements were made towards the stimuli, faces that were presented for longer durations were preferred. However, faces that were centrally presented (thus not requiring an eye movement) were also preferred in the current study. The presence of an exposure duration effect for centrally presented faces casts doubt on the necessity of the eye movement in this decision making process and has implications for decision theories that place an emphasis on the role of eye movements in decision making.

Paradoxical evidence integration in rapid decision processes

Decisions about noisy stimuli require evidence integration over time. Traditionally, evidence integration and decision making are described as a one-stage process: a decision is made when evidence for the presence of a stimulus crosses a threshold. Here, we show that one-stage models cannot explain psychophysical experiments on feature fusion, where two visual stimuli are presented in rapid succession. Paradoxically, the second stimulus biases decisions more strongly than the first one, contrary to predictions of one-stage models and intuition. We present a two-stage model where sensory information is integrated and buffered before it is fed into a drift diffusion process. The model is tested in a series of psychophysical experiments and explains both accuracy and reaction time distributions.

In models of decision making, evidence is accumulated until it crosses a threshold. The amount of evidence is directly related to the strength of the sensory input for the decision alternatives. Such one-stage models predict that if two stimulus alternatives are presented in succession, the stimulus alternative presented first dominates the decision, as the accumulated evidence will reach the threshold for this alternative first. Here, we show that for short stimulus durations decision making is not dominated by the first, but by the second stimulus. This result cannot be explained by classical one-stage decision models. We present a two-stage model where sensory input is first integrated before its outcome is fed into a classical decision process.

Stop Signals Provide Cross Inhibition in Collective Decision-Making by Honeybee Swarms

Honeybee swarms and complex brains show many parallels in how they make decisions. In both, separate populations of units (bees or neurons) integrate noisy evidence for alternatives, and, when one population exceeds a threshold, the alternative it represents is chosen. We show that a key feature of a brain—cross inhibition between the evidence-accumulating populations—also exists in a swarm as it chooses its nesting site. Nest-site scouts send inhibitory stop signals to other scouts producing waggle dances, causing them to cease dancing, and each scout targets scouts’ reporting sites other than her own. An analytic model shows that cross inhibition between populations of scout bees increases the reliability of swarm decision-making by solving the problem of deadlock over equal sites.

Riding the lexical speedway: a critical review on the time course of lexical selection in speech production

Speech requires time. How much time often depends on the amount of labor the brain has to perform in order to retrieve the linguistic information related to the ideas we want to express. Although most psycholinguistic research in the field of language production has focused on the net result of time required to utter words in various experimental conditions, over the last years more and more researchers pursued the objective to flesh out the time course of particular stages implicated in language production. Here we critically review these studies, with particular interest for the time course of lexical selection. First, we evaluate the data underlying the estimates of an influential temporal meta-analysis on language production (Indefrey and Levelt, 2004). We conclude that those data alone are not sufficient to provide a reliable time frame of lexical selection. Next, we discuss recent neurophysiological evidence which we argue to offer more explicit insights into the time course of lexical selection. Based on this evidence we suggest that, despite the absence of a clear time frame of how long lexical selection takes, there is sufficient direct evidence to conclude that the brain initiates lexical access within 200 ms after stimulus presentation, hereby confirming Indefrey and Levelt's estimate. In a final section, we briefly review the proposed mechanisms which could lead to this rapid onset of lexical access, namely automatic spreading activation versus specific concept selection, and discuss novel data which support the notion of spreading activation, but indicate that the speed with which this principle takes effect is driven by a top-down signal in function of the intention to engage in a speech act.

The decision value computations in the vmPFC and striatum use a relative value code that is guided by visual attention

There is a growing consensus in behavioral neuroscience that the brain makes simple choices by first assigning a value to the options under consideration and then comparing them. Two important open questions are whether the brain encodes absolute or relative value signals, and what role attention might play in these computations. We investigated these questions using a human fMRI experiment with a binary choice task in which the fixations to both stimuli were exogenously manipulated to control for the role of visual attention in the valuation computation. We found that the ventromedial prefrontal cortex and the ventral striatum encoded fixation-dependent relative value signals: activity in these areas correlated with the difference in value between the attended and the unattended items. These attention-modulated relative value signals might serve as the input of a comparator system that is used to make a choice.

Bilingualism and creativity: benefits in convergent thinking come with losses in divergent thinking

Bilingualism is commonly assumed to improve creativity but the mechanisms underlying creative acts, and the way these mechanisms are affected by bilingualism, are not very well understood. We hypothesize that learning to master multiple languages drives individuals toward a relatively focused cognitive-control state that exerts strong top-down impact on information processing and creates strong local competition for selection between cognitive codes. Considering the control requirements posed by creativity tasks tapping into convergent and divergent thinking, this predicts that high-proficient bilinguals should outperform low-proficient bilinguals in convergent thinking, while low-proficient bilinguals might be better in divergent thinking. Comparing low- and high-proficient bilinguals on convergent-thinking and divergent-thinking tasks indeed showed a high-proficient bilingual advantage for convergent thinking but a low-proficient bilingual advantage for fluency in divergent thinking. These findings suggest that bilingualism should not be related to “creativity” as a unitary concept but, rather, to the specific processes and mechanisms that underlie creativity.

Neural mechanisms of saccade target selection: gated accumulator model of the visual-motor cascade

We review a new computational model developed to understand how evidence about stimulus salience in visual search is translated into a saccade command. The model uses the activity of visually responsive neurons in the frontal eye field as evidence for stimulus salience that is accumulated in a network of stochastic accumulators to produce accurate and timely saccades. We discovered that only when the input to the accumulation process was gated could the model account for the variability in search performance and predict the dynamics of movement neuron discharge rates. This union of cognitive modeling and neurophysiology indicates how the visual-motor transformation can occur, and provides a concrete mapping between neuron function and specific cognitive processes.

Transformation of stimulus value signals into motor commands during simple choice

Decision-making can be broken down into several component processes: assigning values to stimuli under consideration, selecting an option by comparing those values, and initiating motor responses to obtain the reward. Although much is known about the neural encoding of stimulus values and motor commands, little is known about the mechanisms through which stimulus values are compared, and the resulting decision is transmitted to motor systems. We investigated this process using human fMRI in a task where choices were indicated using the left or right hand. We found evidence consistent with the hypothesis that value signals are computed in the ventral medial prefrontal cortex, they are passed to regions of dorsomedial prefrontal cortex and intraparietal sulcus, implementing a comparison process, and the output of the comparator regions modulates activity in motor cortex to implement the choice. These results describe the network through which stimulus values are transformed into actions during a simple choice task.

Focusing attention on the health aspects of foods changes value signals in vmPFC and improves dietary choice

Attention is thought to play a key role in the computation of stimulus values at the time of choice, which suggests that attention manipulations could be used to improve decision-making in domains where self-control lapses are pervasive. We used an fMRI food choice task with non-dieting human subjects to investigate whether exogenous cues that direct attention to the healthiness of foods could improve dietary choices. Behaviorally, we found that subjects made healthier choices in the presence of health cues. In parallel, stimulus value signals in ventromedial prefrontal cortex were more responsive to the healthiness of foods in the presence of health cues, and this effect was modulated by activity in regions of dorsolateral prefrontal cortex. These findings suggest that the neural mechanisms used in successful self-control can be activated by exogenous attention cues, and provide insights into the processes through which behavioral therapies and public policies could facilitate self-control.

Decision making and reward in frontal cortex: complementary evidence from neurophysiological and neuropsychological studies

Patients with damage to the prefrontal cortex (PFC)—especially the ventral and medial parts of PFC—often show a marked inability to make choices that meet their needs and goals. These decision-making impairments often reflect both a deficit in learning concerning the consequences of a choice, as well as deficits in the ability to adapt future choices based on experienced value of the current choice. Thus, areas of PFC must support some value computations that are necessary for optimal choice. However, recent frameworks of decision making have highlighted that optimal and adaptive decision making does not simply rest on a single computation, but a number of different value computations may be necessary. Using this framework as a guide, we summarize evidence from both lesion studies and single-neuron physiology for the representation of different value computations across PFC areas.

Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions

How do we make decisions when confronted with several alternatives (e.g., on a supermarket shelf)? Previous work has shown that accumulator models, such as the drift-diffusion model, can provide accurate descriptions of the psychometric data for binary value-based choices, and that the choice process is guided by visual attention. However, the computational processes used to make choices in more complicated situations involving three or more options are unknown. We propose a model of trinary value-based choice that generalizes what is known about binary choice, and test it using an eye-tracking experiment. We find that the model provides a quantitatively accurate description of the relationship between choice, reaction time, and visual fixation data using the same parameters that were estimated in previous work on binary choice. Our findings suggest that the brain uses similar computational processes to make binary and trinary choices.

Consumers can make decisions in as little as a third of a second

We make hundreds of decisions every day, many of them extremely quickly and without much explicit deliberation. This motivates two important open questions: What is the minimum time required to make choices with above chance accuracy? What is the impact of additional decision-making time on choice accuracy? We investigated these questions in four experiments in which subjects made binary food choices using saccadic or manual responses, under either “speed” or “accuracy” instructions. Subjects were able to make above chance decisions in as little as 313 ms, and choose their preferred food item in over 70% of trials at average speeds of 404 ms. Further, slowing down their responses by either asking them explicitly to be confident about their choices, or to respond with hand movements, generated about a 10% increase in accuracy. Together, these results suggest that consumers can make accurate every-day choices, akin to those made in a grocery store, at significantly faster speeds than previously reported.

Persistent storage capability impairs decision making in a biophysical network model

Two long-standing questions in neuroscience concern the mechanisms underlying our abilities to make decisions and to store goal-relevant information in memory for seconds at a time. Recent experimental and theoretical advances suggest that NMDA receptors at intrinsic cortical synapses play an important role in both these functions. The long NMDA time constant is suggested to support persistent mnemonic activity by maintaining excitatory drive after the removal of a stimulus and to enable the slow integration of afferent information in the service of decisions. These findings have led to the hypothesis that the local circuit mechanisms underlying decisions must also furnish persistent storage of information. We use a local circuit cortical model of spiking neurons to test this hypothesis, controlling intrinsic drive by scaling NMDA conductance strength. Our simulations provide further evidence that persistent storage and decision making are supported by common mechanisms, but under biophysically realistic parameters, our model demonstrates that the processing requirements of persistent storage and decision making may be incompatible at the local circuit level. Parameters supporting persistent storage lead to strong dynamics that are at odds with slow integration, whereas weaker dynamics furnish the speed-accuracy trade-off common to psychometric data and decision theory.

Gain modulation by an urgency signal controls the speed-accuracy trade-off in a network model of a cortical decision circuit

The speed-accuracy trade-off (SAT) is ubiquitous in decision tasks. While the neural mechanisms underlying decisions are generally well characterized, the application of decision-theoretic methods to the SAT has been difficult to reconcile with experimental data suggesting that decision thresholds are inflexible. Using a network model of a cortical decision circuit, we demonstrate the SAT in a manner consistent with neural and behavioral data and with mathematical models that optimize speed and accuracy with respect to one another. In simulations of a reaction time task, we modulate the gain of the network with a signal encoding the urgency to respond. As the urgency signal builds up, the network progresses through a series of processing stages supporting noise filtering, integration of evidence, amplification of integrated evidence, and choice selection. Analysis of the network's dynamics formally characterizes this progression. Slower buildup of urgency increases accuracy by slowing down the progression. Faster buildup has the opposite effect. Because the network always progresses through the same stages, decision-selective firing rates are stereotyped at decision time.

Decision confidence and uncertainty in diffusion models with partially correlated neuronal integrators

Diffusion models have become essential for describing the performance and statistics of reaction times in human decision making. Despite their success, it is not known how to evaluate decision confidence from them. I introduce a broader class of models consisting of two partially correlated neuronal integrators with arbitrarily time-varying decision boundaries that allow a natural description of confidence. The dependence of decision confidence on the state of the losing integrator, decision time, time-varying boundaries, and correlations is analytically described. The marginal confidence is computed for the half-anticorrelated case using the exact solution of the diffusion process with constant boundaries and compared to that of the independent and completely anticorrelated cases.

Basic impairments in regulating the speed-accuracy tradeoff predict symptoms of attention-deficit/hyperactivity disorder

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is characterized by poor optimization of behavior in the face of changing demands. Theoretical accounts of ADHD have often focused on higher-order cognitive processes and typically assume that basic processes are unaffected. It is an open question whether this is indeed the case.

METHOD

We explored basic cognitive processing in 25 subjects with ADHD and 30 typically developing children and adolescents with a perceptual decision-making paradigm. We investigated whether individuals with ADHD were able to balance the speed and accuracy of decisions.

RESULTS

We found impairments in the optimization of the speed-accuracy tradeoff. Furthermore, these impairments were directly related to the hyperactive and impulsive symptoms that characterize the ADHD-phenotype.

CONCLUSIONS

These data suggest that impairments in basic cognitive processing are central to the disorder. This calls into question conceptualizations of ADHD as a "higher-order" deficit, as such simple decision processes are at the core of almost every paradigm used in ADHD research.

Decision threshold modulation in the human brain

Perceptual decisions are made when sensory evidence accumulated over time reaches a decision threshold. Because decisions are also guided by prior information, one important factor that is likely to shape how a decision is adaptively tuned to its context is the predictability of forthcoming events. However, little is known about the mechanisms underlying this contextual regulation of the perceptual decision-making process. Mathematical models of decision making predict two possible mechanisms supporting this regulation: an adjustment of the distance to the decision threshold, which leads to a change in the amount of accumulated evidence required to make a decision, or a gain control of the sensory evidence, leading to a change in the slope of the sensory evidence accumulation. Here, we show that predictability of the forthcoming event reduces the distance to the threshold of the decision. Then, combining model-driven fMRI and the framework of information theory, we show that the anterior cingulate cortex (ACC) adjusts the distance to the decision threshold in proportion to the current amount of predictive information and that the dorsolateral cortex (DLPFC) codes the accumulation of sensory evidence. Moreover, the information flow from the ACC to the DLPFC region that accumulates sensory evidence increases when optimal adjustment of the distance to the threshold requires more complex computations, reflecting the increased weight of ACC's regulation signals in the decision process. Our results characterize the respective contributions of the ACC and the DLPFC to contextually optimized decision making.

Stretching the limits of visual attention: the case of action video games

Visual attention is the set of mechanisms by which relevant visual information is selected while irrelevant information is suppressed, thus allowing the observer to function in a world made up of nearly infinite visual information. Recently, those who habitually play video games have been documented to outperform novices in a variety of visual attentional capabilities, including attention in space, in time, and to objects. Training studies have established similar improvements in groups of nongamers given experience playing these video games. Critically, not all video games seem to have such a beneficial effect on attention; it seems that fast-paced, embodied visuo-motor tasks that require divided attention (tasks commonly found in popular action games like Halo) have the greatest effect. At the core of these action video game-induced improvements appears to be a remarkable enhancement in the ability to efficiently deploy endogenous attention. The implications of such an enhancement are relevant to a variety of real-world applications, such as work force training, rehabilitation of clinical populations, and improvement of traditional educational approaches. WIREs Cogni Sci 2011 2 222-230 DOI: 10.1002/wcs.116 For further resources related to this article, please visit the WIREs website.

The Drift Diffusion Model can account for the accuracy and reaction time of value-based choices under high and low time pressure

An important open problem is how values are compared to make simple choices. A natural hypothesis is that the brain carries out the computations associated with the value comparisons in a manner consistent with the Drift Diffusion Model (DDM), since this model has been able to account for a large amount of data in other domains. We investigated the ability of four different versions of the DDM to explain the data in a real binary food choice task under conditions of high and low time pressure. We found that a seven-parameter version of the DDM can account for the choice and reaction time data with high-accuracy, in both the high and low time pressure conditions. The changes associated with the introduction of time pressure could be traced to changes in two key model parameters: the barrier height and the noise in the slope of the drift process.

Religion and the attentional blink: depth of faith predicts depth of the blink

Religion is commonly defined as a set of rules, developed as part of a culture. Here we provide evidence that practice in following these rules systematically changes the way people allocate their attention, as indicated by the attentional blink (AB), a deficit in reporting the second of two target stimuli presented in close succession in a rapid sequence of distracters. We provide evidence that Dutch Calvinists and Atheists, brought up in the same country and culture and controlled for race, intelligence, mood, personality traits, and age, differ with respect to the amount of resources invested into processing AB targets. Calvinists showed a larger AB than Atheists, which is consistent with the notion that people's attentional processing style reflects biases rewarded by their religious beliefs.

Human striatal activation during adjustment of the response criterion in visual word recognition

Results of recent computational modelling studies suggest that a general function of the striatum in human cognition is related to shifting decision criteria in selection processes. We used functional magnetic resonance imaging (fMRI) in 21 healthy subjects to examine the hemodynamic responses when subjects shift their response criterion on a trial-by-trial basis in the lexical decision paradigm. Trial-by-trial criterion setting is obtained when subjects respond faster in trials following a word trial than in trials following nonword trials - irrespective of the lexicality of the current trial. Since selection demands are equally high in the current trials, we expected to observe neural activations that are related to response criterion shifting. The behavioural data show sequential effects with faster responses in trials following word trials compared to trials following nonword trials, suggesting that subjects shifted their response criterion on a trial-by-trial basis. The neural responses revealed a signal increase in the striatum only in trials following word trials. This striatal activation is therefore likely to be related to response criterion setting. It demonstrates a role of the striatum in shifting decision criteria in visual word recognition, which cannot be attributed to pure error-related processing or the selection of a preferred response.

Beneficial effects of the NMDA antagonist ketamine on decision processes in visual search

The ability of sensory-motor circuits to integrate sensory evidence over time is thought to underlie the process of decision-making in perceptual discrimination. Recent work has suggested that the NMDA receptor contributes to mediating neural activity integration. To test this hypothesis, we trained three female rhesus monkeys (Macaca mulatta) to perform a visual search task, in which they had to make a saccadic eye movement to the location of a target stimulus presented among distracter stimuli of lower luminance. We manipulated NMDA-receptor function by administering an intramuscular injection of the noncompetitive NMDA antagonist ketamine and assessed visual search performance before and after manipulation. Ketamine was found to lengthen response latency in a dose-dependent fashion. Surprisingly, it was also observed that response accuracy was significantly improved when lower doses were administered. These findings suggest that NMDA receptors play a crucial role in the process of decision-making in perceptual discrimination. They also further support the idea that multiple neural representations compete with one another through mutual inhibition, which may explain the speed-accuracy trade-off rule that shapes discrimination behavior: lengthening integration time helps resolve small differences between choice alternatives, thereby improving accuracy.

A mechanistic account of value computation in the human brain

To make decisions based on the value of different options, we often have to combine different sources of probabilistic evidence. For example, when shopping for strawberries on a fruit stand, one uses their color and size to infer-with some uncertainty-which strawberries taste best. Despite much progress in understanding the neural underpinnings of value-based decision making in humans, it remains unclear how the brain represents different sources of probabilistic evidence and how they are used to compute value signals needed to drive the decision. Here, we use a visual probabilistic categorization task to show that regions in ventral temporal cortex encode probabilistic evidence for different decision alternatives, while ventromedial prefrontal cortex integrates information from these regions into a value signal using a difference-based comparator operation.