Causal role of dorsolateral prefrontal cortex in human perceptual decision making

The way that we interpret and interact with the world entails making decisions on the basis of available sensory evidence. Recent primate neurophysiology [1-6], human neuroimaging [7-13], and modeling experiments [14-19] have demonstrated that perceptual decisions are based on an integrative process in which sensory evidence accumulates over time until an internal decision bound is reached. Here we used repetitive transcranial magnetic stimulation (rTMS) to provide causal support for the role of the dorsolateral prefrontal cortex (DLPFC) in this integrative process. Specifically, we used a speeded perceptual categorization task designed to induce a time-dependent accumulation of sensory evidence through rapidly updating dynamic stimuli and found that disruption of the left DLPFC with low-frequency rTMS reduced accuracy and increased response times relative to a sham condition. Importantly, using the drift-diffusion model, we show that these behavioral effects correspond to a decrease in drift rate, a parameter describing the rate and thereby the efficiency of the sensory evidence integration in the decision process. These results provide causal evidence linking the DLPFC to the mechanism of evidence accumulation during perceptual decision making.     

Noise and correlations in parallel perceptual decision making

Perceptual decisions involve the accumulation of sensory evidence over time, a process that is corrupted by noise [1]. Here, we extend the decision-making framework to crossmodal research [2, 3] and the parallel processing of two distinct signals presented to different sensory modalities like vision and audition. Contrary to the widespread view that multisensory signals are integrated prior to a single decision [4-10], we show that evidence is accumulated for each signal separately and that consequent decisions are flexibly coupled by logical operations. We find that the strong correlation of response latencies from trial to trial is critical to explain the short latencies of multisensory decisions. Most critically, we show that increased noise in multisensory decisions is needed to explain the mean and the variability of response latencies. Precise knowledge of these key factors is fundamental for the study and understanding of parallel decision processes with multisensory signals.     

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.     

The neural basis of perceptual learning

Perceptual learning is a lifelong process. We begin by encoding information about the basic structure of the natural world and continue to assimilate information about specific patterns with which we become familiar. The specificity of the learning suggests that all areas of the cerebral cortex are plastic and can represent various aspects of learned information. The neural substrate of perceptual learning relates to the nature of the neural code itself, including changes in cortical maps, in the temporal characteristics of neuronal responses, and in modulation of contextual influences. Top-down control of these representations suggests that learning involves an interaction between multiple cortical areas.     

The neural mechanisms of perceptual filling-in

Filling-in is a perceptual phenomenon in which a visual attribute such as colour, brightness, texture or motion is perceived in a region of the visual field even though such an attribute exists only in the surround. Filling-in dramatically reveals the dissociation between the retinal input and the percept, and raises fundamental questions about how these two relate to each other. Filling-in is observed in various situations, and is an essential part of our normal surface perception. Here, I review recent experiments examining brain activities associated with filling-in, and discuss possible neural mechanisms underlying this remarkable perceptual phenomenon. The evidence shows that neuronal activities in early visual cortical areas are involved in filling-in, providing new insights into visual cortical functions.     

Flutter discrimination: neural codes,perception, memory and decision making

Recent studies combining psychophysical and neurophysiological experiments in behaving monkeys have provided new insights into how several cortical areas integrate efforts to solve a vibrotactile discrimination task. In particular, these studies have addressed how neural codes are related to perception, working memory and decision making in this model. The primary somatosensory cortex drives higher cortical areas where past and current sensory information are combined, such that a comparison of the two evolves into a behavioural decision. These and other observations in visual tasks indicate that decisions emerge from highly-distributed processes in which the details of a scheduled motor plan are gradually specified by sensory information.     

Selective attention increases choice certainty in human decision making

Choice certainty is a probabilistic estimate of past performance and expected outcome. In perceptual decisions the degree of confidence correlates closely with choice accuracy and reaction times, suggesting an intimate relationship to objective performance. Here we show that spatial and feature-based attention increase human subjects' certainty more than accuracy in visual motion discrimination tasks. Our findings demonstrate for the first time a dissociation of choice accuracy and certainty with a significantly stronger influence of voluntary top-down attention on subjective performance measures than on objective performance. These results reveal a so far unknown mechanism of the selection process implemented by attention and suggest a unique biological valence of choice certainty beyond a faithful reflection of the decision process.     

Seeing at a glance, smelling in a whiff: rapid forms of perceptual decision making

Intuitively, decisions should always improve with more time for the accumulation of evidence, yet psychophysical data show a limit of 200-300 ms for many perceptual tasks. Here, we consider mechanisms that favour such rapid information processing in vision and olfaction. We suggest that the brain limits some types of perceptual processing to short, discrete chunks (for example, eye fixations and sniffs) in order to facilitate the construction of global sensory images.     

A weighted constraint satisfaction approach to human goal-directed decision making

When we plan for long-range goals, proximal information cannot be exploited in a blindly myopic way, as relevant future information must also be considered. But when a subgoal must be resolved first, irrelevant future information should not interfere with the processing of more proximal, subgoal-relevant information. We explore the idea that decision making in both situations relies on the flexible modulation of the degree to which different pieces of information under consideration are weighted, rather than explicitly decomposing a problem into smaller parts and solving each part independently. We asked participants to find the shortest goal-reaching paths in mazes and modeled their initial path choices as a noisy, weighted information integration process. In a base task where choosing the optimal initial path required weighting starting-point and goal-proximal factors equally, participants did take both constraints into account, with participants who made more accurate choices tending to exhibit more balanced weighting. The base task was then embedded as an initial subtask in a larger maze, where the same two factors constrained the optimal path to a subgoal, and the final goal position was irrelevant to the initial path choice. In this more complex task, participants’ choices reflected predominant consideration of the subgoal-relevant constraints, but also some influence of the initially-irrelevant final goal. More accurate participants placed much less weight on the optimality-irrelevant goal and again tended to weight the two initially-relevant constraints more equally. These findings suggest that humans may rely on a graded, task-sensitive weighting of multiple constraints to generate approximately optimal decision outcomes in both hierarchical and non-hierarchical goal-directed tasks.     

The molecular machines that mediate microRNA maturation

MicroRNAs (miRNA) are small RNAs that regulate the translation of thousands of message RNAs and play a profound role in mammalian biology. Over the past 5 years, significant advances have been made towards understanding the pathways that generate miRNAs and the mechanisms by which miRNAs exert their regulatory functions. An emerging theme is that miRNAs are both generated by and utilized by large and complex macromolecular assemblies. Here, we review the biology of mammalian miRNAs with a focus on the macromolecular complexes that generate and control the biogenesis of miRNAs.     

Choosing the greater of two goods: neural currencies for valuation and decision making

To make adaptive decisions, animals must evaluate the costs and benefits of available options. The nascent field of neuroeconomics has set itself the ambitious goal of understanding the brain mechanisms that are responsible for these evaluative processes. A series of recent neurophysiological studies in monkeys has begun to address this challenge using novel methods to manipulate and measure an animal's internal valuation of competing alternatives. By emphasizing the behavioural mechanisms and neural signals that mediate decision making under conditions of uncertainty, these studies might lay the foundation for an emerging neurobiology of choice behaviour.     

A plausible neural circuit for decision making and its formation based on reinforcement learning

A human’s, or lower insects’, behavior is dominated by its nervous system. Each stable behavior has its own inner steps and control rules, and is regulated by a neural circuit. Understanding how the brain influences perception, thought, and behavior is a central mandate of neuroscience. The phototactic flight of insects is a widely observed deterministic behavior. Since its movement is not stochastic, the behavior should be dominated by a neural circuit. Based on the basic firing characteristics of biological neurons and the neural circuit’s constitution, we designed a plausible neural circuit for this phototactic behavior from logic perspective. The circuit’s output layer, which generates a stable spike firing rate to encode flight commands, controls the insect’s angular velocity when flying. The firing pattern and connection type of excitatory and inhibitory neurons are considered in this computational model. We simulated the circuit’s information processing using a distributed PC array, and used the real-time average firing rate of output neuron clusters to drive a flying behavior simulation. In this paper, we also explored how a correct neural decision circuit is generated from network flow view through a bee’s behavior experiment based on the reward and punishment feedback mechanism. The significance of this study: firstly, we designed a neural circuit to achieve the behavioral logic rules by strictly following the electrophysiological characteristics of biological neurons and anatomical facts. Secondly, our circuit’s generality permits the design and implementation of behavioral logic rules based on the most general information processing and activity mode of biological neurons. Thirdly, through computer simulation, we achieved new understanding about the cooperative condition upon which multi-neurons achieve some behavioral control. Fourthly, this study aims in understanding the information encoding mechanism and how neural circuits achieve behavior control. Finally, this study also helps establish a transitional bridge between the microscopic activity of the nervous system and macroscopic animal behavior.