Project description:Brain activity is highly variable even while performing the same cognitive task with consequences for performance. Discovering, characterizing, and linking variability in brain activity to internal processes has primarily relied on experimentally inducing changes (e.g., via attention manipulation) to identify neuronal and behavioral consequences or studying spontaneous changes in ongoing brain dynamics. However, changes in internal processing could arise from many factors, such as variation in strategy or arousal, that are independent of experimental conditions. Here we utilize a data-driven clustering method based on modularity-maximation to identify consistent spatial-temporal EEG activity patterns across individual trials and relate this activity to behavioral performance. Subjects (N = 25) performed a motion direction discrimination task with six interleaved levels of motion coherence. Modularity-maximization based clustering identified two discrete spatial-temporal clusters, or subtypes, of trials with different patterns of brain activity. Surprisingly, even though Subtype 1 occurred more frequently with lower motion coherence, it was nonetheless associated with faster response times. Computational modeling suggests that Subtype 1 was characterized by a lower threshold for reaching a decision. These results highlight trial-to-trial variability in decision processes usually masked to experimenters and provide a method for identifying endogenous brain state variability relevant to cognition and behavior.
Project description:Decision-making requires the accumulation of sensory evidence. However, in everyday life, sensory information is often ambiguous and contains decision-irrelevant features. This means that the brain must disambiguate sensory input and extract decision-relevant features. Sensory information processing and decision-making represent two subsequent stages of the perceptual decision-making process. While sensory processing relies on occipito-parietal neuronal activity during the earlier time window, decision-making lasts for a prolonged time, involving parietal and frontal areas. Although perceptual decision-making is being actively studied, its neuronal mechanisms under ambiguous sensory evidence lack detailed consideration. Here, we analyzed the brain activity of subjects accomplishing a perceptual decision-making task involving the classification of ambiguous stimuli. We demonstrated that ambiguity induced high frontal θ-band power for 0.15 s post-stimulus onset, indicating increased reliance on top-down processes, such as expectations and memory. Ambiguous processing also caused high occipito-parietal β-band power for 0.2 s and high fronto-parietal β-power for 0.35-0.42 s post-stimulus onset. We supposed that the former component reflected the disambiguation process while the latter reflected the decision-making phase. Our findings complemented existing knowledge about ambiguous perception by providing additional information regarding the temporal discrepancy between the different cognitive processes during perceptual decision-making.
Project description:Making optimal decisions in the face of noise requires balancing short-term speed and accuracy. But a theory of optimality should account for the fact that short-term speed can influence long-term accuracy through learning. Here, we demonstrate that long-term learning is an important dynamical dimension of the speed-accuracy trade-off. We study learning trajectories in rats and formally characterize these dynamics in a theory expressed as both a recurrent neural network and an analytical extension of the drift-diffusion model that learns over time. The model reveals that choosing suboptimal response times to learn faster sacrifices immediate reward, but can lead to greater total reward. We empirically verify predictions of the theory, including a relationship between stimulus exposure and learning speed, and a modulation of reaction time by future learning prospects. We find that rats' strategies approximately maximize total reward over the full learning epoch, suggesting cognitive control over the learning process.
Project description:We investigated whether a task requiring concurrent perceptual decision-making and response control can be performed concurrently, whether evidence accumulation and response control are accomplished by the same neurons, and whether perceptual decision-making and countermanding can be unified computationally. Based on neural recordings in a prefrontal area of macaque monkeys, we present behavioral, neural, and computational results demonstrating that perceptual decision-making of varying difficulty can be countermanded efficiently, that single prefrontal neurons instantiate both evidence accumulation and response control, and that an interactive race between stochastic GO evidence accumulators for each alternative and a distinct STOP accumulator fits countermanding choice behavior and replicates neural trajectories. Thus, perceptual decision-making and response control, previously regarded as distinct mechanisms, are actually aspects of a common neuro-computational mechanism supporting flexible behavior.
Project description:Classical models of perceptual decision-making assume that subjects use a single, consistent strategy to form decisions, or that decision-making strategies evolve slowly over time. Here we present new analyses suggesting that this common view is incorrect. We analyzed data from mouse and human decision-making experiments and found that choice behavior relies on an interplay among multiple interleaved strategies. These strategies, characterized by states in a hidden Markov model, persist for tens to hundreds of trials before switching, and often switch multiple times within a session. The identified decision-making strategies were highly consistent across mice and comprised a single 'engaged' state, in which decisions relied heavily on the sensory stimulus, and several biased states in which errors frequently occurred. These results provide a powerful alternate explanation for 'lapses' often observed in rodent behavioral experiments, and suggest that standard measures of performance mask the presence of major changes in strategy across trials.
Project description:In this paper we present, and test in two realistic environments, collaborative Brain-Computer Interfaces (cBCIs) that can significantly increase both the speed and the accuracy of perceptual group decision-making. The key distinguishing features of this work are: (1) our cBCIs combine behavioural, physiological and neural data in such a way as to be able to provide a group decision at any time after the quickest team member casts their vote, but the quality of a cBCI-assisted decision improves monotonically the longer the group decision can wait; (2) we apply our cBCIs to two realistic scenarios of military relevance (patrolling a dark corridor and manning an outpost at night where users need to identify any unidentified characters that appear) in which decisions are based on information conveyed through video feeds; and (3) our cBCIs exploit Event-Related Potentials (ERPs) elicited in brain activity by the appearance of potential threats but, uniquely, the appearance time is estimated automatically by the system (rather than being unrealistically provided to it). As a result of these elements, in the two test environments, groups assisted by our cBCIs make both more accurate and faster decisions than when individual decisions are integrated in more traditional manners.
Project description:Perceptual decisions depend on the ability to exploit available sensory information in order to select the most adaptive option from a set of alternatives. Such decisions depend on the perceptual sensitivity of the organism, which is generally accompanied by a corresponding level of certainty about the choice made. Here, by use of corticocortical paired associative transcranial magnetic stimulation protocol (ccPAS) aimed at inducing plastic changes, we shaped perceptual sensitivity and metacognitive ability in a motion discrimination task depending on the targeted network, demonstrating their functional dissociation. Neurostimulation aimed at boosting V5/MT+-to-V1/V2 back-projections enhanced motion sensitivity without impacting metacognition, whereas boosting IPS/LIP-to-V1/V2 back-projections increased metacognitive efficiency without impacting motion sensitivity. This double-dissociation provides causal evidence of distinct networks for perceptual sensitivity and metacognitive ability in humans.
Project description:Meaningful variation in internal states that impacts cognition and behavior remains challenging to discover and characterize. Here we leverage trial-to-trial fluctuations in the brain-wide signal recorded using functional MRI to test if distinct sets of brain regions are activated on different trials when accomplishing the same task. Across three different perceptual decision-making experiments, we estimate the brain activations for each trial. We then cluster the trials based on their similarity using modularity-maximization, a data-driven classification method. In each experiment, we find multiple distinct but stable subtypes of trials, suggesting that the same task can be accomplished in the presence of widely varying brain activation patterns. Surprisingly, in all experiments, one of the subtypes exhibits strong activation in the default mode network, which is typically thought to decrease in activity during tasks that require externally focused attention. The remaining subtypes are characterized by activations in different task-positive areas. The default mode network subtype is characterized by behavioral signatures that are similar to the other subtypes exhibiting activation with task-positive regions. These findings demonstrate that the same perceptual decision-making task is accomplished through multiple brain activation patterns.
Project description:Perceptual decision-making relies on the gradual accumulation of noisy sensory evidence. It is often assumed that such decisions are degraded by adding noise to a stimulus, or to the neural systems involved in the decision making process itself. But it has been suggested that adding an optimal amount of noise can, under appropriate conditions, enhance the quality of subthreshold signals in nonlinear systems, a phenomenon known as stochastic resonance. Here we asked whether perceptual decisions made by human observers obey these stochastic resonance principles, by adding noise directly to the visual cortex using transcranial random noise stimulation (tRNS) while participants judged the direction of coherent motion in random-dot kinematograms presented at the fovea. We found that adding tRNS bilaterally to visual cortex enhanced decision-making when stimuli were just below perceptual threshold, but not when they were well below or above threshold. We modelled the data under a drift diffusion framework, and showed that bilateral tRNS selectively increased the drift rate parameter, which indexes the rate of evidence accumulation. Our study is the first to provide causal evidence that perceptual decision-making is susceptible to a stochastic resonance effect induced by tRNS, and to show that this effect arises from selective enhancement of the rate of evidence accumulation for sub-threshold sensory events.
Project description:The timing and accuracy of perceptual decision-making is exquisitely sensitive to fluctuations in arousal. Although extensive research has highlighted the role of various neural processing stages in forming decisions, our understanding of how arousal impacts these processes remains limited. Here we isolated electrophysiological signatures of decision-making alongside signals reflecting target selection, attentional engagement and motor output and examined their modulation as a function of tonic and phasic arousal, indexed by baseline and task-evoked pupil diameter, respectively. Reaction times were shorter on trials with lower tonic, and higher phasic arousal. Additionally, these two pupil measures were predictive of a unique set of EEG signatures that together represent multiple information processing steps of decision-making. Finally, behavioural variability associated with fluctuations in tonic and phasic arousal, indicative of neuromodulators acting on multiple timescales, was mediated by its effects on the EEG markers of attentional engagement, sensory processing and the variability in decision processing.