Adaptive Sampling of Information in Perceptual Decision-Making

In many perceptual and cognitive decision-making problems, humans sample multiple noisy information sources serially, and integrate the sampled information to make an overall decision. We derive the optimal decision procedure for two-alternative choice tasks in which the different options are sampled one at a time, sources vary in the quality of the information they provide, and the available time is fixed. To maximize accuracy, the optimal observer allocates time to sampling different information sources in proportion to their noise levels. We tested human observers in a corresponding perceptual decision-making task. Observers compared the direction of two random dot motion patterns that were triggered only when fixated. Observers allocated more time to the noisier pattern, in a manner that correlated with their sensory uncertainty about the direction of the patterns. There were several differences between the optimal observer predictions and human behaviour. These differences point to a number of other factors, beyond the quality of the currently available sources of information, that influences the sampling strategy

Production of Chemokines in Kawasaki Disease, Henoch-Schönlein Purpura and Acute Febrile Illness

We compared the production of three chemokines; interferon-γ-inducible protein-10 (IP-10), monocyte chemoattractant protein-1 (MCP-1) and growth-related oncogene-α (Gro-α) that attracts monocytes or neutrophils, or both, in peripheral blood at acute stage of Kawasaki disease (n=29), Henoch-Schönlein purpura (n=15) and acute febrile illnesses (n=12). The production of the chemokines was assayed by ELISA. The plasma levels of IP-10 were markedly elevated in Kawasaki disease (538.6±336.4 pg/mL) and acute febrile illnesses (417.1±262.2 pg/mL) compared with in Henoch-Schönlein purpura (58.7±95.7 pg/mL) (p<0.05). The MCP-1 levels were elevated in Kawasaki disease (443.0±473.1 pg/mL) and acute febrile illnesses (328.6±261.1 pg/mL) compared with in Henoch-Schönlein purpura (82.9±79.0 pg/mL) (p<0.05). The Gro-α levels were elevated only in acute febrile illnesses (134.3±153.6 pg/mL) compared with in Kawasaki disease (31.8±22.1 pg/mL) or Henoch-Schönlein purpura (29.4±53.3 pg/mL) (p<0.05). According to these results, monocytes may play an important role in Kawasaki disease. In acute febrile illnesses, both monocytes and neutrophils may play an important role. By contrast, Henoch-Schönlein purpura may not be associated with the role of monocytes and neutrophils. Further studies using a larger number of cases are needed

Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study

Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenia's alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia

A Fluctuation-Driven Mechanism for Slow Decision Processes in Reverberant Networks

The spike activity of cells in some cortical areas has been found to be correlated with reaction times and behavioral responses during two-choice decision tasks. These experimental findings have motivated the study of biologically plausible winner-take-all network models, in which strong recurrent excitation and feedback inhibition allow the network to form a categorical choice upon stimulation. Choice formation corresponds in these models to the transition from the spontaneous state of the network to a state where neurons selective for one of the choices fire at a high rate and inhibit the activity of the other neurons. This transition has been traditionally induced by an increase in the external input that destabilizes the spontaneous state of the network and forces its relaxation to a decision state. Here we explore a different mechanism by which the system can undergo such transitions while keeping the spontaneous state stable, based on an escape induced by finite-size noise from the spontaneous state. This decision mechanism naturally arises for low stimulus strengths and leads to exponentially distributed decision times when the amount of noise in the system is small. Furthermore, we show using numerical simulations that mean decision times follow in this regime an exponential dependence on the amplitude of noise. The escape mechanism provides thus a dynamical basis for the wide range and variability of decision times observed experimentally

Action planning and the timescale of evidence accumulation

Perceptual decisions are based on the temporal integration of sensory evidence for different states of the outside world. The timescale of this integration process varies widely across behavioral contexts and individuals, and it is diagnostic for the underlying neural mechanisms. In many situations, the decision-maker knows the required mapping between perceptual evidence and motor response (henceforth termed “sensory-motor contingency”) before decision formation. Here, the integrated evidence can be directly translated into a motor plan and, indeed, neural signatures of the integration process are evident as build-up activity in premotor brain regions. In other situations, however, the sensory-motor contingencies are unknown at the time of decision formation. We used behavioral psychophysics and computational modeling to test if knowledge about sensory-motor contingencies affects the timescale of perceptual evidence integration. We asked human observers to perform the same motion discrimination task, with or without trial-to-trial variations of the mapping between perceptual choice and motor response. When the mapping varied, it was either instructed before or after the stimulus presentation. We quantified the timescale of evidence integration under these different sensory-motor mapping conditions by means of two approaches. First, we analyzed subjects’ discrimination threshold as a function of stimulus duration. Second, we fitted a dynamical decision-making model to subjects’ choice behavior. The results from both approaches indicated that observers (i) integrated motion information for several hundred ms, (ii) used a shorter than optimal integration timescale, and (iii) used the same integration timescale under all sensory-motor mappings. We conclude that the mechanisms limiting the timescale of perceptual decisions are largely independent from long-term learning (under fixed mapping) or rapid acquisition (under variable mapping) of sensory-motor contingencies. This conclusion has implications for neurophysiological and neuroimaging studies of perceptual decision-making

fMRI Evidence for a Dual Process Account of the Speed-Accuracy Tradeoff in Decision-Making

Background: The speed and accuracy of decision-making have a well-known trading relationship: hasty decisions are more prone to errors while careful, accurate judgments take more time. Despite the pervasiveness of this speed-accuracy tradeoff (SAT) in decision-making, its neural basis is still unknown. Methodology/Principal Findings: Using functional magnetic resonance imaging (fMRI) we show that emphasizing the speed of a perceptual decision at the expense of its accuracy lowers the amount of evidence-related activity in lateral prefrontal cortex. Moreover, this speed-accuracy difference in lateral prefrontal cortex activity correlates with the speedaccuracy difference in the decision criterion metric of signal detection theory. We also show that the same instructions increase baseline activity in a dorso-medial cortical area involved in the internal generation of actions. Conclusions/Significance: These findings suggest that the SAT is neurally implemented by modulating not only the amount of externally-derived sensory evidence used to make a decision, but also the internal urge to make a response. We propose that these processes combine to control the temporal dynamics of the speed-accuracy trade-off in decisionmaking

A neural circuit model of decision uncertainty and change-of-mind

Decision-making is often accompanied by a degree of confidence on whether a choice is correct. Decision uncertainty, or lack in confidence, may lead to change-of-mind. Studies have identified the behavioural characteristics associated with decision confidence or change-of-mind, and their neural correlates. Although several theoretical accounts have been proposed, there is no neural model that can compute decision uncertainty and explain its effects on change-of-mind. We propose a neuronal circuit model that computes decision uncertainty while accounting for a variety of behavioural and neural data of decision confidence and change-of-mind, including testable model predictions. Our theoretical analysis suggests that change-of-mind occurs due to the presence of a transient uncertainty-induced choice-neutral stable steady state and noisy fluctuation within the neuronal network. Our distributed network model indicates that the neural basis of change-of-mind is more distinctively identified in motor-based neurons. Overall, our model provides a framework that unifies decision confidence and change-of-mind

Neurobiological Models of Two-Choice Decision Making Can Be Reduced to a One-Dimensional Nonlinear Diffusion Equation

The response behaviors in many two-alternative choice tasks are well described by so-called sequential sampling models. In these models, the evidence for each one of the two alternatives accumulates over time until it reaches a threshold, at which point a response is made. At the neurophysiological level, single neuron data recorded while monkeys are engaged in two-alternative choice tasks are well described by winner-take-all network models in which the two choices are represented in the firing rates of separate populations of neurons. Here, we show that such nonlinear network models can generally be reduced to a one-dimensional nonlinear diffusion equation, which bears functional resemblance to standard sequential sampling models of behavior. This reduction gives the functional dependence of performance and reaction-times on external inputs in the original system, irrespective of the system details. What is more, the nonlinear diffusion equation can provide excellent fits to behavioral data from two-choice decision making tasks by varying these external inputs. This suggests that changes in behavior under various experimental conditions, e.g. changes in stimulus coherence or response deadline, are driven by internal modulation of afferent inputs to putative decision making circuits in the brain. For certain model systems one can analytically derive the nonlinear diffusion equation, thereby mapping the original system parameters onto the diffusion equation coefficients. Here, we illustrate this with three model systems including coupled rate equations and a network of spiking neurons

Neural antecedents of self-initiated actions in secondary motor cortex

The neural origins of spontaneous or self-initiated actions are not well understood and their interpretation is controversial. To address these issues, we used a task in which rats decide when to abort waiting for a delayed tone. We recorded neurons in the secondary motor cortex (M2) and interpreted our findings in light of an integration-to-bound decision model. A first population of M2 neurons ramped to a constant threshold at rates proportional to waiting time, strongly resembling integrator output. A second population, which we propose provide input to the integrator, fired in sequences and showed trial-to-trial rate fluctuations correlated with waiting times. An integration model fit to these data also quantitatively predicted the observed inter-neuronal correlations. Together, these results reinforce the generality of the integration-to-bound model of decision-making. These models identify the initial intention to act as the moment of threshold crossing while explaining how antecedent subthreshold neural activity can influence an action without implying a decision.info:eu-repo/semantics/publishedVersio