Response repetition biases in human perceptual decisions are explained by activity decay in competitive attractor models

The influence of reward and loss outcomes after free- and forced-tasks on voluntary task choice

In four experiments, we investigated the impact of outcomes and processing mode (free versus forced) on subsequent voluntary task-switching behavior. Participants freely chose between two tasks or were forced to perform one, and the feedback they received randomly varied after correct performance (reward or no-reward; loss or no-loss). In general, we reasoned that the most recently applied task goal is usually the most valued one, leading people to prefer task repetitions over switches. However, the task values might be additionally biased by previous outcomes and the previous processing mode. Indeed, negatively reinforcing tasks with no-reward or losses generally resulted in more subsequent switches. Additionally, participants demonstrated a stronger attachment to free- compared to forced-tasks, as indicated by more switches when the previous task was forced, suggesting that people generally value free over forced-choice task goals. Moreover, the reward manipulation had a greater influence on switching behavior following free- compared to forced-tasks in Exp. 1 and Exp. 3, suggesting a stronger emphasis on evaluating rewarding outcomes associated with free-task choices. However, this inflationary effect on task choice seemed to be limited to reward and situations where task choice and performance more strongly overlap. Specifically, there was no evidence that switching behavior was differentially influenced after free-and forced-task as a function of losses (Exp. 2) or reward when task choice and task performance were separated (Exp. 4). Overall, the results provide new insights into how the valuation of task goals based on choice freedom and outcome feedback can influence voluntary task choices.

Galvanic vs. pulsatile effects on decision-making networks: reshaping the neural activation landscape

Objective. Primarily due to safety concerns, biphasic pulsatile stimulation (PS) is the present standard for electrical excitation of neural tissue with a diverse set of applications. While pulses have been shown to be effective to achieve functional outcomes, they have well-known deficits. Due to recent technical advances, galvanic stimulation (GS), delivery of current for extended periods of time (>1 s), has re-emerged as an alternative to PS.Approach. In this paper, we use a winner-take-all decision-making cortical network model to investigate differences between pulsatile and GS in the context of a perceptual decision-making task.Main results. Based on previous work, we hypothesized that GS would produce more spatiotemporally distributed, network-sensitive neural responses, while PS would produce highly synchronized activation of a limited group of neurons. Our results in-silico support these hypotheses for low-amplitude GS but deviate when galvanic amplitudes are large enough to directly activate or block nearby neurons.Significance. We conclude that with careful parametrization, GS could overcome some limitations of PS to deliver more naturalistic firing patterns in the group of targeted neurons.

Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts

Active reinforcement learning enables dynamic prediction and control, where one should not only maximize rewards but also minimize costs such as of inference, decisions, actions, and time. For an embodied agent such as a human, decisions are also shaped by physical aspects of actions. Beyond the effects of reward outcomes on learning processes, to what extent can modeling of behavior in a reinforcement-learning task be complicated by other sources of variance in sequential action choices? What of the effects of action bias (for actions per se) and action hysteresis determined by the history of actions chosen previously? The present study addressed these questions with incremental assembly of models for the sequential choice data from a task with hierarchical structure for additional complexity in learning. With systematic comparison and falsification of computational models, human choices were tested for signatures of parallel modules representing not only an enhanced form of generalized reinforcement learning but also action bias and hysteresis. We found evidence for substantial differences in bias and hysteresis across participants-even comparable in magnitude to the individual differences in learning. Individuals who did not learn well revealed the greatest biases, but those who did learn accurately were also significantly biased. The direction of hysteresis varied among individuals as repetition or, more commonly, alternation biases persisting from multiple previous actions. Considering that these actions were button presses with trivial motor demands, the idiosyncratic forces biasing sequences of action choices were robust enough to suggest ubiquity across individuals and across tasks requiring various actions. In light of how bias and hysteresis function as a heuristic for efficient control that adapts to uncertainty or low motivation by minimizing the cost of effort, these phenomena broaden the consilient theory of a mixture of experts to encompass a mixture of expert and nonexpert controllers of behavior.

Stimulation along the anterior-posterior axis of lateral frontal cortex reduces visual serial dependence

Serial dependence is an attractive pull that recent perceptual history exerts on current judgments. Theory suggests that this bias is due to a form of short-term plasticity prevalent specifically in the frontal lobe. We sought to test the importance of the frontal lobe to serial dependence by disrupting neural activity along its lateral surface during two tasks with distinct perceptual and motor demands. In our first experiment, stimulation of the lateral prefrontal cortex (LPFC) during an oculomotor delayed response task decreased serial dependence only in the first saccade to the target, whereas stimulation posterior to the LPFC decreased serial dependence only in adjustments to eye position after the first saccade. In our second experiment, which used an orientation discrimination task, stimulation anterior to, in, and posterior to the LPFC all caused equivalent decreases in serial dependence. In this experiment, serial dependence occurred only between stimuli at the same location; an alternation bias was observed across hemifields. Frontal stimulation had no effect on the alternation bias. Transcranial magnetic stimulation to parietal cortex had no effect on serial dependence in either experiment. In summary, our experiments provide evidence for both functional differentiation (Experiment 1) and redundancy (Experiment 2) in frontal cortex with respect to serial dependence.

History biases reveal novel dissociations between perceptual and metacognitive decision-making

Human decision-making and self-reflection often depend on context and internal biases. For instance, decisions are often influenced by preceding choices, regardless of their relevance. It remains unclear how choice history influences different levels of the decision-making hierarchy. We used analyses grounded in information and detection theories to estimate the relative strength of perceptual and metacognitive history biases and to investigate whether they emerge from common/unique mechanisms. Although both perception and metacognition tended to be biased toward previous responses, we observed novel dissociations that challenge normative theories of confidence. Different evidence levels often informed perceptual and metacognitive decisions within observers, and response history distinctly influenced first- (perceptual) and second- (metacognitive) order decision-parameters, with the metacognitive bias likely to be strongest and most prevalent in the general population. We propose that recent choices and subjective confidence represent heuristics, which inform first- and second-order decisions in the absence of more relevant evidence.

Longitudinal effects of early psychosocial deprivation on macaque executive function: Evidence from computational modelling

Executive function (EF) describes a group of cognitive processes underlying the organization and control of goal-directed behaviour. Environmental experience appears to play a crucial role in EF development, with early psychosocial deprivation often linked to EF impairment. However, many questions remain concerning the developmental trajectories of EF after exposure to deprivation, especially concerning specific mechanisms. Accordingly, using an 'A-not-B' paradigm and a macaque model of early psychosocial deprivation, we investigated how early deprivation influences EF development longitudinally from adolescence into early adulthood. The contribution of working memory and inhibitory control mechanisms were examined specifically via the fitting of a computational model of decision making to the choice behaviour of each individual. As predicted, peer-reared animals (i.e. those exposed to early psychosocial deprivation) performed worse than mother-reared animals across time, with the fitted model parameters yielding novel insights into the functional decomposition of group-level EF differences underlying task performance. Results indicated differential trajectories of inhibitory control and working memory development in the two groups. Such findings not only extend our knowledge of how early deprivation influences EF longitudinally, but also provide support for the utility of computational modelling to elucidate specific mechanisms linking early psychosocial deprivation to long-term poor outcomes.

Modality-specific sensory and decisional carryover effects in duration perception

BACKGROUND

The brain uses recent history when forming perceptual decisions. This results in carryover effects in perception. Although separate sensory and decisional carryover effects have been shown in many perceptual tasks, their existence and nature in temporal processing are unclear. Here, we investigated whether and how previous stimuli and previous choices affect subsequent duration perception, in vision and audition.

RESULTS

In a series of three experiments, participants were asked to classify visual or auditory stimuli into "shorter" or "longer" duration categories. In experiment 1, visual and auditory stimuli were presented in separate blocks. Results showed that current duration estimates were repelled away from the previous trial's stimulus duration, but attracted towards the previous choice, in both vision and audition. In experiment 2, visual and auditory stimuli were pseudorandomly presented in one block. We found that sensory and decisional carryover effects occurred only when previous and current stimuli were from the same modality. Experiment 3 further investigated the stimulus dependence of carryover effects within each modality. In this experiment, visual stimuli with different shape topologies (or auditory stimuli with different audio frequencies) were pseudorandomly presented in one visual (or auditory) block. Results demonstrated sensory carryover (within each modality) despite task-irrelevant differences in visual shape topology or audio frequency. By contrast, decisional carryover was reduced (but still present) across different visual topologies and completely absent across different audio frequencies.

CONCLUSIONS

These results suggest that serial dependence in duration perception is modality-specific. Moreover, repulsive sensory carryover effects generalize within each modality, whereas attractive decisional carryover effects are contingent on contextual details.

Difference in Network Effects of Pulsatile and Galvanic Stimulation

Biphasic pulsatile stimulation is the present standard for neural prosthetic use, and it is used to understand connectivity and functionality of the brain in brain mapping studies. While pulses have been shown to drive behavioral changes, such as biasing decision making, they have deficits. For example, cochlear implants restore hearing but lack the ability to restore pitch perception. Recent work shows that pulses produce artificial synchrony in networks of neurons and non-linear changes in firing rate with pulse amplitude. Studies also show galvanic stimulation, delivery of current for extended periods of time, produces more naturalistic behavioral responses than pulses. In this paper, we use a winner-take-all decision-making network model to investigate differences between pulsatile and galvanic stimulation at the single neuron and network level while accurately modeling the effects of pulses on neurons for the first time. Results show pulses bias spike timing and make neurons more resistive to natural network inputs than galvanic stimulation at an equivalent current amplitude. Clinical Relevance- This establishes that pulsatile stimulation may disrupt natural spike timing and network-level interactions while certain parameterizations of galvanic stimulation avoid these effects and can drive network firing more naturally.

Cortico-Brainstem Mechanisms of Biased Perceptual Decision-Making in the Context of Pain

Prior expectations can bias how we perceive pain. Using a drift diffusion model, we recently showed that this influence is primarily based on changes in perceptual decision-making (indexed as shift in starting point). Only during unexpected application of high-intensity noxious stimuli, altered information processing (indexed as increase in drift rate) explained the expectancy effect on pain processing. Here, we employed functional magnetic resonance imaging to investigate the neural basis of both these processes in healthy volunteers. On each trial, visual cues induced the expectation of high- or low-intensity noxious stimulation or signaled equal probability for both intensities. Participants categorized a subsequently applied electrical stimulus as either low- or high-intensity pain. A shift in starting point towards high pain correlated negatively with right dorsolateral prefrontal cortex activity during cue presentation underscoring its proposed role of "keeping pain out of mind". This anticipatory right dorsolateral prefrontal cortex signal increase was positively correlated with periaqueductal gray (PAG) activity when the expected high-intensity stimulation was applied. A drift rate increase during unexpected high-intensity pain was reflected in amygdala engagement and increased functional connectivity between amygdala and PAG. Our findings suggest involvement of the PAG in both decision-making bias and altered information processing to implement expectancy effects on pain. PERSPECTIVE: Modulation of pain through expectations has been linked to changes in perceptual decision-making and altered processing of afferent information. Our results suggest involvement of the dorsolateral prefrontal cortex, amygdala, and periaqueductal gray in these processes.

A Future of Current Flow Modelling for Transcranial Electrical Stimulation?

Purpose of Review

Transcranial electrical stimulation (tES) is used to non-invasively modulate brain activity in health and disease. Current flow modeling (CFM) provides estimates of where and how much electrical current is delivered to the brain during tES. It therefore holds promise as a method to reduce commonplace variability in tES delivery and, in turn, the outcomes of stimulation. However, the adoption of CFM has not yet been widespread and its impact on tES outcome variability is unclear. Here, we discuss the potential barriers to effective, practical CFM-informed tES use.

Recent Findings

CFM has progressed from models based on concentric spheres to gyri-precise head models derived from individual MRI scans. Users can now estimate the intensity of electrical fields (E-fields), their spatial extent, and the direction of current flow in a target brain region during tES. Here. we consider the multi-dimensional challenge of implementing CFM to optimise stimulation dose: this requires informed decisions to prioritise E-field characteristics most likely to result in desired stimulation outcomes, though the physiological consequences of the modelled current flow are often unknown. Second, we address the issue of a disconnect between predictions of E-field characteristics provided by CFMs and predictions of the physiological consequences of stimulation which CFMs are not designed to address. Third, we discuss how ongoing development of CFM in conjunction with other modelling approaches could overcome these challenges while maintaining accessibility for widespread use.

Summary

The increasing complexity and sophistication of CFM is a mandatory step towards dose control and precise, individualised delivery of tES. However, it also risks counteracting the appeal of tES as a straightforward, cost-effective tool for neuromodulation, particularly in clinical settings.

Is value-based choice repetition susceptible to medial frontal transcranial direct current stimulation (tDCS)? A preregistered study

In value-based decision making, people have to weigh different options based on their subjective value. This process, however, also is influenced by choice biases, such as choice repetition: in a series of choices, people are more likely to repeat their decision than to switch to a different choice. Previously, it was shown that transcranial direct current stimulation (tDCS) can affect such choice biases. We applied tDCS over the medial prefrontal cortex to investigate whether tDCS can alter choice repetition in value-based decision making. In a preregistered study, we applied anodal, cathodal, and sham tDCS stimulation to 52 participants. While we found robust choice repetition effects, we did not find support for an effect of tDCS stimulation. We discuss these findings within the larger scope of the tDCS literature and highlight the potential roles of interindividual variability and current density strength.

Prefrontal cortex represents heuristics that shape choice bias and its integration into future behavior

Goal-directed behavior requires integrating sensory information with prior knowledge about the environment. Behavioral biases that arise from these priors could increase positive outcomes when the priors match the true structure of the environment, but mismatches also happen frequently and could cause unfavorable outcomes. Biases that reduce gains and fail to vanish with training indicate fundamental suboptimalities arising from ingrained heuristics of the brain. Here, we report systematic, gain-reducing choice biases in highly trained monkeys performing a motion direction discrimination task where only the current stimulus is behaviorally relevant. The monkey's bias fluctuated at two distinct time scales: slow, spanning tens to hundreds of trials, and fast, arising from choices and outcomes of the most recent trials. Our findings enabled single trial prediction of biases, which influenced the choice especially on trials with weak stimuli. The pre-stimulus activity of neuronal ensembles in the monkey prearcuate gyrus represented these biases as an offset along the decision axis in the state space. This offset persisted throughout the stimulus viewing period, when sensory information was integrated, leading to a biased choice. The pre-stimulus representation of history-dependent bias was functionally indistinguishable from the neural representation of upcoming choice before stimulus onset, validating our model of single-trial biases and suggesting that pre-stimulus representation of choice could be fully defined by biases inferred from behavioral history. Our results indicate that the prearcuate gyrus reflects intrinsic heuristics that compute bias signals, as well as the mechanisms that integrate them into the oculomotor decision-making process.

Flexible categorization in perceptual decision making

Perceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.

Impact of brain atrophy on tDCS and HD-tDCS current flow: a modeling study in three variants of primary progressive aphasia

BACKGROUND

During transcranial direct current stimulation (tDCS), the amount and distribution of current that reaches the brain depends on individual anatomy. Many progressive neurodegenerative diseases are associated with cortical atrophy, but the importance of individual brain atrophy during tDCS in patients with progressive atrophy, including primary progressive aphasia (PPA), remains unclear.

OBJECTIVE

In the present study, we addressed the question whether brain anatomy in patients with distinct cortical atrophy patterns would impact brain current intensity and distribution during tDCS over the left IFG.

METHOD

We developed state-of-the-art, gyri-precise models of three subjects, each representing a variant of primary progressive aphasia: non-fluent variant PPA (nfvPPA), semantic variant PPA (svPPA), and logopenic variant PPA (lvPPA). We considered two exemplary montages over the left inferior frontal gyrus (IFG): a conventional pad montage (anode over F7, cathode over the right cheek) and a 4 × 1 high-definition tDCS montage. We further considered whether local anatomical features, specifically distance of the cortex to skull, can directly predict local electric field intensity.

RESULTS

We found that the differences in brain current flow across the three PPA variants fall within the distribution of anatomically typical adults. While clustering of electric fields was often around individual gyri or sulci, the minimal distance from the gyri/sulci to skull was not correlated with electric field intensity.

CONCLUSION

Limited to the conditions and assumptions considered here, this argues against a specific need to adjust the tDCS montage for these patients any more than might be considered useful in anatomically typical adults. Therefore, local atrophy does not, in isolation, reliably predict local electric field. Rather, our results are consistent with holistic head anatomy influencing brain current flow, with tDCS producing diffuse and individualized brain current flow patterns and HD-tDCS producing targeted brain current flow across individuals.

Inferring Causality from Noninvasive Brain Stimulation in Cognitive Neuroscience

Noninvasive brain stimulation (NIBS) techniques, such as transcranial magnetic stimulation or transcranial direct and alternating current stimulation, are advocated as measures to enable causal inference in cognitive neuroscience experiments. Transcending the limitations of purely correlative neuroimaging measures and experimental sensory stimulation, they allow to experimentally manipulate brain activity and study its consequences for perception, cognition, and eventually, behavior. Although this is true in principle, particular caution is advised when interpreting brain stimulation experiments in a causal manner. Research hypotheses are often oversimplified, disregarding the underlying (implicitly assumed) complex chain of causation, namely, that the stimulation technique has to generate an electric field in the brain tissue, which then evokes or modulates neuronal activity both locally in the target region and in connected remote sites of the network, which in consequence affects the cognitive function of interest and eventually results in a change of the behavioral measure. Importantly, every link in this causal chain of effects can be confounded by several factors that have to be experimentally eliminated or controlled to attribute the observed results to their assumed cause. This is complicated by the fact that many of the mediating and confounding variables are not directly observable and dose-response relationships are often nonlinear. We will walk the reader through the chain of causation for a generic cognitive neuroscience NIBS study, discuss possible confounds, and advise appropriate control conditions. If crucial assumptions are explicitly tested (where possible) and confounds are experimentally well controlled, NIBS can indeed reveal cause-effect relationships in cognitive neuroscience studies.

Nonlinear neural network dynamics accounts for human confidence in a sequence of perceptual decisions

Electrophysiological recordings during perceptual decision tasks in monkeys suggest that the degree of confidence in a decision is based on a simple neural signal produced by the neural decision process. Attractor neural networks provide an appropriate biophysical modeling framework, and account for the experimental results very well. However, it remains unclear whether attractor neural networks can account for confidence reports in humans. We present the results from an experiment in which participants are asked to perform an orientation discrimination task, followed by a confidence judgment. Here we show that an attractor neural network model quantitatively reproduces, for each participant, the relations between accuracy, response times and confidence. We show that the attractor neural network also accounts for confidence-specific sequential effects observed in the experiment (participants are faster on trials following high confidence trials). Remarkably, this is obtained as an inevitable outcome of the network dynamics, without any feedback specific to the previous decision (that would result in, e.g., a change in the model parameters before the onset of the next trial). Our results thus suggest that a metacognitive process such as confidence in one's decision is linked to the intrinsically nonlinear dynamics of the decision-making neural network.

Movement Repetition Facilitates Response Preparation

Our sensorimotor system appears to be influenced by the recent history of our movements. Repeating movements toward a particular direction is known to have a dramatic effect on involuntary movements elicited by cortical stimulation-a phenomenon that has been termed use-dependent plasticity. However, analogous effects of repetition on behavior have proven elusive. Here, we show that movement repetition enhances the generation of similar movements in the future by reducing the time required to select and prepare the repeated movement. We further show that this reaction time advantage for repeated movements is attributable to more rapid, but still flexible, preparation of the repeated movement rather than anticipation and covert advance preparation of the previously repeated movement. Our findings demonstrate a powerful and beneficial effect of movement repetition on response preparation, which may represent a behavioral counterpart to use-dependent plasticity effects in primary motor cortex.

Choice history biases subsequent evidence accumulation

Perceptual choices depend not only on the current sensory input but also on the behavioral context, such as the history of one's own choices. Yet, it remains unknown how such history signals shape the dynamics of later decision formation. In models of decision formation, it is commonly assumed that choice history shifts the starting point of accumulation toward the bound reflecting the previous choice. We here present results that challenge this idea. We fit bounded-accumulation decision models to human perceptual choice data, and estimated bias parameters that depended on observers' previous choices. Across multiple task protocols and sensory modalities, individual history biases in overt behavior were consistently explained by a history-dependent change in the evidence accumulation, rather than in its starting point. Choice history signals thus seem to bias the interpretation of current sensory input, akin to shifting endogenous attention toward (or away from) the previously selected interpretation.

Perceptual Decision-Making: Biases in Post-Error Reaction Times Explained by Attractor Network Dynamics

Perceptual decision-making is the subject of many experimental and theoretical studies. Most modeling analyses are based on statistical processes of accumulation of evidence. In contrast, very few works confront attractor network models' predictions with empirical data from continuous sequences of trials. Recently however, numerical simulations of a biophysical competitive attractor network model have shown that such a network can describe sequences of decision trials and reproduce repetition biases observed in perceptual decision experiments. Here we get more insights into such effects by considering an extension of the reduced attractor network model of Wong and Wang (2006), taking into account an inhibitory current delivered to the network once a decision has been made. We make explicit the conditions on this inhibitory input for which the network can perform a succession of trials, without being either trapped in the first reached attractor, or losing all memory of the past dynamics. We study in detail how, during a sequence of decision trials, reaction times and performance depend on nonlinear dynamics of the network, and we confront the model behavior with empirical findings on sequential effects. Here we show that, quite remarkably, the network exhibits, qualitatively and with the correct order of magnitude, post-error slowing and post-error improvement in accuracy, two subtle effects reported in behavioral experiments in the absence of any feedback about the correctness of the decision. Our work thus provides evidence that such effects result from intrinsic properties of the nonlinear neural dynamics.SIGNIFICANCE STATEMENT Much experimental and theoretical work is being devoted to the understanding of the neural correlates of perceptual decision-making. In a typical behavioral experiment, animals or humans perform a continuous series of binary discrimination tasks. To model such experiments, we consider a biophysical decision-making attractor neural network, taking into account an inhibitory current delivered to the network once a decision is made. Here we provide evidence that the same intrinsic properties of the nonlinear network dynamics underpins various sequential effects reported in experiments. Quite remarkably, in the absence of feedback on the correctness of the decisions, the network exhibits post-error slowing (longer reaction times after error trials) and post-error improvement in accuracy (smaller error rates after error trials).

Direction of TDCS current flow in human sensorimotor cortex influences behavioural learning

Background

Recent studies have shown that neurophysiological outcomes of transcranial direct current stimulation (TDCS) are influenced by current flow in brain regions between the electrodes, and in particular the orientation of current flow relative to the cortical surface.

Objective

We asked whether the directional effects of TDCS on physiological measures in the motor system would also be observed on motor behaviours.

Methods

We applied TDCS during the practice of a ballistic movement task to test whether it affected learning or the retention of learning 48 h later. TDCS electrodes were oriented perpendicular to the central sulcus and two current orientations were used (posterior-anterior, TDCS_PA; and anterior-posterior, TDCS_AP). Transcranial magnetic stimulation (TMS) was used to assess whether changes in corticospinal excitability reflected any behavioural changes.

Results

Directional TDCS_AP impaired the retention of learning on the ballistic movement task compared to TDCS_PA and a sham condition. Although TDCS_PA had no effect on learning or retention, it blocked the typical increase in corticospinal excitability after a period of motor practice.

Conclusions

Our results extend on previous reports of TDCS producing directionally specific changes in neurophysiological outcomes by showing that current direction through a cortical target also impacts upon behavioural outcomes. In addition, changes in corticospinal excitability after a period of motor practice are not causally linked to behavioural learning.

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TDCS current direction influences neurophysiological outcomes.

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We show that it also influences behavioural outcomes.

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Behavioural learning is not linked to changes in corticospinal excitability.

Confirmation Bias through Selective Overweighting of Choice-Consistent Evidence

People's assessments of the state of the world often deviate systematically from the information available to them [1]. Such biases can originate from people's own decisions: committing to a categorical proposition, or a course of action, biases subsequent judgment and decision-making. This phenomenon, called confirmation bias [2], has been explained as suppression of post-decisional dissonance [3, 4]. Here, we provide insights into the underlying mechanism. It is commonly held that decisions result from the accumulation of samples of evidence informing about the state of the world [5-8]. We hypothesized that choices bias the accumulation process by selectively altering the weighting (gain) of subsequent evidence, akin to selective attention. We developed a novel psychophysical task to test this idea. Participants viewed two successive random dot motion stimuli and made two motion-direction judgments: a categorical discrimination after the first stimulus and a continuous estimation of the overall direction across both stimuli after the second stimulus. Participants' sensitivity for the second stimulus was selectively enhanced when that stimulus was consistent with the initial choice (compared to both, first stimuli and choice-inconsistent second stimuli). A model entailing choice-dependent selective gain modulation explained this effect better than several alternative mechanisms. Choice-dependent gain modulation was also established in another task entailing averaging of numerical values instead of motion directions. We conclude that intermittent choices direct selective attention during the evaluation of subsequent evidence, possibly due to decision-related feedback in the brain [9]. Our results point to a recurrent interplay between decision-making and selective attention.

Moderate developmental alcohol exposure reduces repetitive alternation in a zebrafish model of fetal alcohol spectrum disorders

The damaging effects of alcohol on a developing fetus are well known and cause a range of conditions known as fetal alcohol spectrum disorder (FASD). High levels of alcohol exposure lead to physical deformity and severe cognitive deficits, but more moderate exposure leads to a range of subtle cognitive effects such as reduced social behavior, higher propensity to develop addictions, and reduced spatial working memory. Previous studies have demonstrated that following exposure to relatively low levels of ethanol during early brain development (equivalent in humans to moderate exposure) zebrafish display a range of social and behavioral differences. Here, our aim was to test the hypothesis that moderate developmental ethanol exposure would affect aspects of learning and memory in zebrafish. In order to do this, we exposed zebrafish embryos to 20 mM [0.12% v/v] ethanol from 2 to 9 dpf to model the effects of moderate prenatal ethanol (MPE) exposure. At 3 months old, adult fish were tested for appetitive and aversive learning, and for spatial alternation in a novel unconditioned y-maze protocol. We found that MPE did not affect appetitive or aversive learning, but exposed-fish showed a robust reduction in repetitive alternations in the y-maze when compared to age matched controls. This study confirms that moderate levels of ethanol exposure to developing embryos have subtle effects on spatial working memory in adulthood. Our data thus suggest that zebrafish may be a promising model system for studying the effects of alcohol on learning and decision-making, but also for developing treatments and interventions to reduce the negative effects of prenatal alcohol.

Adaptive History Biases Result from Confidence-Weighted Accumulation of past Choices

Perceptual decision-making is biased by previous events, including the history of preceding choices: observers tend to repeat (or alternate) their judgments of the sensory environment more often than expected by chance. Computational models postulate that these so-called choice history biases result from the accumulation of internal decision signals across trials. Here, we provide psychophysical evidence for such a mechanism and its adaptive utility. Male and female human observers performed different variants of a challenging visual motion discrimination task near psychophysical threshold. In a first experiment, we decoupled categorical perceptual choices and motor responses on a trial-by-trial basis. Choice history bias was explained by previous perceptual choices, not motor responses, highlighting the importance of internal decision signals in action-independent formats. In a second experiment, observers performed the task in stimulus environments containing different levels of autocorrelation and providing no external feedback about choice correctness. Despite performing under overall high levels of uncertainty, observers adjusted both the strength and the sign of their choice history biases to these environments. When stimulus sequences were dominated by either repetitions or alternations, the individual degree of this adjustment of history bias was about as good a predictor of individual performance as individual perceptual sensitivity. The history bias adjustment scaled with two proxies for observers' confidence about their previous choices (accuracy and reaction time). Together, our results are consistent with the idea that action-independent, confidence-modulated decision variables are accumulated across choices in a flexible manner that depends on decision-makers' model of their environment.

SIGNIFICANCE STATEMENT Decisions based on sensory input are often influenced by the history of one's preceding choices, manifesting as a bias to systematically repeat (or alternate) choices. We here provide support for the idea that such choice history biases arise from the context-dependent accumulation of a quantity referred to as the decision variable: the variable's sign dictates the choice and its magnitude the confidence about choice correctness. We show that choices are accumulated in an action-independent format and a context-dependent manner, weighted by the confidence about their correctness. This confidence-weighted accumulation of choices enables decision-makers to flexibly adjust their behavior to different sensory environments. The bias adjustment can be as important for optimizing performance as one's sensitivity to the momentary sensory input.

Modifying response times in the Simon task with transcranial random noise stimulation

Perceptual decisions pervade our every-day lives, and can align or conflict with inbuilt biases. We investigated these conflicting biases by applying transcranial random noise stimulation (tRNS) while subjects took part in a visual Simon task - a paradigm where irrelevant spatial cues influence the response times of subjects to relevant colour cues. We found that tRNS reduces the response time of subjects independent of the congruence between spatial and colour cues, but dependent on the baseline response time, both between subjects and across conditions within subjects. We consider the reduction in response time to be non-specific to the Simon task, and cast our interpretations in terms of drift-diffusion models, which have been previously used as mechanistic explanations for decision-making processes. However, there have been few extensions of the drift-diffusion model to the Simon effect, and so we first elaborate on this interpretation, and further extend it by incorporating the potential action of tRNS.

Orbitofrontal Cortex: A Neural Circuit for Economic Decisions

Economic choice behavior entails the computation and comparison of subjective values. A central contribution of neuroeconomics has been to show that subjective values are represented explicitly at the neuronal level. With this result at hand, the field has increasingly focused on the difficult question of where in the brain and how exactly subjective values are compared to make a decision. Here, we review a broad range of experimental and theoretical results suggesting that good-based decisions are generated in a neural circuit within the orbitofrontal cortex (OFC). The main lines of evidence supporting this proposal include the fact that goal-directed behavior is specifically disrupted by OFC lesions, the fact that different groups of neurons in this area encode the input and the output of the decision process, the fact that activity fluctuations in each of these cell groups correlate with choice variability, and the fact that these groups of neurons are computationally sufficient to generate decisions. Results from other brain regions are consistent with the idea that good-based decisions take place in OFC and indicate that value signals inform a variety of mental functions. We also contrast the present proposal with other leading models for the neural mechanisms of economic decisions. Finally, we indicate open questions and suggest possible directions for future research.