Context-dependent Decision-making Research Articles

Employment of time-varying sensory evidence to test the mechanisms underlying flexible decision-making.

To make flexible decisions in dynamic environments, the brain must integrate behaviorally relevant information while simultaneously discarding irrelevant information. This study aimed to investigate the mechanisms responsible for discarding irrelevant information during context-dependent decision-making. We trained two macaque monkeys to switch between direction and depth discrimination tasks in successive trials. During decision-making, the strength of the motion or depth signal changes transiently at various times, introducing a brief pulse. We assessed the effects of pulse on behavioral choices. Consistent with previous findings, early relevant pulses, such as motion pulses during direction discrimination, had a significantly larger effect compared to late pulses. Critically, the effects of irrelevant pulses, such as motion pulses during depth discrimination, exhibited an initial minimal effect, followed by an increase and subsequent decrease as a function of pulse timing. Gating mechanisms alone, aimed at discarding irrelevant information, did not account for the observed time course of pulse effects. Instead, the observed increase in the effects of irrelevant pulses with time suggested the involvement of a leaky integration mechanism. The results suggested that the brain controls the amount of disposal in accumulating sensory evidence during flexible decision-making.

Predicting near-term, out-of-sample fish passage, guidance, and movement across diverse river environments by cognitively relating momentary behavioral decisions to multiscale memories of past hydrodynamic experiences

Predicting the behavior of individuals acting under their own motivation is a challenge shared across multiple scientific fields, from economic to ecological systems. In rivers, fish frequently change their orientation even when stimuli are unchanged, which makes understanding and predicting their movement in time-varying environments near built infrastructure particularly challenging. Cognition is central to fish movement, and our lack of understanding is costly in terms of time and resources needed to design and manage water operations infrastructure that is able to meet the multiple needs of human society while preserving valuable living resources. An open question is how best to cognitively account for the multi-modal, -attribute, -alternative, and context-dependent decision-making of fish near infrastructure. Here, we leverage agent- and individual-based modeling techniques to encode a cognitive approach to mechanistic fish movement behavior that operates at the scale in which water operations river infrastructure is engineered and managed. Our cognitive approach to mechanistic behavior modeling uses a Eulerian-Lagrangian-agent method (ELAM) to interpret and quantitatively predict fish movement and passage/entrainment near infrastructure across different and time-varying river conditions. A goal of our methodology is to leverage theory and equations that can provide an interpretable version of animal movement behavior in complex environments that requires a minimal number of parameters in order to facilitate the application to new data in real-world engineering and management design projects. We first describe concepts, theory, and mathematics applicable to animals across aquatic, terrestrial, avian, and subterranean domains. Then, we detail our application to juvenile Pacific salmonids in the Bay-Delta of California. We reproduce observations of salmon movement and passage/entrainment with one field season of measurements, year 2009, using five simulated behavior responses to 3-D hydrodynamics. Then, using the ELAM model calibrated from year 2009 data, we predict the movement and passage/entrainment of salmon for a later field season, year 2014, which included a novel engineered fish guidance boom not present in 2009. Central to the fish behavior model’s performance is the notion that individuals are attuned to more than one hydrodynamic signal and more than one timescale. We find that multi-timescale perception can disentangle multiplex hydrodynamic signals and inform the context-based behavioral choice of a fish. Simulated fish make movement decisions within a rapidly changing environment without global information, knowledge of which direction is downriver/upriver, or path integration. The key hydrodynamic stimuli are water speed, the spatial gradient in water speed, water acceleration, and fish swim bladder pressure. We find that selective tidal stream transport in the Bay-Delta is a superset of the fish-hydrodynamic behavior repertoire that reproduces salmon movement and passage in dam reservoir environments. From a cognitive movement ecology perspective, we describe how a behavior can emerge from a repertoire of multiple fish-hydrodynamic responses that are each tailored to suit the animal’s recent past experience (localized environmental context). From a movement behavior perspective, we describe how different fish swim paths can emerge from the same local hydrodynamic stimuli. Our findings demonstrate that a cognitive approach to mechanistic fish movement behavior modeling does not always require the maximum possible spatiotemporal resolution for representing the river environmental stimuli although there are concomitant tradeoffs in resolving features at different scales. From a water operations perspective, we show that a decision-support tool can successfully operate outside the calibration conditions, which is a necessary attribute for tools informing future engineering design and management actions in a world that will invariably look different than the past.

Geometry of neural computation unifies working memory and planning

Real-world tasks require coordination of working memory, decision-making, and planning, yet these cognitive functions have disproportionately been studied as independent modular processes in the brain. Here, we propose that contingency representations, defined as mappings for how future behaviors depend on upcoming events, can unify working memory and planning computations. We designed a task capable of disambiguating distinct types of representations. In task-optimized recurrent neural networks, we investigated possible circuit mechanisms for contingency representations and found that these representations can explain neurophysiological observations from the prefrontal cortex during working memory tasks. Our experiments revealed that human behavior is consistent with contingency representations and not with traditional sensory models of working memory. Finally, we generated falsifiable predictions for neural data to identify contingency representations in neural data and to dissociate different models of working memory. Our findings characterize a neural representational strategy that can unify working memory, planning, and context-dependent decision-making.

A neurocomputational model for the processing of conflicting information in context-dependent decision tasks.

Context-dependent computation is a relevant characteristic of neural systems, endowing them with the capacity of adaptively modifying behavioral responses and flexibly discriminating between relevant and irrelevant information in a stimulus. This ability is particularly highlighted in solving conflicting tasks. A long-standing problem in computational neuroscience, flexible routing of information, is also closely linked with the ability to perform context-dependent associations. Here we present an extension of a context-dependent associative memory model to achieve context-dependent decision-making in the presence of conflicting and noisy multi-attribute stimuli. In these models, the input vectors are multiplied by context vectors via the Kronecker tensor product. To outfit the model with a noisy dynamic, we embedded the context-dependent associative memory in a leaky competing accumulator model, and, finally, we proved the power of the model in the reproduction of a behavioral experiment with monkeys in a context-dependent conflicting decision-making task. At the end, we discuss the neural feasibility of the tensor product and made the suggestive observation that the capacities of tensor context models are surprisingly in alignment with the more recent experimental findings about functional flexibility at different levels of brain organization.

Orthogonal representations for robust context-dependent task performance in brains and neural networks

SummaryHow do neural populations code for multiple, potentially conflicting tasks? Here we used computational simulations involving neural networks to define “lazy” and “rich” coding solutions to this context-dependent decision-making problem, which trade off learning speed for robustness. During lazy learning the input dimensionality is expanded by random projections to the network hidden layer, whereas in rich learning hidden units acquire structured representations that privilege relevant over irrelevant features. For context-dependent decision-making, one rich solution is to project task representations onto low-dimensional and orthogonal manifolds. Using behavioral testing and neuroimaging in humans and analysis of neural signals from macaque prefrontal cortex, we report evidence for neural coding patterns in biological brains whose dimensionality and neural geometry are consistent with the rich learning regime.

A geometric framework for understanding dynamic information integration in context-dependent computation

SummaryThe prefrontal cortex (PFC) plays a prominent role in performing flexible cognitive functions and working memory, yet the underlying computational principle remains poorly understood. Here, we trained a rate-based recurrent neural network (RNN) to explore how the context rules are encoded, maintained across seconds-long mnemonic delay, and subsequently used in a context-dependent decision-making task. The trained networks replicated key experimentally observed features in the PFC of rodent and monkey experiments, such as mixed selectivity, neuronal sequential activity, and rotation dynamics. To uncover the high-dimensional neural dynamical system, we further proposed a geometric framework to quantify and visualize population coding and sensory integration in a temporally defined manner. We employed dynamic epoch-wise principal component analysis (PCA) to define multiple task-specific subspaces and task-related axes, and computed the angles between task-related axes and these subspaces. In low-dimensional neural representations, the trained RNN first encoded the context cues in a cue-specific subspace, and then maintained the cue information with a stable low-activity state persisting during the delay epoch, and further formed line attractors for sensor integration through low-dimensional neural trajectories to guide decision-making. We demonstrated via intensive computer simulations that the geometric manifolds encoding the context information were robust to varying degrees of weight perturbation in both space and time. Overall, our analysis framework provides clear geometric interpretations and quantification of information coding, maintenance, and integration, yielding new insight into the computational mechanisms of context-dependent computation.

A Geometric Framework for Understanding Dynamic Information Integration in Context-Dependent Computation

Prefrontal cortex plays a prominent role in performing flexible cognitive functions and working memory, yet the underlying computational principle remains poorly understood. Here we trained a rate-based recurrent neural network (RNN) to explore how the context rules are encoded, maintained across seconds-long mnemonic delay, and subsequently used in a context-dependent decision-making task. The trained networks emerged key experimentally observed features in the prefrontal cortex (PFC) of rodent and monkey experiments, such as mixed-selectivity, sparse representations, neuronal sequential activity and rotation dynamics. To uncover the high-dimensional neural dynamical system, we further proposed a geometric framework to quantify and visualize population coding and sensory integration in a temporally-defined manner. We employed dynamic epoch-wise principal component analysis (PCA) to define multiple task-specific subspaces and task-related axes, and computed the angles between task-related axes and these subspaces. In low-dimensional neural representations, the trained RNN first encoded the context cues in a cue-specific subspace, and then maintained the cue information with a stable low-activity state persisting during the delay epoch, and further formed line attractors for sensor integration through low-dimensional neural trajectories to guide decision making. We demonstrated via intensive computer simulations that the geometric manifolds encoding the context information were robust to varying degrees of weight perturbation in both space and time. Overall, our analysis framework provides clear geometric interpretations and quantification of information coding, maintenance and integration, yielding new insight into the computational mechanisms of context-dependent computation.

Roles of the Default Mode and Multiple-Demand Networks in Naturalistic versus Symbolic Decisions.

The default mode network (DMN) is often associated with representing semantic, social, and situational content of contexts and episodes. The DMN may therefore be important for contextual decision-making, through representing situational constraints and simulating common courses of events. Most decision-making paradigms, however, use symbolic stimuli and instead implicate cognitive control regions, such as the multiple demand (MD) system. This fMRI study aimed to contrast the brain mechanisms underlying decision-making based on rich naturalistic contexts or symbolic cues. While performing an ongoing task, 40 human participants (25 female) responded to different sounds. For one sound, the stimulus-response mapping was fixed; responses for the other sounds depended on the visual context: either lifelike scenes or letter symbols, varying across participants. Despite minimal behavioral differences between the groups, posterior DMN regions showed increased activity during context-dependent decision-making using the naturalistic scenes only, compared with symbolic cues. More anterior temporal and frontal DMN regions showed a different pattern, with sensitivity to the need for contextual control, but not to the type of context. Furthermore, in the scenes group, widespread DMN regions showed stronger representation of not just the context but also the sound whose significance it modulated. In comparison, the MD system showed strong univariate activity for every decision, but, intriguingly, somewhat reduced activity in the case of a scene-based but demanding context-dependent decision. Depending on context, we suggest, either DMN or MD regions may play a prominent role in selection and control of appropriate behavior.SIGNIFICANCE STATEMENT Contextual knowledge is widely believed to be important for guiding real-world goal-directed behavior. Much remains to be understood, however, regarding the underlying brain mechanisms. Using a novel paradigm to contrast decisions based on richly meaningful naturalistic scenes with decisions based on symbolic cues, we find that both multiple demand regions and default mode regions may contribute to the cognitive control of behavior. Rich semantic context enhances representation not just of the context itself, but also of the contents of the decision that it controls. Dependence of a decision on naturalistic context can also reverse the common pattern of multiple demand regions responding more, and default mode regions responding less, to more difficult decisions.

Learning Context-dependent Personal Preferences for Adaptive Recommendation

We propose two online-learning algorithms for modeling the personal preferences of users of interactive systems. The proposed algorithms leverage user feedback to estimate user behavior and provide personalized adaptive recommendation for supporting context-dependent decision-making. We formulate preference modeling as online prediction algorithms over a set of learned policies, i.e., policies generated via supervised learning with interaction and context data collected from previous users. The algorithms then adapt to a target user by learning the policy that best predicts that user’s behavior and preferences. We also generalize the proposed algorithms for a more challenging learning case in which they are restricted to a limited number of trained policies at each timestep, i.e., for mobile settings with limited resources. While the proposed algorithms are kept general for use in a variety of domains, we developed an image-filter-selection application. We used this application to demonstrate how the proposed algorithms can quickly learn to match the current user’s selections. Based on these evaluations, we show that (1) the proposed algorithms exhibit better prediction accuracy compared to traditional supervised learning and bandit algorithms, (2) our algorithms are robust under challenging limited prediction settings in which a smaller number of expert policies is assumed. Finally, we conducted a user study to demonstrate how presenting users with the prediction results of our algorithms significantly improves the efficiency of the overall interaction experience.

Institutional Design and the Psychology of the Trial Judge

The psychological research of judicial behaviour and decision-making has evolved markedly over the last few decades. While we are still far from developing a full empirical account of what is happening ‘inside the judicial mind’ (Guthrie, Rachlinski & Wistrich, 2001), substantial progress has been made as studies have gradually expanded in scope and methodology. In particular, an increasing number of studies strive to better capture the unique conditions that are relevant to judicial behaviour. For instance, researchers have moved to conduct laboratory experiments with expert subject pools, including judges and legal professionals (for an overview see, e.g., Rachlinski & Wistrich, 2017), more realistic settings (Spamann & Klohn, 2016), and more sophisticated methods, looking not only at the final outcome reached but also at elements of the decision-making process, such as the order at which judges approach various documents and how much time they devote to reading them (Liu, Klohn & Spamann, 2019). Researchers have also deployed the random assignment of cases to judges in order to isolate causal psychological mechanisms in real-world judicial decisions—documenting how differences in case attributes can lead to context-dependent decisions in criminal (Leibovitch, 2016) and asylum cases (Chen, Moskowitz & Shue, 2016), and how judicial decisions may change in response to emotional shocks (Eren & Mocan, 2018). Alongside growing methodological sophistication, scholars have also started to make headway on the manifold ways in which the psychology of judicial decision-making may be intertwined with wider institutional design aspects (Leibovitch, 2016, 2017; Schaur & Spellman, 2017). This new paradigm highlights the ways in which cognitive biases and heuristics can lead not only to transient effects on decisions but can also affect judgements in more systematic and prolonged ways. At its roots is the understanding that the expertise and repeat experience that characterize judges can lead to the creation of schemas and baselines which judges employ when making decisions in individual cases. The institutional capacity of the courts and the applicable rules of procedure and evidence can foster different decision-making environments, thereby changing the schemas created and nudging judicial decisions in a particular direction. In ‘The Psychology of the Trial Judge’, Judge Hoffman emphasizes how judicial decision-making, like human decision-making in general, is susceptible to various cognitive biases. This commentary complements that discussion by extending from the individual trial judge to the characteristics of the court in which she operates, and reviews the connection between institutional design and the psychology of judging through three prominent examples. The first section discusses how because of context-dependent decision-making, the assignment of jurisdiction and types of cases across courts can change the substantive outcomes reached by the courts. The second section draws attention to the tension between the human tendency to retributivism and the rehabilitative goals set by problem-solving or treatment-oriented courts, and how this tendency may be responsible for some of the unintended consequences of specialized courts. The third section reviews the accumulating findings regarding the difficulty of disregarding irrelevant or inadmissible evidence, and discusses how these could inform the applicable rules of evidence governing trials. As this is a developing field of research, throughout the discussion I also highlight some of the limitations of existing evidence and offer new avenues for future research on these topics.

Ecology of fear: environment-dependent parasite avoidance among ovipositing Drosophila.

Habitat avoidance is an anti-parasite behaviour exhibited by at-risk hosts that can minimize exposure to parasites. Because environments are often heterogeneous, host decision-making with regards to habitat use may be affected by the presence of parasites and habitat quality simultaneously. In this study we examine how the ovipositing behaviour of a cactiphilic fruit fly, Drosophila nigrospiracula, is affected by the presence of an ectoparasitic mite, Macrocheles subbadius, in conjunction with other environmental factors - specifically the presence or absence of conspecific eggs and host plant tissue. We hypothesized that the trade-off between site quality and parasite avoidance should favour ovipositing at mite-free sites even if it is of inferior quality. We found that although flies avoided mites in homogeneous environments (86% of eggs at mite-free sites), site quality overwhelmed mite avoidance. Both conspecific eggs (65% of eggs at infested sites with other Drosophila eggs) and host plant tissue (78% of eggs at infested sites with cactus) overpowered mite avoidance. Our results elucidate the context-dependent decision-making of hosts in response to the presence of parasites in variable environments, and suggest how the ecology of fear and associated trade-offs may influence the relative investment in anti-parasite behaviour in susceptible hosts.

Behavioral Insights into International Arbitration: An Analysis of How to De-Bias Arbitrators

Empirical evidence indicates that national court judges fall prey to cognitive biases and heuristics. The same may be assumed for international arbitrators. Improving third-party adjudication through behavior-ally informed rules on procedure thus seems to be an avenue of research worth being pursued. In applying behavioral law and economics to international commercial arbitration, the present analysis shows (1) that behavioral economics can help to understand arbitrators’ behavior and (2) suggests how the law may mitigate their cognitive biases and heuristics in order to design more effective, efficient, and fair arbitral proceedings under the UNCITRAL Arbitration Rules. The analysis focuses on (i) the representativeness heuristic, (ii) anchoring, (iii) the hindsight bias, (iv) framing effects, and (v) the egocentric bias. Building on their underlying dynamics and recent research on context-dependent decision-making, corresponding debiasing mechanisms may be implemented into arbitral proceedings through either behaviorally informed (model) arbitration clauses or by complementing existing frameworks such as the UNCITRAL Notes on Organizing Arbitral Proceedings in a behaviorally informed manner. Hence, in applying insights from economics and psychology to international arbitration, the present analysis adopts a prescriptive approach, examining how to actively mitigate arbitrators’ cognitive shortcomings as much as possible. Ac-curacy in fact determination – or the search for the truth – is perceived as the central motivation of this approach. As prescriptive insights from behavioral economics are able to allow for more accurate judgment, behaviorally informed rules on procedure not only benefit disputing parties by enhancing the idea of due process, but in doing so, they also empower international arbitration as a legal institution when con-fronted with national legal systems.

A dendritic disinhibitory circuit mechanism for pathway-specific gating.

While reading a book in a noisy café, how does your brain ‘gate in' visual information while filtering out auditory stimuli? Here we propose a mechanism for such flexible routing of information flow in a complex brain network (pathway-specific gating), tested using a network model of pyramidal neurons and three classes of interneurons with connection probabilities constrained by data. We find that if inputs from different pathways cluster on a pyramidal neuron dendrite, a pathway can be gated-on by a disinhibitory circuit motif. The branch-specific disinhibition can be achieved despite dense interneuronal connectivity, even with random connections. Moreover, clustering of input pathways on dendrites can naturally emerge through synaptic plasticity regulated by dendritic inhibition. This gating mechanism in a neural circuit is further demonstrated by performing a context-dependent decision-making task. The model suggests that cognitive flexibility engages top-down signalling of behavioural rule or context that targets specific classes of inhibitory neurons.

Assessment of health in human faces is context-dependent

When making decisions between options, humans are expected to choose the option that returns the highest benefit. In practice, however, adding inferior alternatives to the choice set can alter these decisions. Here we investigated whether decisions over the facial features that people find healthy looking can also be affected by the context in which they see those faces. To do this we examined the effect of choice set on the perception of health of images of faces of light-skinned Caucasian females. We manipulated apparent facial health by changing yellowness of the skin: the healthy faces were moderately yellow and the less healthy faces were either much more yellow or much less yellow. In each experiment, two healthy faces were presented along with a third, less healthy face. When the third face was much more yellow, participants chose the more yellow of the two healthy faces more often as the most healthy. However, when the third face was the least yellow, participants chose the less yellow of the two healthy faces more often. A further experiment confirmed that this result is not due to a generalised preference for an intermediate option. These results extend our understanding of context-dependent decision-making in humans, and suggest that comparative evaluation may be a common feature across many different kinds of choices that humans have to make.

Value-based attentional capture influences context-dependent decision-making.

Normative theories posit that value-based decision-making is context independent. However, decisions between two high-value options can be suboptimally biased by the introduction of a third low-value option. This context-dependent modulation is consistent with the divisive normalization of the value of each stimulus by the total value of all stimuli. In addition, an independent line of research demonstrates that pairing a stimulus with a high-value outcome can lead to attentional capture that can mediate the efficiency of visual information processing. Here we tested the hypothesis that value-based attentional capture interacts with value-based normalization to influence the optimality of decision-making. We used a binary-choice paradigm in which observers selected between two targets and the color of each target indicated the magnitude of their reward potential. Observers also had to simultaneously ignore a task-irrelevant distractor rendered in a color that was previously associated with a specific reward magnitude. When the color of the task-irrelevant distractor was previously associated with a high reward, observers responded more slowly and less optimally. Moreover, as the learned value of the distractor increased, electrophysiological data revealed an attenuation of the lateralized N1 and N2Pc responses evoked by the relevant choice stimuli and an attenuation of the late positive deflection (LPD). Collectively, these behavioral and electrophysiological data suggest that value-based attentional capture and value-based normalization jointly mediate the influence of context on free-choice decision-making.

Spatial statistics and attentional dynamics in scene viewing.

In humans and in foveated animals visual acuity is highly concentrated at the center of gaze, so that choosing where to look next is an important example of online, rapid decision-making. Computational neuroscientists have developed biologically-inspired models of visual attention, termed saliency maps, which successfully predict where people fixate on average. Using point process theory for spatial statistics, we show that scanpaths contain, however, important statistical structure, such as spatial clustering on top of distributions of gaze positions. Here, we develop a dynamical model of saccadic selection that accurately predicts the distribution of gaze positions as well as spatial clustering along individual scanpaths. Our model relies on activation dynamics via spatially-limited (foveated) access to saliency information, and, second, a leaky memory process controlling the re-inspection of target regions. This theoretical framework models a form of context-dependent decision-making, linking neural dynamics of attention to behavioral gaze data.

Individual differences in decision making by foraging hummingbirds

For both humans and animals preference for one option over others can be influenced by the context in which the options occur. In animals, changes in preference could be due to comparative decision-making or to changes in the energy state of the animal when making decisions. We investigated which of these possibilities better explained the response of wild hummingbirds to the addition of a decoy option to a set of two options by presenting Rufous hummingbirds (Selasphorus rufus) with a foraging experiment with two treatments. In each treatment the birds were presented with a binary choice between two options and a trinary choice with three options. In treatment one the binary choice was between a volume option and a concentration option, whereas in treatment two the same volume option was presented alongside an alternative concentration option. In the trinary choice, birds were presented with the same options as in the binary choice plus one of two inferior options. Birds changed their preferences when a poorer option was added to the choice set: birds increased their preference for the same option when in the presence of either decoy. Which option differed across individuals and the changes in preference were not readily explained by either energy maximisation or the decoy effect. The consistency in response within individuals, however, would suggest that the individual itself brings an extra dimension to context-dependent decision-making. This article is part of a Special Issue entitled: Cognition in the wild.

Mate finding, Allee effects and selection for sex-biased dispersal.

Although dispersal requires context-dependent decision-making in three distinct stages (emigration, transit, immigration), these decisions are commonly ignored in simple models of dispersal. For sexually reproducing organisms, mate availability is an important factor in dispersal decisions. Difficulty finding mates can lead to an Allee effect where population growth decreases at low densities. Surprisingly, theoretical studies on mate finding and on sex-biased dispersal produce opposing predictions: in the former, one sex is predicted to move less if the other sex evolves to search more, whereas in the latter, mate-finding difficulties can select for less sex bias in dispersal when mate finding occurs after dispersal. Here, we develop a pair of models to examine the joint evolution of dispersal and settlement behaviour. Our first model resolves the apparent contradiction from the mate search and dispersal literatures. Our second model demonstrates that the relationship between mating system and sex-biased dispersal is more complex than a simple contrast between resource defence monogamy and female defence polygyny. Our results highlight that a key factor is the timing of mating relative to dispersal (before, during, or after). We also show that although movement has the potential to alleviate a mate-finding Allee effect, in some cases, it can actually exacerbate the effect.

Informed consent for exome sequencing in diagnostics: exploring first experiences and views of professionals and patients

Next-generation sequencing is increasingly being chosen as a diagnostic tool for cases of expected genetic, but unresolved origin. The consequential increased need for decisions on disclosure of unsolicited findings poses a challenge for the informed consent procedure. This study explored the first experiences with, and needs for, the informed consent procedure in diagnostic exome sequencing, with the stakeholders involved. Semi-structured interviews were conducted with 11 professional experts and one professional gave a written response. Furthermore, the counseling process was observed in three cases where exome sequencing was offered, followed by interviews with the patient (representative) and the genetic counselor. The respondents not only preferred an opt-out for unsolicited findings but also identified many challenges and therefore more experiences with exome sequencing was considered needed. Context-dependent decision-making was observed and an Advisory Board for unsolicited findings was considered helpful while doubts were raised about the feasibility and the possibility of undermining patients' autonomy. Finally, respondents brought up the complexity of information provision, and division of responsibilities between clinicians and the lab. These challenges and needs, raised by stakeholders involved, provide more insight in the next steps needed for an optimal informed consent procedure for exome sequencing in diagnostics.

Context-dependent decision-making: a simple Bayesian model.

Many phenomena in animal learning can be explained by a context-learning process whereby an animal learns about different patterns of relationship between environmental variables. Differentiating between such environmental regimes or 'contexts' allows an animal to rapidly adapt its behaviour when context changes occur. The current work views animals as making sequential inferences about current context identity in a world assumed to be relatively stable but also capable of rapid switches to previously observed or entirely new contexts. We describe a novel decision-making model in which contexts are assumed to follow a Chinese restaurant process with inertia and full Bayesian inference is approximated by a sequential-sampling schemein which only a single hypothesis about current context is maintained. Actions are selected via Thompson sampling, allowing uncertainty in parameters to drive exploration in a straightforward manner. The model is tested on simple two-alternative choice problems with switching reinforcement schedules and the results compared with rat behavioural data from a number of T-maze studies. The model successfully replicates a number of important behavioural effects: spontaneous recovery, the effect of partial reinforcement on extinction and reversal, the overtraining reversal effect, and serial reversal-learning effects.