Decisions reduce sensitivity to subsequent information

Behavioural studies over half a century indicate that making categorical choices alters beliefs about the state of the world. People seem biased to confirm previous choices, and to suppress contradicting information. These choice-dependent biases imply a fundamental bound of human rationality. However, it remains unclear whether these effects extend to lower level decisions, and only little is known about the computational mechanisms underlying them. Building on the framework of sequential-sampling models of decision-making, we developed novel psychophysical protocols that enable us to dissect quantitatively how choices affect the way decision-makers accumulate additional noisy evidence. We find robust choice-induced biases in the accumulation of abstract numerical (experiment 1) and low-level perceptual (experiment 2) evidence. These biases deteriorate estimations of the mean value of the numerical sequence (experiment 1) and reduce the likelihood to revise decisions (experiment 2). Computational modelling reveals that choices trigger a reduction of sensitivity to subsequent evidence via multiplicative gain modulation, rather than shifting the decision variable towards the chosen alternative in an additive fashion. Our results thus show that categorical choices alter the evidence accumulation mechanism itself, rather than just its outcome, rendering the decision-maker less sensitive to new information.     

Genetic compatibility in long-term intimate relationships: partner similarity at major histocompatibility complex (MHC) genes may reduce in-pair attraction

Theoretical models from evolutionary biology predict that individual mate choice will be influenced by the extent of similarity between potential mates at the major histocompatibility complex (MHC) genes. A number of studies have sought to uncover an effect of MHC similarity on mate choice in humans, but the extent to which MHC similarity influences attraction within existing human relationships has been relatively under-explored. We investigated this question in a sample of 168 heterosexual couples that were typed and matched at 3 classical MHC markers. Findings were mixed with respect to the prediction that higher levels of MHC similarity would be linked to a reduction of in-pair attraction. In the full sample, there were no effects of MHC similarity on any of the dependent variables used to measure in-pair attraction, but there were strong and consistent effects of MHC similarity on these measures in couples with two Asian partners (N couples =44). In sum, our findings are consistent with an effect of MHC similarity on in-pair attraction within existing relationships, but they also suggest that this effect may be moderated by additional factors, particularly the ancestral background of the individual relationship partners.     

A dual role for the zebrafish unplugged gene in motor axon pathfinding and pharyngeal development.

On their way toward their synaptic targets, motor growth cones encounter multiple choice points, where they are confronted with trajectory choices. We have previously shown that the zebrafish unplugged gene acts as a somite-derived cue controlling pathway choice of primary motor axons. Here, we demonstrate that this trajectory choice is not exclusively controlled by a single unplugged-dependent process, but depends on the coordinated function of additional cues. We also show that secondary motor neurons, most similar to those in birds and mammals, depend on the unplugged gene to navigate a choice point, suggesting that primary and secondary motor neurons share common mechanisms controlling axonal path selection. Moreover, we show that the unplugged gene plays an additional role guiding secondary motor axons through a single segmental nerve. Finally, we report that unplugged larvae display a striking pharyngeal arch defect, consistent with a dual function of the unplugged gene in axonal guidance and cell motility.     

Models of social evolution: can we do better to predict ‘who helps whom to achieve what’?

Models of social evolution and the evolution of helping have been classified in numerous ways. Two categorical differences have, however, escaped attention in the field. Models tend not to justify why they use a particular assumption structure about who helps whom: a large number of authors model peer-to-peer cooperation of essentially identical individuals, probably for reasons of mathematical convenience; others are inspired by particular cooperatively breeding species, and tend to assume unidirectional help where subordinates help a dominant breed more efficiently. Choices regarding what the help achieves (i.e. which life-history trait of the helped individual is improved) are similarly made without much comment: fecundity benefits are much more commonly modelled than survival enhancements, despite evidence that these may interact when the helped individual can perform life-history reallocations (load-lightening and related phenomena). We review our current theoretical understanding of effects revealed when explicitly asking ‘who helps whom to achieve what’, from models of mutual aid in partnerships to the very few models that explicitly contrast the strength of selection to help enhance another individual''s fecundity or survival. As a result of idiosyncratic modelling choices in contemporary literature, including the varying degree to which demographic consequences are made explicit, there is surprisingly little agreement on what types of help are predicted to evolve most easily. We outline promising future directions to fill this gap.     

Cognitive mechanisms of risky choice: is there an evaluation cost?

We contrast two classes of choice processes, those assuming time-consuming comparisons and those where stimuli for each option act independently, competing for expression by cross censorship. The Sequential Choice Model (SCM) belongs in the latter category, and has received empirical support in several procedures involving deterministic alternatives. Here we test this model in risky choices. In two treatments, each with five conditions, European starlings (Sturnus vulgaris) faced choices between options with unpredictable outcomes and risk-free alternatives. In the delay treatment the five conditions involved choices between a variable option offering two equiprobable delays to reward and a fixed option with delay differing between conditions. The amount treatment was structurally similar, but amount of reward rather than delay was manipulated. As assumed (and required) by the SCM, latency to respond in no-choice trials reflected each option's richness with respect to the background alternatives, and, crucially, preferences in simultaneous choices were predictable from latencies to each option in forced trials. However, we did not detect reliable differences in response times between forced and choice trials, neither the lengthening expected from evaluation models nor the shortening expected from the SCM.     

Choice in multi-alternative environments: A trial-by-trial implementation of the Sequential Choice Model

The sequential choice model (SCM) proposes that latencies to accept options presented alone can be used to predict preferences between these options when they are presented simultaneously. SCM has been proposed and tested in experiments where only two alternatives were present. To further challenge the model, we trained and tested European starlings (Sturnus vulgaris) in an environment with a background of four alternatives differing in delay to reinforcement. Unexpected binary choices between the six possible pairs of alternatives were used to assess preference. The model's predictions of the strength of preference roughly corresponded to the bird's choices for each of the six choice situations. More importantly, a trial-by-trial test of the model correctly predicted 84% of all individual choice trials.     

Fixation patterns in simple choice reflect optimal information sampling

Simple choices (e.g., eating an apple vs. an orange) are made by integrating noisy evidence that is sampled over time and influenced by visual attention; as a result, fluctuations in visual attention can affect choices. But what determines what is fixated and when? To address this question, we model the decision process for simple choice as an information sampling problem, and approximate the optimal sampling policy. We find that it is optimal to sample from options whose value estimates are both high and uncertain. Furthermore, the optimal policy provides a reasonable account of fixations and choices in binary and trinary simple choice, as well as the differences between the two cases. Overall, the results show that the fixation process during simple choice is influenced dynamically by the value estimates computed during the decision process, in a manner consistent with optimal information sampling.     

How strict is the correlation between STIM1 and Orai1 expression, puncta formation, and ICRAC activation?

Stromal interaction molecule 1 (STIM1) and Orai1 have been identified as crucial elements of the store-operated Ca(2+) entry (SOCE) pathway, but the mechanism of their functional interaction remains controversial. It is now well established that, upon depletion of the stores, both molecules can accumulate and colocalize in specific areas (puncta) where the endoplasmic reticulum comes in close proximity to the plasma membrane. Some models propose a direct interaction between STIM1 and Orai1 as the most straightforward mechanism for signal transduction from the stores to the plasma membrane. To test some of the predictions of a conformational coupling model, we assessed how tight the relationships are between STIM1 and Orai1 expression, puncta formation, and SOCE activation. Here we present evidence that STIM1 accumulates in puncta equally well in the presence or absence of Orai1 expression, that STIM1 accumulation is not sufficient for Orai1 accumulation in the same areas, and that normal Ca(2+) release-activated Ca(2+) current (I(CRAC)) can be activated in STIM1-deficient cells. These data challenge the idea of direct conformational coupling between STIM1 and Orai1 as a viable mechanism of puncta formation and SOCE activation and uncover greater complexity in their relationship, which may require additional intermediate elements.     

A Dual Role for the Zebrafish unplugged Gene in Motor Axon Pathfinding and Pharyngeal Development

On their way toward their synaptic targets, motor growth cones encounter multiple choice points, where they are confronted with trajectory choices. We have previously shown that the zebrafish unplugged gene acts as a somite-derived cue controlling pathway choice of primary motor axons. Here, we demonstrate that this trajectory choice is not exclusively controlled by a single unplugged-dependent process, but depends on the coordinated function of additional cues. We also show that secondary motor neurons, most similar to those in birds and mammals, depend on the unplugged gene to navigate a choice point, suggesting that primary and secondary motor neurons share common mechanisms controlling axonal path selection. Moreover, we show that the unplugged gene plays an additional role guiding secondary motor axons through a single segmental nerve. Finally, we report that unplugged larvae display a striking pharyngeal arch defect, consistent with a dual function of the unplugged gene in axonal guidance and cell motility.     

Design and Analysis of Simple Choice Surveys for Natural Resource Management

Abstract: We used a simple yet powerful method for judging public support for management actions from randomized surveys. We asked respondents to rank choices (representing management regulations under consideration) according to their preference, and we then used discrete choice models to estimate probability of choosing among options (conditional on the set of options presented to respondents). Because choices may share similar unmodeled characteristics, the multinomial logit model, commonly applied to discrete choice data, may not be appropriate. We introduced the nested logit model, which offers a simple approach for incorporating correlation among choices. This forced choice survey approach provides a useful method of gathering public input; it is relatively easy to apply in practice, and the data are likely to be more informative than asking constituents to rate attractiveness of each option separately.     

Deciding Not to Decide: Computational and Neural Evidence for Hidden Behavior in Sequential Choice

Understanding the cognitive and neural processes that underlie human decision making requires the successful prediction of how, but also of when, people choose. Sequential sampling models (SSMs) have greatly advanced the decision sciences by assuming decisions to emerge from a bounded evidence accumulation process so that response times (RTs) become predictable. Here, we demonstrate a difficulty of SSMs that occurs when people are not forced to respond at once but are allowed to sample information sequentially: The decision maker might decide to delay the choice and terminate the accumulation process temporarily, a scenario not accounted for by the standard SSM approach. We developed several SSMs for predicting RTs from two independent samples of an electroencephalography (EEG) and a functional magnetic resonance imaging (fMRI) study. In these studies, participants bought or rejected fictitious stocks based on sequentially presented cues and were free to respond at any time. Standard SSM implementations did not describe RT distributions adequately. However, by adding a mechanism for postponing decisions to the model we obtained an accurate fit to the data. Time-frequency analysis of EEG data revealed alternating states of de- and increasing oscillatory power in beta-band frequencies (14–30 Hz), indicating that responses were repeatedly prepared and inhibited and thus lending further support for the existence of a decision not to decide. Finally, the extended model accounted for the results of an adapted version of our paradigm in which participants had to press a button for sampling more information. Our results show how computational modeling of decisions and RTs support a deeper understanding of the hidden dynamics in cognition.     

The Probability of Extinction of Infectious Salmon Anemia Virus in One and Two Patches

Single-type and multitype branching processes have been used to study the dynamics of a variety of stochastic birth–death type phenomena in biology and physics. Their use in epidemiology goes back to Whittle’s study of a susceptible–infected–recovered (SIR) model in the 1950s. In the case of an SIR model, the presence of only one infectious class allows for the use of single-type branching processes. Multitype branching processes allow for multiple infectious classes and have latterly been used to study metapopulation models of disease. In this article, we develop a continuous time Markov chain (CTMC) model of infectious salmon anemia virus in two patches, two CTMC models in one patch and companion multitype branching process (MTBP) models. The CTMC models are related to deterministic models which inform the choice of parameters. The probability of extinction is computed for the CTMC via numerical methods and approximated by the MTBP in the supercritical regime. The stochastic models are treated as toy models, and the parameter choices are made to highlight regions of the parameter space where CTMC and MTBP agree or disagree, without regard to biological significance. Partial extinction events are defined and their relevance discussed. A case is made for calculating the probability of such events, noting that MTBPs are not suitable for making these calculations.     

A neural network model of basal ganglia’s decision-making circuitry

The basal ganglia have been increasingly recognized as an important structure involved in decision making. Neurons in the basal ganglia were found to reflect the evidence accumulation process during decision making. However, it is not well understood how the direct and indirect pathways of the basal ganglia work together for decision making. Here, we create a recurrent neural network model that is composed of the direct and indirect pathways and test it with the classic random dot motion discrimination task. The direct pathway drives the outputs, which are modulated through a gating mechanism controlled by the indirect pathway. We train the network to learn the task and find that the network reproduces the accuracy and reaction time patterns of previous animal studies. Units in the model exhibit ramping activities that reflect evidence accumulation. Finally, we simulate manipulations of the direct and indirect pathways and find that the manipulations of the direct pathway mainly affect the choice while the manipulations of the indirect pathway affect the model’s reaction time. These results suggest a potential circuitry mechanism of the basal ganglia’s role in decision making with predictions that can be tested experimentally in the future.     

Activity in Inferior Parietal and Medial Prefrontal Cortex Signals the Accumulation of Evidence in a Probability Learning Task

In an uncertain environment, probabilities are key to predicting future events and making adaptive choices. However, little is known about how humans learn such probabilities and where and how they are encoded in the brain, especially when they concern more than two outcomes. During functional magnetic resonance imaging (fMRI), young adults learned the probabilities of uncertain stimuli through repetitive sampling. Stimuli represented payoffs and participants had to predict their occurrence to maximize their earnings. Choices indicated loss and risk aversion but unbiased estimation of probabilities. BOLD response in medial prefrontal cortex and angular gyri increased linearly with the probability of the currently observed stimulus, untainted by its value. Connectivity analyses during rest and task revealed that these regions belonged to the default mode network. The activation of past outcomes in memory is evoked as a possible mechanism to explain the engagement of the default mode network in probability learning. A BOLD response relating to value was detected only at decision time, mainly in striatum. It is concluded that activity in inferior parietal and medial prefrontal cortex reflects the amount of evidence accumulated in favor of competing and uncertain outcomes.     

SIS epidemic attractors in periodic environments

The demographic dynamics are known to drive the disease dynamics in constant environments. In periodic environments, we prove that the demographic dynamics do not always drive the disease dynamics. We exhibit a chaotic attractor in an SIS epidemic model, where the demograhic dynamics are asymptotically cyclic. Periodically forced SIS epidemic models are known to exhibit multiple attractors. We prove that the basins of attraction of these coexisting attractors have infinitely many components.     

Reinforcement and learning

Evidence has been accumulating to support the process of reinforcement as a potential mechanism in speciation. In many species, mate choice decisions are influenced by cultural factors, including learned mating preferences (sexual imprinting) or learned mate attraction signals (e.g., bird song). It has been postulated that learning can have a strong impact on the likelihood of speciation and perhaps on the process of reinforcement, but no models have explicitly considered learning in a reinforcement context. We review the evidence that suggests that learning may be involved in speciation and reinforcement, and present a model of reinforcement via learned preferences. We show that not only can reinforcement occur when preferences are learned by imprinting, but that such preferences can maintain species differences easily in comparison with both autosomal and sex-linked genetically inherited preferences. We highlight the need for more explicit study of the connection between the behavioral process of learning and the evolutionary process of reinforcement in natural systems.     

Conspecific attraction and shelter selection in gregarious insects

During habitat selection, the presence of conspecifics can frequently drive a nonuniform distribution of animals across habitats of equivalent quality. In group-living species, subgroups of individuals might display mutual attraction while differing in their preferences for environmental resources. The final decision to settle requires individuals to integrate both environmental and social cues. This raises the question of the relative importance of sociality and resources preferences in determining habitat choice. In this study, we examined the interactive influence of conspecific attraction on individual resource preferences on refuge choice in groups of cockroaches. Shelters scaled to the sizes of nymphs and adult males were offered to groups of only nymphs and only males and to mixed groups. The choices of males were consistent across social conditions. Conversely, the preferences of nymphs shifted depending on the social context; the presence of males overrode the affinity nymphs had for scaled-size shelters. We developed a numerical model implementing parameters derived from these experiments to test whether the final spatial distribution of individuals originated from a differential attraction between nymphs and males that was associated with their relative body size. Finally, we propose a general framework for understanding how similar mechanisms can promote the skewed distribution of organisms at different spatial scales.     

The importance of urgency in decision making based on dynamic information

A standard view in the literature is that decisions are the result of a process that accumulates evidence in favor of each alternative until such accumulation reaches a threshold and a decision is made. However, this view has been recently questioned by an alternative proposal that suggests that, instead of accumulated, evidence is combined with an urgency signal. Both theories have been mathematically formalized and supported by a variety of decision-making tasks with constant information. However, recently, tasks with changing information have shown to be more effective to study the dynamics of decision making. Recent research using one of such tasks, the tokens task, has shown that decisions are better described by an urgency mechanism than by an accumulation one. However, the results of that study could depend on a task where all fundamental information was noiseless and always present, favoring a mechanism of non-integration, such as the urgency one. Here, we wanted to address whether the same conclusions were also supported by an experimental paradigm in which sensory evidence was removed shortly after it was provided, making working memory necessary to properly perform the task. Here, we show that, under such condition, participants’ behavior could be explained by an urgency-gating mechanism that low-pass filters the mnemonic information and combines it with an urgency signal that grows with time but not by an accumulation process that integrates the same mnemonic information. Thus, our study supports the idea that, under certain situations with dynamic sensory information, decisions are better explained by an urgency-gating mechanism than by an accumulation one.