How We Choose One over Another: Predicting Trial-by-Trial Preference Decision

Preference formation is a complex problem as it is subjective, involves emotion, is led by implicit processes, and changes depending on the context even within the same individual. Thus, scientific attempts to predict preference are challenging, yet quite important for basic understanding of human decision making mechanisms, but prediction in a group-average sense has only a limited significance. In this study, we predicted preferential decisions on a trial by trial basis based on brain responses occurring before the individuals made their decisions explicit. Participants made a binary preference decision of approachability based on faces while their electrophysiological responses were recorded. An artificial neural network based pattern-classifier was used with time-frequency resolved patterns of a functional connectivity measure as features for the classifier. We were able to predict preference decisions with a mean accuracy of 74.3±2.79% at participant-independent level and of 91.4±3.8% at participant-dependent level. Further, we revealed a causal role of the first impression on final decision and demonstrated the temporal trajectory of preference decision formation.     

Synaptic plasticity and connectivity requirements to produce stimulus-pair specific responses in recurrent networks of spiking neurons

Animals must respond selectively to specific combinations of salient environmental stimuli in order to survive in complex environments. A task with these features, biconditional discrimination, requires responses to select pairs of stimuli that are opposite to responses to those stimuli in another combination. We investigate the characteristics of synaptic plasticity and network connectivity needed to produce stimulus-pair neural responses within randomly connected model networks of spiking neurons trained in biconditional discrimination. Using reward-based plasticity for synapses from the random associative network onto a winner-takes-all decision-making network representing perceptual decision-making, we find that reliably correct decision making requires upstream neurons with strong stimulus-pair selectivity. By chance, selective neurons were present in initial networks; appropriate plasticity mechanisms improved task performance by enhancing the initial diversity of responses. We find long-term potentiation of inhibition to be the most beneficial plasticity rule by suppressing weak responses to produce reliably correct decisions across an extensive range of networks.     

Selecting neural networks for a committee decision

To improve recognition results, decisions of multiple neural networks can be aggregated into a committee decision. In contrast to the ordinary approach of utilizing all neural networks available to make a committee decision, we propose creating adaptive committees, which are specific for each input data point. A prediction network is used to identify classification neural networks to be fused for making a committee decision about a given input data point. The jth output value of the prediction network expresses the expectation level that the jth classification neural network will make a correct decision about the class label of a given input data point. The proposed technique is tested in three aggregation schemes, namely majority vote, averaging, and aggregation by the median rule and compared with the ordinary neural networks fusion approach. The effectiveness of the approach is demonstrated on two artificial and three real data sets.     

Neural control of behavioural choice in juvenile crayfish

Natural selection leads to behavioural choices that increase the animal''s fitness. The neuronal mechanisms underlying behavioural choice are still elusive and empirical evidence connecting neural circuit activation to adaptive behavioural output is sparse.We exposed foraging juvenile crayfish to approaching shadows of different velocities and found that slow-moving shadows predominantly activated a pair of giant interneurons, which mediate tail-flips that thrust the animals backwards and away from the approaching threat. Tail-flips also moved the animals farther away from an expected food source, and crayfish defaulted to freezing behaviour when faced with fast-approaching shadows. Under these conditions, tail-flipping, an ineffective and costly escape strategy was suppressed in favour of freezing, a more beneficial choice. The decision to freeze also dominated in the presence of a more desirable resource; however, the increased incentive was less effective in suppressing tail-flipping when paired with slow-moving visual stimuli that reliably evoked tail-flips in most animals. Together this suggests that crayfish make value-based decisions by weighing the costs and benefits of different behavioural options, and they select adaptive behavioural output based on the activation patterns of identifiable neural circuits.     

Posterior Probability Matching and Human Perceptual Decision Making

Probability matching is a classic theory of decision making that was first developed in models of cognition. Posterior probability matching, a variant in which observers match their response probabilities to the posterior probability of each response being correct, is being used increasingly often in models of perception. However, little is known about whether posterior probability matching is consistent with the vast literature on vision and hearing that has developed within signal detection theory. Here we test posterior probability matching models using two tools from detection theory. First, we examine the models’ performance in a two-pass experiment, where each block of trials is presented twice, and we measure the proportion of times that the model gives the same response twice to repeated stimuli. We show that at low performance levels, posterior probability matching models give highly inconsistent responses across repeated presentations of identical trials. We find that practised human observers are more consistent across repeated trials than these models predict, and we find some evidence that less practised observers more consistent as well. Second, we compare the performance of posterior probability matching models on a discrimination task to the performance of a theoretical ideal observer that achieves the best possible performance. We find that posterior probability matching is very inefficient at low-to-moderate performance levels, and that human observers can be more efficient than is ever possible according to posterior probability matching models. These findings support classic signal detection models, and rule out a broad class of posterior probability matching models for expert performance on perceptual tasks that range in complexity from contrast discrimination to symmetry detection. However, our findings leave open the possibility that inexperienced observers may show posterior probability matching behaviour, and our methods provide new tools for testing for such a strategy.     

Risky Decisions and Their Consequences: Neural Processing by Boys with Antisocial Substance Disorder

Background

Adolescents with conduct and substance problems (“Antisocial Substance Disorder” (ASD)) repeatedly engage in risky antisocial and drug-using behaviors. We hypothesized that, during processing of risky decisions and resulting rewards and punishments, brain activation would differ between abstinent ASD boys and comparison boys.

Methodology/Principal Findings

We compared 20 abstinent adolescent male patients in treatment for ASD with 20 community controls, examining rapid event-related blood-oxygen-level-dependent (BOLD) responses during functional magnetic resonance imaging. In 90 decision trials participants chose to make either a cautious response that earned one cent, or a risky response that would either gain 5 cents or lose 10 cents; odds of losing increased as the game progressed. We also examined those times when subjects experienced wins, or separately losses, from their risky choices. We contrasted decision trials against very similar comparison trials requiring no decisions, using whole-brain BOLD-response analyses of group differences, corrected for multiple comparisons. During decision-making ASD boys showed hypoactivation in numerous brain regions robustly activated by controls, including orbitofrontal and dorsolateral prefrontal cortices, anterior cingulate, basal ganglia, insula, amygdala, hippocampus, and cerebellum. While experiencing wins, ASD boys had significantly less activity than controls in anterior cingulate, temporal regions, and cerebellum, with more activity nowhere. During losses ASD boys had significantly more activity than controls in orbitofrontal cortex, dorsolateral prefrontal cortex, brain stem, and cerebellum, with less activity nowhere.

Conclusions/Significance

Adolescent boys with ASD had extensive neural hypoactivity during risky decision-making, coupled with decreased activity during reward and increased activity during loss. These neural patterns may underlie the dangerous, excessive, sustained risk-taking of such boys. The findings suggest that the dysphoria, reward insensitivity, and suppressed neural activity observed among older addicted persons also characterize youths early in the development of substance use disorders.     

Scaling laws of ambush predator ‘waiting’ behaviour are tuned to a common ecology

The decisions animals make about how long to wait between activities can determine the success of diverse behaviours such as foraging, group formation or risk avoidance. Remarkably, for diverse animal species, including humans, spontaneous patterns of waiting times show random ‘burstiness’ that appears scale-invariant across a broad set of scales. However, a general theory linking this phenomenon across the animal kingdom currently lacks an ecological basis. Here, we demonstrate from tracking the activities of 15 sympatric predator species (cephalopods, sharks, skates and teleosts) under natural and controlled conditions that bursty waiting times are an intrinsic spontaneous behaviour well approximated by heavy-tailed (power-law) models over data ranges up to four orders of magnitude. Scaling exponents quantifying ratios of frequent short to rare very long waits are species-specific, being determined by traits such as foraging mode (active versus ambush predation), body size and prey preference. A stochastic–deterministic decision model reproduced the empirical waiting time scaling and species-specific exponents, indicating that apparently complex scaling can emerge from simple decisions. Results indicate temporal power-law scaling is a behavioural ‘rule of thumb’ that is tuned to species’ ecological traits, implying a common pattern may have naturally evolved that optimizes move–wait decisions in less predictable natural environments.     

Low doses of a neonicotinoid insecticide modify pheromone response thresholds of central but not peripheral olfactory neurons in a pest insect

Insect pest management relies mainly on neurotoxic insecticides, including neonicotinoids, leaving residues in the environment. There is now evidence that low doses of insecticides can have positive effects on pest insects by enhancing various life traits. Because pest insects often rely on sex pheromones for reproduction, and olfactory synaptic transmission is cholinergic, neonicotinoid residues could modify chemical communication. We recently showed that treatments with different sublethal doses of clothianidin could either enhance or decrease behavioural sex pheromone responses in the male moth, Agrotis ipsilon. We investigated now effects of the behaviourally active clothianidin doses on the sensitivity of the peripheral and central olfactory system. We show with extracellular recordings that both tested clothianidin doses do not influence pheromone responses in olfactory receptor neurons. Similarly, in vivo optical imaging does not reveal any changes in glomerular response intensities to the sex pheromone after clothianidin treatments. The sensitivity of intracellularly recorded antennal lobe output neurons, however, is upregulated by a lethal dose 20 times and downregulated by a dose 10 times lower than the lethal dose 0. This correlates with the changes of behavioural responses after clothianidin treatment and suggests the antennal lobe as neural substrate involved in clothianidin-induced behavioural changes.     

Comparing alternative models to empirical data: cognitive models of western scrub-jay foraging behavior

Animals often select one item from a set of candidates, as when choosing a foraging site or mate, and are expected to possess accurate and efficient rules for acquiring information and making decisions. Little is known, however, about the decision rules animals use. We compare patterns of information sampling by western scrub-jays (Aphelocoma californica) when choosing a nut with three decision rules: best of n (BN), flexible threshold (FT), and comparative Bayes (CB). First, we use a null hypothesis testing approach and find that the CB decision rule, in which individuals use past experiences to make nonrandom assessment and choice decisions, produces patterns of behavior that more closely correspond to observed patterns of nut sampling in scrub-jays than the other two rules. This approach does not allow us to quantify how much better CB is at predicting scrub-jay behavior than the other decision rules. Second, we use a model selection approach that uses Akaike Information Criteria to quantify how well alternative models approximate observed data. We find that the CB rule is much more likely to produce the observed patterns of scrub-jay behavior than the other rules. This result provides some of the best empirical evidence of the use of Bayesian information updating by a nonhuman animal.     

Introduction. Neuroeconomics: the promise and the profit

Neuroeconomics investigates the neural mechanisms underlying decisions about rewarding or punishing outcomes ('economic' decisions). It combines the knowledge about the behavioural phenomena of economic decisions with the mechanistic explanatory power of neuroscience. Thus, it is about the neurobiological foundations of economic decision making. It is hoped that by 'opening the box' we can understand how decisions about gains and losses are directed by the brain of the individual decision maker. Perhaps we can even learn why some decisions are apparently paradoxical or pathological. The knowledge could be used to create situations that avoid suboptimal decisions and harm.     

The wisdom of stalemates: consensus and clustering as filtering mechanisms for improving collective accuracy

Groups of organisms, from bacteria to fish schools to human societies, depend on their ability to make accurate decisions in an uncertain world. Most models of collective decision-making assume that groups reach a consensus during a decision-making bout, often through simple majority rule. In many natural and sociological systems, however, groups may fail to reach consensus, resulting in stalemates. Here, we build on opinion dynamics and collective wisdom models to examine how stalemates may affect the wisdom of crowds. For simple environments, where individuals have access to independent sources of information, we find that stalemates improve collective accuracy by selectively filtering out incorrect decisions (an effect we call stalemate filtering). In complex environments, where individuals have access to both shared and independent information, this effect is even more pronounced, restoring the wisdom of crowds in regions of parameter space where large groups perform poorly when making decisions using majority rule. We identify network properties that tune the system between consensus and accuracy, providing mechanisms by which animals, or evolution, could dynamically adjust the collective decision-making process in response to the reward structure of the possible outcomes. Overall, these results highlight the adaptive potential of stalemate filtering for improving the decision-making abilities of group-living animals.     

Intuition, functional responses and the formulation of predator-prey models when there is a large disparity in the spatial domains of the interacting species

1. The disparity of the spatial domains used by predators and prey is a common feature of many terrestrial avian and mammalian predatory interactions, as predators are typically more mobile and have larger home ranges than their prey. 2. Incorporating these realistic behavioural features requires formulating spatial predator-prey models having local prey mortality due to predation and its spatial aggregation, in order to generate a numerical response at timescales longer than the local prey consumption. Coupling the population dynamics occurring at different spatial scales is far from intuitive, and involves making important behavioural and demographic assumptions. Previous spatial predator-prey models resorted to intuition to derive local functional responses from non-spatial equivalents, and often involve unrealistic biological assumptions that restrict their validity. 3. We propose a hierarchical framework for deriving generic models of spatial predator-prey interactions that explicitly considers the behavioural and demographic processes occurring at different spatial and temporal scales. 4. The proposed framework highlights the circumstances wherein static spatial patterns emerge and can be a stabilizing mechanism of consumer-resource interactions.     

Integration of sensory and motor processing underlying social behaviour in túngara frogs

Social decision making involves the perception and processing of social stimuli, the subsequent evaluation of that information in the context of the individual's internal and external milieus to produce a decision, and then culminates in behavioural output informed by that decision. We examined brain networks in an anuran communication system that relies on acoustic signals to guide simple, stereotyped motor output. We used egr-1 mRNA expression to measure neural activation in male túngara frogs, Physalaemus pustulosus, following exposure to conspecific and heterospecific calls that evoke competitive or aggressive behaviour. We found that acoustically driven activation in auditory brainstem nuclei is transformed into activation related to sensory-motor interactions in the diencephalon, followed by motor-related activation in the telencephalon. Furthermore, under baseline conditions, brain nuclei typically have correlated egr-1 mRNA levels within brain divisions. Hearing conspecific advertisement calls increases correlations between anatomically distant brain divisions; no such effect was observed in response to calls that elicit aggressive behaviour. Neural correlates of social decision making thus take multiple forms: (i) a progressive shift from sensory to motor encoding from lower to higher stages of neural processing and (ii) the emergence of correlated activation patterns among sensory and motor regions in response to behaviourally relevant social cues.     

Involving the motor system in decision making

The control of behaviour is usually understood in terms of three distinct components: sensory processing, decision making and movement control. Recently, this view has been questioned on the basis of physiological and behavioural data, blurring the distinction between these three stages. This raises the question to what extent the motor system itself can contribute to the interpretation of behavioural situations. To investigate this question we use a neural model of sensory motor integration applied to a behaving mobile robot performing a navigation task. We show that the population response of the motor system provides a substrate for the categorization of behavioural situations. This categorization allows for the assessment of the complexity of a behavioural situation and regulates whether higher-level decision making is required to resolve behavioural conflicts. Our model lends credence to an emerging reconceptualization of behavioural control where the motor system can be considered as part of a high-level perceptual system.     

Long-range, pattern-dependent contextual effects in early human visual cortex

The standard view of neurons in early visual cortex is that they behave like localized feature detectors. Here we demonstrate that processing in early visual areas goes beyond feature detection by showing that neural responses are greater when a feature deviates from its context compared to when it does not deviate from its context. Using psychophysics, fMRI, and electroencephalography methodologies, we measured neural responses to an oriented Gabor ("target") embedded in various visual patterns as defined by the relative orientation of flanking stimuli. We first show using psychophysical contrast adaptation and fMRI that a target that differs from its context results in more neural activity compared to a target that is contained within an alternating sequence, suggesting that neurons in early visual cortex are sensitive to large-scale orientation patterns. Next, we use event-related potentials to show that orientation deviations affect the earliest sensory components of the target response. Finally, we use forced-choice classification of "noise" stimuli to show that we are more likely to "see" orientations that deviate from the context. Our results suggest that early visual cortex is sensitive to global patterns in images in a way that is markedly different from the predictions of standard models of cortical visual processing.     

Candidate neural locus for sex differences in reproductive decisions

Sexual selection and signal detection theories predict that females should be selective in their responses to mating signals in mate choice, while the response of males to signals in male competition should be less selective. The neural processes underlying this behavioural sex difference remain obscure. Differences in behavioural selectivity could result from differences in how sensitive sensory systems are to mating signals, distinct thresholds in motor areas regulating behaviour, or sex differences in selectivity at a gateway relaying sensory information to motor systems. We tested these hypotheses in frogs using the expression of egr-1 to quantify the neural responses of each sex to mating signals. We found that egr-1 expression in a midbrain auditory region was elevated in males in response to both conspecific and heterospecific calls, whereas in females, egr-1 induction occurred only in response to conspecific signals. This differential neural selectivity mirrored the sex differences in behavioural responsiveness to these stimuli. By contrast, egr-1 expression in lower brainstem auditory centres was not different in males and females. Our results support a model in which sex differences in behavioural selectivity arise from sex differences in the neural selectivity in midbrain areas relaying sensory information to the forebrain.     

Protein attributes contribute to halo-stability, bioinformatics approach

Halophile proteins can tolerate high salt concentrations. Understanding halophilicity features is the first step toward engineering halostable crops. To this end, we examined protein features contributing to the halo-toleration of halophilic organisms. We compared more than 850 features for halophilic and non-halophilic proteins with various screening, clustering, decision tree, and generalized rule induction models to search for patterns that code for halo-toleration. Up to 251 protein attributes selected by various attribute weighting algorithms as important features contribute to halo-stability; from them 14 attributes selected by 90% of models and the count of hydrogen gained the highest value (1.0) in 70% of attribute weighting models, showing the importance of this attribute in feature selection modeling. The other attributes mostly were the frequencies of di-peptides. No changes were found in the numbers of groups when K-Means and TwoStep clustering modeling were performed on datasets with or without feature selection filtering. Although the depths of induced trees were not high, the accuracies of trees were higher than 94% and the frequency of hydrophobic residues pointed as the most important feature to build trees. The performance evaluation of decision tree models had the same values and the best correctness percentage recorded with the Exhaustive CHAID and CHAID models. We did not find any significant difference in the percent of correctness, performance evaluation, and mean correctness of various decision tree models with or without feature selection. For the first time, we analyzed the performance of different screening, clustering, and decision tree algorithms for discriminating halophilic and non-halophilic proteins and the results showed that amino acid composition can be used to discriminate between halo-tolerant and halo-sensitive proteins.     

Testing Whether and When Abstract Symmetric Patterns Produce Affective Responses

Symmetry has a central role in visual art, it is often linked to beauty, and observers can detect it efficiently in the lab. We studied what kind of fast and automatic responses are generated by visual presentation of symmetrical patterns. Specifically, we tested whether a brief presentation of novel symmetrical patterns engenders positive affect using a priming paradigm. The abstract patterns were used as primes in a pattern-word interference task. To ensure that familiarity was not a factor, no pattern and no word was ever repeated within each experiment. The task was to classify words that were selected to have either positive or negative valence. We tested irregular patterns, patterns containing vertical and horizontal reflectional symmetry, and patterns containing a 90 deg rotation. In a series of 7 experiments we found that the effect of affective congruence was present for both types of regularity but only when observers had to classify the regularity of the pattern after responding to the word. The findings show that processing abstract symmetrical shapes or random pattern can engender positive or negative affect as long as the regularity of the pattern is a feature that observers have to attend to and classify.     

A cognitive framework for mate choice and species recognition

Mating decisions contribute to both the fitness of individuals and the emergence of evolutionary diversity, yet little is known about their cognitive architecture. We propose a simple model that describes how preferences are translated into decisions and how seemingly disparate patterns of preference can emerge from a single perceptual process. The model proposes that females use error-prone estimates of attractiveness to select mates based on a simple decision rule: choose the most attractive available male that exceeds some minimal criterion. We test the model in the tungara frog, a well-characterized species with an apparent dissociation between mechanisms of mate choice and species recognition. As suggested by our model results, we find that a mate attraction feature alters assessments of species status. Next, we compare female preferences in one-choice and two-choice tests, contexts thought to emphasize species recognition and mate choice, respectively. To do so, we use the model to generate maximum-likelihood estimators of preference strengths from empirical data. We find that a single representation of preferences is sufficient to explain response probabilities in both contexts across a wide range of stimuli. In this species, mate choice and species recognition are accurately and simply summarized by our model. While the findings resolve long-standing anomalies, they also illustrate how models of choice can bridge theoretical and empirical treatments of animal decisions. The data demonstrate a remarkable congruity of perceptual processes across contexts, tasks, and taxa.