Characterizing the Impact of Category Uncertainty on Human Auditory Categorization Behavior

Categorization is an important cognitive process. However, the correct categorization of a stimulus is often challenging because categories can have overlapping boundaries. Whereas perceptual categorization has been extensively studied in vision, the analogous phenomenon in audition has yet to be systematically explored. Here, we test whether and how human subjects learn to use category distributions and prior probabilities, as well as whether subjects employ an optimal decision strategy when making auditory-category decisions. We asked subjects to classify the frequency of a tone burst into one of two overlapping, uniform categories according to the perceived tone frequency. We systematically varied the prior probability of presenting a tone burst with a frequency originating from one versus the other category. Most subjects learned these changes in prior probabilities early in testing and used this information to influence categorization. We also measured each subject''s frequency-discrimination thresholds (i.e., their sensory uncertainty levels). We tested each subject''s average behavior against variations of a Bayesian model that either led to optimal or sub-optimal decision behavior (i.e. probability matching). In both predicting and fitting each subject''s average behavior, we found that probability matching provided a better account of human decision behavior. The model fits confirmed that subjects were able to learn category prior probabilities and approximate forms of the category distributions. Finally, we systematically explored the potential ways that additional noise sources could influence categorization behavior. We found that an optimal decision strategy can produce probability-matching behavior if it utilized non-stationary category distributions and prior probabilities formed over a short stimulus history. Our work extends previous findings into the auditory domain and reformulates the issue of categorization in a manner that can help to interpret the results of previous research within a generative framework.     

The neglected tool in the Bayesian ecologist's shed: a case study testing informative priors' effect on model accuracy

Despite benefits for precision, ecologists rarely use informative priors. One reason that ecologists may prefer vague priors is the perception that informative priors reduce accuracy. To date, no ecological study has empirically evaluated data‐derived informative priors' effects on precision and accuracy. To determine the impacts of priors, we evaluated mortality models for tree species using data from a forest dynamics plot in Thailand. Half the models used vague priors, and the remaining half had informative priors. We found precision was greater when using informative priors, but effects on accuracy were more variable. In some cases, prior information improved accuracy, while in others, it was reduced. On average, models with informative priors were no more or less accurate than models without. Our analyses provide a detailed case study on the simultaneous effect of prior information on precision and accuracy and demonstrate that when priors are specified appropriately, they lead to greater precision without systematically reducing model accuracy.     

On the Origins of Suboptimality in Human Probabilistic Inference

Humans have been shown to combine noisy sensory information with previous experience (priors), in qualitative and sometimes quantitative agreement with the statistically-optimal predictions of Bayesian integration. However, when the prior distribution becomes more complex than a simple Gaussian, such as skewed or bimodal, training takes much longer and performance appears suboptimal. It is unclear whether such suboptimality arises from an imprecise internal representation of the complex prior, or from additional constraints in performing probabilistic computations on complex distributions, even when accurately represented. Here we probe the sources of suboptimality in probabilistic inference using a novel estimation task in which subjects are exposed to an explicitly provided distribution, thereby removing the need to remember the prior. Subjects had to estimate the location of a target given a noisy cue and a visual representation of the prior probability density over locations, which changed on each trial. Different classes of priors were examined (Gaussian, unimodal, bimodal). Subjects'' performance was in qualitative agreement with the predictions of Bayesian Decision Theory although generally suboptimal. The degree of suboptimality was modulated by statistical features of the priors but was largely independent of the class of the prior and level of noise in the cue, suggesting that suboptimality in dealing with complex statistical features, such as bimodality, may be due to a problem of acquiring the priors rather than computing with them. We performed a factorial model comparison across a large set of Bayesian observer models to identify additional sources of noise and suboptimality. Our analysis rejects several models of stochastic behavior, including probability matching and sample-averaging strategies. Instead we show that subjects'' response variability was mainly driven by a combination of a noisy estimation of the parameters of the priors, and by variability in the decision process, which we represent as a noisy or stochastic posterior.     

Prediction precedes control in motor learning

Skilled motor behavior relies on the brain learning both to control the body and predict the consequences of this control. Prediction turns motor commands into expected sensory consequences, whereas control turns desired consequences into motor commands. To capture this symmetry, the neural processes underlying prediction and control are termed the forward and inverse internal models, respectively. Here, we investigate how these two fundamental processes are related during motor learning. We used an object manipulation task in which subjects learned to move a hand-held object with novel dynamic properties along a prescribed path. We independently and simultaneously measured subjects' ability to control their actions and to predict their consequences. We found different time courses for predictor and controller learning, with prediction being learned far more rapidly than control. In early stages of manipulating the object, subjects could predict the consequences of their actions, as measured by the grip force they used to grasp the object, but could not generate appropriate actions for control, as measured by their hand trajectory. As predicted by several recent theoretical models of sensorimotor control, our results indicate that people can learn to predict the consequences of their actions before they can learn to control their actions.     

Inferring visuomotor priors for sensorimotor learning

Sensorimotor learning has been shown to depend on both prior expectations and sensory evidence in a way that is consistent with Bayesian integration. Thus, prior beliefs play a key role during the learning process, especially when only ambiguous sensory information is available. Here we develop a novel technique to estimate the covariance structure of the prior over visuomotor transformations--the mapping between actual and visual location of the hand--during a learning task. Subjects performed reaching movements under multiple visuomotor transformations in which they received visual feedback of their hand position only at the end of the movement. After experiencing a particular transformation for one reach, subjects have insufficient information to determine the exact transformation, and so their second reach reflects a combination of their prior over visuomotor transformations and the sensory evidence from the first reach. We developed a Bayesian observer model in order to infer the covariance structure of the subjects' prior, which was found to give high probability to parameter settings consistent with visuomotor rotations. Therefore, although the set of visuomotor transformations experienced had little structure, the subjects had a strong tendency to interpret ambiguous sensory evidence as arising from rotation-like transformations. We then exposed the same subjects to a highly-structured set of visuomotor transformations, designed to be very different from the set of visuomotor rotations. During this exposure the prior was found to have changed significantly to have a covariance structure that no longer favored rotation-like transformations. In summary, we have developed a technique which can estimate the full covariance structure of a prior in a sensorimotor task and have shown that the prior over visuomotor transformations favor a rotation-like structure. Moreover, through experience of a novel task structure, participants can appropriately alter the covariance structure of their prior.     

Speeding up the Consensus Clustering methodology for microarray data analysis

Background

Position-specific priors (PSP) have been used with success to boost EM and Gibbs sampler-based motif discovery algorithms. PSP information has been computed from different sources, including orthologous conservation, DNA duplex stability, and nucleosome positioning. The use of prior information has not yet been used in the context of combinatorial algorithms. Moreover, priors have been used only independently, and the gain of combining priors from different sources has not yet been studied.

Results

We extend RISOTTO, a combinatorial algorithm for motif discovery, by post-processing its output with a greedy procedure that uses prior information. PSP's from different sources are combined into a scoring criterion that guides the greedy search procedure. The resulting method, called GRISOTTO, was evaluated over 156 yeast TF ChIP-chip sequence-sets commonly used to benchmark prior-based motif discovery algorithms. Results show that GRISOTTO is at least as accurate as other twelve state-of-the-art approaches for the same task, even without combining priors. Furthermore, by considering combined priors, GRISOTTO is considerably more accurate than the state-of-the-art approaches for the same task. We also show that PSP's improve GRISOTTO ability to retrieve motifs from mouse ChiP-seq data, indicating that the proposed algorithm can be applied to data from a different technology and for a higher eukaryote.

Conclusions

The conclusions of this work are twofold. First, post-processing the output of combinatorial algorithms by incorporating prior information leads to a very efficient and effective motif discovery method. Second, combining priors from different sources is even more beneficial than considering them separately.     

Higher frequency of social learning in China than in the West shows cultural variation in the dynamics of cultural evolution

Cultural evolutionary models have identified a range of conditions under which social learning (copying others) is predicted to be adaptive relative to asocial learning (learning on one''s own), particularly in humans where socially learned information can accumulate over successive generations. However, cultural evolution and behavioural economics experiments have consistently shown apparently maladaptive under-utilization of social information in Western populations. Here we provide experimental evidence of cultural variation in people''s use of social learning, potentially explaining this mismatch. People in mainland China showed significantly more social learning than British people in an artefact-design task designed to assess the adaptiveness of social information use. People in Hong Kong, and Chinese immigrants in the UK, resembled British people in their social information use, suggesting a recent shift in these groups from social to asocial learning due to exposure to Western culture. Finally, Chinese mainland participants responded less than other participants to increased environmental change within the task. Our results suggest that learning strategies in humans are culturally variable and not genetically fixed, necessitating the study of the ‘social learning of social learning strategies'' whereby the dynamics of cultural evolution are responsive to social processes, such as migration, education and globalization.     

Risk-Sensitivity in Bayesian Sensorimotor Integration

Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decision-makers are, however, risk-neutral in the sense that they weigh all possibilities based on prior expectation and sensory evidence when they choose the action with highest expected value. In contrast, risk-sensitive decision-makers are sensitive to model uncertainty and bias their decision-making processes when they do inference over unobserved variables. In particular, they allow deviations from their probabilistic model in cases where this model makes imprecise predictions. Here we test for risk-sensitivity in a sensorimotor integration task where subjects exhibit Bayesian information integration when they infer the position of a target from noisy sensory feedback. When introducing a cost associated with subjects'' response, we found that subjects exhibited a characteristic bias towards low cost responses when their uncertainty was high. This result is in accordance with risk-sensitive decision-making processes that allow for deviations from Bayes optimal decision-making in the face of uncertainty. Our results suggest that both Bayesian integration and risk-sensitivity are important factors to understand sensorimotor integration in a quantitative fashion.     

Expert elicitation and data noise learning for material flow analysis using Bayesian inference

Bayesian inference allows the transparent communication and systematic updating of model uncertainty as new data become available. When applied to material flow analysis (MFA), however, Bayesian inference is undermined by the difficulty of defining proper priors for the MFA parameters and quantifying the noise in the collected data. We start to address these issues by first deriving and implementing an expert elicitation procedure suitable for generating MFA parameter priors. Second, we propose to learn the data noise concurrent with the parametric uncertainty. These methods are demonstrated using a case study on the 2012 US steel flow. Eight experts are interviewed to elicit distributions on steel flow uncertainty from raw materials to intermediate goods. The experts' distributions are combined and weighted according to the expertise demonstrated in response to seeding questions. These aggregated distributions form our model parameters' informative priors. Sensible, weakly informative priors are adopted for learning the data noise. Bayesian inference is then performed to update the parametric and data noise uncertainty given MFA data collected from the United States Geological Survey and the World Steel Association. The results show a reduction in MFA parametric uncertainty when incorporating the collected data. Only a modest reduction in data noise uncertainty was observed using 2012 data; however, greater reductions were achieved when using data from multiple years in the inference. These methods generate transparent MFA and data noise uncertainties learned from data rather than pre-assumed data noise levels, providing a more robust basis for decision-making that affects the system.     

Social Learning of a Novel Foraging Task by Big Brown Bats (Eptesicus fuscus)

Acquiring information via observation of others can be an efficient way to respond to changing situations or learn skills, particularly for inexperienced individuals. Many bat species are gregarious, yet few studies have investigated their capacity for learning from conspecifics. We tested whether big brown bats (Eptesicus fuscus) can learn a novel foraging task by interacting with knowledgeable conspecifics. In experimental trials 11 na?ve bats (7 juveniles, 4 adults) interacted freely with trained bats that were capturing tethered mealworms, while in control trials 11 na?ve bats (7 juveniles, 4 adults) flew with untrained bats. Na?ve bats were then assessed for their ability to capture tethered mealworms. While no bat in the control group learned the task, a significant number of experimental bats, including juveniles with little or no experience foraging, showed evidence of learning. Eighty-two per cent of experimental bats and 27% of control bats directed feeding buzzes (echolocation calls associated with prey capture) at the mealworm. Furthermore, seven experimental bats (64%) showed evidence of learning by attacking and/or capturing the mealworm, while no bat in the control group attacked or captured the prey. Analyses of high-speed stereo video recordings revealed increased interaction with demonstrators among bats attacking or capturing the mealworm. At the time they displayed evidence of learning, bats flew closer together during feeding buzzes than during other portions of trials. Our results demonstrate that social interaction with experienced bats, and listening to feeding buzzes in particular, may play an integral role in development of foraging skills in bats.     

Automated auditory recognition training and testing

Laboratory training and testing of auditory recognition skills in animals is important for understanding animal communication systems that depend on auditory cues. Songbirds are commonly studied because of their exceptional ability to learn complex vocalizations. In recent years, mounting interest in the perceptual abilities of songbirds has increased the demand for laboratory behavioural training and testing paradigms. Here, we describe and demonstrate the success of a method for auditory discrimination experiments, including all the necessary hardware, training procedures and freely-available, versatile software. The system can run several behavioural training and testing paradigms, including operant (go-nogo, stimulus preference, and two-alternative forced choice) and classical conditioning tasks. The software and some hardware components can be used with any laboratory animal that learns and responds to sensory cues. The peripheral hardware and training procedures are designed for use with songbirds and auditory stimuli. Using the go-nogo paradigm of the training system, we show that adult zebra finches learn to recognize and correctly classify individual female calls and male songs. We also show that learning the task generalizes to new stimulus classes; birds that learned the task with calls subsequently learned to recognize songs faster than did birds that learned the task and songs at the same time.     

Fragment-based learning of visual object categories

When we perceive a visual object, we implicitly or explicitly associate it with a category we know. It is known that the visual system can use local, informative image fragments of a given object, rather than the whole object, to classify it into a familiar category. How we acquire informative fragments has remained unclear. Here, we show that human observers acquire informative fragments during the initial learning of categories. We created new, but naturalistic, classes of visual objects by using a novel "virtual phylogenesis" (VP) algorithm that simulates key aspects of how biological categories evolve. Subjects were trained to distinguish two of these classes by using whole exemplar objects, not fragments. We hypothesized that if the visual system learns informative object fragments during category learning, then subjects must be able to perform the newly learned categorization by using only the fragments as opposed to whole objects. We found that subjects were able to successfully perform the classification task by using each of the informative fragments by itself, but not by using any of the comparable, but uninformative, fragments. Our results not only reveal that novel categories can be learned by discovering informative fragments but also introduce and illustrate the use of VP as a versatile tool for category-learning research.     

Reward quality influences the development of learned olfactory biases in honeybees

Plants produce flowers with complex visual and olfactory signals, but we know relatively little about the way that signals such as floral scents have evolved. One important factor that may direct the evolution of floral signals is a pollinator''s ability to learn. When animals learn to associate two similar signals with different outcomes, biases in their responses to new signals can be formed. Here, we investigated whether or not pollinators develop learned biases towards floral scents that depend on nectar reward quality by training restrained honeybees to learn to associate two similar odour signals with different outcomes using a classical conditioning assay. Honeybees developed learned biases towards odours as a result of differential conditioning, and the extent to which an olfactory bias could be produced depended upon the difference in the quality of the nectar rewards experienced during conditioning. Our results suggest that differences in reward quality offered by flowers influence odour recognition by pollinators, which in turn could influence the evolution of floral scents in natural populations of co-flowering plants.     

Memory and prediction in natural gaze control

In addition to stimulus properties and task factors, memory is an important determinant of the allocation of attention and gaze in the natural world. One way that the role of memory is revealed is by predictive eye movements. Both smooth pursuit and saccadic eye movements demonstrate predictive effects based on previous experience. We have previously shown that unskilled subjects make highly accurate predictive saccades to the anticipated location of a ball prior to a bounce in a virtual racquetball setting. In this experiment, we examined this predictive behaviour. We asked whether the period after the bounce provides subjects with visual information about the ball trajectory that is used to programme the pursuit movement initiated when the ball passes through the fixation point. We occluded a 100 ms period of the ball''s trajectory immediately after the bounce, and found very little effect on the subsequent pursuit movement. Subjects did not appear to modify their strategy to prolong the fixation. Neither were we able to find an effect on interception performance. Thus, it is possible that the occluded trajectory information is not critical for subsequent pursuit, and subjects may use an estimate of the ball''s trajectory to programme pursuit. These results provide further support for the role of memory in eye movements.     

Task-irrelevant learning occurs only when the irrelevant feature is weak

The role of attention in perceptual learning has been controversial. Numerous studies have reported that learning does not occur on stimulus features that are irrelevant to a subject's task [1,2] and have concluded that focused attention on a feature is necessary for a feature to be learned. In contrast, another line of studies has shown that perceptual learning occurs even on task-irrelevant features that are subthreshold, and concluded that attention on a feature is not required to learn that feature [3-5]. Here we attempt to reconcile these divergent findings by systematically exploring the relation between signal strength of the motion stimuli used during training and the resultant magnitude of perceptual learning. Our results show that performance improvements only occurred for the motion-stimuli trained at low, parathreshold, coherence levels. The results are in accord with the hypothesis that weak task-irrelevant signals fail to be 'noticed', and consequently to be suppressed, by the attention system and thus are learned, while stronger stimulus signals are detected, and suppressed [6], and are not learned. These results provide a parsimonious explanation of why task-irrelevant learning is found in some studies but not others, and could give an important clue to resolving a long-standing controversy.     

The effect of protein synthesis inhibition in the central nervous system on long-term memory formation in some behavioral tasks

The effect of the maximum protein synthesis inhibition in brain and spinal cord on long-term memory formation in extreme situations was studied in various new behavioral tasks in rats. Cycloheximide injected bilaterally into the lateral ventricles three hours before learning suppressed protein synthesis in the central nervous system by 96% during one hour after learning. Forty-four hours after learning in a standard Morris water maze, the information about the platform position was not retained, whereas no memory disorder was observed in case of learning in a simplified Morris maze or a new test learned jump-out-of-water task. A more prolonged suppression of protein synthesis (76%, ten hours after learning) elicited amnesia in five out of eight rats learned in a simplified Morris maze but not disturbed information storage after 48 h and 14 days in the learned jump-out-of-water task. It was concluded that protein synthesis inhibitors are not a universal tool for disrupting formation of long-term memory. It was assumed that under extreme conditions, sometimes procedural long-term (to two weeks) memory is formed without de novo protein synthesis.     

Task-induced neural covariability as a signature of approximate Bayesian learning and inference

Perception is often characterized computationally as an inference process in which uncertain or ambiguous sensory inputs are combined with prior expectations. Although behavioral studies have shown that observers can change their prior expectations in the context of a task, robust neural signatures of task-specific priors have been elusive. Here, we analytically derive such signatures under the general assumption that the responses of sensory neurons encode posterior beliefs that combine sensory inputs with task-specific expectations. Specifically, we derive predictions for the task-dependence of correlated neural variability and decision-related signals in sensory neurons. The qualitative aspects of our results are parameter-free and specific to the statistics of each task. The predictions for correlated variability also differ from predictions of classic feedforward models of sensory processing and are therefore a strong test of theories of hierarchical Bayesian inference in the brain. Importantly, we find that Bayesian learning predicts an increase in so-called “differential correlations” as the observer’s internal model learns the stimulus distribution, and the observer’s behavioral performance improves. This stands in contrast to classic feedforward encoding/decoding models of sensory processing, since such correlations are fundamentally information-limiting. We find support for our predictions in data from existing neurophysiological studies across a variety of tasks and brain areas. Finally, we show in simulation how measurements of sensory neural responses can reveal information about a subject’s internal beliefs about the task. Taken together, our results reinterpret task-dependent sources of neural covariability as signatures of Bayesian inference and provide new insights into their cause and their function.     

Consolidation of dynamic motor learning is not disrupted by rTMS of primary motor cortex

Motor skills, once learned, are often retained over a long period of time. However, such learning first undergoes a period of consolidation after practice. During this time, the motor memory is susceptible to being disrupted by the performance of another motor-learning task. Recently, it was shown that repetitive transcranial magnetic stimulation (rTMS) over the primary motor cortex could disrupt the retention of a newly learned ballistic task in which subjects had to oppose their index finger and thumb as rapidly as possible. Here we investigate whether the motor cortex is similarly involved during the consolidation that follows learning novel dynamics. We applied rTMS to primary motor cortex shortly after subjects had either learned to compensate for a dynamic force field applied to their index finger or learned a ballistic finger abduction task. rTMS severely degraded the retention of the learning for the ballistic task but had no effect on retention of the dynamic force-field learning. This suggests that, unlike learning of simple ballistic skills, learning of dynamics may be stored in a more distributed manner, possibly outside the primary motor cortex.     

Reversal of a trimethyltin-induced learning deficit by desglycinamide-8-arginine vasopressin

Trimethyltin (TMT) is an organometal neurotoxin which produces lesions primarily in the limbic system. Selectivity seems to depend upon the dose, but the hippocampus and related entorhinal cortical structures, of importance for learning and memory, are most often described as target sites. We have previously demonstrated that subjects treated with a moderate dose of TMT prior to acquisition sessions, are unable to learn a forward autoshaping task with a 6 sec delay of reinforcement, but are capable of acquiring the same task when no delay of reinforcement is used. These data suggested that the performance deficit is one of learning (i.e. consolidation) rather than of memory (i.e. storage), retrieval, or sensorimotor impairment. To more rigorously test this hypothesis, we determined if performance of a task already learned would be impaired by the neurotoxin. Adult male Long Evans rats were given 10 acquisition sessions of 24 trials, following which TMT (6.0 mg/kg, p.o.) was administered. One month later, these rats performed the lever-touching behavior as well as controls, despite the fact that the same dose of TMT interfered with learning if given one month prior to acquisition sessions, thus confirming our hypothesis. In a second experiment we determined if the peptide analog of vasopressin, desglycinamide-8-arginine vasopressin (DGAVP), could reverse a learning deficit in a population of non-learners. Rats were treated with TMT or water vehicle one month prior to autoshaping. TMT significantly retarded acquisition. After 10 sessions of 12 trials each, non-learners (i.e. rats treated with TMT that failed to associate the lever with delivery of a reinforcer) were administered saline or DGAVP (7.5 micrograms/kg, s.c.) 1 hr before sessions 11-13; treatment was discontinued prior to sessions 14 and 15. Peptide treated subjects showed evidence of acquisition and exhibited higher levels of lever-directed behavior than saline treated nonlearners. Performance was maintained after DGAVP treatment was discontinued, indicating that the learning-enhancing action of DGAVP was not transient or state-dependent.     

Goffin's cockatoos discriminate objects based on weight alone

Paying attention to weight is important when deciding upon an object''s efficacy or value in various contexts (e.g. tool use, foraging). Proprioceptive discrimination learning, with objects that differ only in weight, has so far been investigated almost exclusively in primate species. Here, we show that while Goffin''s cockatoos learn faster when additional colour cues are used, they can also quickly learn to discriminate between objects on the basis of their weight alone. Ultimately, the birds learned to discriminate between visually identical objects on the basis of weight much faster than primates, although methodological differences between tasks should be considered.