Characterizing sequence knowledge using online measures and hidden Markov models

Response times are affected by mispredictions in a stochastic game

Acting as a goalkeeper in a video-game, a participant is asked to predict the successive choices of the penalty taker. The sequence of choices of the penalty taker is generated by a stochastic chain with memory of variable length. It has been conjectured that the probability distribution of the response times is a function of the specific sequence of past choices governing the algorithm used by the penalty taker to make his choice at each step. We found empirical evidence that besides this dependence, the distribution of the response times depends also on the success or failure of the previous prediction made by the participant. Moreover, we found statistical evidence that this dependence propagates up to two steps forward after the prediction failure.

Validation of a Novel Reaction Time Test Specific for Military Personnel

A military-specific reaction time (RT) test was developed to explore its reliability and sensitivity to discriminate between military personnel and sport science students. Fifteen male professional Spanish soldiers and 16 male sport science students completed two RT test modalities: military-specific and nonspecific RT tests. For each RT test modality, both the Simple (i.e., one stimulus, one response) and the Go, No-Go RT (i.e., true, and false stimuli, one response) were tested. The military-specific RT test consisted of a video presented through virtual reality glasses of a forest environment in which soldiers would appear from behind different bushes (stimuli) and the response consisted of pressing the button of a gun-shaped mouse (when they saw a soldier pointing a rifle at them). Both Simple and Go, No-Go RT reached acceptable reliability in both populations (coefficient of variation ≤ 9.64%). Military personnel presented a lower RT than sport science students during the military-specific RT test (p ≤ .001), while no differences were obtained during the nonspecific RT test. RT values were not significantly correlated between the military-specific and nonspecific RT tests (r ≤ .02). These findings collectively suggest that the novel military-specific RT test is an ecologically valid alternative to evaluate the information processing abilities of military personnel.

Inferring an Observer's Prediction Strategy in Sequence Learning Experiments

Cognitive systems exhibit astounding prediction capabilities that allow them to reap rewards from regularities in their environment. How do organisms predict environmental input and how well do they do it? As a prerequisite to answering that question, we first address the limits on prediction strategy inference, given a series of inputs and predictions from an observer. We study the special case of Bayesian observers, allowing for a probability that the observer randomly ignores data when building her model. We demonstrate that an observer's prediction model can be correctly inferred for binary stimuli generated from a finite-order Markov model. However, we can not necessarily infer the model's parameter values unless we have access to several "clones" of the observer. As stimuli become increasingly complicated, correct inference requires exponentially more data points, computational power, and computational time. These factors place a practical limit on how well we are able to infer an observer's prediction strategy in an experimental or observational setting.

Pre-specified Anxiety Predicts Future Decision-Making Performances Under Different Temporally Constrained Conditions

In real-life circumstances, people occasionally require making forced decisions when encountering unpredictable events and situations that yield socially and privately unfavorable consequences. In order to prevent future negative consequences, it is beneficial to successfully predict future decision-making behaviors based on various types of information, including behavioral traits and/or psychological states. For this prospective purpose, the present study used the Iowa Gambling Task, which simulates multiple aspects of real-life decision-making processes, such as choice preference, selection and evaluation of output feedback, and investigated how anxiety profiles predict decision-making performances under conditions with different temporal pressures on task execution. To conduct a temporally causal analysis, we assessed the trait and state anxiety profiles of 33 young participants prior to the task and analyzed their subsequent decision-making performances. We separated two disadvantageous card decks with high rewards and losses into high- and middle-risk decks, and calculated local performance indexes for decision-making immediately after salient penalty events for the high-risk deck in addition to traditional global performance indexes concerning overall trial outcomes such as final winnings and net scores. For global decision-making, higher trait anxiety predicted more risky choices solely in the self-paced condition without temporal pressure. For local decision-making, state anxiety predicted risk-taking performances differently in the self- and forced-paced conditions. In the self-paced condition, higher state anxiety predicted higher risk-avoidance. In the forced-paced condition, higher state anxiety predicted more frequent choices of the middle-risk deck. These findings suggest not only that pre-specified anxiety profiles can effectively predict future decision-making behaviors under different temporal pressures, but also newly indicate that behavioral mechanisms for moderate risk-taking under an emergent condition should be focused on to effectively prevent future unfavorable consequences when actually encountering negative events.

New insights into statistical learning and chunk learning in implicit sequence acquisition

Implicit sequence learning is ubiquitous in our daily life. However, it is unclear whether the initial acquisition of sequences results from learning to chunk items (i.e., chunk learning) or learning the underlying statistical regularities (i.e., statistical learning). By grouping responses with or without a distinct chunk or statistical structure into segments and comparing these responses, previous studies have demonstrated both chunk and statistical learning. However, few studies have considered the response sequence as a whole and examined the temporal dependency of the entire sequence, where the temporal dependencies could disclose the internal representations of chunk and statistical learning. Participants performed a serial reaction time (SRT) task under different stimulus interval conditions. We found that sequence learning reflected by reaction time (RT) rather than motor improvements represented by movement time (MT). The temporal dependency of RT and MT revealed that both RT and MT displayed recursive patterns caused by biomechanical effects of response locations and foot transitions. Chunking was noticeable only in the presence of the recurring RT or MT but vanished after the recursive component was removed, implying that chunk formation may result from biomechanical constraints rather than learning itself. In addition, we observed notable first-order autocorrelations in RT. This trial-to-trial association enhanced as learning progressed regardless of stimulus intervals, reflecting the internal cognitive representation of the first-order stimulus contingencies. Our results suggest that initial acquisition of implicit sequences may arise from first-order statistical learning rather than chunk learning.

Parameter recovery, bias and standard errors in the linear ballistic accumulator model

The linear ballistic accumulator (LBA) model (Brown & Heathcote, , Cogn. Psychol., 57, 153) is increasingly popular in modelling response times from experimental data. An R package, glba, has been developed to fit the LBA model using maximum likelihood estimation which is validated by means of a parameter recovery study. At sufficient sample sizes parameter recovery is good, whereas at smaller sample sizes there can be large bias in parameters. In a second simulation study, two methods for computing parameter standard errors are compared. The Hessian-based method is found to be adequate and is (much) faster than the alternative bootstrap method. The use of parameter standard errors in model selection and inference is illustrated in an example using data from an implicit learning experiment (Visser et al., , Mem. Cogn., 35, 1502). It is shown that typical implicit learning effects are captured by different parameters of the LBA model.

Discovery of a Recursive Principle: An Artificial Grammar Investigation of Human Learning of a Counting Recursion Language

Learning is typically understood as a process in which the behavior of an organism is progressively shaped until it closely approximates a target form. It is easy to comprehend how a motor skill or a vocabulary can be progressively learned-in each case, one can conceptualize a series of intermediate steps which lead to the formation of a proficient behavior. With grammar, it is more difficult to think in these terms. For example, center embedding recursive structures seem to involve a complex interplay between multiple symbolic rules which have to be in place simultaneously for the system to work at all, so it is not obvious how the mechanism could gradually come into being. Here, we offer empirical evidence from a new artificial language (or "artificial grammar") learning paradigm, Locus Prediction, that, despite the conceptual conundrum, recursion acquisition occurs gradually, at least for a simple formal language. In particular, we focus on a variant of the simplest recursive language, a (n) b (n) , and find evidence that (i) participants trained on two levels of structure (essentially ab and aabb) generalize to the next higher level (aaabbb) more readily than participants trained on one level of structure (ab) combined with a filler sentence; nevertheless, they do not generalize immediately; (ii) participants trained up to three levels (ab, aabb, aaabbb) generalize more readily to four levels than participants trained on two levels generalize to three; (iii) when we present the levels in succession, starting with the lower levels and including more and more of the higher levels, participants show evidence of transitioning between the levels gradually, exhibiting intermediate patterns of behavior on which they were not trained; (iv) the intermediate patterns of behavior are associated with perturbations of an attractor in the sense of dynamical systems theory. We argue that all of these behaviors indicate a theory of mental representation in which recursive systems lie on a continuum of grammar systems which are organized so that grammars that produce similar behaviors are near one another, and that people learning a recursive system are navigating progressively through the space of these grammars.

Sequential detection of learning in cognitive diagnosis

In order to look more closely at the many particular skills examinees utilize to answer items, cognitive diagnosis models have received much attention, and perhaps are preferable to item response models that ordinarily involve just one or a few broadly defined skills, when the objective is to hasten learning. If these fine-grained skills can be identified, a sharpened focus on learning and remediation can be achieved. The focus here is on how to detect when learning has taken place for a particular attribute and efficiently guide a student through a sequence of items to ultimately attain mastery of all attributes while administering as few items as possible. This can be seen as a problem in sequential change-point detection for which there is a long history and a well-developed literature. Though some ad hoc rules for determining learning may be used, such as stopping after M consecutive items have been successfully answered, more efficient methods that are optimal under various conditions are available. The CUSUM, Shiryaev-Roberts and Shiryaev procedures can dramatically reduce the time required to detect learning while maintaining rigorous Type I error control, and they are studied in this context through simulation. Future directions for modelling and detection of learning are discussed.

Juvenile zebra finches learn the underlying structural regularities of their fathers' song

Natural behaviors, such as foraging, tool use, social interaction, birdsong, and language, exhibit branching sequential structure. Such structure should be learnable if it can be inferred from the statistics of early experience. We report that juvenile zebra finches learn such sequential structure in song. Song learning in finches has been extensively studied, and it is generally believed that young males acquire song by imitating tutors (Zann, 1996). Variability in the order of elements in an individual’s mature song occurs, but the degree to which variation in a zebra finch’s song follows statistical regularities has not been quantified, as it has typically been dismissed as production error (Sturdy et al., 1999). Allowing for the possibility that such variation in song is non-random and learnable, we applied a novel analytical approach, based on graph-structured finite-state grammars, to each individual’s full corpus of renditions of songs. This method does not assume syllable-level correspondence between individuals. We find that song variation can be described by probabilistic finite-state graph grammars that are individually distinct, and that the graphs of juveniles are more similar to those of their fathers than to those of other adult males. This grammatical learning is a new parallel between birdsong and language. Our method can be applied across species and contexts to analyze complex variable learned behaviors, as distinct as foraging, tool use, and language.

Modeling dyadic processes using Hidden Markov Models: A time series approach to mother-infant interactions during infant immunization

The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at two and six months of age, used hidden Markov modeling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a 4-state model for the dyadic responses to a two-month inoculation whereas a 6-state model best described the dyadic process at six months. Two of the states at two months and three of the states at six months suggested a progression from high intensity crying to no crying with parents using vestibular and auditory soothing methods. The use of feeding and/or pacifying to soothe the infant characterized one two-month state and two six-month states. These data indicate that with maturation and experience, the mother-infant dyad is becoming more organized around the soothing interaction. Using hidden Markov modeling to describe individual differences, as well as normative processes, is also presented and discussed.

Costs and benefits of automatization in category learning of ill-defined rules

Learning ill-defined categories (such as the structure of Medin & Schaffer, 1978) involves multiple learning systems and different corresponding category representations, which are difficult to detect. Application of latent Markov analysis allows detection and investigation of such multiple latent category representations in a statistically robust way, isolating low performers and quantifying shifts between latent strategies. We reanalyzed data from three experiments presented in Johansen and Palmeri (2002), which comprised prolonged training of ill-defined categories, with the aim of studying the changing interactions between underlying learning systems. Our results broadly confirm the original conclusion that, in most participants, learning involved a shift from a rule-based to an exemplar-based strategy. Separate analyses of latent strategies revealed that (a) shifts from a rule-based to an exemplar-based strategy resulted in an initial decrease of speed and an increase of accuracy; (b) exemplar-based strategies followed a power law of learning, indicating automatization once an exemplar-based strategy was used; (c) rule-based strategies changed from using pure rules to rules-plus-exceptions, which appeared as a dual processes as indicated by the accuracy and response-time profiles. Results suggest an additional pathway of learning ill-defined categories, namely involving a shift from a simple rule to a complex rule after which this complex rule is automatized as an exemplar-based strategy.

Infants' Transitions out of a Fussing/Crying State Are Modifiable and Are Related to Weight Status

Currently, about 10% of infants have a weight for length greater than the 95th percentile for their age and sex, which puts them at risk for obesity as they grow. In a pilot obesity prevention study, primiparous mothers and their newborn infants were randomly assigned to a control group or a Soothe/Sleep intervention. Previously, it has been demonstrated that this intervention contributed to lower weight-for-length percentiles at 1 year; the aim of the present study was to examine infant behavior diary data collected during the intervention. Markov modeling was used to characterize infants' patterns of behavioral transitions at ages 3 and 16 weeks. Results showed that heavier mothers were more likely to follow their infants' fussing/crying episodes with a feeding. The intervention increased infants' likelihood of transitioning from a fussing/crying state to an awake/calm state. A shorter latency to feed in response to fussing/crying was associated with a higher subsequent weight status. This study provides preliminary evidence that infants' transitions out of fussing/crying are characterized by inter-individual differences, are modifiable, and are linked to weight outcomes, suggesting that they may be promising targets for early behavioral obesity interventions, and highlighting the methodology used in this study as an appropriate and innovative tool to assess the impact of such interventions.