Probable Sequence Research Articles

Probabilistic prediction and context tree identification in the Goalkeeper game

In this article we address two related issues on the learning of probabilistic sequences of events. First, which features make the sequence of events generated by a stochastic chain more difficult to predict. Second, how to model the procedures employed by different learners to identify the structure of sequences of events. Playing the role of a goalkeeper in a video game, participants were told to predict step by step the successive directions—left, center or right—to which the penalty kicker would send the ball. The sequence of kicks was driven by a stochastic chain with memory of variable length. Results showed that at least three features play a role in the first issue: (1) the shape of the context tree summarizing the dependencies between present and past directions; (2) the entropy of the stochastic chain used to generate the sequences of events; (3) the existence or not of a deterministic periodic sequence underlying the sequences of events. Moreover, evidence suggests that best learners rely less on their own past choices to identify the structure of the sequences of events.

Implicit visuospatial sequence representations are accessible in both the practice and the transfer hand

A serial reaction time task was used to test whether the representations of a probabilistic second-order sequence structure are (i) stored in an effector-dependent, effector-independent intrinsic or effector-independent visuospatial code and (ii) are inter-manually accessible. Participants were trained either with the dominant or non-dominant hand. Tests were performed with both hands in the practice sequence, a random sequence, and a mirror sequence. Learning did not differ significantly between left and right-hand practice, suggesting symmetric intermanual transfer from the dominant to the non-dominant hand and vice versa. In the posttest, RTs were shorter for the practice sequence than for the random sequence, and longest for the mirror sequence. Participants were unable to freely generate or recognize the practice sequence, indicating implicit knowledge of the probabilistic sequence structure. Because sequence-specific learning did not differ significantly between hands, we conclude that representations of the probabilistic sequence structure are stored in an effector-independent visuospatial code.

Prefrontal theta—gamma transcranial alternating current stimulation improves non-declarative visuomotor learning in older adults

The rise in the global population of older adults underscores the significance to investigate age-related cognitive disorders and develop early treatment modalities. Previous research suggests that non-invasive transcranial Alternating Current Stimulation (tACS) can moderately improve cognitive decline in older adults. However, non-declarative cognition has received relatively less attention. This study investigates whether repeated (16-day) bilateral theta—gamma cross-frequency tACS targeting the Dorsolateral Prefrontal Cortex (DLPFC) enhances non-declarative memory. Computerized cognitive training was applied alongside stimulation to control for the state-of-the-brain. The Alternating Serial Reaction Time (ASRT) task was employed to assess non-declarative functions such as visuomotor skill and probabilistic sequence learning. Results from 35 participants aged 55–82 indicated that active tACS led to more substantial improvements in visuomotor skills immediately after treatment, which persisted 3 months later, compared to sham tACS. Treatment benefit was more pronounced in older adults of younger age and those with pre-existing cognitive decline. However, neither intervention group exhibited modulation of probabilistic sequence learning. These results suggest that repeated theta—gamma tACS can selectively improve distinct non-declarative cognitive aspects when targeting the DLPFC. Our findings highlight the therapeutic potential of tACS in addressing deficits in learning and retaining general skills, which could have a positive impact on the quality of life for cognitively impaired older individuals by preserving independence in daily activities.

Extracting sequential frequent itemsets from probabilistic sequences database

Extracting sequential frequent itemsets from probabilistic sequences database

Energy management of green charging station integrated with photovoltaics and energy storage system based on electric vehicles classification

As the number of electric vehicles (EVs) increases, EV charging demand is also growing rapidly. In the smart grid environment, there is an urgent need for green charging stations (GCS) to effectively manage the internal photovoltaic (PV), energy storage system (ESS), charging behaviors of EVs and energy transactions with entities. In this paper, a novel EV classification approach was proposed for GCS, of which the objective is to minimize the total cost of energy trading between charging station and entities. Based on EV charging requirements and risk preference for participating in demand response (DR), EVs are classified into three categories: (1) regular; (2) conservative; and (3) vehicle to grid(V2G). For different types of EVs, charging price schemes are proposed for GCS to stimulate more EVs to interact with the grid and duly dis-charging their power in the capacity of V2G runners. In dealing with the uncertainty issues caused by photovoltaic generation, chance-constrained and probabilistic sequence methods are employed to solve the proposed framework with satisfactory computational efficiency. Case studies based on genuine PV generation, load and pricing data are carried out to verify the proposed framework. It is demonstrated V2G EVs can dramatically reduce the total cost to the GCS. Besides, V2G EVs also can greatly reduce the charging cost compared to regular and conservative EVs.

Modality-specific and modality-independent neural representations work in concert in predictive processes during sequence learning.

Probabilistic sequence learning supports the development of skills and enables predictive processing. It remains contentious whether visuomotor sequence learning is driven by the representation of the visual sequence (perceptual coding) or by the representation of the response sequence (motor coding). Neurotypical adults performed a visuomotor sequence learning task. Learning occurred incidentally as it was evidenced by faster responses to high-probability than to low-probability targets. To uncover the neurophysiology of the learning process, we conducted both univariate analyses and multivariate pattern analyses (MVPAs) on the temporally decomposed EEG signal. Univariate analyses showed that sequence learning modulated the amplitudes of the motor code of the decomposed signal but not in the perceptual and perceptual-motor signals. However, MVPA revealed that all 3 codes of the decomposed EEG contribute to the neurophysiological representation of the learnt probabilities. Source localization revealed the involvement of a wider network of frontal and parietal activations that were distinctive across coding levels. These findings suggest that perceptual and motor coding both contribute to the learning of sequential regularities rather than to a neither-nor distinction. Moreover, modality-specific encoding worked in concert with modality-independent representations, which suggests that probabilistic sequence learning is nonunitary and encompasses a set of encoding principles.

Scanning a compressed ordered representation of the future.

Several authors have suggested a deep symmetry between the psychological processes that underlie our ability to remember the past and make predictions about the future. The judgment of recency (JOR) task measures temporal order judgments for the past by presenting pairs of probe stimuli; participants choose the probe that was presented more recently. We performed a short-term relative JOR task and introduced a novel judgment of imminence (JOI) task to study temporal order judgments for the future. In the JOR task, participants were presented with a sequence of stimuli and asked to choose which of two probe stimuli was presented closer to the present. In the JOI task, participants were trained on a probabilistic sequence. After training, the sequence was interrupted with probe stimuli. Participants were asked to choose which of two probe stimuli was expected to be presented closer to the present. Replicating prior work on JOR, we found that RT results supported a backward self-terminating search model operating on a temporally organized representation of the past. We also showed that RT distributions are consistent with this model and that the temporally organized representation is compressed. Critically, results for the JOI task probing expectations of the future suggest a forward self-terminating search model operating on a temporally organized representation of the future. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Do temporal factors affect whether our performance accurately reflects our underlying knowledge? The effects of stimulus presentation rates on the performance versus competence dissociation.

Ample evidence shows that the momentary performance can dissociate from the underlying knowledge (competence). Under what circumstances such dissociation occurs, however, remains unclear. Here we tested how temporal factors, and more specifically, the elapsed time between subsequent events affects the dissociation between performance and competence by systematically manipulating the stimulus presentation rates during and after learning. Participants completed a probabilistic sequence learning task with a fast (120msec) or a slow (850msec) response-to-stimulus-interval (RSI) during the Learning phase and they were tested with both RSIs 24h later (Testing phase). We also tested whether they gained explicit knowledge about the sequence or their knowledge remained implicit. Our results revealed higher reaction time learning scores when tested with the fast RSI, irrespective of the RSI during learning, suggesting that faster presentation rates can help better express the acquired knowledge, leading to increased performance measures. For accuracy, participants showed higher learning scores when tested with the same presentation rate as the one that they encountered during learning. The acquired knowledge remained implicit in both groups, suggesting that the observed findings were not confounded by differences in awareness gained in the two groups. Overall, our study highlights that the momentary performance does not always accurately reflect the underlying knowledge, and temporal factors seem to influence this dissociation. Our findings have theoretical, methodological, and translational implications that likely extend beyond learning and memory to other functions and domains as well, including aspects of decision-making, perception, theory of mind, and language.

Speed and accuracy instructions affect two aspects of skill learning differently

Procedural learning is key to optimal skill learning and is essential for functioning in everyday life. The findings of previous studies are contradictory regarding whether procedural learning can be modified by prioritizing speed or accuracy during learning. The conflicting results may be due to the fact that procedural learning is a multifaceted cognitive function. The purpose of our study is to determine whether and how speed and accuracy instructions affect two aspects of procedural learning: the learning of probability-based and serial-order-based regularities. Two groups of healthy individuals were instructed to practice on a cued probabilistic sequence learning task: one group focused on being fast and the other on being accurate during the learning phase. The speed instruction resulted in enhanced expression of probability-based but not serial-order-based knowledge. After a retention period, we instructed the participants to focus on speed and accuracy equally, and we tested their acquired knowledge. The acquired knowledge was comparable between groups in both types of learning. These findings suggest that different aspects of procedural learning can be affected differently by instructions. However, only momentary performance might be boosted by speed instruction; the acquired knowledge remains intact. In addition, as the accuracy instruction resulted in accuracy near ceiling level, the results illustrate that response errors are not needed for humans to learn in the procedural domain and draw attention to the fact that different instructions can separate competence from performance.

Symmetric uncertainty-incorporated probabilistic sequence-based ant colony optimization for feature selection in classification

Feature selection (FS), which aims to select informative feature subsets and improve classification performance, is a crucial data-mining technique. Recently, swarm intelligence has attracted considerable attention and has been successfully applied to FS. Ant colony optimization (ACO), a swarm intelligence algorithm, has shown great potential in FS owing to its graphical representation and search ability. However, designing an effective ACO-based approach for FS is challenging because of issues originating from feature interactions and premature convergence problems. In this study, a novel ACO is proposed that incorporates symmetric uncertainty (SU). By constructing a probabilistic sequence-based graphical representation, the proposed algorithm significantly outperformed six other algorithms on 16 problems in terms of the classification error rate. This study also considers an extensive investigation of the contribution of the two components, namely, probabilistic sequence and SU. The experimental results indicated that these components significantly improved the performance of the ACO-based approach.

Deep probabilistic time series forecasting using augmented recurrent input for dynamic systems

The demand of probabilistic time series forecasting has been recently raised in various dynamic system scenarios, for example, system identification and prognostic and health management of machines. To this end, we combine the advances in both deep generative models and state space model (SSM) to come up with a novel, data-driven deep probabilistic sequence model. Specifically, we follow the popular encoder–decoder generative structure to build the recurrent neural networks (RNN) assisted variational sequence model on an augmented recurrent input space, which could induce rich stochastic sequence dependency. Besides, in order to alleviate the inconsistency issue of the posterior between training and predicting as well as improving the mining of dynamic patterns, we (i) propose using a lagged hybrid output as input for the posterior at next time step, which brings training and predicting into alignment; and (ii) further devise a generalized auto-regressive strategy that encodes all the historical dependencies for the posterior. Thereafter, we first investigate the methodological characteristics of the proposed deep probabilistic sequence model on toy cases, and then comprehensively demonstrate the superiority of our model against existing deep probabilistic SSM models through extensive numerical experiments on eight system identification benchmarks from various dynamic systems. Finally, we apply our sequence model to a real-world centrifugal compressor forecasting problem, and again verify its outstanding performance by quantifying the time series predictive distribution.

Accurately predicting nitrosylated tyrosine sites using probabilistic sequence information

Post-translational modification (PTM) is defined as the enzymatic changes of proteins after the translation process in protein biosynthesis. Nitrotyrosine, which is one of the most important modifications of proteins, is interceded by the active nitrogen molecule. It is known to be associated with different diseases including autoimmune diseases characterized by chronic inflammation and cell damage. Currently, nitrotyrosine sites are identified using experimental approaches which are laborious and costly. In this study, we propose a new machine learning method called PredNitro to accurately predict nitrotyrosine sites. To build PredNitro, we use sequence coupling information from the neighboring amino acids of tyrosine residues along with a support vector machine as our classification technique.Our results demonstrates that PredNitro achieves 98.0% accuracy with more than 0.96 MCC and 0.99 AUC in both 5-fold cross-validation and jackknife cross-validation tests which are significantly better than those reported in previous studies. PredNitro is publicly available as an online predictor at: http://103.99.176.239/PredNitro.

How does the length of short rest periods affect implicit probabilistic learning?

Memory consolidation has been mainly investigated for extended periods, from hours to days. Recent studies focused on memory consolidation occurring within shorter periods, from seconds to minutes. Yet, these studies focused on explicit sequence learning with fixed rest periods. Our study aimed at determining whether short rest periods enhance implicit probabilistic sequence learning and whether the length of rest duration influences such offline changes. Participants performed an implicit probabilistic sequence learning task throughout 45 blocks. Between blocks, participants were allowed to rest and then to continue the task at their pace. The results show that probabilistic sequence knowledge decreased from pre-to post-rest periods, and this decrement was not related to the length of rest duration. These results suggest that probabilistic sequence knowledge decays during short rest periods and that such forgetting is not time-dependent. Overall, our findings highlight that ultra-fast consolidation differently affects distinct cognitive processes.

Lateral prefrontal cortex is a hub for music production from structural rules to movements.

Complex sequential behaviors, such as speaking or playing music, entail flexible rule-based chaining of single acts. However, it remains unclear how the brain translates abstract structural rules into movements. We combined music production with multimodal neuroimaging to dissociate high-level structural and low-level motor planning. Pianists played novel musical chord sequences on a muted MR-compatible piano by imitating a model hand on screen. Chord sequences were manipulated in terms of musical harmony and context length to assess structural planning, and in terms of fingers used for playing to assess motor planning. A model of probabilistic sequence processing confirmed temporally extended dependencies between chords, as opposed to local dependencies between movements. Violations of structural plans activated the left inferior frontal and middle temporal gyrus, and the fractional anisotropy of the ventral pathway connecting these two regions positively predicted behavioral measures of structural planning. A bilateral frontoparietal network was instead activated by violations of motor plans. Both structural and motor networks converged in lateral prefrontal cortex, with anterior regions contributing to musical structure building, and posterior areas to movement planning. These results establish a promising approach to study sequence production at different levels of action representation.

The generative capacity of probabilistic protein sequence models

Potts models and variational autoencoders (VAEs) have recently gained popularity as generative protein sequence models (GPSMs) to explore fitness landscapes and predict mutation effects. Despite encouraging results, current model evaluation metrics leave unclear whether GPSMs faithfully reproduce the complex multi-residue mutational patterns observed in natural sequences due to epistasis. Here, we develop a set of sequence statistics to assess the “generative capacity” of three current GPSMs: the pairwise Potts Hamiltonian, the VAE, and the site-independent model. We show that the Potts model’s generative capacity is largest, as the higher-order mutational statistics generated by the model agree with those observed for natural sequences, while the VAE’s lies between the Potts and site-independent models. Importantly, our work provides a new framework for evaluating and interpreting GPSM accuracy which emphasizes the role of higher-order covariation and epistasis, with broader implications for probabilistic sequence models in general.

Statistical learning occurs during practice while high-order rule learning during rest period

Knowing when the brain learns is crucial for both the comprehension of memory formation and consolidation and for developing new training and neurorehabilitation strategies in healthy and patient populations. Recently, a rapid form of offline learning developing during short rest periods has been shown to account for most of procedural learning, leading to the hypothesis that the brain mainly learns during rest between practice periods. Nonetheless, procedural learning has several subcomponents not disentangled in previous studies investigating learning dynamics, such as acquiring the statistical regularities of the task, or else the high-order rules that regulate its organization. Here we analyzed 506 behavioral sessions of implicit visuomotor deterministic and probabilistic sequence learning tasks, allowing the distinction between general skill learning, statistical learning, and high-order rule learning. Our results show that the temporal dynamics of apparently simultaneous learning processes differ. While high-order rule learning is acquired offline, statistical learning is evidenced online. These findings open new avenues on the short-scale temporal dynamics of learning and memory consolidation and reveal a fundamental distinction between statistical and high-order rule learning, the former benefiting from online evidence accumulation and the latter requiring short rest periods for rapid consolidation.

Prompting teaching modulates children's encoding of novel information by facilitating higher-level structure learning and hindering lower-level statistical learning

Young children are not only prepared to learn from teaching, but they also start to spontaneously teach others, indicating that teaching is a natural instinct of the humankind. During childhood, teaching seems to precede the emergence of several cognitive abilities, so the question arises: how does teaching affect the development of later emerging cognitive skills? Since teaching requires explicit, accessible representations of the knowledge of the teacher, we hypothesized that the motivation to teach might impact the way children encode novel information, by biasing them towards a model-based encoding, which can help them to structure the incoming information in a more abstract and explicitly accessible way. In our study, 7–10-year-old children were presented with a well-established probabilistic sequence learning task on two consecutive days, after receiving an instruction that on the second day, they would have to teach a peer about the task. During the task, we could simultaneously measure two different types of learning: model-free learning of local (lower-level) statistical correlations and model-based learning of the global (higher-level) statistical structures of the sequences. We predicted that in case the motivation to teach facilitates model-based encoding, children who received the instruction to teach would perform better in learning the higher-level statistical structures than children in the control group, who did not receive an instruction to teach. Furthermore, since previous studies showed competition between the two types of encoding processes during development, we also predicted that facilitating children's model-based learning will impair their model-free learning of the lower-level statistical correlations. Our results confirmed both predictions, showing an improved model-based higher-level structure learning and an impaired model-free lower-level statistical correlation learning in the Teaching Group, compared to the controls. Thus, prompting teaching affects children's encoding of the novel information, by biasing them to learn in a model-based way, which can help to build more abstract and explicitly accessible representations that could be shared with others.

Clone-structured graph representations enable flexible learning and vicarious evaluation of cognitive maps

Cognitive maps are mental representations of spatial and conceptual relationships in an environment, and are critical for flexible behavior. To form these abstract maps, the hippocampus has to learn to separate or merge aliased observations appropriately in different contexts in a manner that enables generalization and efficient planning. Here we propose a specific higher-order graph structure, clone-structured cognitive graph (CSCG), which forms clones of an observation for different contexts as a representation that addresses these problems. CSCGs can be learned efficiently using a probabilistic sequence model that is inherently robust to uncertainty. We show that CSCGs can explain a variety of cognitive map phenomena such as discovering spatial relations from aliased sensations, transitive inference between disjoint episodes, and formation of transferable schemas. Learning different clones for different contexts explains the emergence of splitter cells observed in maze navigation and event-specific responses in lap-running experiments. Moreover, learning and inference dynamics of CSCGs offer a coherent explanation for disparate place cell remapping phenomena. By lifting aliased observations into a hidden space, CSCGs reveal latent modularity useful for hierarchical abstraction and planning. Altogether, CSCG provides a simple unifying framework for understanding hippocampal function, and could be a pathway for forming relational abstractions in artificial intelligence.

Dissociation between two aspects of procedural learning in Tourette syndrome: Enhanced statistical and impaired sequence learning

ABSTRACT Tourette syndrome (TS) is a childhood-onset neurodevelopmental disorder that primarily affects the cortico-basal ganglia-thalamo-cortical (CBGTC) circuitry and is characterized by motor and vocal tics. Previous studies have found enhancement in procedural memory, which depends on the CBGTC circuitry and plays an important role in the learning and processing of numerous motor, social, and cognitive skills and habits. Based on these studies, procedural hyperfunctioning in TS has been proposed. However, the neurocognitive mechanism underlying such hyperfunctioning is poorly understood. Here, we investigated how two aspects of procedural learning, namely 1) frequency-based statistical learning and 2) order-based sequence learning, are affected in TS. Twenty-one children with TS between the ages of ten and fifteen as well as 21 typically developing controls were tested on a probabilistic sequence learning task that enables the parallel assessment of these two aspects. We found that children with TS showed enhanced sensitivity to statistical information but impaired sequence learning compared to typically developing children. The deconstruction of procedural memory suggests that procedural hyperfunctioning in TS may be supported by enhanced sensitivity to statistical information. These results can provide a potential path for improving therapy methods and skill-oriented educational programs for TS.

Retrieving the structure of probabilistic sequences of auditory stimuli from EEG data

Using a new probabilistic approach we model the relationship between sequences of auditory stimuli generated by stochastic chains and the electroencephalographic (EEG) data acquired while 19 participants were exposed to those stimuli. The structure of the chains generating the stimuli are characterized by rooted and labeled trees whose leaves, henceforth called contexts, represent the sequences of past stimuli governing the choice of the next stimulus. A classical conjecture claims that the brain assigns probabilistic models to samples of stimuli. If this is true, then the context tree generating the sequence of stimuli should be encoded in the brain activity. Using an innovative statistical procedure we show that this context tree can effectively be extracted from the EEG data, thus giving support to the classical conjecture.