Investigation of sequence processing: A cognitive and computational neuroscience perspective

Serial order processing or sequence processing underlies many human activities such as speech, language, skill learning, planning, problem-solving, etc. Investigating the neural bases of sequence processing enables us to understand serial order in cognition and also helps in building intelligent devices. In this article, we review various cognitive issues related to sequence processing with examples. Experimental results that give evidence for the involvement of various brain areas will be described. Finally, a theoretical approach based on statistical models and reinforcement learning paradigm is presented. These theoretical ideas are useful for studying sequence learning in a principled way. This article also suggests a two-way process diagram integrating experimentation (cognitive neuroscience) and theory/ computational modelling (computational neuroscience). This integrated framework is useful not only in the present study of serial order, but also for understanding many cognitive processes

A Multi-disciplinary Approach to the Investigation of Aspects of Serial Order in Cognition

Serial order processing or Sequence processing underlies many human activities such as speech, language, skill learning, planning, problem solving, etc. Investigating the\ud neural bases of sequence processing enables us to understand serial order in cognition and helps us building intelligent devices. In the current paper, various\ud cognitive issues related to sequence processing will be discussed with examples. Some of the issues are: distributed versus local representation, pre-wired versus\ud adaptive origins of representation, implicit versus explicit learning, fixed/flat versus hierarchical organization, timing aspects, order information embedded in sequences, primacy versus recency in list learning and aspects of sequence perception such as recognition, recall and generation. Experimental results that give evidence for the involvement of various brain areas will be described. Finally, theoretical frameworks based on Markov models and Reinforcement Learning paradigm will be presented. These theoretical ideas are useful for studying sequential phenomena in a principled way

Learning and Production of Movement Sequences: Behavioral, Neurophysiological, and Modeling Perspectives

A growing wave of behavioral studies, using a wide variety of paradigms that were introduced or greatly refined in recent years, has generated a new wealth of parametric observations about serial order behavior. What was a mere trickle of neurophysiological studies has grown to a more steady stream of probes of neural sites and mechanisms underlying sequential behavior. Moreover, simulation models of serial behavior generation have begun to open a channel to link cellular dynamics with cognitive and behavioral dynamics. Here we summarize the major results from prominent sequence learning and performance tasks, namely immediate serial recall, typing, 2XN, discrete sequence production, and serial reaction time. These populate a continuum from higher to lower degrees of internal control of sequential organization. The main movement classes covered are speech and keypressing, both involving small amplitude movements that are very amenable to parametric study. A brief synopsis of classes of serial order models, vis-à-vis the detailing of major effects found in the behavioral data, leads to a focus on competitive queuing (CQ) models. Recently, the many behavioral predictive successes of CQ models have been joined by successful prediction of distinctively patterend electrophysiological recordings in prefrontal cortex, wherein parallel activation dynamics of multiple neural ensembles strikingly matches the parallel dynamics predicted by CQ theory. An extended CQ simulation model-the N-STREAMS neural network model-is then examined to highlight issues in ongoing attemptes to accomodate a broader range of behavioral and neurophysiological data within a CQ-consistent theory. Important contemporary issues such as the nature of working memory representations for sequential behavior, and the development and role of chunks in hierarchial control are prominent throughout.Defense Advanced Research Projects Agency/Office of Naval Research (N00014-95-1-0409); National Institute of Mental Health (R01 DC02852

Moving in time: simulating how neural circuits enable rhythmic enactment of planned sequences

Many complex actions are mentally pre-composed as plans that specify orderings of simpler actions. To be executed accurately, planned orderings must become active in working memory, and then enacted one-by-one until the sequence is complete. Examples include writing, typing, and speaking. In cases where the planned complex action is musical in nature (e.g. a choreographed dance or a piano melody), it appears to be possible to deploy two learned sequences at the same time, one composed from actions and a second composed from the time intervals between actions. Despite this added complexity, humans readily learn and perform rhythm-based action sequences. Notably, people can learn action sequences and rhythmic sequences separately, and then combine them with little trouble (Ullén & Bengtsson 2003). Related functional MRI data suggest that there are distinct neural regions responsible for the two different sequence types (Bengtsson et al. 2004). Although research on musical rhythm is extensive, few computational models exist to extend and inform our understanding of its neural bases. To that end, this article introduces the TAMSIN (Timing And Motor System Integration Network) model, a systems-level neural network model capable of performing arbitrary item sequences in accord with any rhythmic pattern that can be represented as a sequence of integer multiples of a base interval. In TAMSIN, two Competitive Queuing (CQ) modules operate in parallel. One represents and controls item order (the ORD module) and the second represents and controls the sequence of inter-onset-intervals (IOIs) that define a rhythmic pattern (RHY module). Further circuitry helps these modules coordinate their signal processing to enable performative output consistent with a desired beat and tempo.Accepted manuscrip

Learning curve assessment of rule use provides evidence for spared implicit sequence learning in a mouse model of mental retardation

Humans with Fragile X Syndrome (FXS) have a mental retardation of which a notable characteristic is a weakness in recalling sequences of information. A mouse model of the disorder exists which exhibits behavioral and neurologic changes, but cognitive testing has not revealed learning deficits seemingly comparable in magnitude to that seen in the human condition. A working memory task for olfactory sequences was employed to test learning set acquisition in mice, half of which had a disruption of the gene responsible for FXS in humans. The task protected against reward detection artifact and demonstrated stringency-dependent task acquisition. A comparable image-based sequence learning set task was used to test humans. The performances of human subjects who did and did not report consciously acquiring the task rules were used as positive and negative controls to assess the mouse learning curves. Learning curve plateau error fluctuation for individual mice was comparable to that of human subjects who never acquired an explicit rule to perform the task, but different from those of human subjects who could state a rule to solve the problem. Sliding window error plots and nonparametric statistical analysis discriminated between the consciously rule-based human performances and that of the mice and humans who did not explicitly obtain the rule. Based on comparison to the human results, wild-type and FX mouse learning curves with a continuingly variable terminal plateau error rate in sliding epochs were classified as “implicit”. Although a moderately large difference in performance of the olfactory task was observed among mouse strains, there was no significant effect of FX genotype. The wild-type performance of the FX mice in this sequence task suggests that implicit learning may be relatively spared in FXS

The Actions and Feelings Questionnaire in Autism and Typically Developed Adults

Open access via Springer Compact Agreement We are grateful to Simon Baron-Cohen and Paula Smith of the Cambridge Autism Centre for the use of the ARC database in distributing the questionnaire, to all participants for completing it, to Eilidh Farquar for special efforts in distributing the link and to Gemma Matthews for advice on using AMOS 23. JHGW is supported by the Northwood Trust.Peer reviewedPublisher PD