Previous experiments have shown a serial-position effect (SPE) in paired-associate (PA) learning where the pairs contained stimuli pre- viously learned in serial order. The present experiment extended the number of pairs from 10 to 14. Pairs containing stimuli from terminal serial positions were learned with significantly fewer errors than pairs whose stimuli derived from central positions. The latter produced a dip in the PA error distribution suggesting the presence of sequential associations in SL between items (...) occupying central positions. (shrink)

Studies of implicit learning have shown that individuals exposed to a rule-governed environment often learn to exploit 'rules' which describe the structural relationship between environmental events. While some authors have interpreted such demonstrations as evidence for functionally separate implicit learning systems, others have argued that the observed changes in performance result from explicit knowledge which has been inadequately assessed. In this paper we illustrate this issue by considering one commonly used implicit learning task, the Serial reaction (...) time task, and outline what we see as an important problem associated with each of the commonly used methods used to assess explicit knowledge. This is that each measure requires a form of response which is dependent on the subjects having some knowledge of the serial-order of the sequence. We argue that such methods, or more specifically their analyses, seriously underestimate other sources of knowledge, which may be available to subjects during their performance of the SRT task. In support of this argument we demonstrate that subjects' serial-order knowledge can, in principle, be independent of subjects' knowledge of the statistical structure of the sequence, and we propose an alternative method for analysing performance on the Generate task which avoids this problem. (shrink)

If artificial neural networks are ever to form the foundation for higher level cognitive behaviors in machines or to realize their full potential as explanatory devices for human cognition, they must show signs of autonomy, multifunction operation, and intersystem integration that are absent in most existing models. This model begins to address these issues by integrating predictive learning, sequence interleaving, and sequence creation components to simulate a spectrum of higher-order cognitive behaviors which have eluded the grasp of simpler (...) systems. Its capabilities are described based on simulations calling for increasing levels of functionality and are used to show how the model can progress from fundamental sequence learning and recall tasks to sophisticated behaviors such as an ability to solve simple mathematical expressions and a creative capacity for the formation and application of inductive rules. (shrink)

We demonstrated unconscious learning of task-irrelevant perceptual regularities in a Serial Reaction Time task in both visual and auditory domains. Participants were required to respond to different letters or syllables which occurred in random order. Unbeknownst to participants, the color of the two letters or the tone of the syllables varied according to certain rules. Reaction times indicated that people indeed learnt both the color and tonal regularities indicating that task-irrelevant sequence structure can be learned perceptually. In (...) a subsequent prediction test of knowledge of the color or tonal cues using subjective measures, we showed that people could acquire task irrelevant knowledge unconsciously. (shrink)

Statistical learning enables humans to involuntarily process and utilize different kinds of patterns from the environment. However, the cognitive mechanisms underlying the simultaneous acquisition of multiple regularities from different perceptual modalities remain unclear. A novel multidimensional serial reaction time task was developed to test 40 participants’ ability to learn simple first‐order and complex second‐order relations between uni‐modal visual and cross‐modal audio‐visual stimuli. Using the difference in reaction times between sequenced and random stimuli as the index of (...) domain‐general statistical learning, a significant difference and dissociation of learning occurred between the initial and final learning phases. Furthermore, we used a negative and positive occurrence‐frequency‐and‐reaction‐time correlation to indicate implicit and explicit learning, respectively, and found that learning simple uni‐modal patterns involved an implicit‐to‐explicit segue, while acquiring complex cross‐modal patterns required an explicit‐to‐implicit segue, resulting in a X‐shape crossing of regularity learning. Thus, we propose an X‐way hypothesis to elucidate the dynamic interplay between the implicit and explicit systems at two distinct stages when acquiring various regularities in a multidimensional probability space. (shrink)

The computations involved in statistical learning have long been debated. Here, we build on work suggesting that a basic memory process, chunking, may account for the processing of statistical regularities into larger units. Drawing on methods from the memory literature, we developed a novel paradigm to test statistical learning by leveraging a robust phenomenon observed in serial recall tasks: that short‐term memory is fundamentally shaped by long‐term distributional learning. In the statistically induced chunking recall (SICR) task, (...) participants are exposed to an artificial language, using a standard statistical learning exposure phase. Afterward, they recall strings of syllables that either follow the statistics of the artificial language or comprise the same syllables presented in a random order. We hypothesized that if individuals had chunked the artificial language into word‐like units, then the statistically structured items would be more accurately recalled relative to the random controls. Our results demonstrate that SICR effectively captures learning in both the auditory and visual modalities, with participants displaying significantly improved recall of the statistically structured items, and even recall specific trigram chunks from the input. SICR also exhibits greater test–retest reliability in the auditory modality and sensitivity to individual differences in both modalities than the standard two‐alternative forced‐choice task. These results thereby provide key empirical support to the chunking account of statistical learning and contribute a valuable new tool to the literature. (shrink)

Although the existence of implicit motor learning is now widely accepted, the findings concerning perceptual implicit learning are ambiguous. Some researchers have observed perceptual learning whereas other authors have not. The review of the literature provides different reasons to explain this ambiguous picture, such as differences in the underlying learning processes, selective attention, or differences in the difficulty to express this knowledge. In three experiments, we investigated implicit visual learning within the original serial reaction (...) time task. We used different response devices in order to manipulate selective attention towards response dimensions. Results showed that visual and motor sequence learning differed in terms of RT-benefits, but not in terms of the amount of knowledge assessed after training. Furthermore, visual sequence learning was modulated by selective attention. However, the findings of all three experiments suggest that selective attention did not alter implicit but rather explicit learning processes. (shrink)

In this article, we explore the extraction of recursive nested structure in the processing of binary sequences. Our aim was to determine whether humans learn the higher-order regularities of a highly simplified input where only sequential-order information marks the hierarchical structure. To this end, we implemented a sequence generated by the Fibonacci grammar in a serial reaction time task. This deterministic grammar generates aperiodic but self-similar sequences. The combination of these two properties allowed us to evaluate hierarchical (...) learning while controlling for the use of low-level strategies like detecting recurring patterns. The deterministic aspect of the grammar allowed us to predict precisely which points in the sequence should be subject to anticipation. Results showed that participants’ pattern of anticipation could not be accounted for by “flat” statistical learning processes and was consistent with them anticipating upcoming points based on hierarchical assumptions. We also found that participants were sensitive to the structure constituency, suggesting that they organized the signal into embedded constituents. We hypothesized that the participants built this structure by merging recursively deterministic transitions. (shrink)

Nissen and Bullemer's serial reaction time task has proven to be a useful model task for exploring implicit sequence learning. Neuropsychological research indicates that SRT learning may depend on the integrity of the basal ganglia, but not on medial temporal and diencephalic structures that are crucial for explicit learning. Recent neuroimaging research demonstrates that motor cortical areas , prefrontal, and parietal cortex also may be involved. This paper reviews this neuropsychological and neuroimaging research, but finds it (...) lacking specific links between structure and function. In order to promote better functional hypotheses, the second part of the paper examines the function of these brain areas from a broader perspective. Neuroimaging and neuropsychological research with human subjects, as well as neurophysiological and lesion research with animals, suggests a number component operations that these brain mechanisms may contribute to learning in the SRT task. (shrink)

If the cortex is an associative memory, strongly connected cell assemblies will form when neurons in different cortical areas are frequently active at the same time. The cortical distributions of these assemblies must be a consequence of where in the cortex correlated neuronal activity occurred during learning. An assembly can be considered a functional unit exhibiting activity states such as full activation (“ignition”) after appropriate sensory stimulation (possibly related to perception) and continuous reverberation of excitation within the assembly (a (...) putative memory process). This has implications for cortical topographies and activity dynamics of cell assemblies forming during language acquisition, in particular for those representing words. Cortical topographies of assemblies should be related to aspects of the meaning of the words they represent, and physiological signs of cell assembly ignition should be followed by possible indicators of reverberation. The following postulates are discussed in detail: (1) assemblies representing phonological word forms are strongly lateralized and distributed over perisylvian cortices; (2) assemblies representing highly abstract words such as grammatical function words are also strongly lateralized and restricted to these perisylvian regions; (3) assemblies representing concrete content words include additional neurons in both hemispheres; (4) assemblies representing words referring to visual stimuli include neurons in visual cortices; and (5) assemblies representing words referring to actions include neurons in motor cortices. Two main sources of evidence are used to evaluate these proposals: (a) imaging studies focusing on localizing word processing in the brain, based on stimulus-triggered event-related potentials (ERPs), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI), and (b) studies of the temporal dynamics of fast activity changes in the brain, as revealed by high-frequency responses recorded in the electroencephalogram (EEG) and magnetoencephalogram (MEG). These data provide evidence for processing differences between words and matched meaningless pseudowords, and between word classes, such as concrete content and abstract function words, and words evoking visual or motor associations. There is evidence for early word class-specific spreading of neuronal activity and for equally specific high-frequency responses occurring later. These results support a neurobiological model of language in the Hebbian tradition. Competing large-scale neuronal theories of language are discussed in light of the data summarized. Neurobiological perspectives on the problem of serial order of words in syntactic strings are considered in closing. Key Words: associative learning; cell assembly; cognition; cortex; ERP; EEG; fMRI; language; lexicon; MEG; PET; word category. (shrink)

This paper focuses on the theme of the application of deep learning in the field of basketball sports, using research methods such as literature research, video analysis, comparative research, and mathematical statistics to explore deep learning in real-time analysis of basketball sports data. The basketball posture action recognition and analysis system proposed for basketball movement is composed of two parts serially. The first part is based on the bottom-up posture estimation method to locate the joint points and is (...) used to extract the posture sequence of the target in the video. The second part is the analysis and research of the action recognition algorithm based on the convolution of the space-time graph. According to the extracted posture sequence, the basketball action of the set classification is recognized. In order to obtain more accurate and three-dimensional information, a multitraining target method can be used in training; that is, multiple indicators can be detected and feedback is provided at the same time to correct player errors in time; the other is an auxiliary method, which is compared with ordinary training. The method can actively correct technical movements, train players to form muscle memory, and improve their abilities. Through the research of this article, it provides a theoretical basis for promoting the application of deep learning in the field of basketball and also provides a theoretical reference for the wider application of deep learning in the field of sports. At the same time, the designed real-time analysis system of basketball data also provides more actual reference values for coaches and athletes. (shrink)

The Ouroboros Model proposes a brain inspired cognitive architecture including detailed suggestions for the main processing steps in an overall conceptualization of cognition as embodied and embedded computing. All memories are structured into schemata, which are firmly grounded in the body of an actor. A cyclic and iterative data-acquisition and -processing loop forms the backbone of all cognitive activity. Ever more sophisticated schemata are built up incrementally from the wide combination of neural activity, concurrent at the point in time when (...) the memory is established; i.e., distinct representations are accrued. Later on, an entire schema can be reinstated from diverse subsets of its constituent features. In order to decode or compile ever more elaborate constructions, extant building blocks are concatenated. They are serially linked via common or “connection-attributes” of different representations and symbols, and they are employed for serial perception, processing and action, in particular, also for language production. At various levels, commonalities between different schemata lead to a similar preferred use of their respective tokens and subsequently shared categorizations. Rules, for the concatenation of words, akin to the ones governing chemical reactions, can be abstracted. As special kinds of symbols, words and word-classes along with the whole grammar of a language can thus be seen as a direct consequence of the processes outlined in the Ouroboros Model. Strong emphasis is put on the dimension of time over short intervals during active experience and performance, and, in particular, during interactions with other agents. (shrink)

Efficient prospective motor control, evident in human activity from birth, reveals an adaptive intentionality of a primary, pre-reflective, and pre-conceptual nature that we identify here as sensorimotor intentionality. We identify a structural continuity between the emergence of this earliest form of prospective movement and the structure of mental states as intentional or content-directed in more advanced forms. We base our proposal on motor control studies, from foetal observations through infancy. These studies reveal movements are guided by anticipations of future effects, (...) even from before birth. This implies that these movements, even if they are simple and discrete, are the actions of an intentional agent. We develop this notion to present a theory of the developing organisation of a core feature of cognition as embodied agent action, from early single actions with proximal prospectivity to the complex serial ordering of actions into projects to reach distal goals. We claim the prospective structural continuity from early and simple actions to later complex projects of serially-ordered actions confirms the existence of an ontogenetically primary form of content–directedness that is a driver for learning and development. Its implications for understanding autism are discussed. (shrink)

Human control of action in routine situations involves a flexible interplay between (a) task-dependent serial ordering constraints; (b) top-down, or intentional, control processes; and (c) bottom-up, or environmentally triggered, affordances. In addition, the interaction between these influences is modulated by learning mechanisms that, over time, appear to reduce the need for top-down control processes while still allowing those processes to intervene at any point if necessary or if desired. We present a model of the acquisition and control of (...) goal-directed action that goes beyond existing models by operationalizing an interface between two putative systems—a routine and a non-routine system—thereby demonstrating how explicitly represented goals can interact with the emergent task representations that develop through learning in the routine system. The gradual emergence of task representations offers an explanation for the transfer of control with experience from the non-routine goal-based system to the routine system. At the same time it allows action selection to be sensitive both to environmental triggers and to biasing from multiple levels within the goal system. (shrink)

Children's imitation of adults plays a prominent role in human cognitive development. However, few studies have investigated how children represent the complex structure of observed actions which underlies their imitation. We integrate theories of action segmentation, memory, and imitation to investigate whether children's event representation is organized according to veridical serial order or a higher level goal structure. Children were randomly assigned to learn novel event sequences either through interactive hands-on experience or via storybook. Results demonstrate that children's (...) representation of observed actions is organized according to higher level goals, even at the cost of representing the veridical temporal ordering of the sequence. We argue that prioritizing goal structure enhances event memory, and that this mental organization is a key mechanism of social-cognitive development in real-world, dynamic environments. It supports cultural learning and imitation in ecologically valid settings when social agents are multitasking and not demonstrating one isolated goal at a time. (shrink)

Sequence learning is fundamental to a wide range of cognitive functions. Explaining how sequences—and the relations between the elements they comprise—are learned is a fundamental challenge to cognitive science. However, although hundreds of articles addressing this question are published each year, the actual learning mechanisms involved in the learning of sequences are rarely investigated. We present three experiments that seek to examine these mechanisms during a typing task. Experiments 1 and 2 tested learning during typing single (...) letters on each trial. Experiment 3 tested for “chunking” of these letters into “words.” The results of these experiments were used to examine the mechanisms that could best account for them, with a focus on two particular proposals: statistical transitional probability learning and discriminative error-driven learning. Experiments 1 and 2 showed that error-driven learning was a better predictor of response latencies than either n-gram frequencies or transitional probabilities. No evidence for chunking was found in Experiment 3, probably due to interspersing visual cues with the motor response. In addition, learning occurred across a greater distance in Experiment 1 than Experiment 2, suggesting that the greater predictability that comes with increased structure leads to greater learnability. These results shed new light on the mechanism responsible for sequence learning. Despite the widely held assumption that transitional probability learning is essential to this process, the present results suggest instead that the sequences are learned through a process of discriminative learning, involving prediction and feedback from prediction error. (shrink)