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)

Learning to play a musical instrument involves mapping visual + auditory cues to motor movements and anticipating transitions. Inspired by the serial reaction time task and artificial grammar learning, we investigated explicit and implicit knowledge of statistical learning in a sensorimotor task. Using a between-subjects design with four groups, one group of participants were provided with visual cues and followed along by tapping the corresponding fingertip to their thumb, while using a computer glove. Another group additionally (...) received accompanying auditory tones; the final two groups received sensory cues but did not provide a motor response—all together following a 2 × 2 design. Implicit knowledge was measured by response time, whereas explicit knowledge was assessed using probe tests. Findings indicate that explicit knowledge was best with only the single modality, but implicit knowledge was best when all three modalities were involved. (shrink)

: The purpose of this paper and its sister paper (Farrell and Hooker, b) is to present, evaluate and elaborate a proposed new model for the process of scientific development: self-directed anticipative learning (SDAL). The vehicle for its evaluation is a new analysis of a well-known historical episode: the development of ape-language research. In this first paper we outline five prominent features of SDAL that will need to be realized in applying SDAL to science: 1) interactive exploration of possibility (...) space; 2) self-directedness; 3) localization of success and error; 4) Synergistic increase in learning capacity; and 5) continuity of SDAL process across scientific change. In this paper we examine the first three features of SDAL in relation to the early history of ape-language research. We show that this history is readily explicated as a self-directed, ever-finer, delineation of possibility space that enables the localization of both success and error. Paper II examines the last two features against this history. (shrink)

: The purpose of this paper and its sister paper I (Farrell and Hooker, a) is to present, evaluate and elaborate a proposed new model for the process of scientific development: self-directed anticipative learning. The vehicle for its evaluation is a new analysis of a well-known historical episode: the development of ape language research. Paper I examined the basic features of SDAL in relation to the early history of ape-language research. In this second paper we examine the reconceptualization of (...) ape-language research following what many conceived to be Terrace's refutation of ape-language. We show that the apparent 'revolution' in our understanding of ape linguistic capacities was not based upon 'revolutionary' research different in kind from 'normal' research. The same processes of self-directed interactive exploration of possibility space, that enables a homing-in upon both error and success, is present in all phases of productive science. Moreover, conceiving science as an SDAL process explains how scientists learn how to learn about their research domain. (shrink)

This paper outlines an original interactivist-constructivist approach to modelling intelligence and learning as a dynamical embodied form of adaptiveness and explores some applications of I-C to understanding the way cognitive learning is realized in the brain. Two key ideas for conceptualizing intelligence within this framework are developed. These are: intelligence is centrally concerned with the capacity for coherent, context-sensitive, self-directed management of interaction; and the primary model for cognitive learning is anticipative skill construction. Self-directedness is a capacity (...) for integrative process modulation which allows a system to "steer" itself through its world by anticipatively matching its own viability requirements to interaction with its environment. Because the adaptive interaction processes required of intelligent systems are too complex for effective action to be prespecified learning is an important component of intelligence. A model of self-directed anticipative learning is formulated based on interactive skill construction, and argued to constitute a central constructivist process involved in cognitive development. SDAL illuminates the capacity of intelligent learners to start with the vague, poorly defined problems typically posed in realistic learning situations and progressively refine them, transforming them into problems with sufficient structure to guide the construction of a solution. Finally, some of the implications of I-C for modelling of the neuronal basis of intelligence and learning are explored; in particular, Quartz and Sejnowski's recent neural constructivism paradigm, enriched by Montague and Sejnowski's dopaminergic model of anticipative-predictive neural learning, is assessed as a promising, but incomplete, contribution to this approach. The paper concludes with a fourfold reflection on the divergence in cognitive modelling philosophy between the I-C and the traditional computational information processing approaches. (shrink)

Error is protean, ubiquitous and crucial in scientific process. In this paper it is argued that understanding scientific process requires what is currently absent: an adaptable, context-sensitive functional role for error in science that naturally harnesses error identification and avoidance to positive, success-driven, science. This paper develops a new account of scientific process of this sort, error and success driving Self-Directed Anticipative Learning (SDAL) cycling, using a recent re-analysis of ape-language research as test example. The example shows the limitations (...) of other accounts of error, in particular Mayo’s (Error and the growth of experimental knowledge, 1996) error-statistical approach, and SDAL cycling shows how they can be fruitfully contextualised. (shrink)

Cognitive Science, Volume 46, Issue 2, February 2022.

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)