Learning to learn: Single session acquisition of new rules by freely moving mice

Learning from examples and adapting to new circumstances are fundamental attributes of human cognition. However, it is unclear what conditions allow for fast and successful learning, especially in nonhuman subjects. To determine how rapidly freely moving mice can learn a new discrimination criterion (DC), we design a two-alternative forced-choice visual discrimination paradigm in which the DCs governing the task can change between sessions. We find that experienced animals can learn a new DC after being exposed to only five training and three testing trials. The propensity for single session learning improves over time and is accurately predicted based on animal experience and criterion difficulty. After establishing the procedural learning of a paradigm, mice continuously improve their performance in new circumstances. Thus, mice learn to learn.

The developmental cognitive mechanism of learning algebraic rules from the dual-process theory perspective

Rule learning is an important ability that enables human beings to adapt to nature and develop civilizations. There have been many discussions on the mechanism and characteristics of algebraic rule learning, but there are still controversies due to the lack of theoretical guidance. Based on the dual-process theory, this study discussed the following arguments for algebraic rule learning across human and animal studies: whether algebraic rule learning is simply Type 1 processing, whether algebraic rule learning is a domain-general ability, whether algebraic rule learning is shared by humans and animals, and whether an algebraic rule is learned consciously. Moreover, we propose that algebraic rule learning is possibly a cognitive process that combines both Type 1 and Type 2 processing. Further exploration is required to establish the essence and neural basis of algebraic rule learning.

More Than the Sum of Its Parts: Visual-Tactile Integration in the Behaving Rat

We experience the world by constantly integrating cues from multiple modalities to form unified sensory percepts. Once familiar with multimodal properties of an object, we can recognize it regardless of the modality involved. In this chapter we will examine the case of a visual-tactile orientation categorization experiment in rats. We will explore the involvement of the cerebral cortex in recognizing objects through multiple sensory modalities. In the orientation categorization task, rats learned to examine and judge the orientation of a raised, black and white grating using touch, vision, or both. Their multisensory performance was better than the predictions of linear models for cue combination, indicating synergy between the two sensory channels. Neural recordings made from a candidate associative cortical area, the posterior parietal cortex (PPC), reflected the principal neuronal correlates of the behavioral results: PPC neurons encoded both graded information about the object and categorical information about the animal's decision. Intriguingly single neurons showed identical responses under each of the three modality conditions providing a substrate for a neural circuit in the cortex that is involved in modality-invariant processing of objects.

Sequence representation as an early step in the evolution of language

Human language is unique in its compositional, open-ended, and sequential form, and its evolution is often solely explained by advantages of communication. However, it has proven challenging to identify an evolutionary trajectory from a world without language to a world with language, especially while at the same time explaining why such an advantageous phenomenon has not evolved in other animals. Decoding sequential information is necessary for language, making domain-general sequence representation a tentative basic requirement for the evolution of language and other uniquely human phenomena. Here, using formal evolutionary analyses of the utility of sequence representation we show that sequence representation is exceedingly costly and that current memory systems found in animals may prevent abilities necessary for language to emerge. For sequence representation to evolve, flexibility allowing for ignoring irrelevant information is necessary. Furthermore, an abundance of useful sequential information and extensive learning opportunities are required, two conditions that were likely fulfilled early in human evolution. Our results provide a novel, logically plausible trajectory for the evolution of uniquely human cognition and language, and support the hypothesis that human culture is rooted in sequential representational and processing abilities.

A test of memory for stimulus sequences in great apes

Identifying cognitive capacities underlying the human evolutionary transition is challenging, and many hypotheses exist for what makes humans capable of, for example, producing and understanding language, preparing meals, and having culture on a grand scale. Instead of describing processes whereby information is processed, recent studies have suggested that there are key differences between humans and other animals in how information is recognized and remembered. Such constraints may act as a bottleneck for subsequent information processing and behavior, proving important for understanding differences between humans and other animals. We briefly discuss different sequential aspects of cognition and behavior and the importance of distinguishing between simultaneous and sequential input, and conclude that explicit tests on non-human great apes have been lacking. Here, we test the memory for stimulus sequences-hypothesis by carrying out three tests on bonobos and one test on humans. Our results show that bonobos' general working memory decays rapidly and that they fail to learn the difference between the order of two stimuli even after more than 2,000 trials, corroborating earlier findings in other animals. However, as expected, humans solve the same sequence discrimination almost immediately. The explicit test on whether bonobos represent stimulus sequences as an unstructured collection of memory traces was not informative as no differences were found between responses to the different probe tests. However, overall, this first empirical study of sequence discrimination on non-human great apes supports the idea that non-human animals, including the closest relatives to humans, lack a memory for stimulus sequences. This may be an ability that sets humans apart from other animals and could be one reason behind the origin of human culture.

Food-caching mountain chickadees can learn abstract rules to solve a complex spatial-temporal pattern

The use of abstract rules in behavioral decisions is considered evidence of executive functions associated with higher-level cognition. Laboratory studies across taxa have shown that animals may be capable of learning abstract concepts, such as the relationships between items, but often use simpler cognitive abilities to solve tasks. Little is known about whether or how animals learn and use abstract rules in natural environments. Here, we tested whether wild, food-caching mountain chickadees (Poecile gambeli) could learn an abstract rule in a spatial-temporal task in which the location of a food reward rotated daily around an 8-feeder square spatial array for up to 34 days. Chickadees initially searched for the daily food reward by visiting the most recently rewarding locations and then moving backward to visit previously rewarding feeders, using memory of previous locations. But by the end of the task, chickadees were more likely to search forward in the correct direction of rotation, moving away from the previously rewarding feeders. These results suggest that chickadees learned the direction rule for daily feeder rotation and used this to guide their decisions while searching for a food reward. Thus, chickadees appear to use an executive function to make decisions on a foraging-based task in the wild. VIDEO ABSTRACT.

Exploring the neurobiology of Merge at a basic level: insights from a novel artificial grammar paradigm

Introduction

Human language allows us to generate an infinite number of linguistic expressions. It's proposed that this competence is based on a binary syntactic operation, Merge, combining two elements to form a new constituent. An increasing number of recent studies have shifted from complex syntactic structures to two-word constructions to investigate the neural representation of this operation at the most basic level.

Methods

This fMRI study aimed to develop a highly flexible artificial grammar paradigm for testing the neurobiology of human syntax at a basic level. During scanning, participants had to apply abstract syntactic rules to assess whether a given two-word artificial phrase could be further merged with a third word. To control for lower-level template-matching and working memory strategies, an additional non-mergeable word-list task was set up.

Results

Behavioral data indicated that participants complied with the experiment. Whole brain and region of interest (ROI) analyses were performed under the contrast of "structure > word-list." Whole brain analysis confirmed significant involvement of the posterior inferior frontal gyrus [pIFG, corresponding to Brodmann area (BA) 44]. Furthermore, both the signal intensity in Broca's area and the behavioral performance showed significant correlations with natural language performance in the same participants. ROI analysis within the language atlas and anatomically defined Broca's area revealed that only the pIFG was reliably activated.

Discussion

Taken together, these results support the notion that Broca's area, particularly BA 44, works as a combinatorial engine where words are merged together according to syntactic information. Furthermore, this study suggests that the present artificial grammar may serve as promising material for investigating the neurobiological basis of syntax, fostering future cross-species studies.

Tactile cognition in rodents

Since the discovery 50 years ago of the precisely ordered representation of the whiskers in somatosensory cortex, the rodent tactile sensory system has been a fertile ground for the study of sensory processing. With the growing sophistication of touch-based behavioral paradigms, together with advances in neurophysiological methodology, a new approach is emerging. By posing increasingly complex perceptual and memory problems, in many cases analogous to human psychophysical tasks, investigators now explore the operations underlying rodent problem solving. We define the neural basis of tactile cognition as the transformation from a stage in which neuronal activity encodes elemental features, local in space and in time, to a stage in which neuronal activity is an explicit representation of the behavioral operations underlying the current task. Selecting a set of whisker-based behavioral tasks, we show that rodents achieve high level performance through the workings of neuronal circuits that are accessible, decodable, and manipulatable. As a means towards exploring tactile cognition, this review presents leading psychophysical paradigms and, where known, their neural correlates.

Statistical Learning in Vision

Vision and learning have long been considered to be two areas of research linked only distantly. However, recent developments in vision research have changed the conceptual definition of vision from a signal-evaluating process to a goal-oriented interpreting process, and this shift binds learning, together with the resulting internal representations, intimately to vision. In this review, we consider various types of learning (perceptual, statistical, and rule/abstract) associated with vision in the past decades and argue that they represent differently specialized versions of the fundamental learning process, which must be captured in its entirety when applied to complex visual processes. We show why the generalized version of statistical learning can provide the appropriate setup for such a unified treatment of learning in vision, what computational framework best accommodates this kind of statistical learning, and what plausible neural scheme could feasibly implement this framework. Finally, we list the challenges that the field of statistical learning faces in fulfilling the promise of being the right vehicle for advancing our understanding of vision in its entirety. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Modelling how cleaner fish approach an ephemeral reward task demonstrates a role for ecologically tuned chunking in the evolution of advanced cognition

What makes cognition “advanced” is an open and not precisely defined question. One perspective involves increasing the complexity of associative learning, from conditioning to learning sequences of events (“chaining”) to representing various cue combinations as “chunks.” Here we develop a weighted graph model to study the mechanism enabling chunking ability and the conditions for its evolution and success, based on the ecology of the cleaner fish Labroides dimidiatus. In some environments, cleaners must learn to serve visitor clients before resident clients, because a visitor leaves if not attended while a resident waits for service. This challenge has been captured in various versions of the ephemeral reward task, which has been proven difficult for a range of cognitively capable species. We show that chaining is the minimal requirement for solving this task in its common simplified laboratory format that involves repeated simultaneous exposure to an ephemeral and permanent food source. Adding ephemeral–ephemeral and permanent–permanent combinations, as cleaners face in the wild, requires individuals to have chunking abilities to solve the task. Importantly, chunking parameters need to be calibrated to ecological conditions in order to produce adaptive decisions. Thus, it is the fine-tuning of this ability, which may be the major target of selection during the evolution of advanced associative learning.

What makes cognition ‘advanced’ is an open and not precisely defined question. In this study, a cognitive model of cleaner fish learning the ephemeral-reward task demonstrates how a critical step in cognitive evolution may be understood as the evolution of chunking and its tuning to fit ecological conditions.

Anterior thalamic NMDA-induced damage impairs extrapolation relying on serial stimulus patterns, in rats

Extrapolation of serial stimulus patterns seems to depend upon identification and application of patterns relating sequences of stimuli stored in memory, thus allowing prediction of pending events never experienced before. There have been proposals that such a "generator of predictions system" would include the subiculum, mammillary bodies, anteroventral thalamus and cingulate cortex (e.g., Gray, 1982). The anteroventral thalamus (AVT) seems to be in a strategic position, both hodologically and experimentally, to allow testing of this hypothesis. This study investigated the effect of NMDA-induced damage to the anteroventral thalamus [part of the anterodorsal (AD) thalamus was also damaged in some animals], following stereotaxic minute topic microinjections, on the ability of male Wistar rats to extrapolate relying on serial stimulus patterns. Corresponding sham-operated controls received phosphate-saline buffer microinjections at the same stereotaxic coordinates. The subjects were trained to run through a straight alleyway along 31 sessions, one session per day, to get rewarded. Each session included four successive trials. Subjects exposed to the monotonic serial pattern received 14, 7, 3, 1 sunflower seeds along trials. Subjects exposed to the non-monotonic serial pattern received 14, 3, 7, 1 sunflower seeds. On the 32nd testing session, a fifth trial, never experienced before, was included immediately after the fourth trial. Sham-operated control subjects exposed to the monotonic serial pattern were expected to exhibit longer running times, since the content of their prediction in the fifth trial should be "less than 1 sunflower seeds". In contrast, control subjects exposed to the non-monotonic serial pattern were expected to exhibit shorter running times, since the content of their prediction would be "more than 1 sunflower seeds". Confirming these predictions, control subjects exposed to the monotonic serial pattern exhibited longer running times as compared to both, their own running times in previous trials within the same session and control subjects exposed to the non-monotonic schedule, thus indicating the occurrence of extrapolation. In contrast, AVT/AD lesioned subjects exposed to the monotonic schedule did not exhibit this increase in running times on the fifth trial, indicating lack of extrapolation. These results indicate that extrapolation relying on serial stimulus patterns is disrupted following extensive NMDA-induced damage to AVT and part of the AD. This represents the first consistent demonstration that the anterior thalamic nuclei are required for extrapolation of serial stimulus patterns and generation of predictions.

Analogy as a Catalyst for Cumulative Cultural Evolution

Analogies, broadly defined, map novel concepts onto familiar concepts, making them essential for perception, reasoning, and communication. We argue that analogy-building served a critical role in the evolution of cumulative culture by allowing humans to learn and transmit complex behavioural sequences that would otherwise be too cognitively demanding or opaque to acquire. The emergence of a protolanguage consisting of simple labels would have provided early humans with the cognitive tools to build explicit analogies and to communicate them to others. This focus on analogy-building can shed new light on the coevolution of cognition and culture and addresses recent calls for better integration of the field of cultural evolution with cognitive science.

Extracting the dynamics of behavior in sensory decision-making experiments

Decision-making strategies evolve during training and can continue to vary even in well-trained animals. However, studies of sensory decision-making tend to characterize behavior in terms of a fixed psychometric function that is fit only after training is complete. Here, we present PsyTrack, a flexible method for inferring the trajectory of sensory decision-making strategies from choice data. We apply PsyTrack to training data from mice, rats, and human subjects learning to perform auditory and visual decision-making tasks. We show that it successfully captures trial-to-trial fluctuations in the weighting of sensory stimuli, bias, and task-irrelevant covariates such as choice and stimulus history. This analysis reveals dramatic differences in learning across mice and rapid adaptation to changes in task statistics. PsyTrack scales easily to large datasets and offers a powerful tool for quantifying time-varying behavior in a wide variety of animals and tasks.

Learning boosts the decoding of sound sequences in rat auditory cortex

Continuous acoustic streams, such as speech signals, can be chunked into segments containing reoccurring patterns (e.g., words). Noninvasive recordings of neural activity in humans suggest that chunking is underpinned by low-frequency cortical entrainment to the segment presentation rate, and modulated by prior segment experience (e.g., words belonging to a familiar language). Interestingly, previous studies suggest that also primates and rodents may be able to chunk acoustic streams. Here, we test whether neural activity in the rat auditory cortex is modulated by previous segment experience. We recorded subdural responses using electrocorticography (ECoG) from the auditory cortex of 11 anesthetized rats. Prior to recording, four rats were trained to detect familiar triplets of acoustic stimuli (artificial syllables), three were passively exposed to the triplets, while another four rats had no training experience. While low-frequency neural activity peaks were observed at the syllable level, no triplet-rate peaks were observed. Notably, in trained rats (but not in passively exposed and naïve rats), familiar triplets could be decoded more accurately than unfamiliar triplets based on neural activity in the auditory cortex. These results suggest that rats process acoustic sequences, and that their cortical activity is modulated by the training experience even under subsequent anesthesia.

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Rats could behaviourally differentiate acoustic stimulus triplets after training.

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Learning relatively increased auditory cortical entrainment to triplets.

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Learning improved decoding of familiar stimuli based on auditory cortical activity.

Nonadjacent dependency processing in monkeys, apes, and humans

The ability to track syntactic relationships between words, particularly over distances ("nonadjacent dependencies"), is a critical faculty underpinning human language, although its evolutionary origins remain poorly understood. While some monkey species are reported to process auditory nonadjacent dependencies, comparative data from apes are missing, complicating inferences regarding shared ancestry. Here, we examined nonadjacent dependency processing in common marmosets, chimpanzees, and humans using "artificial grammars": strings of arbitrary acoustic stimuli composed of adjacent (nonhumans) or nonadjacent (all species) dependencies. Individuals from each species (i) generalized the grammars to novel stimuli and (ii) detected grammatical violations, indicating that they processed the dependencies between constituent elements. Furthermore, there was no difference between marmosets and chimpanzees in their sensitivity to nonadjacent dependencies. These notable similarities between monkeys, apes, and humans indicate that nonadjacent dependency processing, a crucial cognitive facilitator of language, is an ancestral trait that evolved at least ~40 million years before language itself.

Non-adjacent Dependency Learning in Humans and Other Animals

Learning and processing natural language requires the ability to track syntactic relationships between words and phrases in a sentence, which are often separated by intervening material. These nonadjacent dependencies can be studied using artificial grammar learning paradigms and structured sequence processing tasks. These approaches have been used to demonstrate that human adults, infants and some nonhuman animals are able to detect and learn dependencies between nonadjacent elements within a sequence. However, learning nonadjacent dependencies appears to be more cognitively demanding than detecting dependencies between adjacent elements, and only occurs in certain circumstances. In this review, we discuss different types of nonadjacent dependencies in language and in artificial grammar learning experiments, and how these differences might impact learning. We summarize different types of perceptual cues that facilitate learning, by highlighting the relationship between dependent elements bringing them closer together either physically, attentionally, or perceptually. Finally, we review artificial grammar learning experiments in human adults, infants, and nonhuman animals, and discuss how similarities and differences observed across these groups can provide insights into how language is learned across development and how these language-related abilities might have evolved.

Structured Sequence Learning: Animal Abilities, Cognitive Operations, and Language Evolution

Human language is a salient example of a neurocognitive system that is specialized to process complex dependencies between sensory events distributed in time, yet how this system evolved and specialized remains unclear. Artificial Grammar Learning (AGL) studies have generated a wealth of insights into how human adults and infants process different types of sequencing dependencies of varying complexity. The AGL paradigm has also been adopted to examine the sequence processing abilities of nonhuman animals. We critically evaluate this growing literature in species ranging from mammals (primates and rats) to birds (pigeons, songbirds, and parrots) considering also cross-species comparisons. The findings are contrasted with seminal studies in human infants that motivated the work in nonhuman animals. This synopsis identifies advances in knowledge and where uncertainty remains regarding the various strategies that nonhuman animals can adopt for processing sequencing dependencies. The paucity of evidence in the few species studied to date and the need for follow-up experiments indicate that we do not yet understand the limits of animal sequence processing capacities and thereby the evolutionary pattern. This vibrant, yet still budding, field of research carries substantial promise for advancing knowledge on animal abilities, cognitive substrates, and language evolution.

Invertible generalized synchronization: A putative mechanism for implicit learning in neural systems

Regardless of the marked differences between biological and artificial neural systems, one fundamental similarity is that they are essentially dynamical systems that can learn to imitate other dynamical systems whose governing equations are unknown. The brain is able to learn the dynamic nature of the physical world via experience; analogously, artificial neural systems such as reservoir computing networks (RCNs) can learn the long-term behavior of complex dynamical systems from data. Recent work has shown that the mechanism of such learning in RCNs is invertible generalized synchronization (IGS). Yet, whether IGS is also the mechanism of learning in biological systems remains unclear. To shed light on this question, we draw inspiration from features of the human brain to propose a general and biologically feasible learning framework that utilizes IGS. To evaluate the framework's relevance, we construct several distinct neural network models as instantiations of the proposed framework. Regardless of their particularities, these neural network models can consistently learn to imitate other dynamical processes with a biologically feasible adaptation rule that modulates the strength of synapses. Further, we observe and theoretically explain the spontaneous emergence of four distinct phenomena reminiscent of cognitive functions: (i) learning multiple dynamics; (ii) switching among the imitations of multiple dynamical systems, either spontaneously or driven by external cues; (iii) filling-in missing variables from incomplete observations; and (iv) deciphering superimposed input from different dynamical systems. Collectively, our findings support the notion that biological neural networks can learn the dynamic nature of their environment through the mechanism of IGS.

Habit learning in hierarchical cortex-basal ganglia loops

How do the multiple cortico-basal ganglia-thalamo-cortical loops interact? Are they parallel and fully independent or controlled by an arbitrator, or are they hierarchically organized? We introduce here a set of four key concepts, integrated and evaluated by means of a neuro-computational model, that bring together current ideas regarding cortex-basal ganglia interactions in the context of habit learning. According to key concept 1, each loop learns to select an intermediate objective at a different abstraction level, moving from goals in the ventral striatum to motor in the putamen. Key concept 2 proposes that the cortex integrates the basal ganglia selection with environmental information regarding the achieved objective. Key concept 3 claims shortcuts between loops, and key concept 4 predicts that loops compute their own prediction error signal for learning. Computational benefits of the key concepts are demonstrated. Contrasting with former concepts of habit learning, the loops collaborate to select goal-directed actions while training slower shortcuts develops habitual responses.

Engineering a Less Artificial Intelligence

Despite enormous progress in machine learning, artificial neural networks still lag behind brains in their ability to generalize to new situations. Given identical training data, differences in generalization are caused by many defining features of a learning algorithm, such as network architecture and learning rule. Their joint effect, called "inductive bias," determines how well any learning algorithm-or brain-generalizes: robust generalization needs good inductive biases. Artificial networks use rather nonspecific biases and often latch onto patterns that are only informative about the statistics of the training data but may not generalize to different scenarios. Brains, on the other hand, generalize across comparatively drastic changes in the sensory input all the time. We highlight some shortcomings of state-of-the-art learning algorithms compared to biological brains and discuss several ideas about how neuroscience can guide the quest for better inductive biases by providing useful constraints on representations and network architecture.

A Simple, Biologically Plausible Feature Detector for Language Acquisition

Language has a complex grammatical system we still have to understand computationally and biologically. However, some evolutionarily ancient mechanisms have been repurposed for grammar so that we can use insight from other taxa into possible circuit-level mechanisms of grammar. Drawing upon recent evidence for the importance of disinhibitory circuits across taxa and brain regions, I suggest a simple circuit that explains the acquisition of core grammatical rules used in 85% of the world's languages: grammatical rules based on sameness/difference relations. This circuit acts as a sameness detector. “Different” items are suppressed through inhibition, but presenting two “identical” items leads to inhibition of inhibition. The items are thus propagated for further processing. This sameness detector thus acts as a feature detector for a grammatical rule. I suggest that having a set of feature detectors for elementary grammatical rules might make language acquisition feasible based on relatively simple computational mechanisms.

Positional encoding in cotton-top tamarins (Saguinus oedipus)

Strategies used in artificial grammar learning can shed light into the abilities of different species to extract regularities from the environment. In the A(X)ⁿB rule, A and B items are linked, but assigned to different positional categories and separated by distractor items. Open questions are how widespread is the ability to extract positional regularities from A(X)ⁿB patterns, which strategies are used to encode positional regularities and whether individuals exhibit preferences for absolute or relative position encoding. We used visual arrays to investigate whether cotton-top tamarins (Saguinusoedipus) can learn this rule and which strategies they use. After training on a subset of exemplars, two of the tested monkeys successfully generalized to novel combinations. These tamarins discriminated between categories of tokens with different properties (A, B, X) and detected a positional relationship between non-adjacent items even in the presence of novel distractors. The pattern of errors revealed that successful subjects used visual similarity with training stimuli to solve the task and that successful tamarins extracted the relative position of As and Bs rather than their absolute position, similarly to what has been observed in other species. Relative position encoding appears to be favoured in different tasks and taxa. Generalization, though, was incomplete, since we observed a failure with items that during training had always been presented in reinforced arrays, showing the limitations in grasping the underlying positional rule. These results suggest the use of local strategies in the extraction of positional rules in cotton-top tamarins.

Is the consonant bias specifically human? Long-Evans rats encode vowels better than consonants in words

In natural languages, vowels tend to convey structures (syntax, prosody) while consonants are more important lexically. The consonant bias, which is the tendency to rely more on consonants than on vowels to process words, is well attested in human adults and infants after the first year of life. Is the consonant bias based on evolutionarily ancient mechanisms, potentially present in other species? The current study investigated this issue in a species phylogenetically distant from humans: Long-Evans rats. During training, the animals were presented with four natural word-forms (e.g., mano, "hand"). We then compared their responses to novel words carrying either a consonant (pano) or a vowel change (meno). Results show that the animals were less disrupted by consonantal alterations than by vocalic alterations of words. That is, word recognition was more affected by the alteration of a vowel than a consonant. Together with previous findings in very young human infants, this reliance on vocalic information we observe in rats suggests that the emergence of the consonant bias may require a combination of vocal, cognitive and auditory skills that rodents do not seem to possess.

Auditory and Visual Sequence Learning in Humans and Monkeys using an Artificial Grammar Learning Paradigm

Language flexibly supports the human ability to communicate using different sensory modalities, such as writing and reading in the visual modality and speaking and listening in the auditory domain. Although it has been argued that nonhuman primate communication abilities are inherently multisensory, direct behavioural comparisons between human and nonhuman primates are scant. Artificial grammar learning (AGL) tasks and statistical learning experiments can be used to emulate ordering relationships between words in a sentence. However, previous comparative work using such paradigms has primarily investigated sequence learning within a single sensory modality. We used an AGL paradigm to evaluate how humans and macaque monkeys learn and respond to identically structured sequences of either auditory or visual stimuli. In the auditory and visual experiments, we found that both species were sensitive to the ordering relationships between elements in the sequences. Moreover, the humans and monkeys produced largely similar response patterns to the visual and auditory sequences, indicating that the sequences are processed in comparable ways across the sensory modalities. These results provide evidence that human sequence processing abilities stem from an evolutionarily conserved capacity that appears to operate comparably across the sensory modalities in both human and nonhuman primates. The findings set the stage for future neurobiological studies to investigate the multisensory nature of these sequencing operations in nonhuman primates and how they compare to related processes in humans.

Spontaneous Learning of Visual Structures in Domestic Chicks

Simple Summary

Our aim is to investigate the recognition of the structure of multi-element configurations; one mechanism that supports communicative functions in different species. Cognitive mechanisms involved in this ability might not have evolved specifically for communicative use, but derive from other functions. Thus, it is crucial to study these abilities in species that are not vocal learners and with stimuli from other modalities. We know already that domestic chicks can learn the temporal statistical structure of sequences of visual shapes, however their abilities to encode the spatial structure of visual patterns (configurations composed of multiple visual elements presented simultaneously side-by-side) is much less known. Using filial imprinting learning, we showed that chicks spontaneously recognize the structure of their imprinting stimulus, preferring it to one composed of the same elements in different configurations. Moreover, we found that in their affiliative responses chicks give priority to information located at the stimulus edges, a phenomenon that was so far observed only with temporal sequences. This first evidence of a spontaneous edge bias with spatial stimuli further stresses the importance of studying similarities and differences between the processing of linguistic and nonlinguistic stimuli and of stimuli presented in various sensory modalities.

Abstract

Effective communication crucially depends on the ability to produce and recognize structured signals, as apparent in language and birdsong. Although it is not clear to what extent similar syntactic-like abilities can be identified in other animals, recently we reported that domestic chicks can learn abstract visual patterns and the statistical structure defined by a temporal sequence of visual shapes. However, little is known about chicks’ ability to process spatial/positional information from visual configurations. Here, we used filial imprinting as an unsupervised learning mechanism to study spontaneous encoding of the structure of a configuration of different shapes. After being exposed to a triplet of shapes (ABC or CAB), chicks could discriminate those triplets from a permutation of the same shapes in different order (CAB or ABC), revealing a sensitivity to the spatial arrangement of the elements. When tested with a fragment taken from the imprinting triplet that followed the familiar adjacency-relationships (AB or BC) vs. one in which the shapes maintained their position with respect to the stimulus edges (AC), chicks revealed a preference for the configuration with familiar edge elements, showing an edge bias previously found only with temporal sequences.

Artificial Grammar Learning Capabilities in an Abstract Visual Task Match Requirements for Linguistic Syntax

Whether pattern-parsing mechanisms are specific to language or apply across multiple cognitive domains remains unresolved. Formal language theory provides a mathematical framework for classifying pattern-generating rule sets (or "grammars") according to complexity. This framework applies to patterns at any level of complexity, stretching from simple sequences, to highly complex tree-like or net-like structures, to any Turing-computable set of strings. Here, we explored human pattern-processing capabilities in the visual domain by generating abstract visual sequences made up of abstract tiles differing in form and color. We constructed different sets of sequences, using artificial "grammars" (rule sets) at three key complexity levels. Because human linguistic syntax is classed as "mildly context-sensitive," we specifically included a visual grammar at this complexity level. Acquisition of these three grammars was tested in an artificial grammar-learning paradigm: after exposure to a set of well-formed strings, participants were asked to discriminate novel grammatical patterns from non-grammatical patterns. Participants successfully acquired all three grammars after only minutes of exposure, correctly generalizing to novel stimuli and to novel stimulus lengths. A Bayesian analysis excluded multiple alternative hypotheses and shows that the success in rule acquisition applies both at the group level and for most participants analyzed individually. These experimental results demonstrate rapid pattern learning for abstract visual patterns, extending to the mildly context-sensitive level characterizing language. We suggest that a formal equivalence of processing at the mildly context sensitive level in the visual and linguistic domains implies that cognitive mechanisms with the computational power to process linguistic syntax are not specific to the domain of language, but extend to abstract visual patterns with no meaning.

Production of Supra-regular Spatial Sequences by Macaque Monkeys

Understanding and producing embedded sequences in language, music, or mathematics, is a central characteristic of our species. These domains are hypothesized to involve a human-specific competence for supra-regular grammars, which can generate embedded sequences that go beyond the regular sequences engendered by finite-state automata. However, is this capacity truly unique to humans? Using a production task, we show that macaque monkeys can be trained to produce time-symmetrical embedded spatial sequences whose formal description requires supra-regular grammars or, equivalently, a push-down stack automaton. Monkeys spontaneously generalized the learned grammar to novel sequences, including longer ones, and could generate hierarchical sequences formed by an embedding of two levels of abstract rules. Compared to monkeys, however, preschool children learned the grammars much faster using a chunking strategy. While supra-regular grammars are accessible to nonhuman primates through extensive training, human uniqueness may lie in the speed and learning strategy with which they are acquired.

Selection for associative learning of color stimuli reveals correlated evolution of this learning ability across multiple stimuli and rewards

We are only starting to understand how variation in cognitive ability can result from local adaptations to environmental conditions. A major question in this regard is to what extent selection on cognitive ability in a specific context affects that ability in general through correlated evolution. To address this question, we performed artificial selection on visual associative learning in female Nasonia vitripennis wasps. Using appetitive conditioning in which a visual stimulus was offered in association with a host reward, the ability to learn visual associations was enhanced within 10 generations of selection. To test for correlated evolution affecting this form of learning, the ability to readily form learned associations in females was also tested using an olfactory instead of a visual stimulus in the appetitive conditioning. Additionally, we assessed whether the improved associative learning ability was expressed across sexes by color-conditioning males with a mating reward. Both females and males from the selected lines consistently demonstrated an increased associative learning ability compared to the control lines, independent of learning context or conditioned stimulus. No difference in relative volume of brain neuropils was detected between the selected and control lines.

Behavioral evaluation of auditory stream segregation in rats

Perceptual organization of sound sequences into separate sound sources or streams is called auditory stream segregation. Neural substrates for this process in both the spectral and temporal domains remain to be elucidated. Despite abundant knowledge about their auditory physiology, behavioral evidence for auditory streaming in rodents is still limited. We provided behavioral evidence for auditory streaming in the go/no-go discrimination task, but not in the two-alternative choice task. In the go/no-go discrimination phase, rats were able to discriminate different rhythms corresponding to segregated or integrated tone sequences in both short inter-tone interval (ITI) and long ITI conditions. Nevertheless, performance was poorer in the long ITI group. In probe testing, which assessed the ability to discriminate one of the segregated tone sequences from ABA- tone sequences, the detection rate increased with the difference in frequency (ΔF) for short (100 ms), but not long (200 ms) ITIs. Our results indicate that auditory streaming in rats on both the spectral and temporal features in the ABA- tone paradigm is qualitatively analogous to that observed in human psychophysics studies. This suggests that rodents are a valuable model for investigating the neural substrates of auditory streaming.

Constraints on Statistical Learning Across Species

Both human and nonhuman organisms are sensitive to statistical regularities in sensory inputs that support functions including communication, visual processing, and sequence learning. One of the issues faced by comparative research in this field is the lack of a comprehensive theory to explain the relevance of statistical learning across distinct ecological niches. In the current review we interpret cross-species research on statistical learning based on the perceptual and cognitive mechanisms that characterize the human and nonhuman models under investigation. Considering statistical learning as an essential part of the cognitive architecture of an animal will help to uncover the potential ecological functions of this powerful learning process.

Assessing the uniqueness of language: Animal grammatical abilities take center stage

Questions related to the uniqueness of language can only be addressed properly by referring to sound knowledge of the relevant cognitive abilities of nonhuman animals. A key question concerns the nature and extent of animal rule-learning abilities. I discuss two approaches used to assess these abilities. One is comparing the structures of animal vocalizations to linguistic ones, and another is addressing the grammatical rule- and pattern-learning abilities of animals through experiments using artificial grammars. Neither of these approaches has so far provided unambiguous evidence of advanced animal abilities. However, when we consider how animal vocalizations are analyzed, the types of stimuli and tasks that are used in artificial grammar learning experiments, the limited number of species examined, and the groups to which these belong, I argue that the currently available evidence is insufficient to arrive at firm conclusions concerning the limitations of animal grammatical abilities. As a consequence, the gap between human linguistic rule-learning abilities and those of nonhuman animals may be smaller and less clear than is currently assumed. This means that it is still an open question whether a difference in the rule-learning and rule abstraction abilities between animals and humans played the key role in the evolution of language.

Memory for stimulus sequences: a divide between humans and other animals?

Humans stand out among animals for their unique capacities in domains such as language, culture and imitation, yet it has been difficult to identify cognitive elements that are specifically human. Most research has focused on how information is processed after it is acquired, e.g. in problem solving or 'insight' tasks, but we may also look for species differences in the initial acquisition and coding of information. Here, we show that non-human species have only a limited capacity to discriminate ordered sequences of stimuli. Collating data from 108 experiments on stimulus sequence discrimination (1540 data points from 14 bird and mammal species), we demonstrate pervasive and systematic errors, such as confusing a red-green sequence of lights with green-red and green-green sequences. These errors can persist after thousands of learning trials in tasks that humans learn to near perfection within tens of trials. To elucidate the causes of such poor performance, we formulate and test a mathematical model of non-human sequence discrimination, assuming that animals represent sequences as unstructured collections of memory traces. This representation carries only approximate information about stimulus duration, recency, order and frequency, yet our model predicts non-human performance with a 5.9% mean absolute error across 68 datasets. Because human-level cognition requires more accurate encoding of sequential information than afforded by memory traces, we conclude that improved coding of sequential information is a key cognitive element that may set humans apart from other animals.

Sequence learning modulates neural responses and oscillatory coupling in human and monkey auditory cortex

Learning complex ordering relationships between sensory events in a sequence is fundamental for animal perception and human communication. While it is known that rhythmic sensory events can entrain brain oscillations at different frequencies, how learning and prior experience with sequencing relationships affect neocortical oscillations and neuronal responses is poorly understood. We used an implicit sequence learning paradigm (an "artificial grammar") in which humans and monkeys were exposed to sequences of nonsense words with regularities in the ordering relationships between the words. We then recorded neural responses directly from the auditory cortex in both species in response to novel legal sequences or ones violating specific ordering relationships. Neural oscillations in both monkeys and humans in response to the nonsense word sequences show strikingly similar hierarchically nested low-frequency phase and high-gamma amplitude coupling, establishing this form of oscillatory coupling-previously associated with speech processing in the human auditory cortex-as an evolutionarily conserved biological process. Moreover, learned ordering relationships modulate the observed form of neural oscillatory coupling in both species, with temporally distinct neural oscillatory effects that appear to coordinate neuronal responses in the monkeys. This study identifies the conserved auditory cortical neural signatures involved in monitoring learned sequencing operations, evident as modulations of transient coupling and neuronal responses to temporally structured sensory input.

Artificial grammar learning in tamarins (Saguinus oedipus) in varying stimulus contexts

The human ability to detect regularities in sound sequences is a fundamental substrate of our language faculty. However, is this an ability exclusive to human language processing, or have we usurped a more general learning mechanism for this purpose, one shared with other species? The current study is an attempt to replicate and extend Hauser, Weiss, and Marcus's (2002) retracted study (2010) of artificial grammar learning in tamarins to determine if tamarins can detect an underlying grammatical structure in a pattern of sounds. Human language consonant-vowel (CV) combinations from Hauser et al.'s original study, newly created tone sequences, and newly created monkey vocalizations made into sequences were used to familiarize tamarins to an AAB or ABB pattern. Tests of novel sounds in each condition were presented that either were consistent with the familiarized pattern or were different from it. Longer looking times toward the sound source (an audio speaker with a specific location in the auditory field) indicated recognition of novelty. Tamarins looked toward the speaker significantly longer with inconsistent human language CV sequences and with inconsistent tone sequences but not when an inconsistent monkey vocalization was presented. Moreover, tamarins showed differential rates of habituation to the different types of sound patterns, with more robust habituation to CV sequences and tone sequences than to monkey call sequences. The implications of these findings for the generality of learning mechanisms for linguistic and nonlinguistic input across species and the importance of testing across various stimuli are discussed. (PsycINFO Database Record

Conserved Sequence Processing in Primate Frontal Cortex

An important aspect of animal perception and cognition is learning to recognize relationships between environmental events that predict others in time, a form of relational knowledge that can be assessed using sequence-learning paradigms. Humans are exquisitely sensitive to sequencing relationships, and their combinatorial capacities, most saliently in the domain of language, are unparalleled. Recent comparative research in human and nonhuman primates has obtained behavioral and neuroimaging evidence for evolutionarily conserved substrates involved in sequence processing. The findings carry implications for the origins of domain-general capacities underlying core language functions in humans. Here, we synthesize this research into a 'ventrodorsal gradient' model, where frontal cortex engagement along this axis depends on sequencing complexity, mapping onto the sequencing capacities of different species.

Statistical learning: a powerful mechanism that operates by mere exposure

How do infants learn so rapidly and with little apparent effort? In 1996, Saffran, Aslin, and Newport reported that 8-month-old human infants could learn the underlying temporal structure of a stream of speech syllables after only 2 min of passive listening. This demonstration of what was called statistical learning, involving no instruction, reinforcement, or feedback, led to dozens of confirmations of this powerful mechanism of implicit learning in a variety of modalities, domains, and species. These findings reveal that infants are not nearly as dependent on explicit forms of instruction as we might have assumed from studies of learning in which children or adults are taught facts such as math or problem solving skills. Instead, at least in some domains, infants soak up the information around them by mere exposure. Learning and development in these domains thus appear to occur automatically and with little active involvement by an instructor (parent or teacher). The details of this statistical learning mechanism are discussed, including how exposure to specific types of information can, under some circumstances, generalize to never-before-observed information, thereby enabling transfer of learning. WIREs Cogn Sci 2017, 8:e1373. doi: 10.1002/wcs.1373 For further resources related to this article, please visit the WIREs website.

Serial pattern learning in pigeons: Rule-based or associative?

Extensive research has documented evidence for rule learning in sequential behavior tasks in both rats and humans. We adapted the 2-choice serial multiple choice (SMC) task developed for use with rats (Fountain & Rowan, 1995a) to study sequence behavior in pigeons. Pigeons were presented with 8 disks arranged in a circular array on a touchscreen, and pecking to an illuminated disk could lead to reward. Correct responding consisted of serial patterns involving "run" chunks of 3 elements (123 234, etc.). Some pigeons experienced a violation of the chunk rule in the final chunk. Unlike rats, pigeons made fewer errors on violation chunks than run chunks, suggesting the use of low-level cues to guide choices. Removal of low-level cues and increasing the number of simultaneously illuminated disks to an 8-choice SMC task resulted in more errors on the violation chunk. Pigeons were able to use the rule when the array of disks was contracted or expanded, and when chunk length was extended to 4 and 5 elements, but not when disks were removed from or added to the array. Pigeons were also able to abstract structure from a "trill" pattern (121 232 etc.), as shown by high error rates on a violation trial. These results suggest that pigeons, like rats and humans, can abstract sequence structure, but do so primarily in the absence of specific low-level feature-based information. (PsycINFO Database Record

Learning multiple rules simultaneously: Affixes are more salient than reduplications

Language learners encounter numerous opportunities to learn regularities, but need to decide which of these regularities to learn, because some are not productive in their native language. Here, we present an account of rule learning based on perceptual and memory primitives (Endress, Dehaene-Lambertz, & Mehler, Cognition, 105(3), 577–614, 2007; Endress, Nespor, & Mehler, Trends in Cognitive Sciences, 13(8), 348–353, 2009), suggesting that learners preferentially learn regularities that are more salient to them, and that the pattern of salience reflects the frequency of language features across languages. We contrast this view with previous artificial grammar learning research, which suggests that infants “choose” the regularities they learn based on rational, Bayesian criteria (Frank & Tenenbaum, Cognition, 120(3), 360–371, 2013; Gerken, Cognition, 98(3)B67–B74, 2006, Cognition, 115(2), 362–366, 2010). In our experiments, adult participants listened to syllable strings starting with a syllable reduplication and always ending with the same “affix” syllable, or to syllable strings starting with this “affix” syllable and ending with the “reduplication”. Both affixation and reduplication are frequently used for morphological marking across languages. We find three crucial results. First, participants learned both regularities simultaneously. Second, affixation regularities seemed easier to learn than reduplication regularities. Third, regularities in sequence offsets were easier to learn than regularities at sequence onsets. We show that these results are inconsistent with previous Bayesian rule learning models, but mesh well with the perceptual or memory primitives view. Further, we show that the pattern of salience revealed in our experiments reflects the distribution of regularities across languages. Ease of acquisition might thus be one determinant of the frequency of regularities across languages.

A Unified Theoretical Framework for Cognitive Sequencing

The capacity to sequence information is central to human performance. Sequencing ability forms the foundation stone for higher order cognition related to language and goal-directed planning. Information related to the order of items, their timing, chunking and hierarchical organization are important aspects in sequencing. Past research on sequencing has emphasized two distinct and independent dichotomies: implicit vs. explicit and goal-directed vs. habits. We propose a theoretical framework unifying these two streams. Our proposal relies on brain's ability to implicitly extract statistical regularities from the stream of stimuli and with attentional engagement organizing sequences explicitly and hierarchically. Similarly, sequences that need to be assembled purposively to accomplish a goal require engagement of attentional processes. With repetition, these goal-directed plans become habits with concomitant disengagement of attention. Thus, attention and awareness play a crucial role in the implicit-to-explicit transition as well as in how goal-directed plans become automatic habits. Cortico-subcortical loops basal ganglia-frontal cortex and hippocampus-frontal cortex loops mediate the transition process. We show how the computational principles of model-free and model-based learning paradigms, along with a pivotal role for attention and awareness, offer a unifying framework for these two dichotomies. Based on this framework, we make testable predictions related to the potential influence of response-to-stimulus interval (RSI) on developing awareness in implicit learning tasks.

The power of associative learning and the ontogeny of optimal behaviour

Behaving efficiently (optimally or near-optimally) is central to animals' adaptation to their environment. Much evolutionary biology assumes, implicitly or explicitly, that optimal behavioural strategies are genetically inherited, yet the behaviour of many animals depends crucially on learning. The question of how learning contributes to optimal behaviour is largely open. Here we propose an associative learning model that can learn optimal behaviour in a wide variety of ecologically relevant circumstances. The model learns through chaining, a term introduced by Skinner to indicate learning of behaviour sequences by linking together shorter sequences or single behaviours. Our model formalizes the concept of conditioned reinforcement (the learning process that underlies chaining) and is closely related to optimization algorithms from machine learning. Our analysis dispels the common belief that associative learning is too limited to produce 'intelligent' behaviour such as tool use, social learning, self-control or expectations of the future. Furthermore, the model readily accounts for both instinctual and learned aspects of behaviour, clarifying how genetic evolution and individual learning complement each other, and bridging a long-standing divide between ethology and psychology. We conclude that associative learning, supported by genetic predispositions and including the oft-neglected phenomenon of conditioned reinforcement, may suffice to explain the ontogeny of optimal behaviour in most, if not all, non-human animals. Our results establish associative learning as a more powerful optimizing mechanism than acknowledged by current opinion.

Artificial grammar learning in zebra finches and human adults: XYX versus XXY

Abstracting syntactic rules is critical to human language learning. It is debated whether this ability, already present in young infants, is human- and language specific or can also be found in non-human animals, indicating it may arise from more general cognitive mechanisms. Current studies are often ambiguous and few have directly compared rule learning by humans and non-human animals. In a series of discrimination experiments, we presented zebra finches and human adults with comparable training and tests with the same artificial stimuli consisting of XYX and XXY structures, in which X and Y were zebra finch song elements. Zebra finches readily discriminated the training stimuli. Some birds also discriminated novel stimuli when these were composed of familiar element types, but none of the birds generalized the discrimination to novel element types. We conclude that zebra finches show evidence of simple rule abstraction related to positional learning, suggesting stimulus-bound generalization, but found no evidence for a more abstract rule generalization. This differed from the human adults, who categorized novel stimuli consisting of novel element types into different groups according to their structure. The limited abilities for rule abstraction in zebra finches may indicate what the precursors of more complex abstraction as found in humans may have been like.

Maze learning by a hybrid brain-computer system

The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity. Here, we show how rule operations conducted by computing components contribute to a novel hybrid brain-computer system, i.e., ratbots, exhibit superior learning abilities in a maze learning task, even when their vision and whisker sensation were blocked. We anticipate that our study will encourage other researchers to investigate combinations of various rule operations and other artificial intelligence algorithms with the learning and memory processes of organic brains to develop more powerful cyborg intelligence systems. Our results potentially have profound implications for a variety of applications in intelligent systems and neural rehabilitation.

Developmental Trajectory of Rule Detection in Four- to Six-Year-Old Children

Children younger than three years old are able to detect hidden rules in numerical sequences, and this ability matches that of adults by age seven. However, the developmental trajectory of this ability during the ages of four to six remains unknown. The present study adopted a modified Brixton task to address this issue. In this task, children were presented with sequences of moving circles and were asked to predict which circle would next turn blue based on hidden rules that were either simple (e.g. + 2) or complex (e.g. + 2 – 1). Results suggested that (a) four-year-olds were only able to detect comparably few simple rules, whereas children older than 4.5 years were able to successfully detect most of the simple rules hidden in number sequences; (b) although all children performed significantly poorer when attempting to identify complex rules as compared with simple rules, rule detection (RD) ability improved rapidly with age, and children older than five were able to identify most complex rules. These findings extended previous work on rule learning by revealing the developmental trajectory of RD among preschoolers.

Budgerigars and zebra finches differ in how they generalize in an artificial grammar learning experiment

The ability to abstract a regularity that underlies strings of sounds is a core mechanism of the language faculty but might not be specific to language learning or even to humans. It is unclear whether and to what extent nonhuman animals possess the ability to abstract regularities defining the relation among arbitrary auditory items in a string and to generalize this abstraction to strings of acoustically novel items. In this study we tested these abilities in a songbird (zebra finch) and a parrot species (budgerigar). Subjects were trained in a go/no-go design to discriminate between two sets of sound strings arranged in an XYX or an XXY structure. After this discrimination was acquired, each subject was tested with test strings that were structurally identical to the training strings but consisted of either new combinations of known elements or of novel elements belonging to other element categories. Both species learned to discriminate between the two stimulus sets. However, their responses to the test strings were strikingly different. Zebra finches categorized test stimuli with previously heard elements by the ordinal position that these elements occupied in the training strings, independent of string structure. In contrast, the budgerigars categorized both novel combinations of familiar elements as well as strings consisting of novel element types by their underlying structure. They thus abstracted the relation among items in the XYX and XXY structures, an ability similar to that shown by human infants and indicating a level of abstraction comparable to analogical reasoning.

Generalization of visual regularities in newly hatched chicks (Gallus gallus)

Evidence of learning and generalization of visual regularities in a newborn organism is provided in the present research. Domestic chicks have been trained to discriminate visual triplets of simultaneously presented shapes, implementing AAB versus ABA (Experiment 1), AAB versus ABB and AAB versus BAA (Experiment 2). Chicks distinguished pattern-following and pattern-violating novel test triplets in all comparisons, showing no preference for repetition-based patterns. The animals generalized to novel instances even when the patterns compared were not discriminable by the presence or absence of reduplicated elements or by symmetry (e.g., AAB vs. ABB). These findings represent the first evidence of learning and generalization of regularities at the onset of life in an animal model, revealing intriguing differences with respect to human newborns and infants. Extensive prior experience seems to be unnecessary to drive the process, suggesting that chicks are predisposed to detect patterns characterizing the visual world.