We argue that atomistic learning?learning that requires training only on a novel item to be learned?is problematic for networks in which every weight is available for change in every learning situation. This is potentially significant because atomistic learning appears to be commonplace in humans and most non-human animals. We briefly review various proposed fixes, concluding that the most promising strategy to date involves training on pseudo-patterns along with novel items, a form of learning that is (...) not strictly atomistic, but which looks very much like it ?from the outside? (shrink)

The aberrations of a gene can influence it and the functions of its neighbour genes in gene interaction network, leading to the development of carcinogenesis of normal cells. In consideration of gene interaction network as a complex network, previous studies have made efforts on the driver attribute filling of genes via network properties of nodes and network propagation of mutations. However, there are still obstacles from problems of small size of cancer samples and the existence of drivers without property of (...) network neighbours, limiting the discovery of cancer driver genes. To address these obstacles, we propose an efficient modularity subspace based concept learning model. Our model can overcome the curse of dimensionality due to small samples via dimension reduction in the task of attribute concept learning and explore the features of genes through modularity subspace beyond the network neighbours. The evaluation analysis also demonstrates the superiority of our model in the task of driver attribute filling on two gene interaction networks. Generally, our model shows a promising prospect in the application of interaction network analysis of tumorigenesis. (shrink)

Cultural evolutionists typically emphasize the informational aspect of social transmission, that of the learning, stabilizing, and transformation of mental representations along cultural lineages. Social transmission also depends on the production of public displays such as utterances, behaviors, and artifacts, as these displays are what social learners learn from. However, the generative processes involved in the production of public displays are usually abstracted away in both theoretical assessments and formal models. The aim of this paper is to complement the informational (...) view with a generative dimension, emphasizing how the production of public displays both enable and constrain the production of modular cultural recipes through the process of innovation by recombination. In order to avoid a circular understanding of cultural recombination and cultural modularity, we need to take seriously the nature and structure of the generative processes involved in the maintenance of cultural traditions. A preliminary analysis of what recombination and modularity consist of is offered. It is shown how the study of recombination and modularity depends on a finer understanding of the generative processes involved in the production phase of social transmission. Finally, it is argued that the recombination process depends on the inventive production of an interface between modules and the complex recipes in which they figure, and that such interfaces are the direct result of the generative processes involved in the production of these recipes. The analysis is supported by the case study of the transition from the Oldowan to the Early Acheulean flake detachment techniques. (shrink)

A fundamental task for any prospective cognitive architecture is information control: routing information to the relevant mechanisms to support a variety of tasks. Jerry Fodor has argued that the Massive Modularity Hypothesis cannot account for flexible information control due to its architectural commitments and its reliance on heuristic information processing. I argue instead that the real trouble lies in its commitment to nativism—recent massive modularity models, despite incorporating mechanisms for learning and self-organization, still cannot learn to control information flexibly (...) enough. After defending this position, I close the paper by discussing wider implications for control and cognitive architecture. (shrink)

Modularity in the human brain remains a controversial issue, with disagreement over the nature of the modules that exist, and why, when, and how they emerge. It is a natural assumption that modularity offers some form of computational advantage, and hence evolution by natural selection has translated those advantages into the kind of modular neural structures familiar to cognitive scientists. However, simulations of the evolution of simplified neural systems have shown that, in many cases, it is actually non-modular (...) architectures that are most efficient. In this paper, the relevant issues are discussed and a series of simulations are presented that reveal crucial dependencies on the details of the learning algorithms and tasks that are being modelled, and the importance of taking into account known physical brain constraints, such as the degree of neural connectivity. A pattern is established which provides one explanation of why modularity should emerge reliably across a range of neural processing tasks. (shrink)

In this dissertation I argue for a wider and more robust notion of the modularity of mind thesis. The developmental disorder of autism is the prime analytic tool for developing this approach. I argue that a variety of other approaches are deeply flawed in that they cannot account for the autistic spectrum disorder. I mean by this the autistic profile of deficits such as the lack of social interaction and the avoidance of social contact. I begin with Fodorian modularity. I (...) argue that autism presents us with a case that threatens the division Fodor has between modules and central systems. The autistic disorder exemplifies an area of higher cognition that has many of the properties commonly associated with modular processing. Since Fodor cannot opt for a modular account of theory of mind it must be that his account of central systems is incorrect. I next argue that Baron-Cohen's amended modular architecture cannot explain autism since the autistic deficit cannot be due to a defective module for processing intentional action. Furthermore, his use of modularity threatens to make his view of cognition incoherent. Finally I take up Gopnik and Meltzoff's approach that eschews any type of modularity and instead posits a general learning mechanism. If autism, as they claim, were a general theory-building problem, then one should expect to see other behavioral deficits in other areas of autistic cognition. We do not. I then offer an alternative version of modularity inspired by Karmiloff-Smith . It gives us advantages. On Karmiloff-Smith's account we would expect the autistic deficit to have more perceptually basic components and recent research is bearing this out. Progressive modularity also provides us with a framework in which to understand the ways autistic persons understand the social world. This approach also seeks to unify the cognitive work being done on development with burgeoning work on development in neuroscience. (shrink)

Previous neural network models of concept learning were mainly implemented with supervised learning schemes. However, studies of human conceptual memory have shown that concepts may be learned without a teacher who provides the category name to associate with exemplars. A modular neural network architecture that realizes concept acquisition through two functionally distinct operations, categorizing and naming, is proposed as an alternative. An unsupervised algorithm realizes the categorizing module by constructing representations of categories compatible with prototype theory. The (...) naming module associates category names to the output of the categorizing module in a supervised mode. In such a modular architecture, the interface between the modules can be conceived of as an “information relay” that encodes, constrains, and propagates important information. Five experiments were conducted to analyze the relationships among internal conceptual codes and simple conceptual and lexical development. The first two experiments show a prototype effect and illustrate some basic characteristics of the system. The third experiment presents a bottom‐up model of the narrowing down of children's early lexical categories that honors mutual exclusivity. The fourth experiment introduces top‐down constraints on conceptual coding. The fifth experiment exhibits how hierarchical relationships between concepts are learned by the architecture, and also demonstrates how a spectrum of conceptual expertise may gradually emerge as a consequence of experiencing more with certain categories than with others. (shrink)

Beyond modularityattempts a synthesis of Fodor's anticonstructivist nativism and Piaget's antinativist constructivism. Contra Fodor, I argue that: (1) the study of cognitive development is essential to cognitive science, (2) the module/central processing dichotomy is too rigid, and (3) the mind does not begin with prespecified modules; rather, development involves a gradual process of “modularization.” Contra Piaget, I argue that: (1) development rarely involves stagelike domain-general change and (2) domainspecific predispositions give development a small but significant kickstart by focusing the infant's (...) attention on proprietary inputs. Development does not stop at efficient learning. A fundamental aspect of human development (“representational redescription”) is the hypothesized process by which information that isina cognitive system becomes progressively explicit knowledgetothat system. Development thus involves two complementary processes of progressive modularization and progressive “explicitation.” Empirical findings on the child as linguist, physicist, mathematician, psychologist, and notator are discussed in support of the theoretical framework. Each chapter concentrates first on the initial state of the infant mind/brain and on subsequent domain-specific learning in infancy and early childhood. It then goes on to explore data on older children's problem solving and theory building, with particular focus on evolving cognitive flexibility. Emphasis is placed throughout on the status of representations underlying different capacities and on the multiple levels at which knowledge is stored and accessible. Finally, consideration is given to the need for more formal developmental models, and a comparison is made between representational redescription and connectionist simulations of development. In conclusion, I consider what is special about human cognition by speculating on the status of representations underlying the structure of behavior in other species. (shrink)

An introduction to the notion of modularity of mind and an argument as to why only formal semantic theories are compatible with the claim that semantic comprehension is the product of a modular system. This chapter also looks at some initial challenges to formal semantics stemming from the apparent place of pragmatic reasoning in our grasp of meaning. These include arguments concerning the nature of speech acts, the analysis of implicatures, word learning, and ambiguity.

Principles of constructivism are used here to explore how organisms develop tools, subagents, scaffolds, signs, and adaptations. Here I discuss reasons why organisms have composite nature and include diverse subagents that interact in partially cooperating and partially conflicting ways. Such modularity is necessary for efficient and robust functionality, including mutual construction and adaptability at various time scales. Subagents interact via material and semiotic relations, some of which force or prescribe actions of partners. Other interactions, which I call “guiding”, do not (...) have immediate effects and do not disrupt the evolution and learning capacity of partner agents. However, they modify the extent of learning and evolutionary possibilities of partners via establishment of scaffolds and constraints. As a result, subagents construct reciprocal scaffolding for each other to rebalance their communal evolution and learning. As an example, I discuss guiding interactions between the body and mind of animals, where the pain system adjusts mind-based learning to the physical and physiological constraints of the body. Reciprocal effects of mind and behaviors on the development and evolution of the body includes the effects of Lamarck and Baldwin. (shrink)

While no one seems to believe that business schools or their faculties bear entire responsibility for the ethical decision-making processes of their students, these same institutions do have some burden of accountability for educating students surrounding these skills. To that end, the standards promulgated by the Association to Advance Collegiate School of Business, their global accrediting body, require that students learn ethics as part of a business degree. However, since the AACSB does not require the inclusion of a specific course (...) to achieve this objective, it may be satisfied by establishing a stand-alone course in ethical decision-making, by integrating ethical decision-making into the existing curricula, by some combination of the two strategies, or through some alternative mechanism. Notwithstanding the choice of delivery process, though, the institution must ensure that it is able to demonstrate the students' achievement of learning with regard to ethics, a bar that was raised, or arguably simply modified, in 2003. With learning objectives designed precisely to measure the student delta based on content, process and engagement in a particular class, those programs that have opted for stand-alone ethics courses may be more prepared to respond to assessment-related inquiries regarding their programs or satisfaction of the standards. The relevance of the AACSB standards modification to the current efforts at ethics integration in business programs is instead a re-examination of how to create a program of integration that is designed to ensure the most effective learning results possible, while responding to the challenges presented by the integrated approach. The purpose of this article is to explore some of those challenges that may be somewhat universal to business school programs implementing the integrated approach, and to share one large university's response to those challenges, along with lessons learned. (shrink)

This paper proposes a learning-efficiency explanation of modular structure in language. An optimal grammar arises as the solution to the problem of learning a language from a minimal number of observations of instances of the use of the language. Agents face symmetry constraints that limit their ability to make a priori distinctions among symbols used in the language and among objects (interpreted as facts, events, speaker’s intentions) that are to be represented by messages in the language. It (...) is shown that if it is commonly known that the object space is modular and messages are strings, then modularity of the language is sufficient and (essentially) necessary for learning efficiency. Journal of Economic Literature Classification Number: C72. (shrink)

Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which (...) they are presumptuous. After elaborating this moral concern, I explore the possibility that carefully procuring the training data for image recognition systems could ensure that the systems avoid the problem. The lesson of this paper extends beyond just the particular case of image recognition systems and the challenge of responsibly identifying a person’s intentions. Reflection on this particular case demonstrates the importance (as well as the difficulty) of evaluating machine learning systems and their training data from the standpoint of moral considerations that are not encompassed by ordinary assessments of predictive accuracy. (shrink)

Modularity theorists have challenged that there are, or could be, general learning mechanisms that explain theory-of-mind development. In response, supporters of the ‘scientific theory-theory’ account of theory-of-mind development have appealed to children's use of auxiliary hypotheses and probabilistic causal modeling. This article argues that these general learning mechanisms are not sufficient to meet the modularist's challenge. The article then explores an alternative domain-general learning mechanism by proposing that children grasp the concept belief through the progressive alignment of (...) relational structure that occurs as a result of structural-comparison. The article also explores the implications of the proposed account for Fodor's puzzle of conceptual learning. (shrink)

We argue that the core social being system is unlike other core systems in that it participates in frequent, widespread learning. As a result, the social being system is less constant throughout the lifespan and less informationally encapsulated than other core systems. This learning supports the development of the precursors of bias, but also provides avenues for preempting it.

Research on learning, the structure of attained knowledge, and the use of this competence in performance has repeatedly returned to longstanding proposals about how to better understand proficient use of knowledge and how humans acquire it. The following article takes up an exchange between Chiappe & Gardner and Barrett & Kurzban on the concept of modularity, one of these proposals. Despite the disagreements expressed, a careful reading of the contributions shows that they also left us with lines of discussion (...) that will eventually sort out the relevant hypotheses and integrate findings for future research. These lines of work will contribute to a clearer understanding of an updated version of the modularity hypothesis that is also compatible with evolutionary science perspectives on learning. How might the categories of domain-specific and domain-general correspond to the distinction between competence and performance and to that of narrow faculty and broad faculty? (shrink)

Education has always occupied a contradictory position in society, expected to ensure compliance and continuity and yet to encourage critique and renewal. Since the early 1980s, however, successive UK governments have directly mobilised education, and higher education in particular, as an ideological tool in the task of embedding neo-liberalism as ‘common sense’. Modularisation has been in the vanguard, first in the universities, more latterly at secondary level. The effect has been disastrous: here as elsewhere, choice has become depressingly fetishised; knowledge, (...) and with it learning, have been fragmented and commodified; academics, like others, have been de-professionalised; and students, like the rest of us, have been transformed into clients or customers. The details of the history and impact of all this are too close to readers’ experience to require elaboration here: its significance, however, has been too little considered. In particular, the question of how we allowed it to come about is one worth asking – if only so that we can learn from our complicity. Since, however, that judgement depends on the negative view of modularisation just outlined, I start by giving a summary argument for such an evaluation (section 1). I then offer some remarks about the specifically ideological character of modularisation (section 2) as an introduction to some initial reflections on our complicity in this aspect of the neo-liberals’ efforts to make universities safe in their attempt to ensure that the future belongs to them (section 3). Finally I offer a brief suggestion about what we might yet do to overcome the disaster (section 4). (shrink)

Some regularities enjoy only an attenuated existence in a body of training data. These are regularities whose statistical visibility depends on some systematic recoding of the data. The space of possible recodings is, however, infinitely large – it is the space of applicable Turing machines. As a result, mappings that pivot on such attenuated regularities cannot, in general, be found by brute-force search. The class of problems that present such mappings we call the class of “type-2 problems.” Type-1 problems, by (...) contrast, present tractable problems of search insofar as the relevant regularities can be found by sampling the input data as originally coded. Type-2 problems, we suggest, present neither rare nor pathological cases. They are rife in biologically realistic settings and in domains ranging from simple animat behaviors to language acquisition. Not only are such problems rife – they are standardly solved! This presents a puzzle. How, given the statistical intractability of these type-2 cases, does nature turn the trick? One answer, which we do not pursue, is to suppose that evolution gifts us with exactly the right set of recoding biases so as to reduce specific type-2 problems to type-1 mappings. Such a heavy-duty nativism is no doubt sometimes plausible. But we believe there are other, more general mechanisms also at work. Such mechanisms provide general strategies for managing problems of type-2 complexity. Several such mechanisms are investigated. At the heart of each is a fundamental ploy – namely, the maximal exploitation of states of representation already achieved by prior, simpler learning so as to reduce the amount of subsequent computational search. Such exploitation both characterizes and helps make unitary sense of a diverse range of mechanisms. These include simple incremental learning, modular connectionism, and the developmental hypothesis of “representational redescription”. In addition, the most distinctive features of human cognition – language and culture – may themselves be viewed as adaptations enabling this representation/computation trade-off to be pursued on an even grander scale. (shrink)

This essay explores the universal cognitive bases of biological taxonomy and taxonomic inference using cross-cultural experimental work with urbanized Americans and forest-dwelling Maya Indians. A universal, essentialist appreciation of generic species appears as the causal foundation for the taxonomic arrangement of biodiversity, and for inference about the distribution of causally-related properties that underlie biodiversity. Universal folkbiological taxonomy is domain-specific: its structure does not spontaneously or invariably arise in other cognitive domains, like substances, artifacts or persons. It is plausibly an innately-determined (...) evolutionary adaptation to relevant and recurrent aspects of ancestral hominid environments, such as the need to recognize, locate, react to, and profit from many ambient species. Folkbiological concepts are special players in cultural evolution, whose native stability attaches to more variable and difficult-to-learn representational forms, thus enhancing the latter's prospects for regularity and recurrence in transmission within and across cultures. This includes knowledge that cumulatively enriches (folk expertise), overrides (religious belief) or otherwise transcends (science) the commonsense ontology prescribed by folkbiology. Finally, the studies summarized here indicate that results gathered from “standard populations” in regard to biological categorization and reasoning more often than not fail to generalize in straightforward ways to humanity at large. This suggests the need for much more serious attention to cross-cultural research on basic cognitive processes. (shrink)

Evolutionary developmental biology (“evo-devo”) may provide insights and new methods for studies of cognition and cultural evolution. For example, I propose using cultural selection and individual learning to examine constraints on cultural evolution. Modularity, the idea that traits vary independently, can facilitate evolution (increase “evolvability”), because evolution can act on one trait without disrupting another. I explore links between cognitive modularity, evolutionary modularity, and cultural evolvability. (Published Online November 9 2006).

Standard models of concept learning generally focus on deriving statistical properties of a category based on data (i.e., category members and the features that describe them) but fail to give appropriate weight to the contact between people's intuitive theories and these data. Two experiments explored the role of people's prior knowledge or intuitive theories on category learning by manipulating the labels associated with the category. Learning differed dramatically when categories of children's drawings were meaningfully labeled (e.g., “done (...) by creative children”) compared to when they were labeled in a neutral manner. When categories are meaningfully labeled, people bring intuitive theories to the learning context. Learning then involves a process in which people search for evidence in the data that supports abstract features or hypotheses that have been activated by the intuitive theories. In contrast, when categories are labeled in a neutral manner, people search for simple features that distinguish one category from another. Importantly, the final study suggests that learning involves an interaction of people's intuitive theories with data, in which theories and data mutually influence each other. The results strongly suggest that straight‐forward, relatively modular ways of incorporating prior knowledge into models of category learning are inadequate. More telling, the results suggest that standard models may have fundamental limitations. We outline a speculative model of learning in which the interaction of theory and data is tightly coupled. The article concludes by comparing the results to recent artificial intelligence systems that use prior knowledge during learning. (shrink)

We argue that existing learning algorithms are often poorly equipped to solve problems involving a certain type of important and widespread regularity that we call “type-2 regularity.” The solution in these cases is to trade achieved representation against computational search. We investigate several ways in which such a trade-off may be pursued including simple incremental learning, modular connectionism, and the developmental hypothesis of “representational redescription.”.

The paper presents an approach for developing multi-agent reinforcement learning systems that are made up of a coalition of modular agents. We focus on learning to segment sequences (sequential decision tasks) to create modular structures, through a bidding process that is based on reinforcements received during task execution. The approach segments sequences (and divides them up among agents) to facilitate the learning of the overall task. Notably, our approach does not rely on a priori knowledge (...) or a priori structures. Initial experiments demonstrated the basic promise of the approach. This work shows how bidding and reinforcement learning can be usefully combined, thus pointing to a new research direction. (shrink)

This paper shows how to define probability distributions over linguistically realistic syntactic structures in a way that permits us to define language learning and language comprehension as statistical problems. We demonstrate our approach using lexical‐functional grammar (LFG), but our approach generalizes to virtually any linguistic theory. Our probabilistic models are maximum entropy models. In this paper we concentrate on statistical inference procedures for learning the parameters that define these probability distributions. We point out some of the practical problems (...) that make straightforward ways of estimating these distributions infeasible, and develop a “pseudo‐likelihood” estimation procedure that overcomes some of these problems. This method raises interesting questions concerning the nature of the data available to a language learner and the modularity of language learning and processing. (shrink)

The article examines the question of how learning multiple tasks interacts with neural architectures and the flow of information through those architectures. It approaches the question by using the idealization of an artificial neural network where it is possible to ask more precise questions about the effects of modular versus nonmodular architectures as well as the effects of sequential versus simultaneous learning of tasks. A prior work has demonstrated a clear advantage of modular architectures when the (...) two tasks must be learned at the same time from the start, but this advantage may disappear when one task is first learned to a criterion before the second task is undertaken. Indeed, in some cases of sequential learning, nonmodular networks achieve success levels comparable to those of modular networks. In particular, if a nonmodular network is to learn two tasks of different difficulty and the more difficult task is presented first and learned to a criterion, then the network will learn the second, easier one without permanent degradation of the first one. In contrast, if the easier task is learned first, a nonmodular task may perform significantly less well than a modular one. It seems that the reason for this difference has to do with the fact that the sequential presentation of the more difficult task first minimizes interference between the two tasks. More broadly, the studies summarized in this article seem to imply that no single learning architecture is optimal for all situations. (shrink)

Theories that propose a modular basis for developing a “theory of mind” have no problem accommodating social interaction or social environment factors into either the learning process, or into the genotypes underlying the growth of the neurocognitive modules. Instead, they can offer models which constrain and hence explain the mechanisms through which variations in social interaction affect development. Cognitive models of both competence and performance are critical to evaluating the basis of correlations between variations in social interaction and (...) performance on mental state reasoning tasks. (shrink)

In order to extract the regularities underlying a continuous sensory input, the individual elements constituting the stream have to be encoded and their transitional probabilities (TPs) should be learned. This suggests that variance in statistical learning (SL) performance reflects efficiency in encoding representations as well as efficiency in detecting their statistical properties. These processes have been taken to be independent and temporally modular, where first, elements in the stream are encoded into internal representations, and then the co‐occurrences between (...) them are computed and registered. Here, we entertain a novel hypothesis that one unifying construct—the rate of information in the sensory input—explains learning performance. This theoretical approach merges processes related to encoding of events and those related to learning their regularities into a single computational principle. We present data from two large‐scale experiments with over 800 participants tested in support for this hypothesis, showing that rate of information in a visual stream clearly predicts SL performance, and that similar rate of information values leads to similar SL performance. We discuss the implications for SL theory and its relation to regularity learning. (shrink)

A lot of research has been done on the detection of mental workload using various bio-signals. Recently, deep learning has allowed for novel methods and results. A plethora of measurement modalities have proven to be valuable in this task, yet studies currently often only use a single modality to classify MWL. The goal of this research was to classify perceived mental workload using a deep neural network that flexibly makes use of multiple modalities, in order to allow for feature (...) sharing between modalities. To achieve this goal, an experiment was conducted in which MWL was simulated with the help of verbal logic puzzles. The puzzles came in five levels of difficulty and were presented in a random order. Participants had 1 h to solve as many puzzles as they could. Between puzzles, they gave a difficulty rating between 1 and 7, seven being the highest difficulty. Galvanic skin response, photoplethysmograms, functional near-infrared spectrograms and eye movements were collected simultaneously using LabStreamingLayer. Marker information from the puzzles was also streamed on LSL. We designed and evaluated a novel intermediate fusion multimodal DNN for the classification of PMWL using the aforementioned four modalities. Two main criteria that guided the design and implementation of our DNN are modularity and generalisability. We were able to classify PMWL within-level accurate on a seven-level workload scale using the aforementioned modalities. The model architecture allows for easy addition and removal of modalities without major structural implications because of the modular nature of the design. Furthermore, we showed that our neural network performed better when using multiple modalities, as opposed to a single modality. The dataset and code used in this paper are openly available. (shrink)

The posterior cortex, hippocampus, and prefrontal cortex in the Leabra architecture are specialized in terms of various neural parameters, and thus are predilections for learning and processing, but domain-general in terms of cognitive functions such as face recognition. Also, these areas are not encapsulated and violate Fodorian criteria for modularity. Anderson's terminology obscures these important points, but we applaud his overall message.

Recent years have yielded many discussions on how to endow autonomous agents with the ability to make ethical decisions, and the need for explicit ethical reasoning and transparency is a persistent theme in this literature. We present a modular and transparent approach to equip autonomous agents with the ability to comply with ethical prescriptions, while still enacting pre-learned optimal behaviour. Our approach relies on a normative supervisor module, that integrates a theorem prover for defeasible deontic logic within the control (...) loop of a reinforcement learning agent. The supervisor operates as both an event recorder and an on-the-fly compliance checker w.r.t. an external norm base. We successfully evaluated our approach with several tests using variations of the game Pac-Man, subject to a variety of “ethical” constraints. (shrink)

Cognitive science of religion is a fairly young discipline with the aim of studying the cognitive basis of religious belief. Despite the great variation in theories a number of common features can be distilled and most theories can be situated in the cognitivist and modular paradigm. In this paper, I investigate how cognitive science of religion (CSR) can be made better by insights from John Dewey. I chose Dewey because he offered important insights in cognition long before there was (...) cognitive science and because his ideas are influential in the recent enactivist movement. The relevance of Dewey’s thought for CSR will be discussed under three headers: embodiedness, embeddedness and anti-modularity. I focus on these points because embodiedness and embeddedness are important features of Dewey’s view on cognition and because his ideas are useful for criticizing modularity. I will first give a brief overview of the most influential theories in CSR. Then I will discuss how existing theories in CSR can be improved on the first two points and criticized on the third. (shrink)

Human motor learning is governed by a suite of interacting mechanisms each one of which modifies behavior in distinct ways and rely on different neural circuits. In recent years, much attention has been given to one type of motor learning, called motor adaptation. Here, the field has generally focused on the interactions of three mechanisms: sensory prediction error SPE-driven, explicit, and reinforcement learning. Studies of these mechanisms have largely treated them as modular, aiming to model how (...) the outputs of each are combined in the production of overt behavior. However, when examined closely the results of some studies also suggest the existence of additional interactions between the sub-components of each learning mechanism. In this perspective, we propose that these sub-component interactions represent a critical means through which different motor learning mechanisms are combined to produce movement; understanding such interactions is critical to advancing our knowledge of how humans learn new behaviors. We review current literature studying interactions between SPE-driven, explicit, and reinforcement mechanisms of motor learning. We then present evidence of sub-component interactions between SPE-driven and reinforcement learning as well as between SPE-driven and explicit learning from studies of people with cerebellar degeneration. Finally, we discuss the implications of interactions between learning mechanism sub-components for future research in human motor learning. (shrink)

Imitation has been understood in different ways: as a cognitive adaptation subtended by genetically specified cognitive mechanisms; as an aspect of domain general human cognition. The second option has been advanced by Cecilia Heyes who treats imitation as an instance of associative learning. Her argument is part of a deflationary treatment of the “mirror neuron” phenomenon. I agree with Heyes about mirror neurons but argue that Kim Sterelny has provided the tools to provide a better account of the nature (...) and role of human imitation. What we call imitative learning is an instance of social learning. It has little to do with empathy, emotional contagion, or mind reading. (shrink)

How Children Learn the Meanings of Words (HCLMW) defends the theory that words are learned through sophisticated and early-emerging cognitive abilities that have evolved for other purposes; there is no dedicated mental mechanism that is special to word learning. The commentators raise a number of challenges to this theory: Does it correctly characterize the nature and development of early abilities? Does it attribute too much to children, or too little? Does it only apply to nouns, or can it also (...) explain the acquisition of words such as verbs and determiners? More general issues are discussed as well, including the role of the input, the relationship between words and concepts, and debates over nativism, adaptationism, and modularity. (shrink)

GoalThis paper presents an immersive Virtual Reality system to analyze and train Executive Functions of soccer players. EFs are important cognitive functions for athletes. They are a relevant quality that distinguishes amateurs from professionals.MethodThe system is based on immersive technology, hence, the user interacts naturally and experiences a training session in a virtual world. The proposed system has a modular design supporting the extension of various so-called game modes. Game modes combine selected game mechanics with specific simulation content to (...) target particular training aspects. The system architecture decouples selection/parameterization and analysis of training sessions via a coaching app from an Unity3D-based VR simulation core. Monitoring of user performance and progress is recorded by a database that sends the necessary feedback to the coaching app for analysis.ResultsThe system is tested for VR-critical performance criteria to reveal the usefulness of a new interaction paradigm in the cognitive training and analysis of EFs. Subjective ratings for overall usability show that the design as VR application enhances the user experience compared to a traditional desktop app; whereas the new, unfamiliar interaction paradigm does not negatively impact the effort for using the application.ConclusionThe system can provide immersive training of EF in a fully virtual environment, eliminating potential distraction. It further provides an easy-to-use analyzes tool to compare user but also an automatic, adaptive training mode. (shrink)

It is widely appreciated that the difficulty of a particluar computation varies according to how the input data are presented. What is less understood is the effect of this computation/representation tradeoff within familiar learning paradigms. We argue that existing learning algoritms are often poorly equipped to solve problems involving a certain type of important and widespread regularity, which we call 'type-2' regularity. The solution in these cases is to trade achieved representation against computational search. We investigate several ways (...) in which such a trade-off may be pursued including simple incremental learning, modular connectionism, and the developmental hypothesis of 'representational redescription'. In addition, the most distinctive features of human cognition- language and culture- may themselves be viewed as adaptions enabling this representation/computation trade-off to be pursued on an even grander scale. (shrink)

A serial reaction time experiment tested the hypothesis that there are two independent forms of implicit learning: learning that is linked to making judgments about stimuli, and learning that is linked to motor processing. Participants performed 2, 6, or 12 blocks of single task SRT, dual task SRT, or observation with one of five sequences; each sequence had the same underlying structure. Participants then performed two implicit tests, SRT and pattern judgment, as well as a generation test (...) and an explicit knowledge test. There was a double dissociation of the effect of sequence surface structure on SRT and pattern judgment. SRT and pattern judgment competence developed independently of each other across length of acquisition task. Performance on both implicit tasks did not depend on explicit knowledge. These results indicate that the SRT and pattern judgment measures tap independent forms of implicit learning, which is consistent with the modular nature of memory and cognition. (shrink)

Bloom masterfully captures the state-of-the-art in the study of lexical acquisition. He also exposes the extent of our ignorance about the learning of names for non-observables. HCLMW adopts an innatist position without adopting modularity of mind; however, it seems likely that modularity is needed to bridge the gap between object names and the rest of the lexicon.

Clahsen's theory raises problems that make it seem untenable. As an alternative, a constructivist neural network model is reported that develops a modular architecture and in which a single associative mechanism produces all inflections, displaying an emergent dissociation between regular and irregular verbs. Thus, Clahsen's rejection of associative models of inflection concerns only a subgroup of these models.

It is becoming increasingly clear that acquiring cognitive skills is feasible only with significant developmental constraints. However, recent research provides the strongest evidence to date for constructivist development. Here, we examine how these two apparently conflicting perspectives may be reconciled. Specifically, we suggest that subcortical and cortical structures possess divergent developmental strategies, with many subcortical structures satisfying Fodor's criteria for modularity. These structures constitute an early behavioral system that guides the construction of later emerging cortical structures, for which there is (...) little evidence for modularity. Thus, we focus on how the dynamic time course of development itself implicitly constrains the emergence of cortical representations, reducing the requirement for built-in encodings of knowledge in cortical circuits, as on the traditional nativist conception. (shrink)

The fundamental reason for this research study is to point out the challenges encountered by the teachers, students, schools, and parents in facing and handling the oral speech communication subjects during the pandemic. Given that, most of the medium of instruction used is distance learning. It poses issues and concerns on how our respondents dealt with the situation. A descriptive- survey research design was used to obtain themes and phenomena to the questions provided. The questionnaire includes questions that seek (...) to gather information on their basic profile, current experiences, and behavior towards the problem. It was found out that the different problems encountered by the teachers, students, school, and parents include the validity of the issue, the lack of motivation which was a very complicated problem because it deals with emotional readiness and stability, and the difficulty in comprehending the topics of the module because lessons are not explained personally by the teachers. Therefore, oral speech communication requires an in-depth shift of lesson delivery to cater to the needs for improvement. (shrink)

The presence of general intelligence poses a major evolutionary puzzle, which has led to increased interest in its presence in nonhuman animals. The aim of this review is to critically evaluate this question and to explore the implications for current theories about the evolution of cognition. We first review domain-general and domain-specific accounts of human cognition in order to situate attempts to identify general intelligence in nonhuman animals. Recent studies are consistent with the presence of general intelligence in mammals (rodents (...) and primates). However, the interpretation of a psychometricgfactor as general intelligence needs to be validated, in particular in primates, and we propose a range of such tests. We then evaluate the implications of general intelligence in nonhuman animals for current theories about its evolution and find support for the cultural intelligence approach, which stresses the critical importance of social inputs during the ontogenetic construction of survival-relevant skills. The presence of general intelligence in nonhumans implies that modular abilities can arise in two ways, primarily through automatic development with fixed content and secondarily through learning and automatization with more variable content. The currently best-supported model, for humans and nonhuman vertebrates alike, thus construes the mind as a mix of skills based on primary and secondary modules. The relative importance of these two components is expected to vary widely among species, and we formulate tests to quantify their strength. (shrink)

The appropriate methodology for psychological research depends on whether one is studying mental algorithms or their implementation. Mental algorithms are abstract specifications of the steps taken by procedures that run in the mind. Implementational issues concern the speed and reliability of these procedures. The algorithmic level can be explored only by studying across-task variation. This contrasts with psychology's dominant methodology of looking for within-task generalities, which is appropriate only for studying implementational issues.The implementation-algorithm distinction is related to a number of (...) other “levels” considered in cognitive science. Its realization in Anderson's ACT theory of cognition is discussed. Research at the algorithmic level is more promising because it is hard to make further fundamental scientific progress at the implementational level with the methodologies available. Protocol data, which are appropriate only for algorithm-level theories, provide a richer source than data at the implementational level. Research at the algorithmic level will also yield more insight into fundamental properties of human knowledge because it is the level at which significant learning transitions are defined.The best way to study the algorithmic level is to look for differential learning outcomes in pedagogical experiments that manipulate instructional experience. This provides control and prediction in realistically complex learning situations. The intelligent tutoring paradigm provides a particularly fruitful way to implement such experiments.The implications of this analysis for the issue of modularity of mind, the status of language, research on human/computer interaction, and connectionist models are also examined. (shrink)

Our intent with this article, given its space limitations, is converging two contemporary models of mind domain. Both are fundamental heuristic tools, being the first, the modular theory of mind, which enables us to understand important aspects of human cognition such as language, memory, learning (cognitive sciences). The second, the ornamental mind theory, explains certain apparent wasteful behaviors. The fundamental presuppositions of both metaphors are distinct: whilst the modular model is a product of cognitive sciences, the ornamental (...) model is the yield of the sexual selection principle. Basically, as heuristic tools, the modular theory demonstrates the mind as a computation, as a product of selection (natural selection) which processes information; on its turn, the ornamental mind theory proposes an awkward idea, which Amotz Zahavi called the handicap principle (sexual selection). (shrink)