Crain on the relation between natural language and classical logic , by providing us with a computational system that creates recursive and hierarchically structured expressions that display productivity and systematicity and that we use to, amongst other uses, talk and think about the world cf. In what follows, then, I want to pursue the stronger claim in regard to language being an instrument of thought, and the evidence that may be adduced in its favour. The type of evidence and sorts of arguments can be divided into two kinds: the first is the argument from linguistics , according to which the externalisation of language — in, say, verbal communication — is a peripheral phenomenon because the phonological features of expressions in linguistic computations are secondary and perhaps irrelevant to the conceptual-intentional features of the expressions.
The second is the design-features argument , according to which the design features of language, especially when seen from the perspective of their internal structure, suggest that language developed and functions for purposes that are not primarily those of communication. A strong argument in favour of language being primarily an instrument of thought has to do with the phonological properties of lexical items.
Briefly, the idea is that the internal computational processes of the language faculty syntax in a broad sense generate linguistic objects that are employed by the conceptual-intentional systems systems of thought and the sensorimotor systems to yield language production and comprehension. Notice that on this view the language faculty is embedded within, but separate from, the performance systems. Phon contains information in a form interpretable by the sensorimotor systems, including linear precedence, stress, temporal order, prosodic and syllable structure, and other articulatory features.
Sem contains information interpretable by the systems of thought, including event and quantification structure, and certain arrays of semantic features. The expression Exp is generated by the operation Merge, which takes objects already constructed and constructs from them a new object. If two objects are merged, and principles of efficient computation hold, then neither will be changed — this is indeed the result of the recursive operation that generates Exp. Such expressions are not the same as linguistic utterances but rather provide the information required for the sensorimotor systems and the systems of thought to function, largely in language-independent ways.
In other words, the sensorimotor systems and the systems of thought operate independently of but at times in close interaction with the faculty of language. A mapping to two interfaces is necessary because the systems have different and often conflicting requirements.
That is, the systems of thought require a particular sort of hierarchical structure in order to, for example, calculate relations such as scope; the sensorimotor systems, on the other hand, often require the elimination of this hierarchy because, for example, pronunciation must take place serially. The instructions at the Sem interface that are interpreted by the performance systems are used in acts of talking and thinking about the world — in, say, reasoning or organising action. On this view, then, linguistic expressions provide a perspective in the form of a conceptual structure on the world, for it is only via language that certain perspectives are available to us and to our thought processes.
This is the sense in which I take language to be an instrument of thought. Language does not structure human thought in a Whorfian way, nor does it merely express pre-formed thoughts; rather, language with its expressions arranged hierarchically and recursively provides us with a unique way of thinking and talking about the world. Lexical items, then, and all expressions generated from them, are linguistic objects with a double interface property: they have phonological and semantic features through which the linguistic computations can interact with other cognitive systems — indeed, the only principles allowed under the minimalist program are those that can function at the interfaces.
Thus, if one were to imagine an order of operations, the process would be as follows: first a lexical item is created with syntactic, phonological, and semantic features. Then, in the process known as Spell Out, the phonological features are sent to the sensorimotor interface, leaving the syntactic and semantic features together to be sent to the conceptual-intentional interface cf.
Burton-Roberts This is strong evidence in favour of the thesis that language is an instrument of thought, for the central computations in which lexical meanings are produced are carried out independently of any consideration as to how or whether they are to be communicated.
Thus, the externalisation of language is a peripheral phenomenon in the sense that the phonological features of expressions in linguistic computations are peripheral to the syntactic and semantic features of these expressions. In addition to the above, we have independent evidence from comparative, neuropathological, developmental, and neuroscientific research that supports the existence of an asymmetry between the interfaces in favour of the semantic side, pushing externalisation to the periphery.
The work of Laura-Ann Petitto, for example, has shown that speech per se is not critical to the human language acquisition process. That is, the acquisition of language occurs in the same way in all healthy children, irrespective of the modality in which the child is exposed to language speech in hearing children, sign in deaf children, and even the tactile modality.
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This suggests that the brain is hardwired to tune in to the structure and meaning of what is expressed, but that the modality through which this is transmitted is irrelevant Petitto In other words, the syntax and semantics of language are processed in the same brain site regardless of the modality in which they are expressed and perceived. Such evidence gives weight to the biolinguistic argument that syntax and semantics are computed together without recourse to the way in which if at all the product of this computation say, lexical meanings is to be externalised.
There is further evidence of this sort: it appears that the neural specialization for processing language structure is not modifiable, whereas the neural pathways for externalising language are highly modifiable Petitto et al. This again suggests that the language areas of the brain are optimized for processing linguistic structures and meaning, and that their externalisation is not only secondary but also that their type is not fixed — any modality would do as long as the brain can interpret the required linguistic patterns in the input.
Recent work by Ding et al. These cortical circuits track abstract linguistic structures that are internally constructed and that are based on syntax. Further evidence of the modality independence of language, indeed the condition under which it is most acute, comes from cases where there is practically no externalisation perhaps only the ability to say a few phonemes but where the receptive language ability is completely intact. This form of developmental speech dyspraxia suggests that the ability to comprehend language and make normal grammaticality judgments does not depend on normal language production Stromswold In other words, as the work of Caplan et al.
That is, the linguistic competence at the syntactic and semantic levels remains intact but these patients have difficulty in linking this competence with the performance systems — they have difficulty in externalising the internally constructed expressions. The above is direct evidence in support of the claim that there exists a separation in the underlying mechanisms of language between, on the one hand, the processing of structure and meaning, and, on the other hand, their externalisation. That is, not only is the processing of non-language information dissociated from the processing of information used in language, but also that the processing of the language information itself is separated into Phon and Sem , just as biolinguistics predicts.
Note that this asymmetry regards the underlying mechanisms of language and thus does not apply in the same way to natural languages. So whilst it makes sense to separate Phon from Sem when one studies the underlying mechanisms of language, specific natural languages are a different matter.
That is, a natural language encapsulates the use of the Phon and Sem interfaces — in conjunction with other modules — in the act of communication via sound or sign, and so the Phon interface is inseparable from what a natural language is and the way it is used. In contrast to this, the claim that language is an instrument of thought regards the part of the underlying mechanisms of natural languages that creates the hierarchical and recursive expressions that provide humans with a unique way of thinking about the world.
This part on its own is of course not yet a particular natural language, for it is not yet in a form in which it can be externalised. In order to become a natural language it needs to be paired with the Phon interface and then, together with other systems, be used in the act of communication. Returning to the double interface object, one might wonder why the asymmetry between the interfaces is in favour the semantic side, pushing externalisation to the periphery. I think the answer to this comes in the form of the design-features argument.
If one does not share the general framework of biolinguistics, then they will perhaps be unconvinced by the argument from linguistics above. The design-features argument , on the other hand, has much wider scope and is not entirely dependent upon a particular linguistics school of thought. By design features I mean the kind of features one discovers upon investigating language as a system in its own right. Such features include, amongst many others, displacement, linear order, agreement, and anaphora.
One may then investigate the communicative and computational efficiency of these features as they relate to language as a whole system, and ask whether these features are better optimised for communication or for computation. Of course, many comparisons of this sort can be made, and some particular selection that depicts a conflict between communicative efficiency and computational efficiency might seem tendentious, but I think that the conflicts of the sort highlighted below, in which computational efficiency wins out, represent one of several chinks in the armour of the orthodoxy that assumes that the function of language is communication.
Let us now consider the case of the explanation of the linear order of expressions. The linear order imposed on verbal expressions is not a language-specific constraint: it is not a consequence of the structure of the language faculty. Rather, it is a necessary consequence of the structure of the sensorimotor systems and the obvious fact that expressions cannot be produced or comprehended in parallel.
Assuming this is the case, then, what is the effect of such constraints on, say, the computations involved in parsing sound inputs into linguistic representations? If language is optimised for communication and if sound is our main source of externalisation, then one would predict that many of the features of language would respect linear order and favour operations that support it even if they conflict with computational efficiency.
Closer investigation, however, suggests that this is not the case. Consider, for example, how co-reference is interpreted in sentences such as In her study, Jane is mostly productive , where her and Jane are interpreted as being co-referential. It was initially thought Langacker ; Jackendoff ; Lasnik that in order to explain the difference between, say, 1 and 2 below, a linear relationship of precede-and-command was needed, according to which the pronoun cannot both precede and command its antecedent.
The explanation used to be that in 1 the pronoun precedes and commands the full noun phrase and therefore the co-referential interpretation is blocked. In 2 , conversely, it was claimed that the pronoun precedes but does not command the full noun phrase and therefore a co-referential interpretation is permitted.
However, as Reinhart shows, the domains over which the precede-and-command operations are defined are quite arbitrary; the parts of the expressions that are preceded or commanded by other parts often do not correspond to independently characterisable syntactic units. On independent grounds, then, it would be surprising if such an arbitrary linear relationship would turn out to be the operative co-referential explanation. This is clear in 3 and 4 below, which cannot be explained by precede-and-command operations cf.
Reinhart 36ff. In 3a the pronoun cannot refer to Mary , whereas in 3b the co-referential interpretation is permitted. However, when we consider 4 , which is the pre-preposed version of the sentences in 3 , the co-referential interpretation is blocked in both 4a and 4b. Thus, no ordering explanation such as precede-and-command can account for the difference between 3a and 3b. Or compare 5a and 5b , both of which are allowed by the relation of precede-and-command but only one of which has an acceptable co-referential reading.
As Reinhart shows with a range of other examples, there is good reason to think that, instead of a linear order operation, the explanation of co-reference has to do with the structural properties of the expressions. According to the structure-dependent analysis, coreferential interpretations are only permitted when anaphors are bound by another nominal.
This binding is a structure sensitive and asymmetric relation according to which a subject can bind an object, but an object cannot bind a subject. In regard to the above examples, there is an asymmetry between the coreference options of subjects and those of objects or non-subjects , for in cases with preposed constituents forward pronominalisation is impossible where the pronoun is the subject — as in 3a and 5a — but possible where the pronoun is not the subject — as in 3b and 5b.
Thus, the hierarchical relation of binding, involving both c-command and coindexation, supplies us with a more encompassing and much improved explanation of the phenomena — it explains not only what the relation of precede-and-command explains, but also the cases that cannot be explained by invoking ordering relationships. The structural relation of c-command has been shown to be a fundamental relation in syntax that underlies many diverse linguistic phenomena cf.
It should be noted that this holds for a specific kind of computation instantiated in the human brain — biological computation, if you will. If modern computers could consistently parse natural language expressions by methods that assume that the expressions are based on linear distance or statistical regularities, that would be an interesting and valuable outcome that could be put to numerous practical uses. However, whether or not computers can or would be able to do this is not relevant to this discussion because our brains do not work in that way: language appears to use structure-dependent operations almost entirely cf.
Moro ; These operations are often irrelevant to externalisation and in many cases are in direct conflict with the efficient operation or needs of the sensorimotor systems as they are used in communicating. Linear distance is more efficient, arguably less taxing for the parser, and simpler from the point of view of communication but is largely absent in the crucial cases where one would expect it.
As Chomsky argues, one explanation for this phenomenon is that linear distance is simply not available to the child during language acquisition; they are instead guided by a principle that dictates that there is no such thing as linear order and that only structure-dependent operations are to be considered. But structure-dependent operations cause problems for communication that would not arise if, say, linear distance was used instead.
Linguistic expressions seem to be optimised for computational efficiency, they are not structured in a way that favours ease of communication. A key source of evidence in favour of the claim that language is optimised for computational efficiency is that computational efficiency appears to be a feature of biological systems, which of course include the human brain and the language faculty within it.
In other words, evidence for the computational efficiency of the human brain is also evidence for the computational efficiency of the language faculty because the latter is part of the former. The neural connections in the brain are a highly constrained and finite resource, especially the longer range ones that are subject to constraints due to volume and signal-propagation times.
Computational Thinking: I Do Not Think It Means What You Think It Means
There are innumerable local maxima that would do for the task at hand, but the brain appears to be structured in an optimal way that is closer or indeed at the global maximum, asymptotically close to being the best of all possible brains given the constraints at hand and the initial conditions Cherniak et al. The optimal structure of biological neural systems was first confirmed by studying the neural system of the millimetre-long roundworm, C. Subsequent studies have observed even finer wiring optimisation in the layout of the cerebral cortex of rats, cats and macaque monkeys Cherniak et al.
Since the way in which the human brain works is optimised in the above sense, and since the language faculty is in the brain, there is no reason to expect that the language faculty would not also respect the principle of efficient computation. Rather, they can be found in work across many species, from the inception of modern biology Thompson ; Turing ; Leiber to current thinking Maynard Smith ; Kauffman ; Stewart ; Gould ; Fox Keller The asymmetry in favour of computational efficiency is dramatically illustrated by the presence in natural language of structural ambiguity and garden path sentences.
These clearly cause problems for communication and so one might ask why natural language has them at all. Phillips shows that many well-known ambiguities such as John said Bill left yesterday can in fact be explained by the same computational principle Branch Right that forces certain structural biases in the parsing of expressions cf.
This suggests that externalising language with intent to communicate is a peripheral aspect of language. But we find that not only is this not the case, but that the underlying mechanisms of language are in fact structured in a way to maximise computational efficiency, which ends up causing communicative problems. There is the computational system and there is the parser, the latter of which is part of the systems that externalise language. The closer these two are to each other, the more that is gained in terms of overall efficiency.
The further away they are from each other, however, the more questions that arise as to why? I have suggested that the two are further away than is commonly thought. In order to externalise language, the parser must respect the computational and structural features of the syntax-LF bundle. The latter, however, causes problems for the externalisation of language in communication ambiguity, garden path sentences, etc.
The problems for communication arise when the computational system, which operates along an independent path, is used in the act of communicating. In other words, impediments to successful and smooth communication are the result of the computational operations of the underlying mechanisms of language being asked to perform a function, externalising language with intent to communicate, that is not their primary function.
To recap, there is a conflict between computational efficiency and communicative efficiency. If the function of language were primarily for communication, one would predict that the language system would opt for an operation that aids in the communication of propositional thoughts, or at the very least one that does not hinder parsing or interpretation. But a look at the evidence from linguistic, comparative, neuropathological, developmental, and neuroscientific research shows that this is not the case. This suggests that the language system is composed of computational operations that are optimised for computation, not for communicative efficiency.
It follows from the above that the nature of language, when taking into account its design features and its internal structure, is not as it is widely assumed to be.
That is, language is meaning with externalisation in sound, sign, etc. Speech, sign, or any other kind of externalisation are secondary properties of language. The fundamental property of language is the internal construction of indefinitely many expressions by a generative procedure that yields a uniquely human perspective in the form of a conceptual structure on the world.
It is in this sense that language not a particular natural language but rather its underlying computational mechanisms is an instrument of thought; it provides us with a unique way of structuring the world around us, which we use for various purposes such as thinking and talking about the world. Their arguments are not the same but the conclusion they reach is cf. Carruthers ff. Ryle and Slezak for criticism of this view. A current hypothesis, which I favour, is that of Hauser et al. The debate continued in Fitch et al.
Therefore, a primary function of the human language faculty is to support communication. Baronchelli et al. Cummins for a good discussion of systematicity. But such limitations are due to systems outside of the language faculty but internal to the brain and thus do not change the nature of the language faculty itself. As I detail below, however, I think that narrow syntax as construed within biolinguistics is a better explanation for the specifically human type of thought.
As he argues, the relationship between the two clauses satisfy the condition of minimal structural distance and not the much simpler computational operation of minimal linear distance. I would like to thank the two anonymous Glossa referees and the associate editor, Waltraud Paul, who provided invaluable and in-depth comments and criticisms. This article has benefited a great deal from their feedback. Parts of this research have been presented at the Australasian Association of Philosophy conference at Macquarie University.
Akmajian, Adrian, Richard A. New York, NY: Routledge. Pragmatics: Critical concepts. Asoulin, Eran. The creative aspect of language use and the implications for linguistic science. Biolinguistics 7. Linguistic communication and speech acts. A theory of command relations. Linguistics and Philosophy The biological origins of linguistic diversity. Berwick, Robert C. Songs to syntax: The linguistics of birdsong. Trends in Cognitive Sciences 15 3. Boeckx, Cedric. Biolinguistics: A brief guide for the perplexed. Linguistic Sciences 10 5. Language as a natural object: Linguistics as a natural science.
The Linguistic Review Rethinking the Cartesian theory of linguistic productivity. Philosophical Psychology 22 3. Burton-Roberts, Noel. On the grounding of syntax and the role of phonology in human cognition. Lingua A study of syntactic processing in aphasia I: Behavioral psycholinguistic aspects. Brain and Language Carruthers, Peter. The cognitive functions of language. Behavioral and Brain Sciences Cherniak, Christopher. Philosophy and computational neuroanatomy. Philosophical Studies Neural component placement. Trends in Neuroscience Innateness and brain-wiring optimization: Non-genomic nativism.
Global optimization of cerebral cortex layout. Proceedings National Academy of Sciences Optimal-wiring models of neuroanatomy. Ascoli ed. Totowa, New Jersey: Humana Press. Chklovskii, Dmitri B. Wiring optimization in cortical circuits. Neuron Chomsky, Noam. The minimalist program. Minimalist inquiries: The framework. Biolinguistic explorations: Design, development, evolution. International Journal of Philosophical Studies 15 1. What kind of creatures are we?
Lecture 1: What is language?. The Journal of Philosophy 90 Problems of projection. Crain, Stephen. The emergence of meaning. Cambridge: Cambridge University Press. Cummins, Robert. In the second round, it was taken by eight graduate level and nine undergraduate level students.
All had elementary programming background in either C, Matlab, or Pascal a programming course is mandatory for all Technion undergraduate students. Participants were required to submit five home assignments, each including programming tasks and theoretical questions. In the first round, a take-home exam was given at the end, which was replaced in the second round by a final research project: students chose topics that they found interesting among the course subjects, extended them in some manner, and applied them to real biological data.
Additional details regarding the projects, and specific project examples, appear in the supplementary Text S2. At the end of the semester, students were either interviewed by the lecturer or asked to fill a survey for feedback. These feedbacks are summarized in the supplementary Text S3. To examine the effect of the course on how students view computer science, they were asked to define this discipline before and after the course.
Prior to the course, students related the field mostly to the computer as a machine and to software and tools. At the end of the course, however, they tended to relate CS to broader and more abstract terms, such as problem solving and modeling see Figure 2. We believe this shift in the view of the discipline, especially considering the prior exposure of our students to programming, strengthens the rationale for such a course. Numbers indicate how many students among the responders included the notion in their definition for the discipline.
Obviously, there is more than a single way to expose life sciences students to computational thinking. Yet, based on our experience, and on numerous discussions with life scientists and bioinformaticians, we feel that a single one-semester course, which does not assume a basic programming course as a prerequisite, is likely to miss the goal of teaching computational thinking and computational concepts to life science students.
If basic programming is taught from scratch, not enough time will be left for the higher level computational concepts and their relations to biology, so the depth of coverage of computational thinking will be smaller. On the other hand, having such a basic programming prerequisite, as in our course, enables us to take the students a step further, beyond programming and tool handling.
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We believe that these days, a basic programming course is a crucial component of every science curricula. This leads to the recommendation that basic programming should be taught separately, prior to a computational thinking course. Such a prerequisite will allow the students to digest programming issues well before, so they need not be preoccupied with technical issues while taking a computational thinking course.
Teachers engaged with computational education for biologists are sometimes tempted to make their course as practical as they can and many students feel more comfortable staying away from abstract topics. While practical skills are, of course, important and motivating, we believe that time and educational effort must be spent on abstract notions and thinking processes: naming, discussing, and reflecting upon them.
Most of these conclusions are supported by the surveys and interviews conducted among course students during the two semesters it was taught. Clearly, a more in-depth evaluation of the course, based on a larger number of participants, is called for. This is planned to take place in future offerings of the course. In our view, an essential part of any course aiming to teach computational thinking to life scientists is the interaction in class, with an able instructor who is knowledgeable in both computer and life sciences. Class interactions in the form of discussions, guided solutions to problems, naming of thinking processes, and exposure of students to alternative including incorrect approaches are at the heart of the learning process in this course.
Our four-step pipeline instruction model prevents spending too much time on technical aspects since part of the time is explicitly dedicated to reflection and discussion in class. We strongly believe that we have an important message to deliver. We propose a way to take life scientists' computational education a step further. Even small steps in this direction are likely to have substantial consequences in life or medical science practices and research in the long run. A A microscope slide containing Bacilli anthracis cells and spores image taken from . B Endospores identified white spots in the original image.
C Vegetative cells identified dark spots in the original image. We thank Metsada Pasmanik-Chor for her constructive criticism on an earlier version of this manuscript. Abstract We join the increasing call to take computational education of life science students a step further, beyond teaching mere programming and employing existing software tools. Download: PPT. Figure 1. Biological modules of the course and related computational topics.
Table 1. Choice of topics The course topics span several algorithmic and logical concepts that lie at the heart of CS. Emphasis on discrete notions One important choice in the course's design was to exclusively concentrate on discrete approaches such as finite graphs, strings, digital images represented as a matrix of discrete elements—pixels , finite state automata, etc. Level of formalism We choose a level of formalism that matches students' background.
Learning Outcomes and Evaluation Upon successful completion of the course, we expect students to: Be familiar with several fundamental concepts and notions in CS, and their applicability to life sciences. Figure 2. Students' views of the important facets of CS before and after the course. Discussion Obviously, there is more than a single way to expose life sciences students to computational thinking.
Computational Thinking in Life Science Education
Figure S1. Figure S2. Figure S3. Emergence of a more complete picture of biological systems depends on successful methods for integration of data from these different perspectives. With this workshop, we aim to encourage researchers to develop new methodologies, analytical models, and high-throughput computing workflow that best utilize various types of biomedical data in ways that meaningful structures present but hidden in data can be revealed.
All papers will undergo peer review by the conference program committee. Authors of selected papers will be invited to extend their papers for submission to special issues in prestigious Journals. Paper Submission: Please submit a full-length paper up to 8 pages in IEEE two-column format through the online submission system.
Electronic submissions in pdf format are required. For paper submission click on the following link: wi-lab. It will include information useful for both beginners and more advanced users. We will start by introducing general concepts of comparative genomics. On this basis, we will then continue to describe all major analysis steps from the raw sequencing data via the identification of variations to an assessment of their impact on the phenotype.
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Attendees should have a background in biology. There will be a mix of lectures and hands-on practical exercises using command line Linux. We will therefore dedicate one session to introduce basic and advanced Linux concepts for processing data on Amazon cloud AWS. Attendees should have also some familiarity with genomic data such as that arising from NGS sequencing experiments.
Sedlazeck fritzsedlazeck. Ingo Ebersberger scholar. Here is the full list of our courses and Workshops: www. Mark Andrews. OVERVIEW Python is one of the most widely used and highly valued programming languages in the world, and is especially widely used in data science, machine learning, and in other scientific computing applications. This course provides both a general introduction to programming with Python and a comprehensive introduction to using Python for data science, machine learning, and scientific computing.
The major topics that we will cover include the following: the fundamentals of general purpose programming in Python; using Jupyter notebooks as a reproducible interactive Python programming environment; numerical computing using numpy; data processing and manipulations using pandas; data visualization using matplotlib, seaborn, ggplot, bokeh, altair, etc; symbolic mathematics using sympy; data science and machine learning using scikit-learn, keras, and tensorflow; Bayesian modelling using PyMC3 and PyStan; high performance computing with Cython, Numba, IPyParallel, Dask.
Overall, this course aims to provide a solid introduction to Python generally as a programming language, and to its principal tools for doing data science, machine learning, and scientific computing. Note that this course will focus on Python 3 exclusively given that Python 2 has now reached it end of life.
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