Biological Cognition: Syntax 5. Biological Cognition: Control 6. Biological Cognition: Memory and Learning 7. The Semantic Pointer Architecture. Evaluating Cognitive Theories 9. Theories of Cognition Consequences and Challenges. The author provides a very extensive page Bibliography that lists numerous materials as references for further study and exploration on the highly complex nature of the human brain and its many faculties. This is a definitive and pioneering work on the study of the human brain because Eliasmith presents a unified theory of cognition using the Semantic Pointer Architecture or SPA.
How to build a brain provides a detailed, guided exploration of a new cognitive architecture that takes biological detail seriously, while addressing cognitive phenomena.
Publications about Nengo
The Semantic Pointer Architecture SPA introduced in this book provides a set of tools for constructing a wide range of perceptual, cognitive, and motor models at the level of individual spiking neurons. Many examples of such models are provided, and they are shown to explain a wide range of data including single cell recordings, neural population activity, reaction times, error rates, choice behavior, and fMR Many examples of such models are provided, and they are shown to explain a wide range of data including single cell recordings, neural population activity, reaction times, error rates, choice behavior, and fMRI signals.
Each of these models is introduced to explain a major feature of biological cognition addressed in the book, including semantics, syntax, control, learning, and memory. The last half of this book compares the Semantic Pointer Architecture with the current state-of-the-art, addressing issues of theory construction in the behavioral sciences, semantic compositionality, and scalability, among other considerations. Along the way, the book considers neural coding, concept representation, neural dynamics, working memory, neuroanatomy, reinforcement learning, and spike-timing dependent plasticity.
The book includes detailed, hands-on tutorials exploiting the free Nengo neural simulation environment, providing practical experience with the concepts and models presented throughout. Keywords: neural simulation , brain models , spiking neurons , syntax , neural dynamics , neural representation , cognitive architecture , cognitive science , semantics.
Forgot password? Don't have an account? All Rights Reserved. OSO version 0. University Press Scholarship Online. Sign in. Not registered? Sign up. The open-class words in the interrogative sentences used for the test were modified accordingly. By iterating this procedure, we generated a database of declarative sentences and interrogative sentences. This database was used as an independent test set for testing the four instances of the system that were trained on the original database during the four rounds of the cross-validation, respectively. Table 7 shows the number of correct answers produced by the four instances of the system over the number of interrogative sentences for the three extended datasets people, parts of the body and categorization.
The results demonstrate that the system is able to generalize the acquired knowledge to learned constructions with different open-class words. The four instances of the system are the ones obtained by training the system on the original datasets during the four rounds of the cross-validation, respectively. The compositional generalization capacity of the system was evaluated through an experiment of sentence-to-meaning mapping, based on a task that was developed by Caplan et al.
In the Caplan task, an aphasic subject listens to sentences and then he is required to indicate the meaning by pointing to images depicting the agent, object and recipient, always in that canonical order. In formal terms, the input is the sequence of words in the sentence, and the output is the sequence agent, object, and recipient, corresponding to a standardized representation of the meaning in terms of thematic role assignment.
In our implementation, the surface form of the input sentences is presented word-by-word to the system, which is trained to assign the the thematic roles predicate, agent, object, recipient of the open-class words. For this experiment we used a dataset of distinct grammatical constructions developed by Hinaut and Dominey [ 25 ], who used a context free grammar to generate systematically distinct grammatical constructions, each consisting of between 1 and 6 nouns, with 1 to 2 levels of hierarchical structure i.
- Fishery Science: The Unique Contributions of Early Life Stages.
- Large-scale modeling of the behaving brain - IEEE Life Sciences.
- Scientific Chairs;
Each grammatical construction of this dataset has a surface form and a coded meaning. The surface form is composed by the word groups shown in Table 8. The X represents a closed-class word. In case of ambiguities, the system is trained to use the largest groups. The architecture of our model does not include a structure where the open-class words can be explicitly mapped to their thematic role.
In order to perform this task without modifying the system architecture, our approach was to explicitly ask the system for the thematic roles:. This approach is more close to the Caplan-task protocol. It should also be noted that our model, in contrast to other approaches to the same problem, does not require a prior specification of the distinction between open-class and closed-class words. Following the same approach of Ref. The dataset was divided in ten partitions eight partitions with 46 sentences, and two with 47 sentences.
In each round of the cross-validation, the system was trained on nine partitions and tested with the one not used for training. This procedure was performed ten times, so that all partitions were used for the test. Table 9 reports the results of the cross validation. Meaning error is the percentage of incorrect thematic role assignment. Sentence error is the percentage of sentences in which there is at least one wrong thematic role assignment. As illustrated in the table, the cross validation yielded 9. The model proposed by Hinaut and Dominey achieved 9. This means that the number of errors in thematic role assignment is the same, however in their work the assignment errors are concentrated in a smaller number of sentences.
It should be considered that while that work is focused on the problem of thematic role assignment, our model is not optimized for this specific task, because it addresses a wider range of aspects of human language processing. In recent years, there has been a growing interest in the development of different types of conversational agents, ranging from chatterbots to dialog systems for automated online assistance.
Chatterbot programs try to provide a more or less adequate imitation of how humans conversate, without developing real understanding of language [ 6 , 50 ]. Most of them try to bring the human interlocutor into stereotyped conversations, and they largely use the so-called Eliza effect, which is the tendency of people to unconsciously assume computer behaviors are analogous to human behaviors. In general, those conversational agents do not model the cognitive processes that sustain language development. For this reason, they are not useful for understanding how humans develop language skills and how verbal information is processed in the brain.
The connectionist approach has been successfully applied to several aspects of human language cognition and memory. However, it has been difficult to build comprehensive neural models able to perform and to control the wide variety of cognitive abilities that are involved in verbal communication [ 23 ]. This difficulty is mainly due to to the lack of a suitable system that controls the flow of information among the STM subsystems.
We propose that this control can be done by mental actions that operates though neural gating mechanisms. This hypothesis is compatible with recent research, which demonstrates that neural gating mechanisms play a fundamental role in the flow of information in the cortex. We also provide a model of how the central executive can learn how to associate mental actions to the internal states of the system through a rewarding procedure. Our work suggests that those mental actions, which have already been studied in previous working memory models, have also a fundamental role in the development of language skills.
According to our model, language processing at the sentence level is performed through such kind of mental actions, which are controlled by executive functions. The central executive acquires the procedural knowledge for controlling mental actions through rewarding mechanisms, which modify the connections of the state-action association system through the Hebbian learning rule. The proposed architecture is based on a multi-component working memory model, which reflects the functional role of different subsystems, rather than their anatomical location in the brain.
Nevertheless, the neural-network structure and the use of biologically motivated learning principles make this system suitable for understanding how observed behavior is related to the low level neural processes that occur in the brain and that support it. The results of this work show that the ANNABELL model is able to learn, starting from a tabula rasa condition, how to execute and to coordinate different cognitive tasks, as processing verbal information, storing and retrieving it from long-term memory, directing attention to relevant items and organizing language production.
The proposed model can help to understand the development of such abilities in the human brain, and the role of reward processes in this development.
For instance, let us consider the age-comparison example, described in Sect. The first example involves counting skills, ability to compare small numbers, ability to associate the words "your friend" to a known person, ability to retrieve information about her age from the LTM, ability to use personal pronouns. The system is able to learn how to answer this question through a rewarding procedure, and to generalize the acquired knowledge to similar questions involving different people with different ages. In the second example "how many games did you play?
It is important to point out that our model does not include a specialized structure for counting, or a specialized structure for number comparison, or a specialized structure for mapping names into personal pronouns…. All its abilities arise from a relatively small set of mental actions that are compatible with psychological findings. In the past decades, many researchers emphasized the contrast between localist and distributed models.
A limitation of the current version of the system is that it uses a localist model for word representation. In the subnetworks that represent individual words, different words are represented by vectors that are orthogonal to each other. This representation limits the ability of the system to learn to recognize similarities among words.
On the other hand, in general different STM states are not orthogonal to each other. Another limitation of the localist representation is that it makes the system very sensitive to word position in phrases. This problem is partially attenuated by the fact that the system learns to scan the words of the working phrase and to transfer them to the current-word buffer one by one. After each step of this scanning procedure, the system has to decide whether or not the current word should be transferred to the focus of attention.
As the current word itself is part of the input of the state-action association system, this decision process can be partially decoupled from the position of the word in the sentence. The localist representation of words can be regarded as a simplification, mainly motivated by the need for computational efficiency. Conversely, the use of a sparse signal representation is a basic feature of our model. In principle, our model could be modified to use a distributed but still sparse representation of the words. This could be a subject for a future work. It is important to point out that word representation is not a central point of our work, which is more focused on the role of executive functions in language processing at the sentence level.
The central executive, which is the heart of our system, follows a distributed model, and our work emphasizes that the decision processes operated by this component are not based on pre-coded rules, but statistical. In the context of human-computer interaction, human language understanding is often associated to the ability to translate a linguistic input into a standardized functional form.
This type of understanding involves the capacity to recognize the thematic role of the open-class words in the surface form of sentences. Meaning in this case is interpreted as a mapping from the surface form to the functional form. Our model does not have a structure where this mapping is explicit, however its ability to identify thematic roles can be tested through a question-answer approach, as in the Caplan task that was discussed in Sect.
The previous notion of understanding is insufficient for the purpose of our work. Question answering and, more generally, communicative interactions involve a kind of procedural knowledge, which is used to process the linguistic input and to produce the output. This type of understanding refers to the ability to perform the sequences of mental operations that are needed to respond to a verbal input.
Our work is an attempt to implement a working cognitive model that helps to understand the development of this procedural knowledge. Many researchers argued that a true understanding can not be achieved if language is not grounded in the agent's physical environment through actions and perceptions [ 51 ].
An active field of research is devoted to grounding open-class words to objects visual elements, bodily sensations and other types of perceptions and grounding sentences to scenes and actions. Morse et al. Dominey and Boucher [ 54 ] argued that we learn to translate the surface form of language into a functional form through the integration of speech inputs and non-speech inputs. In Baddeley's working memory model, this integration occurs in the episodic buffer.
Chris Eliasmith - Citace Google Scholar
A limit of the current version of our model is that language is not grounded. Language grounding would require the combination of our model with a visual system, or its embodiment in a larger system that integrates language with other forms of perceptions and actions. The results of the validation show that, compared to previous cognitive neural models of language, the ANNABELL model is able to develop a broad range of functionalities, starting from a tabula rasa condition. The system processes verbal information through sequences of mental operations that are compatible with psychological findings.
Those results support the hypothesis that executive functions play a fundamental role for the elaboration of verbal information. Our work emphasizes that the decision processes operated by the central executive are not based on pre-coded rules. On the contrary, they are statistical decision processes, which are learned by exploration-reward mechanisms. The reward is based on Hebbian changes of the learnable connections of the central executive.
A neural architecture is suitable for modeling the development of the procedural knowledge that determines those decision processes. The current version of the system sets the scene for subsequent experiments on the fluidity of the brain and its robustness in the response to noisy or altered input signals. Moreover, the addition of sensorimotor knowledge to the system e. We are deeply grateful to Prof. Risto Miikkulainen for his invaluable discussions and suggestions for the improvement of our work.
Designed the software: BG. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Communicative interactions involve a kind of procedural knowledge that is used by the human brain for processing verbal and nonverbal inputs and for language production. Introduction The attempts to build artificial systems capable of simulating important aspects of human cognitive abilities have a long history, and have contributed to the debate among two different theoretical approaches, the computationalism and the connectionism.
Working memory models Although there are different perspectives regarding the organization of memory in the human brain, all approaches recognize at least two types of memory: the s hort-term memory STM and the long-term memory LTM.
Download: PPT. The mental action sequence In classical tasks used to study working memory capacity [ 31 ], a subject is asked to hold in mind a short sequence of digits and to perform some simple process on each of these digits or on a subset , for example adding the number two to each digit. Therefore, the previous sequence should be extended by including at the beginning, before step 1, two other operations, such as: transfer the phrase " add the number two " to the phonological store; transfer this phrase or some coded form of it to a goal-task store.
A minimal system that can perform this sequence should include at least the following components: - a phonological store; - a focus of attention; - a retrieval structure that uses the focus of attention as a cue to retrieve information from LTM; - a goal store i.
Suppose that an artificial model of the working memory was trained to respond to the "add the number two" task described above, and that it is tested on a similar task, but with different numbers: " add the number three to each of the following digits : 7 8 2 5 " Since this sentence is similar to that of the first task, the central executive will provide the same output, i. Localization of the verbal working memory in the brain Localization of brain areas that are involved in language comprehension and production requires the combination of findings from neuroimaging and psycholinguistic research.
Fig 2. Brodmann's areas are marked by numbers. Neural gating mechanisms Neural gating mechanisms play an important role in the cortex and in other regions of the brain [ 39 ]. Fig 3. Learning mechanisms and signal flow control The ANNABELL system is entirely composed of interconnected artificial neurons, and all processes are achieved at the neural level. Three types of connections are used: fixed-weight connections, which do not change during the learning process; variable-weight learnable connections, which are modified by the learning process; forcing connections, which are variable-weight connections that have a positive or negative weight much greater in absolute value than that of the other two connection types, thus they can force the target neurons to a high-level or to a low-level state.
Fig 4. Global organization of the model The global organization of our model is compatible with the M-WM framework. Acquisition actions. Those actions are used during the acquisition and during the association phases, for acquiring the input phrases, memorizing them and building the associations between word groups and memorized phrases. Elaboration actions. Those actions are used during the exploration and during the exploitation phases, for extracting word groups from the working phrase, for retrieving memorized phrases from word groups through the association mechanism, for retrieving memorized phrases belonging to the same context, for composing output phrases.
Reward actions. Those actions are used by the rewarding system and can be executed in parallel to the elaboration actions. They are used for memorizing the state-action sequences produced during the exploration and during the exploitation phases, for retrieving such sequences after a reward signal and for triggering the changes of the state-action-association connection weights.
A complete list of the actions is presented in S5 Appendix. The database The database of sentences used for training and testing the system is organized in five datasets, each devoted to a thematic group, i. The people dataset The first dataset is devoted to the subject people , and it is partially inspired by the Language Development Survey work of Rescorla et al.
The parts of the body dataset The second dataset is devoted to the main parts of the body, and it is also partially based on the words of this subject category included in the Language Development Survey. The categorization dataset The third dataset is used for evaluating the categorization capabilities of the system. In this case, the human asks the system two consecutive questions, as in the following example: Q: what is the turtle? A: it is an animal Q: what kind of animal?
A: a reptile Other questions in this dataset are used to evaluate the system capability to combine information on categories and adjectives, as in the following example: Q: tell me a big reptile A: crocodile.
How to build a brain: from function to implementation
The virtual environment dataset The fifth dataset represents a text-based virtual environment, where the system is trained to perform simple tasks by means of verbal commands. Fig 6. Map of the virtual house used to build the sentences for the test set of the virtual-environment dataset. Results The training procedure is organized in five incremental language training sessions, one for each dataset. Table 3. Number of declarative sentences, number of interrogative sentences used for training, number of interrogative sentences used for the test and number of output sentences in the five learning sessions.
Fig 7. Fig 8. Distribution of the number of words and of the word classes in the input and output sentences. Table 4. Number of correct answers over the total number of expected answers in the test stage of the cross-validation rounds for the first three datasets. Table 5. Number of tasks that are performed correctly by the system on the virtual environment dataset over the total number of assigned tasks in the test stage of the cross validation rounds, as a function of the number of training examples used in the training stage.
Fig 9. Fig Comparison between some distributions related to the output sentences produced by the system in the communicative interaction test, based on the Warren-Leubecker corpus from the CHILDES database, and to the utterances of the real child for the same part of the corpus: distribution of the number of words in the output sentences a,b ; distribution of the words used in the output sentences among different word classes c,d ; percentage of word classes in the output sentences e,f.
Percentage of correct answers after removal of the connections from the STM components to the central executive, and after complete removal of the components. Percentage of correct answers over the total number of expected answers, evaluated on the first three learning sessions: [ a and b ] as functions of the weight-saturation value W max for the main components of the system; c as a function of the parameter k used for the k -winner-take-all algorithm in the state-action-association subnetwork. Generalization Our study is focused on the children age range between about 3 to 5 years, which is a crucial range for the acquisition of linguistic competencies, and therefore is considered particularly interesting for studies on language development.
We can distinguish two types of generalization [ 24 ]: handling learned grammatical constructions with new open-class words; compositional generalization, i. Generalization 1 For this experiment, we built an extended database by replacing the open-class words of the three datasets people , parts of the body and categorization , with new, randomly generated words.
Table 7. Number of correct answers produced by the four instances of the system over the number of interrogative sentences for the three extended datasets people, parts of the body and categorization. Generalization 2 The compositional generalization capacity of the system was evaluated through an experiment of sentence-to-meaning mapping, based on a task that was developed by Caplan et al. Table 8. Group of words that compose the sentences of the database used for this experiment. Table 9. Meaning errors and sentence errors on the ten rounds of the ten-fold cross validation.
Discussion In recent years, there has been a growing interest in the development of different types of conversational agents, ranging from chatterbots to dialog systems for automated online assistance. Conclusion The results of the validation show that, compared to previous cognitive neural models of language, the ANNABELL model is able to develop a broad range of functionalities, starting from a tabula rasa condition. Supporting Information. S1 Appendix. Examples from the learning sessions.
S2 Appendix. S3 Appendix. Examples of neural activation patterns. S4 Appendix. Mathematical properties of the state-action association system. S5 Appendix. References 1. Artif Intell 1— View Article Google Scholar 4. Hum Comput Int 4 12 : — View Article Google Scholar 5. Langley P An adaptive architecture for physical agents. View Article Google Scholar 8. Science : — View Article Google Scholar Information and Control 2: — Elman JL Distributed representations, simple recurrent networks, and grammatical structure. Mach Learn 7 2—3 : — Miikkulainen R Script-based inference and memory retrieval in subsymbolic story processing.
Applied Intelligence 5 2 — Dominey PF Recurrent temporal networks and language acquisition—from corticostriatal neurophysiology to reservoir computing. Front Psychol 4. In Bower G. New York: Academic Press. Baddeley AD The episodic buffer: a new component of working memory? Trends in Cognitive Science 4: — Annual Review of Psychology 1— Cowan N Attention and memory: an integrated framework. Oxford [Oxfordshire]: Oxford University Press. McElree B Working memory and focal attention. Oberauer K Access to information in working memory: exploring the focus of attention.
Journal of Experimental Psychology. Learning, Memory, and Cognition 28 3 : — Bryck RL, Mayr U On the role of verbalization during task set selection: switching or serial order control? Psychologica Belgica, 52 2—3 , —