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We present a computational model capable of simulating aspects of human knowledge for thousands of real-world concepts. Our approach involves a pretrained transformer network that is further fine-tuned on large data sets of participant-generated feature norms. We show that such a model can successfully extrapolate from its training data, and predict human knowledge for new concepts and features. We apply our model to stimuli from 25 previous experiments in semantic cognition research and show that it reproduces many findings on semantic verification, concept typicality, feature distribution, and semantic similarity. We also compare our model against several variants, and by doing so, establish the model properties that are necessary for good prediction. The success of our approach shows how a combination of language data and (laboratory-based) psychological data can be used to build models with rich world knowledge. Such models can be used in the service of new psychological applications, such as the modeling of naturalistic semantic verification and knowledge retrieval, as well as the modeling of real-world categorization, decision-making, and reasoning. [ABSTRACT FROM AUTHOR]

Brain imaging studies of English past tense inflection have found dissociations between regular and irregular verbs, but no coherent picture has emerged to explain how these dissociations arise. Here we use synthetic brain imaging on a neural network model to provide a mechanistic account of the origins of such dissociations. The model suggests that dissociations between regional activation patterns in verb inflection emerge in an adult processing system that has been shaped through experience-dependent structural brain development. Although these dissociations appear to be between regular and irregular verbs, they arise in the model from a combination of statistical properties including frequency, relationships to other verbs, and phonological complexity, without a causal role for regularity or semantics. These results are consistent with the notion that all inflections are produced in a single associative mechanism. The model generates predictions about the patterning of active brain regions for different verbs that can be tested in future imaging studies.

Brain imaging studies of English past tense inflection have found dissociations between regular and irregular verbs, but no coherent picture has emerged to explain how these dissociations arise. Here we use synthetic brain imaging on a neural network model to provide a mechanistic account of the origins of such dissociations. The model suggests that dissociations between regional activation patterns in verb inflection emerge in an adult processing system that has been shaped through experience-dependent structural brain development. Although these dissociations appear to be between regular and irregular verbs, they arise in the model from a combination of statistical properties including frequency, relationships to other verbs, and phonological complexity, without a causal role for regularity or semantics. These results are consistent with the notion that all inflections are produced in a single associative mechanism. The model generates predictions about the patterning of active brain regions for different verbs that can be tested in future imaging studies. [ABSTRACT FROM AUTHOR]

Knowledge of common events is central to many aspects of cognition. Intuitively, it seems as though events are linear chains of the activities of which they are comprised. In line with this intuition, a number of theories of the temporal structure of event knowledge have posited mental representations (data structures) consisting of linear chains of activities. Competing theories focus on the hierarchical nature of event knowledge, with representations comprising ordered scenes, and chains of activities within those scenes. We present evidence that the temporal structure of events typically is not well‐defined, but it is much richer and more variable both within and across events than has usually been assumed. We also present evidence that prediction‐based neural network models can learn these rich and variable event structures and produce behaviors that reflect human performance. We conclude that knowledge of the temporal structure of events in the human mind emerges as a consequence of prediction‐based learning. McRae, Brown and Elman argue against the view that events are structured as frequently‐occurring sequences of world stimuli. They underline the importance of temporal structure defining event types and advance a more complex temporal structure, which allows for some variance in the component elements. [ABSTRACT FROM AUTHOR]

While the ability to acquire non-linearly separable (NLS) classifications is well documented in the study of human category learning, the relative ease of learning compared to a linear separable structure is difficult to evaluate without potential confounds. Medin and Schwanenflugel (Journal of Experimental Psychology: Human Learning and Memory, 7, 355-368, 1981) were the first to demonstrate that NLS classifications are not more difficult to acquire than linearly separable ones when structures are equated in terms of within- and between-category similarities. However, their evidence is less sturdy than might be expected due to non-standard methodology and low sample size. We conducted a conceptual replication to clarify the behavioral picture and perform qualitative testing of formal models. The behavioral results not only showed a lack of advantage for the linearly separable (LS) structure, but revealed a stronger finding: the NLS structure was reliably easier to acquire. Differences in the relative ease of NLS learners to master certain items yielded evidence for the existence of distinct learner subgroups, one marked by significantly easier (not harder) learning of exception items. Comparing the qualitative fits of leading computational models to the human learning performance confirmed that a pure prototype account, even with contemporary updates, remains incompatible with the data. However, exemplar models and similarity-based models grounded in sophisticated forms of abstraction-based learning successfully account for the NLS advantage. In sum, evidence against a linear separability constraint is redoubled, and the observed NLS advantage along with behavioral patterns seen at the subgroup and item level provide a valuable basis for comprehensive evaluation of competing theoretical accounts and models. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a tutorial for creating neural network models of personality processes. Such models enable researchers to create explicit models of both personality structure and personality dynamics, and to address issues of recent concern in personality, such as, "If personality is stable, then how is it possible that within subject variability in personality states can be as large as or larger than between subject variability in personality?" or "Is it possible to understand personality dynamics and personality structure within a common framework?" We discuss why one should want to use neural networks, review what a neural network model is, review a previous model we have constructed, discuss how to conceptualize issues in such a way that they can be computationally modeled, show how that conceptualization can be translated into a model, and discuss the utility of such models for understanding personality structure and personality dynamics. To build our model we use a neural network modeling package called emergent that is freely available, and a specific architecture called Leabra to build a runnable model that addresses one of the questions posed above: How can within subject variability in personality related states be as large as between subject variability in personality? [ABSTRACT FROM AUTHOR]

Nativist theories have argued that language involves syntactic principles which are unlearnable from the input children receive. A paradigm case of these innate principles is the structure dependence of auxiliary inversion in complex polar questions (Chomsky, 1968, 1975, 1980). Computational approaches have focused on the properties of the input in explaining how children acquire these questions. In contrast, we argue that messages are structured in a way that supports structure dependence in syntax. We demonstrate this approach within a connectionist model of sentence production (Chang, 2009) which learned to generate a range of complex polar questions from a structured message without positive exemplars in the input. The model also generated different types of error in development that were similar in magnitude to those in children (e.g., auxiliary doubling, Ambridge, Rowland, & Pine, 2008; Crain & Nakayama, 1987). Through model comparisons we trace how meaning constraints and linguistic experience interact during the acquisition of auxiliary inversion. Our results suggest that auxiliary inversion rules in English can be acquired without innate syntactic principles, as long as it is assumed that speakers who ask complex questions express messages that are structured into multiple propositions. [ABSTRACT FROM AUTHOR]

The connectionist model is one approach to studying language processing from a computational perspective. And building a representation in the connectionist model study is just as important as making the structure of the model in that it determines the level of learning and performance of the model. The connectionist model has been constructed in two different ways: localist representation and distributed representation. However, the localist representation used in the previous studies had limitations in that the unit of the output layer having a rare target activation value is inactivated, and the past distributed representation has the limitation of difficulty in confirming the result by the opacity of the displayed information. This has been a limitation of the overall connection model study. In this paper, we present a new method to induce distributed representation with local representation using abstraction of information, which is a feature of restricted Boltzmann machine, with respect to the limitation of such representation of the past. As a result, our proposed method effectively solves the problem of conventional representation by using the method of information compression and inverse transformation of distributed representation into local representation. [ABSTRACT FROM AUTHOR]

The recent "deep learning revolution" in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems. [ABSTRACT FROM AUTHOR]

Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage.

It has been argued that the mental representation resulting from sentence comprehension is not (just) an abstract symbolic structure but a “mental simulation” of the state-of-affairs described by the sentence. We present a particular formalization of this theory and show how it gives rise to quantifications of the amount of syntactic and semantic information conveyed by each word in a sentence. These information measures predict simulated word-processing times in a dynamic connectionist model of sentence comprehension as mental simulation. A quantitatively similar relation between information content and reading time is known to be present in human reading-time data.

The question of whether, and if so how, learning can be transfered from previously experienced games to novel games has recently attracted the attention of the experimental game theory literature. Existing research presumes that learning operates over actions, beliefs or decision rules. This study instead uses a connectionist approach that learns a direct mapping from game payoffs to a probability distribution over own actions. Learning is operationalized as a backpropagation rule that adjusts the weights of feedforward neural networks in the direction of increasing the probability of an agent playing a myopic best response to the last game played. One advantage of this approach is that it expands the scope of the model to any possible nxn normal-form game allowing for a comprehensive model of transfer of learning. Agents are exposed to games drawn from one of seven classes of games with significantly different strategic characteristics and then forced to play games from previously unseen classes. I find significant transfer of learning, i.e., behavior that is path-dependent, or conditional on the previously seen games. Cooperation is more pronounced in new games when agents are previously exposed to games where the incentive to cooperate is stronger than the incentive to compete, i.e., when individual incentives are aligned. Prior exposure to Prisoner's dilemma, zero-sum and discoordination games led to a significant decrease in realized payoffs for all the game classes under investigation. A distinction is made between superficial and deep transfer of learning both---the former is driven by superficial payoff similarities between games, the latter by differences in the incentive structures or strategic implications of the games. I examine whether agents learn to play the Nash equilibria of games, how they select amongst multiple equilibria, and whether they transfer Nash equilibrium behavior to unseen games. Sufficient exposure to a strategically he

The extent to which meaning is involved in reading aloud has proven an area of longstanding debate, and current computational models differ on this dimension. The connectionist triangle model proposes that normal individuals rely on semantic information for correct reading of words with atypical spelling-sound relationships, but to varying degrees. This proposed individual difference would account for the varying stage of decline at which patients with semantic dementia first show the reading impairment known as surface dyslexia. Recent neuroimaging data has provided validation of this view, showing that individual differences in degree of semantic reliance during exception word reading predict the amount of activation in left anterior temporal regions associated with semantic processing. This study aimed to establish the cognitive correlates of individual differences in semantic reliance during exception word reading. Experiment 1 used a subgrouping approach with 32 participants and found larger imageability and semantic priming effects specifically for exception word reading amongst high relative to low semantic reliance readers. High semantic reliance readers also tended to read nonwords more slowly than low semantic reliance readers. A second experiment used a regression approach with 129 readers and confirmed the relationship of degree of semantic reliance both to imageability effects in exception word reading and speed of nonword reading. Further, while the performance of the higher semantic readers revealed no significant association with semantic processing tasks, there was a negative relationship with rhyme processing tasks. We therefore speculate that differences in phonological abilities may be responsible for varying degrees of semantic reliance in reading aloud. This proposal accords with the results of functional imaging showing that higher semantic reliance during exception word reading corresponds to lower activation in left pre-central gyrus, an area associated with direct spelling sound mapping and phonological processing. Our results therefore establish the nature of systematic individual differences in degree of semantic involvement amongst normal readers, and suggest directions for future neuroimaging and computational modeling research to uncover their origins. [ABSTRACT FROM AUTHOR]

Connectionist modeling was used to investigate the brain mechanisms responsible for pain's ability to shift attention away from another stimulus modality and toward itself. Different connectionist model architectures were used to simulate the different possible brain mechanisms underlying this attentional bias, where nodes in the model simulated the brain areas thought to mediate the attentional bias, and the connections between the nodes simulated the interactions between the brain areas. Mathematical optimization techniques were used to find the model parameters, such as connection strengths, that produced the best quantitative fits of reaction time and event-related potential data obtained in our previous work. Of the several architectures tested, two produced excellent quantitative fits of the experimental data. One involved an unexpected pain stimulus activating somatic threat detectors in the dorsal posterior insula. This threat detector activity was monitored by the medial prefrontal cortex, which in turn evoked a phasic response in the locus coeruleus. The locus coeruleus phasic response resulted in a facilitation of the cortical areas involved in decision and response processes time-locked to the painful stimulus. The second architecture involved the presence of pain causing an increase in general arousal. The increase in arousal was mediated by locus coeruleus tonic activity, which facilitated responses in the cortical areas mediating the sensory, decision, and response processes involved in the task. These two neural network architectures generated competing predictions that can be tested in future studies. [ABSTRACT FROM AUTHOR]

Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage. [ABSTRACT FROM AUTHOR]

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. [ABSTRACT FROM AUTHOR]

An influential view of the nature of the language system is that of an evolved biological system in which a set of rules is combined with a lexicon that contains the words of the language together with a representation of their context. Alternative views, usually based on connectionist modeling, attempt to explain the structure of language on the basis of complex associative processes. Here, I put forward a third view that stresses experience-dependent structural development of the brain circuits supporting language as a core principle of the organization of the language system. In this view, embodied in a recent neuroconstructivist neural network of past tense development and processing, initial domain-general predispositions enable the development of functionally specialized brain structures through interactions between experience-dependent brain development and statistical learning in a structured environment. Together, these processes shape a biological adult language system that appears to separate into distinct mechanism for processing rules and exceptions, whereas in reality those subsystems co-develop and interact closely. This view puts experience-dependent brain development in response to a specific language environment at the heart of understanding not only language development but adult language processing as well. [ABSTRACT FROM AUTHOR]

Understanding spoken words involves a rapid mapping from speech to conceptual representations. One distributed feature-based conceptual account assumes that the statistical characteristics of concepts' features-the number of concepts they occur in ( distinctiveness/ sharedness) and likelihood of co-occurrence ( correlational strength)-determine conceptual activation. To test these claims, we investigated the role of distinctiveness/sharedness and correlational strength in speech-to-meaning mapping, using a lexical decision task and computational simulations. Responses were faster for concepts with higher sharedness, suggesting that shared features are facilitatory in tasks like lexical decision that require access to them. Correlational strength facilitated responses for slower participants, suggesting a time-sensitive co-occurrence-driven settling mechanism. The computational simulation showed similar effects, with early effects of shared features and later effects of correlational strength. These results support a general-to-specific account of conceptual processing, whereby early activation of shared features is followed by the gradual emergence of a specific target representation. [ABSTRACT FROM AUTHOR]

The question of whether, and if so how, learning can be transfered from previously experienced games to novel games has recently attracted the attention of the experimental game theory literature. Existing research presumes that learning operates over actions, beliefs or decision rules. This study instead uses a connectionist approach that learns a direct mapping from game payoffs to a probability distribution over own actions. Learning is operationalized as a backpropagation rule that adjusts the weights of feedforward neural networks in the direction of increasing the probability of an agent playing a myopic best response to the last game played. One advantage of this approach is that it expands the scope of the model to any possible n × n normal-form game allowing for a comprehensive model of transfer of learning. Agents are exposed to games drawn from one of seven classes of games with significantly different strategic characteristics and then forced to play games from previously unseen classes. I find significant transfer of learning, i.e., behavior that is path-dependent, or conditional on the previously seen games. Cooperation is more pronounced in new games when agents are previously exposed to games where the incentive to cooperate is stronger than the incentive to compete, i.e., when individual incentives are aligned. Prior exposure to Prisoner's dilemma, zero-sum and discoordination games led to a significant decrease in realized payoffs for all the game classes under investigation. A distinction is made between superficial and deep transfer of learning both-the former is driven by superficial payoff similarities between games, the latter by differences in the incentive structures or strategic implications of the games. I examine whether agents learn to play the Nash equilibria of games, how they select amongst multiple equilibria, and whether they transfer Nash equilibrium behavior to unseen games. Sufficient exposure to a strategically heterogeneous set of games is found to be a necessary condition for deep learning (and transfer) across game classes. Paradoxically, superficial transfer of learning is shown to lead to better outcomes than deep transfer for a wide range of game classes. The simulation results corroborate important experimental findings with human subjects, and make several novel predictions that can be tested experimentally. [ABSTRACT FROM AUTHOR]

Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981) is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative) learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition.

Theories of embodied cognition suppose that perception, action and cognition are tightly intertwined and share common representations and processes. Indeed, numerous empirical studies demonstrate interaction between stimulus perception, response planning and response execution. In this paper, we present an experiment and a connectionist model that show how the Simon effect, a canonical example of perception-action congruency, can be moderated by the (cognitive representation of the) task instruction. To date, no representational account of this influence exists. In the experiment, a two-dimensional Simon task was used, with critical stimuli being colored arrows pointing in one of four directions (backward, forward, left or right). Participants stood on a Wii balance board, oriented diagonally towards the screen displaying the stimuli. They were either instructed to imagine standing on a snowboard or on a pair of skis and to respond to the stimulus color by leaning towards either the left or right foot. We expected that participants in the snowboard condition would encode these movements as forward or backward, resulting in a Simon effect on this dimension. This was confirmed by the results. The left–right congruency effect was larger in the ski condition, whereas the forward–backward congruency effect appeared only in the snowboard condition. The results can be readily accounted for by HiTEC, a connectionist model that aims at capturing the interaction between perception and action at the level of representations, and the way this interaction is mediated by cognitive control. Together, the empirical work and the connectionist model contribute to a better understanding of the complex interaction between perception, cognition, and action.

Recent research on the effects of letter transposition in Indo-European Languages has shown that readers are surprisingly tolerant of these manipulations in a range of tasks. This evidence has motivated the development of new computational models of reading that regard flexibility in positional coding to be a core and universal principle of the reading process. Here we argue that such approach does not capture cross-linguistic differences in transposed-letter effects, nor does it explain them. To address this issue, we investigated how a simple domain-general connectionist architecture performs in tasks such as letter-transposition and letter substitution when it had learned to process words in the context of different linguistic environments. The results show that in spite of the neurobiological noise involved in registering letter-position in all languages, flexibility and inflexibility in coding letter order is also shaped by the statistical orthographic properties of words in a language, such as the relative prevalence of anagrams. Our learning model also generated novel predictions for targeted empirical research, demonstrating a clear advantage of learning models for studying visual word recognition. [ABSTRACT FROM AUTHOR]

Consciousness remains a mystery-'a phenomenon that people do not know how to think about-yet' (Dennett, , p. 21). Here, I consider how the connectionist perspective on information processing may help us progress toward the goal of understanding the computational principles through which conscious and unconscious processing differ. I begin by delineating the conceptual challenges associated with classical approaches to cognition insofar as understanding unconscious information processing is concerned, and to highlight several contrasting computational principles that are constitutive of the connectionist approach. This leads me to suggest that conscious and unconscious processing are fundamentally connected, that is, rooted in the very same computational principles. I further develop a perspective according to which the brain continuously and unconsciously learns to redescribe its own activity itself based on constant interaction with itself, with the world, and with other minds. The outcome of such interactions is the emergence of internal models that are metacognitive in nature and that function so as to make it possible for an agent to develop a (limited, implicit, practical) understanding of itself. In this light, plasticity and learning are constitutive of what makes us conscious, for it is in virtue of our own experiences with ourselves and with other people that our mental life acquires its subjective character. The connectionist framework continues to be uniquely positioned in the Cognitive Sciences to address the challenge of identifying what one could call the 'computational correlates of consciousness' (Mathis & Mozer, ) because it makes it possible to focus on the mechanisms through which information processing takes place. [ABSTRACT FROM AUTHOR]

Task-cuing experiments are usually intended to explore control of task set. But when small stimulus sets are used, they plausibly afford learning of the response associated with a combination of cue and stimulus, without reference to tasks. In 3 experiments we presented the typical trials of a task-cuing experiment: a cue (colored shape) followed, after a short or long interval, by a digit to which 1 of 2 responses was required. In a tasks condition, participants were (as usual) directed to interpret the cue as an instruction to perform either an odd/even or a high/low classification task. In a cue + stimulus → response (CSR) condition, to induce learning of mappings between cue-stimulus compound and response, participants were, in Experiment 1, given standard task instructions and additionally encour-aged to learn the CSR mappings; in Experiment 2, informed of all the CSR mappings and asked to learn them, without standard task instructions; in Experiment 3, required to learn the mappings by trial and error. The effects of a task switch, response congruence, preparation, and transfer to a new set of stimuli differed substantially between the conditions in ways indicative of classification according to task rules in the tasks condition, and retrieval of responses specific to stimulus-cue combinations in the CSR conditions. Qualitative features of the latter could be captured by an associative learning network. Hence associatively based compound retrieval can serve as the basis for performance with a small stimulus set. But when organization by tasks is apparent, control via task set selection is the natural and efficient strategy. [ABSTRACT FROM AUTHOR]

Connectionist models have been applied to many phenomena in infant development including perseveration, language learning, categorization, and causal perception. In this article, we discuss the benefits of connectionist networks for the advancement of theories of early development. In particular, connectionist models contribute novel testable predictions, instantiate the theorized mechanism of change, and create a unifying framework for understanding infant learning and development. We relate these benefits to the 2 primary approaches used in connectionist models of infant development. The first approach employs changes in neural processing as the basis for developmental changes, and the second employs changes in infants' experiences. The review sheds light on the unique hurdles faced by each approach as well as the challenges and solutions related to both, particularly with respect to the identification of critical model components, parameter specification, availability of empirical data, and model comparison. Finally, we discuss the future of modeling work as it relates to the study of development. We propose that connectionist networks stand to make a powerful contribution to the generation and revision of theories of early child development. Furthermore, insights from connectionist models of early development can improve the understanding of developmental changes throughout the life span. [ABSTRACT FROM AUTHOR]

Temporal discounting denotes the fact that individuals prefer smaller rewards delivered sooner over larger rewards delivered later, often to a higher extent than suggested by normative economical theories. In this article, we identify three lines of research studying this phenomenon which aim (i) to describe temporal discounting mathematically, (ii) to explain observed choice behavior psychologically, and (iii) to predict the influence of specific factors on intertemporal decisions. We then opt for an approach integrating postulated mechanisms and empirical findings from these three lines of research. Our approach focuses on the dynamical properties of decision processes and is based on computational modeling. We present a dynamic connectionist model of intertemporal choice focusing on the role of self-control and time framing as two central factors determining choice behavior. Results of our simulations indicate that the two influences interact with each other, and we present experimental data supporting this prediction. We conclude that computational modeling of the decision process dynamics can advance the integration of different strands of research in intertemporal choice.

We introduce a computational model of the negative priming (NP) effect that includes perception, memory, attention, decision making, and action. The model is designed to provide a coherent picture across competing theories of NP and to relate psychological experiments to physiological measurements. The model is formulated in terms of an abstract dynamics of activations of features, their binding into object entities or their semantic categorization as well as related memories and implied reactions. The dynamical variables interact in a connectionist network which is shown to be adaptable to a variety of experimental paradigms. We find that selective attention can be modeled by means of inhibitory processes and by a threshold dynamics. Considering the implementation it becomes obvious that the specificity of the experimental paradigm must be taken into account when predicting the nature of the NP effect.

Mastery of language can be a struggle for some children. Amongst those that succeed in achieving this feat there is variability in proficiency. Cognitive scientists remain intrigued by this variation. A now substantial body of research suggests that language acquisition is underpinned by a child's capacity for statistical learning. Moreover, a growing body of research has demonstrated that variability in statistical learning is associated with variability in language proficiency. Yet, there is a striking lack of longitudinal data. To date, there has been no comprehensive investigation of whether a capacity for statistical learning in young children is, in fact, associated with language proficiency in subsequent years. Here we review key studies that have led to the need for this longitudinal research. Advancing the language acquisition debate via longitudinal research has the potential to transform our understanding of typical development as well as disorders such as autism, specific language impairment and dyslexia.

Errors in choice tasks are preceded by gradual changes in brain activity presumably related to fluctuations in cognitive control that promote the occurrence of errors. In the present paper, we use connectionist modeling to explore the hypothesis that these fluctuations reflect (mal-)adaptive adjustments of cognitive control. We considered ERP data from a study in which the probability of conflict in an Eriksen flanker task was manipulated in sub-blocks of trials. Errors in these data were preceded by a gradual decline of N2 amplitude. After fitting a connectionist model of conflict adaptation to the data, we analyzed simulated N2 amplitude, simulated response times and stimulus history preceding errors in the model, and found that the model produced the same pattern as obtained in the empirical data. Moreover, this pattern is not found in alternative models in which cognitive control varies randomly or in an oscillating manner. Our simulations suggest that the decline of N2 amplitude preceding errors reflects an increasing adaptation of cognitive control to specific task demands, which leads to an error when these task demands change. Taken together, these results provide evidence that error-preceding brain activity can reflect adaptive adjustments rather than unsystematic fluctuations of cognitive control, and therefore, that these errors are actually a consequence of the adaptiveness of human cognition.

The recent outburst of interest in cognitive developmental robotics is fueled by the ambition to propose ecologically plausible mechanisms of how, among other things, a learning agent/robot could ground linguistic meanings in its sensorimotor behaviour. Along this stream, we propose a model that allows the simulated iCub robot to learn the meanings of actions (point, touch and push) oriented towards objects in robot's peripersonal space. In our experiments, the iCub learns to execute motor actions and comment on them. Architecturally, the model is composed of three neural-network-based modules that are trained in different ways. The first module, a two-layer perceptron, is trained by back-propagation to attend to the target position in the visual scene, given the low-level visual information and the feature-based target information. The second module, having the form of an actor-critic architecture, is the most distinguishing part of our model, and is trained by a continuous version of reinforcement learning to execute actions as sequences, based on a linguistic command. The third module, an echo-state network, is trained to provide the linguistic description of the executed actions. The trained model generalises well in case of novel action-target combinations with randomised initial arm positions. It can also promptly adapt its behavior if the action/target suddenly changes during motor execution.

Numerous studies suggest that preverbal infants possess the ability to make sociomoral judgements and demonstrate a preference for prosocial agents. Some theorists argue that infants possess an "innate moral core" that guides their sociomoral reasoning. However, we propose that infants' capacity for putative sociomoral evaluation and reasoning can just as likely be driven by a domain-general associative-learning mechanism that is sensitive to agent action. We implement this theoretical account in a connectionist computational model and show that it can account for the pattern of results in Hamlin et al. (2007), Hamlin and Wynn (2011), Hamlin (2013), and Hamlin, Wynn, Bloom, and Mahajan (2011). These are pioneering studies in this area and were among the first to examine sociomoral evaluation in preverbal infants. Based on the results of 9 computer simulations, we suggest that an associative-learning mechanism—instantiated as a computational (connectionist) model—can account for previous findings on preverbal infants' capacity for sociomoral evaluation. These results suggest that an innate moral core may not be necessary to account for sociomoral evaluation in infants. • An associative-learning mechanism can account for infant social evaluation. • Infants' choices in helping-hindering studies reflect expectations for interaction. • An 'innate moral core' may not be necessary for infant social evaluation. [ABSTRACT FROM AUTHOR]

Abstract: Computational models are tools for testing mechanistic theories of learning and development. Formal models allow us to instantiate theories of cognitive development in computer simulations. Model behavior can then be compared to real performance. Connectionist models, loosely based on neural information processing, have been successful in capturing a range of developmental phenomena, in particular on-line within-task category learning by young infants. Here we describe two new models. One demonstrates how age dependent changes in neural receptive field sizes can explain observed changes in on-line category learning between 3 and 10 months of age. The other aims to reconcile two conflicting views of infant categorization by focusing on the different task requirements of preferential looking and manual exploration studies. A dual-memory hypothesis posits that within-task category learning that drives looking time behaviors is based on a fast-learning memory system, whereas categorization based on background experience and assessed by paradigms requiring complex motor behavior relies on a second, slow-learning system. The models demonstrate how emphasizing the mechanistic causes of behaviors leads to discovery of deeper, more explanatory accounts of learning and development. [Copyright &y& Elsevier]

We analyzed stimulus class formation in a human study and in a connectionist model (CM) with a yes/no procedure, using compound stimuli. In the human study, the participants were six female undergraduate students; the CM was a feed-forward back-propagation network. Two 3-member stimulus classes were trained with a similar procedure in both the human participants and the model. During training, the compound stimuli A1B1, A2B2, B1C1, B2C2 were associated with the yes response, and A1B2, A2B1, B1C2, B2C1 were associated with the no response. During testing, we probe the emergence of the yes and no responses in the presence of new compound stimuli (A1C1, A2C2, A1C2, A2C1). Four participants and five out of six runs of the CM exhibited stimulus class formation. These results show the usefulness of the yes/no procedure for simulating stimulus class formation in a CM in contrast with the models inspired in matching-to-sample procedures. [ABSTRACT FROM AUTHOR]

It has been argued that the mental representation resulting from sentence comprehension is not (just) an abstract symbolic structure but a "mental simulation" of the state-of-affairs described by the sentence. We present a particular formalization of this theory and show how it gives rise to quantifications of the amount of syntactic and semantic information conveyed by each word in a sentence. These information measures predict simulated word-processing times in a dynamic connectionist model of sentence comprehension as mental simulation. A quantitatively similar relation between information content and reading time is known to be present in human reading-time data. [ABSTRACT FROM AUTHOR]

Abstract: Empirical evidence demonstrating that sentence meaning is rapidly reconciled with the visual environment has been broadly construed as supporting the seamless interaction of visual and linguistic representations during situated comprehension. Based on recent behavioral and neuroscientific findings, however, we argue for the more deeply rooted coordination of the mechanisms underlying visual and linguistic processing, and for jointly considering the behavioral and neural correlates of scene–sentence reconciliation during situated comprehension. The Coordinated Interplay Account (CIA; Knoeferle, P., & Crocker, M. W. (2007). The influence of recent scene events on spoken comprehension: Evidence from eye movements. Journal of Memory and Language, 57(4), 519–543) asserts that incremental linguistic interpretation actively directs attention in the visual environment, thereby increasing the salience of attended scene information for comprehension. We review behavioral and neuroscientific findings in support of the CIA’s three processing stages: (i) incremental sentence interpretation, (ii) language-mediated visual attention, and (iii) the on-line influence of non-linguistic visual context. We then describe a recently developed connectionist model which both embodies the central CIA proposals and has been successfully applied in modeling a range of behavioral findings from the visual world paradigm (Mayberry, M. R., Crocker, M. W., & Knoeferle, P. (2009). Learning to attend: A connectionist model of situated language comprehension. Cognitive Science). Results from a new simulation suggest the model also correlates with event-related brain potentials elicited by the immediate use of visual context for linguistic disambiguation (Knoeferle, P., Habets, B., Crocker, M. W., & Münte, T. F. (2008). Visual scenes trigger immediate syntactic reanalysis: Evidence from ERPs during situated spoken comprehension. Cerebral Cortex, 18(4), 789–795). Finally, we argue that the mechanisms underlying interpretation, visual attention, and scene apprehension are not only in close temporal synchronization, but have co-adapted to optimize real-time visual grounding of situated spoken language, thus facilitating the association of linguistic, visual and motor representations that emerge during the course of our embodied linguistic experience in the world. [Copyright &y& Elsevier]

According to Bowers (2009), the finding that there are neurons with highly selective responses to familiar stimuli supports theories positing localist representations over approaches positing the type of distributed representations typically found in parallel distributed processing (PDP) models. However, his conclusions derive from an overly narrow view of the range of possible distributed representations and of the role that PDP models can play in exploring their properties. Although it is true that current distributed theories face challenges in accounting for both neural and behavioral data, the proposed localist account—to the extent that it is articulated at all—runs into more fundamental difficulties. Central to these difficulties is the problem of specifying the set of entities a localist unit represents. [ABSTRACT FROM AUTHOR]

A substantial body of experimental evidence has demonstrated that labels have an impact on infant categorization processes. Yet little is known regarding the nature of the mechanisms by which this effect is achieved. We distinguish between two competing accounts: supervised name-based categorization and unsupervised feature-based categorization. We describe a neurocomputational model of infant visual categorization, based on self-organizing maps, that implements the unsupervised feature-based approach. The model successfully reproduces experiments demonstrating the impact of labeling on infant visual categorization reported in Plunkett, Hu, and Cohen (2008) . It mimics infant behavior in both the familiarization and testing phases of the procedure, using a training regime that involves only single presentations of each stimulus and using just 24 participant networks per experiment. The model predicts that the observed behavior in infants is due to a transient form of learning that might lead to the emergence of hierarchically organized categorical structure and that the impact of labels on categorization is influenced by the perceived similarity and the sequence in which the objects are presented. The results suggest that early in development, say before 12 months old, labels need not act as invitations to form categories nor highlight the commonalities between objects, but they may play a more mundane but nevertheless powerful role as additional features that are processed in the same fashion as other features that characterize objects and object categories. [ABSTRACT FROM AUTHOR]

Evidence from numerous studies using the visual world paradigm has revealed both that spoken language can rapidly guide attention in a related visual scene and that scene information can immediately influence comprehension processes. These findings motivated the coordinated interplay account ( Knoeferle & Crocker, 2006 ) of situated comprehension, which claims that utterance-mediated attention crucially underlies this closely coordinated interaction of language and scene processing. We present a recurrent sigma-pi neural network that models the rapid use of scene information, exploiting an utterance-mediated attentional mechanism that directly instantiates the CIA. The model is shown to achieve high levels of performance (both with and without scene contexts), while also exhibiting hallmark behaviors of situated comprehension, such as incremental processing, anticipation of appropriate role fillers, as well as the immediate use, and priority, of depicted event information through the coordinated use of utterance-mediated attention to the scene. [ABSTRACT FROM AUTHOR]

This article presents a sobering view of the discipline of cognitive neuropsychology as practiced over the last three or four decades. Our judgment is that, although the study of abnormal cognition resulting from brain injury or disease in previously normal adults has produced a catalogue of fascinating and highly selective deficits, it has yielded relatively little advance in understanding how the brain accomplishes its cognitive business. We question the wisdom of the following three “choices” in mainstream cognitive neuropsychology: (a) single-case methodology, (b) dissociation between functions as the most important source of evidence, and (c) a central goal of diagramming the functional architecture of cognition rather than specifying how its components work. These choices may all stem from an excessive commitment to strict and fine-grained modularity. Although different brain regions are undoubtedly specialized for different functions, we argue that parallel and interactive processing is a better assumption about cognitive processing. The essential goal of specifying representations and processes can, we claim, be significantly assisted by computational modeling which, by its very nature, requires such specification. [ABSTRACT FROM AUTHOR]

The present study investigated the mechanisms underlying error detection in the error signaling response. The authors tested between a response monitoring account and a conflict monitoring account. By implementing each account within the neural network model of N. Yeung, M. M. Botvinick, and J. D. Cohen (2004), they demonstrated that both accounts make different predictions as to how error signaling performance is influenced by varying the participants' response criterion. These predictions were tested in an experiment using the Eriksen-flanker task. The qualitative pattern as well as a model fit favored the response monitoring account, which states that error detection is mediated by detecting internal error corrections. [ABSTRACT FROM AUTHOR]

Connectionist-model simulations of competing hypotheses of cognition in schizophrenia were constructed and tested. Emphasis was placed on judgment of affect, a prominent area of disturbance in this disorder with potential implications for social impairment. Participants with paranoid or nonparanoid schizophrenia and control participants provided judgments of affect as expressed in photographic faces. Schizophrenia groups were less accurate than control groups, and the paranoid group had greater latencies than did other groups. Model predictions simultaneously addressed judgment content and latencies for each trial. Results provide a connectionist extension of an account of deficits in schizophrenia that originated at the computational (stochastic modeling) level of analysis. This account postulates extra stages of item encoding but no reduction in formally defined processing capacity. It also provides for abnormalities in both judgment patterns and duration and is consistent with biological accounts of schizophrenia deficits. The substantive findings are supported by strategic innovations in the construction and testing of connectionist models. [ABSTRACT FROM AUTHOR]

Two studies provided evidence that holsters the Marsh, Adams, and Kleck hypothesis that the morphology of certain emotion expressions reflects an evolved adaptation to mimic babies or mature adults. Study 1 found differences in emotion expressions' resemblance to babies using objective indices of babyfaceness provided by connectionist models that are impervious to overlapping cultural stereotypes about babies and the emotions. Study 2 not only replicated parallels between impressions of certain emotions and babies versus adults hut also showed that objective indices of babyfaceness partially mediated impressions of the emotion expressions. babyface effects were independent of strong effects of attractiveness, and babyfaceness did not mediate impressions of happy expressions, to which the evolutionary hypothesis would not apply. [ABSTRACT FROM AUTHOR]

Plaut and Booth (2000) developed a distributed connectionist model of written word comprehension and evaluated it against empirical findings on individual and developmental differences in semantic priming in visual lexical decision. Borowsky and Besner (2006) raised a number of challenges for this model. First, the model was not shown to be capable of accurately distinguishing words from orthographically matched nonwords. Second, its use of a semantic measure for performing lexical decision appears inconsistent with evidence of normal lexical decision in brain-damaged patients with semantic impairments. Third, the explanation offered for additive and interactive effects in the model appears incompatible with certain aspects of existing empirical findings on the joint effects word frequency, priming context, and stimulus quality. In this reply, the authors demonstrate with additional modeling that none of these issues is problematic for the model. [ABSTRACT FROM AUTHOR]

To model behavior, scientists need to know how models behave. This means learning what other behaviors a model can produce besides the one generated by participants in an experiment. This is a difficult problem because of the complexity of psychological models (e.g., their many parameters) and because the behavioral precision of models (e.g., interval-scale performance) often mismatches their testable precision in experiments, where qualitative, ordinal predictions are the norm. Parameter space partitioning is a solution that evaluates model performance at a qualitative level. There exists a partition on the model's parameter space that divides it into regions that correspond to each data pattern. Three application examples demonstrate its potential and versatility for studying the global behavior of psychological models. [ABSTRACT FROM AUTHOR]