Spreading Activation in an Attractor Network With Latching Dynamics: Automatic Semantic Priming Revisited

Localist models of spreading activation (SA) and models assuming distributed representations offer very different takes on semantic priming, a widely investigated paradigm in word recognition and semantic memory research. In this study, we implemented SA in an attractor neural network model with distributed representations and created a unified framework for the two approaches. Our models assume a synaptic depression mechanism leading to autonomous transitions between encoded memory patterns (latching dynamics), which account for the major characteristics of automatic semantic priming in humans. Using computer simulations, we demonstrated how findings that challenged attractor‐based networks in the past, such as mediated and asymmetric priming, are a natural consequence of our present model’s dynamics. Puzzling results regarding backward priming were also given a straightforward explanation. In addition, the current model addresses some of the differences between semantic and associative relatedness and explains how these differences interact with stimulus onset asynchrony in priming experiments.     

Cognitive and neural modeling of dynamics of trust in competitive trustees

Trust dynamics can be modeled in relation to experiences. In this paper two models to represent human trust dynamics are introduced, namely a model on a cognitive level and a neural model. These models include a number of parameters, providing the possibility to express certain relations between trustees. The behavior of each of the models is further analyzed by means of simulation experiments and formal verification techniques. Thereafter, both models have been compared to see whether they can produce patterns that are comparable. As each of the models has its own specific set of parameters, with values that depend on the type of person modeled, such a comparison is non-trivial. To address this, a special comparison approach is introduced, based on mutual mirroring of the models in each other. More specifically, for a given parameter values set for one model, by an automated parameter estimation procedure the most optimal values for the parameter values of the other model are determined in order to show the same behavior. Roughly spoken the results are that the models can mirror each other up to an accuracy of around 90%.     

Why we view the brain as a computer

The view that the brain is a sort of computer has functioned as a theoretical guideline both in cognitive science and, more recently, in neuroscience. But since we can view every physical system as a computer, it has been less than clear what this view amounts to. By considering in some detail a seminal study in computational neuroscience, I first suggest that neuroscientists invoke the computational outlook to explain regularities that are formulated in terms of the information content of electrical signals. I then indicate why computational theories have explanatory force with respect to these regularities:in a nutshell, they underscore correspondence relations between formal/mathematical properties of the electrical signals and formal/mathematical properties of the represented objects. I finally link my proposal to the philosophical thesis that content plays an essential role in computational taxonomy.     

Testing neural network models of personality

Attempts to develop neural network models of personality have not generally used empirical data for training and validating the models. Two illustrations are provided which demonstrate the incorporation of empirical data into the modeling of behavioral responses to situations varying in closeness and hierarchical role relationships. An event-contingent recording procedure is utilized to obtain data from the same participant in multiple events for multiple situations. This data is then used in the training and validation of the neural networks. The first illustration models dominant and submissive behaviors in response to situations varying in social role status. The second illustration models agreeable and quarrelsome behaviors in response to situations varying in closeness and gender of the interaction partner. The predictions from both neural network models are consistent with previous research.     

Rating the similarity of simple perceptual stimuli: asymmetries induced by manipulating exposure frequency.

When judging the similarity of two stimuli, people's ratings often differ depending on the order in which the comparison is presented (A vs. B or B vs. A). Such directional asymmetries have typically been demonstrated using complex concepts that have a large number of semantic features and a standard explanation is that different sets of features are emphasized depending on the direction of the comparison. In this study, we show that directional asymmetries in the similarity of simple perceptual stimuli can be predictably manipulated merely by presenting each member of a pair with different frequency. Participants rated the similarity of color patches before and after performing an irrelevant training task in which a subset of colors was presented ten times more frequently than others. The similarity ratings after training were significantly more asymmetric than the ratings before training. We discuss the implications of these findings for models of similarity judgment and propose a computationally explicit explanation based on asymmetries in representational stability.     

Neural control of fundamental frequency rise and fall in Mandarin tones

The neural mechanisms used in tone rises and falls in Mandarin were investigated. Nine participants were scanned while they named one-character pictures that required rising or falling tone responses in Mandarin: the left insula and right putamen showed stronger activation between rising and falling tones; the left brainstem showed weaker activation between rising and falling tones. Connectivity analysis showed that the significant projection from the laryngeal motor cortex to the brainstem which was present in rising tones was absent in falling tones. Additionally, there was a significant difference between the connection from the insula to the laryngeal motor cortex which was negative in rising tones but positive in falling tones. These results suggest that the significant projection from the laryngeal motor cortex to the brainstem used in rising tones was not active in falling tones. The connection from the left insula to the laryngeal motor cortex that differs between rising and falling tones may control whether the rise mechanism is active or not.     

Children can solve Bayesian problems: the role of representation in mental computation

Can children reason the Bayesian way? We argue that the answer to this question depends on how numbers are represented, because a representation can do part of the computation. We test, for the first time, whether Bayesian reasoning can be elicited in children by means of natural frequencies. We show that when information was presented to fourth, fifth, and sixth graders in terms of probabilities, their ability to estimate the Bayesian posterior probability was zero. Yet when the same information was presented in natural frequencies, Bayesian reasoning showed a steady increase from fourth to sixth grade, reaching an average level of 19, 39, and 53%, respectively, in two studies. Sixth graders' performance with natural frequencies matched the performance of adults with probabilities. But this general increase was accompanied by striking individual differences. More than half of the sixth graders solved most or all problems, whereas one third could not solve a single one. An analysis of the children's responses provides evidence for the use of three non-Bayesian strategies. These follow an overlapping wave model of development and continue to be observed in the minds of adults. More so than adults' probabilistic reasoning, children's reasoning depends on a proper representation of information.     

Connectionism and the Philosophical Foundations of Cognitive Science

This is an overview of recent philosophical discussion about connectionism and the foundations of cognitive science. Connectionist modeling in cognitive science is described. Three broad conceptions of the mind are characterized, and their comparative strengths and weaknesses are discussed: (1) the classical computation conception in cognitive science; (2) a popular foundational interpretation of connectionism that John Tienson and I call "non-sentential computationalism"; and (3) an alternative interpretation of connectionism we call "dynamical cognition." Also discussed are two recent philosophical attempts to enlist connectionism in defense of eliminativism about folk psychology.     

A Poetics of Psychological Explanation

Intentional, 'commonsense,' or 'folk' psychology is, as Jerry Fodor has remarked, ubiquitous. Explanations of what we say and do in terms of our reasons for acting are the stock in trade of intentional psychology. But there is a question whether explanations in terms of reasons are properly explanatory. Donald Davidson and Daniel Dennett, to name two, have defended intentional psychology and its reason-explanations. Still, many philosophers – including Fodor, Davidson and Dennett – fail to pay due attention to the narrative basis of such agent-centered accounts of action. In this paper, I argue that psychological explanation is an agent-centered, narrative-based interpretive practice. To make my case, I present a poetics of psychological explanation: seven elements which collectively describe what makes psychological explanations work. Narrative form allows us to represent the temporal arc of agents' actions – as well as the temporal arc of their reasoning about their actions, both prospective and retrospective. It allows us to negotiate between the canonical and the exceptional in human experience, and thus to account for actions that strike us as puzzling or unusual – whether the puzzle originates in our suboptimal understanding or the agent's suboptimal reasoning. And it allows us to juxtapose different perspectives on any action. Such juxtapositioning gives us a mechanism for coming to see how an action that strikes us as misguided might have been construed by the agent as reasonable given her understanding of her circumstances. After establishing the seven elements of the poetics, I address the objection that narrative-based accounts of intentional action are not properly explanatory.