An analysis with Bion: an interview with James Gooch. Interview by JoAnn Culbert-Koehn

In 1968 Wilfred Bion moved to Los Angeles, escaping the perils of fame in London. He lived in Los Angeles until a few months before his death in Oxford in 1979. He made a deep impact on psychoanalysis in Los Angeles through those he analysed and what he wrote. James Gooch, psychiatrist and founding president of the Psychoanalytic Center of California describes in detail the transformative experience of his analysis with Bion in an interview with JoAnn Culbert-Koehn, Jungian analyst. Dr. Gooch describes important differences between his analysis with Bion and his classical Freudian analysis during his analytic training.     

Reorganization of functional and effective connectivity during real-time fMRI-BCI modulation of prosody processing

Mechanisms of cortical reorganization underlying the enhancement of speech processing have been poorly investigated. In the present study, we addressed changes in functional and effective connectivity induced in subjects who learned to deliberately increase activation in the right inferior frontal gyrus (rIFG), and improved their ability to identify emotional intonations by using a real-time fMRI Brain-Computer Interface. At the beginning of their training process, we observed a massive connectivity of the rIFG to a widespread network of frontal and temporal areas, which decreased and lateralized to the right hemisphere with practice. Volitional control of activation strengthened connectivity of this brain region to the right prefrontal cortex, whereas training increased its connectivity to bilateral precentral gyri. These findings suggest that changes of connectivity in a functionally specific manner play an important role in the enhancement of speech processing. Also, these findings support previous accounts suggesting that motor circuits play a role in the comprehension of speech.     

Effects of practice on task architecture: combined evidence from interference experiments and random-walk models of decision making

Does extensive practice reduce or eliminate central interference in dual-task processing? We explored the reorganization of task architecture with practice by combining interference analysis (delays in dual-task experiment) and random-walk models of decision making (measuring the decision and non-decision contributions to RT). The main delay observed in the Psychologically Refractory Period at short stimulus onset asynchronies (SOA) values was largely unaffected by training. However, the range of SOAs over which this interference regime held diminished with learning. This was consistent with an overall shift observed in single-task performance from a highly variable decision time to a reliable (non-decision time) contribution to response time. Executive components involved in coordinating dual-task performance decreased (and became more stable) after extensive practice. The results suggest that extensive practice reduces the duration of central decision stages, but that the qualitative property of central seriality remains a structural invariant.     

Connecting instances to promote children's relational reasoning

The practice of learning from multiple instances seems to allow children to learn about relational structure. The experiments reported here focused on two issues regarding relational learning from multiple instances: (a) what kind of perceptual situations foster such learning and (b) how particular object properties, such as complexity and similarity, interact with relational learning. Two kinds of perceptual situations were of interest here: simultaneous view, where instances are viewed at once, and sequential view, where instances are viewed one at a time (one right after the other). We examined the influence of particular perceptual situations and object properties using two tests of relational reasoning: a common match-to-sample task, where new instances are compared with a common sample, and a variable match-to-sample task, where new instances are compared with a sample that varies on each trial. Experiments 1 and 2 indicate that simultaneous presentation of even highly dissimilar instances, one simple and one complex, effectively connects them together and improves relational generalization in both match-to-sample tasks. Experiment 3 shows that simple samples are more effective than complex ones in the common match-to-sample task. However, when one instance is not used a common sample and various pairs of instances are simply compared, as in Experiment 4, simple and rich instances are equally effective at promoting relational learning. These results bear on our understanding of how children connect instances and how those initial connections affect learning and generalization.     

Residential Mobility and Prosocial Development Within a Single City

Neighborhood social dynamics have been shown to impact behavioral development in residents, including levels of prosociality (i.e. positive social behavior). This study explores whether residential moves to neighborhoods with different social dynamics can influence further prosocial development. Prosociality, five domains of social support, and residential location were tracked between 2006 and 2009 in 397 adolescents across a small city in upstate New York. Analysis compared the role of the different forms of social support in prosocial development for movers versus non-movers. The effects of one's neighborhood of residence at Time 2 were also compared between movers and non-movers. Prosocial development in these two groups responded similarly to all forms of social support, including from neighbors. Movers experienced a greater increase in prosociality the more residentially stable the adolescent population of their new neighborhood of residence. Such neighborhood characteristics were not influential in the prosocial development of non-movers.     

Exclusion constraints facilitate statistical word learning

The roles of linguistic, cognitive, and social-pragmatic processes in word learning are well established. If statistical mechanisms also contribute to word learning, they must interact with these processes; however, there exists little evidence for such mechanistic synergy. Adults use co-occurrence statistics to encode speech-object pairings with detailed sensitivity in stochastic learning environments (Vouloumanos, 2008). Here, we replicate this statistical work with nonspeech sounds and compare the results with the previous speech studies to examine whether exclusion constraints contribute equally to the statistical learning of speech-object and nonspeech-object associations. In environments in which performance could benefit from exclusion, we find a learning advantage for speech over nonspeech, revealing an interaction between statistical and exclusion processes in associative word learning.     

A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word‐Order Universal

In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language‐learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners’ input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment ( Culbertson, Smolensky, & Legendre, 2012 ) targeting the learning of word‐order patterns in the nominal domain. The model identifies internal biases of the experimental participants, providing evidence that learners impose (possibly arbitrary) properties on the grammars they learn, potentially resulting in the cross‐linguistic regularities known as typological universals. Learners exposed to mixtures of artificial grammars tended to shift those mixtures in certain ways rather than others; the model reveals how learners’ inferences are systematically affected by specific prior biases. These biases are in line with a typological generalization—Greenberg's Universal 18—which bans a particular word‐order pattern relating nouns, adjectives, and numerals.     

Symbolically speaking: a connectionist model of sentence production

The ability to combine words into novel sentences has been used to argue that humans have symbolic language production abilities. Critiques of connectionist models of language often center on the inability of these models to generalize symbolically (Fodor & Pylyshyn, 1988; Marcus, 1998). To address these issues, a connectionist model of sentence production was developed. The model had variables (role‐concept bindings) that were inspired by spatial representations (Landau & Jackendoff, 1993). In order to take advantage of these variables, a novel dual‐pathway architecture with event semantics is proposed and shown to be better at symbolic generalization than several variants. This architecture has one pathway for mapping message content to words and a separate pathway that enforces sequencing constraints. Analysis of the model's hidden units demonstrated that the model learned different types of information in each pathway, and that the model's compositional behavior arose from the combination of these two pathways. The model's ability to balance symbolic and statistical behavior in syntax acquisition and to model aphasic double dissociations provided independent support for the dual‐pathway architecture.