Evolving cellular automata schemes for protein folding modeling using the Rosetta atomic representation

Protein folding is the dynamic process by which a protein folds into its final native structure. This is different to the traditional problem of the prediction of the final protein structure, since it requires a modeling of how protein components interact over time to obtain the final folded structure. In this study we test whether a model of the folding process can be obtained exclusively through machine learning. To this end, protein folding is considered as an emergent process and the cellular automata tool is used to model the folding process. A neural cellular automaton is defined, using a connectionist model that acts as a cellular automaton through the protein chain to define the dynamic folding. Differential evolution is used to automatically obtain the optimized neural cellular automata that provide protein folding. We tested the methods with the Rosetta coarse-grained atomic model of protein representation, using different proteins to analyze the modeling of folding and the structure refinement that the modeling can provide, showing the potential advantages that such methods offer, but also difficulties that arise.

Addressing Word Recognition Difficulties of Adolescents With Dyslexia

This chapter provides a comprehensive overview of evidence-based word analysis approaches for promoting accurate and fluent reading of complex words by adolescents with a specific reading disability (i.e., dyslexia). First, research has been reviewed to pinpoint the characteristics and causes of dyslexia as a specific learning disability. Specifically, two theories of dyslexia, the phonological theory of dyslexia and the magnocellular theory of dyslexia, have been discussed to ascertain the causal attributes of phonological awareness deficits and auditory and visual sequencing deficits to word recognition difficulties of adolescents with dyslexia. Next, two theories of word recognition, particularly the dual-route model of word recognition and connectionist model of word recognition, have been discussed to clarify the mechanism underlying the manifestation of dyslexia and resultant difficulties with word recognition. Finally, evidence-based word analysis programs have been described as approaches for improving word reading ability of adolescents with dyslexia.

Reading skill correlates in frontal cortex during semantic and phonological processing

Previous studies have generally shown that reading skill is related to a left hemisphere network involving temporal, parietal, or frontal components. A limitation of many of these studies, however, is the neuroimaging of a single reading task, so we know less about how skill modulates the engagement of reading network during various reading tasks. Within the connectionist model, reading engages both phonological and semantic processing regardless of whether it is for pronunciation or meaning. Both target [i.e., ortho-phonological (OP) or ortho-semantic (OS) ] and non-target [i.e., ortho-phono-semantic (OPS) or ortho-sem-phonological (OSP)] paths are likely simultaneously involved in reading. However, readers may vary in their division of labor across target and non-target paths as a function of task and reading skill. Therefore, the goal of the current study was to examine how skill modulates the neural mechanism of reading depending on the task. Children (aged 8 to 15) were given two reading tasks, namely, a rhyming judgment task tapping into orthographic-to-phonological mapping and a meaning judgment task tapping into orthographic-to-semantic mapping. Brain activation during these two reading tasks was then correlated with reading skill. Consistent with previous research showing functional separation of the dorsal versus ventral left inferior frontal gyrus (IFG), we found that better readers showed greater engagement of the opercularis for the rhyming task, whereas they showed a trend for greater engagement of the triangularis for the meaning task. A novel component of the study was to determine whether these skill related regions identified during the reading tasks were also correlated with activation during parallel rhyming and meaning tasks in the auditory modality. We found that better readers only reliably showed greater engagement of opercularis during auditory phonological processing, but there were trends for overall greater engagement of frontal regions with increasing skill. We did not find evidence for compensatory mechanisms for lower skill readers, either in the left or right hemisphere. Taken together, our study suggests some specificity of the frontal cortex for phonological versus semantic processing during reading, but that more effective access to posterior representations by the frontal cortex seems to be a general characteristic of better readers

MT-aIAT: Integrating mouse tracking into memory-detection aIAT

This study assesses the validity of a newly integrated memory detection method, MT-aIAT, which is a combination of the autobiographical Implicit Association Test (aIAT) and the mouse-tracking method. Participants were assigned to steal a credit card and then performed the aIAT while mouse tracker was recording their motor trajectories. Replicating previous work, we found a RT congruency effect. Critically, the mouse trajectories indicate a congruency effect and a block order effect, suggesting the validity of mouse-tracking technique in unraveling real-time measurement of the IAT congruency effect. Lastly, to test the computational modeling in MT-aIAT, we posited a connectionist model combined with the drift-discussion model to simulate participants’ behavioral performance. Our model captures the ubiquitous implicit bias towards the autobiographical event. Implications of the MT-aIAT in identifying autobiographical memories, the combination of MT-aIAT with computational modeling approach were discussed.

Semantic Systematicity in Connectionist Language Production

Decades of studies trying to define the extent to which artificial neural networks can exhibit systematicity suggest that systematicity can be achieved by connectionist models but not by default. Here we present a novel connectionist model of sentence production that employs rich situation model representations originally proposed for modeling systematicity in comprehension. The high performance of our model demonstrates that such representations are also well suited to model language production. Furthermore, the model can produce multiple novel sentences for previously unseen situations, including in a different voice (actives vs. passive) and with words in new syntactic roles, thus demonstrating semantic and syntactic generalization and arguably systematicity. Our results provide yet further evidence that such connectionist approaches can achieve systematicity, in production as well as comprehension. We propose our positive results to be a consequence of the regularities of the microworld from which the semantic representations are derived, which provides a sufficient structure from which the neural network can interpret novel inputs.

Effects of Orthographic Consistency on Bilingual Reading: Human and Computer Simulation Data

English serves as today’s lingua franca, a role not eased by the inconsistency of its orthography. Indeed, monolingual readers of more consistent orthographies such as Italian or German learn to read more quickly than monolingual English readers. Here, we assessed whether long-lasting bilingualism would mitigate orthography-specific differences in reading speed and whether the order in which orthographies with a different regularity are learned matters. We studied high-proficiency Italian-English and English-Italian bilinguals, with at least 20 years of intensive daily exposure to the second language and its orthography and we simulated sequential learning of the two orthographies with the CDP++ connectionist model of reading. We found that group differences in reading speed were comparatively bigger with Italian stimuli than with English stimuli. Furthermore, only Italian bilinguals took advantage of a blocked presentation of Italian stimuli compared to when stimuli from both languages were presented in mixed order, suggesting a greater ability to keep language-specific orthographic representations segregated. These findings demonstrate orthographic constraints on bilingual reading, whereby the level of consistency of the first learned orthography affects later learning and performance on a second orthography. The computer simulations were consistent with these conclusions.

Optimum Integration Procedure for Connectionist and Dynamic Field Equations

Connectionist and dynamic field models consist of a set of coupled first-order differential equations describing the evolution in time of different units. We compare three numerical methods for the integration of these equations: the Euler method, and two methods we have developed and present here: a modified version of the fourth-order Runge Kutta method, and one semi-analytical method. We apply them to solve a well-known nonlinear connectionist model of retrieval in single-digit multiplication, and show that, in many regimes, the semi-analytical and modified Runge Kutta methods outperform the Euler method, in some regimes by more than three orders of magnitude. Given the outstanding difference in execution time of the methods, and that the EM is widely used, we conclude that the researchers in the field can greatly benefit from our analysis and developed methods.

The Expressive Triad: Structure, Color, and Texture Similarity of Emotion Expressions Predict Impressions of Neutral Faces

Previous research has demonstrated how emotion resembling cues in the face help shape impression formation (i. e., emotion overgeneralization). Perhaps most notable in the literature to date, has been work suggesting that gender-related appearance cues are visually confounded with certain stereotypic expressive cues (see Adams et al., 2015 for review). Only a couple studies to date have used computer vision to directly map out and test facial structural resemblance to emotion expressions using facial landmark coordinates to estimate face shape. In one study using a Bayesian network classifier trained to detect emotional expressions structural resemblance to a specific expression on a non-expressive (i.e., neutral) face was found to influence trait impressions of others (Said et al., 2009). In another study, a connectionist model trained to detect emotional expressions found different emotion-resembling cues in male vs. female faces (Zebrowitz et al., 2010). Despite this seminal work, direct evidence confirming the theoretical assertion that humans likewise utilize these emotion-resembling cues when forming impressions has been lacking. Across four studies, we replicate and extend these prior findings using new advances in computer vision to examine gender-related, emotion-resembling structure, color, and texture (as well as their weighted combination) and their impact on gender-stereotypic impression formation. We show that all three (plus their combination) are meaningfully related to human impressions of emotionally neutral faces. Further when applying the computer vision algorithms to experimentally manipulate faces, we show that humans derive similar impressions from them as did the computer.

Ideology, communication and polarization

Ideologically committed minds form the basis of political polarization, but ideologically guided communication can further entrench and exacerbate polarization depending on the structures of ideologies and social network dynamics on which cognition and communication operate. Combining a well-established connectionist model of cognition and a well-validated computational model of social influence dynamics on social networks, we develop a new model of ideological cognition and communication on dynamic social networks and explore its implications for ideological political discourse. In particular, we explicitly model ideologically filtered interpretation of social information, ideological commitment to initial opinion, and communication on dynamically evolving social networks, and examine how these factors combine to generate ideologically divergent and polarized political discourse. The results show that ideological interpretation and commitment tend towards polarized discourse. Nonetheless, communication and social network dynamics accelerate and amplify polarization. Furthermore, when agents sever social ties with those that disagree with them (i.e. structure their social networks by homophily), even non-ideological agents may form an echo chamber and form a cluster of opinions that resemble an ideological group. This article is part of the theme issue ‘The political brain: neurocognitive and computational mechanisms’.