New approaches to neural circuits in behavior

A fundamental goal of neuroscience is to understand how the brain represents the world. If we consider the neurons of the brain to be one system and the external world to be another system, how do the two systems interact, and by what translational code does the former represent the latter? Recent advances in imaging neural activity, genetically altering specific neural circuits, and genetic tools for the direct manipulation of neural activity are beginning to shed light on this critical question. We review recent advances in these areas that illustrate a path to addressing this fundamental question.     

Through neural stimulation to behavior manipulation: a novel method for analyzing dynamical cognitive models

The dynamical systems' approach to cognition (Dynamicism) promises computational models that effectively embed cognitive processing within its more natural behavioral context. Dynamical cognitive models also pose difficult, analytical challenges, which motivate the development of new analytical methodology. We start by illustrating the challenge by applying two conventional analytical methods to a well-known Dynamicist model of categorical perception. We then introduce our own analysis, which works by analogy with neural stimulation methods, and which yields some novel insights into the way the model works. We then extend and apply the method to a second Dynamicist model, which captures the key psychophysical trends that emerge when humans and animals compare two numbers. The results of the analysis-which reveals units with tuning functions that are monotonically related to the magnitudes of the numbers that the agents must compare-offer a clear contribution to the contentious debate concerning the way number information is encoded in the brain.     

Artificial neural networks for analyzing inter-limb coordination: the golf chip shot

Motor control research relies on theories, such as coordination dynamics, adapted from physical sciences to explain the emergence of coordinated movement in biological systems. Historically, many studies of coordination have involved inter-limb coordination of relatively few degrees of freedom. This study looked at the high-dimensional inter-limb coordination used to perform the golf chip shot toward six different target distances. This study also introduces a visualization of high-dimensional coordination relevant within the coordination dynamics theoretical framework. A specific type of Artificial Neural Network (ANN), the Self-Organizing Map (SOM), was used for the analysis. In this study, the trajectory of consecutive best-matching nodes on the output map was used as a collective variable and subsequently fed into a second SOM which was used to create visualization of coordination stability. The SOM trajectories showed changes in coordination between movement patterns used for short chip shots and movement patterns used for long chip shots. The attractor diagrams showed non-linear phase transitions for three out of four players. The methods used in this study may offer a solution for researchers from a coordination dynamics perspective who intend to use data obtained from discrete high-dimensional movements.     

Laser printers as powerful tools for the scientific visualization of behavior

Laser printers are powerful tools for the scientific visualization of behavior. Because of their broad software support and their 300 dot per inch resolution, they are not only more cost-effective than plotters and cumulative recorders, but also capable of producing visualizations that would otherwise be impossible.     

Localizing complex neural circuits with MEG data

During cognitive processing, the various cortical areas, with specialized functions, supply for different tasks. In most cases then, the information flows are processed in a parallel way by brain networks which work together integrating the single performances for a common goal. Such a step is generally performed at higher processing levels in the associative areas. The frequency range at which neuronal pools oscillate is generally wider than the one which is detectable by bold changes in fMRI studies. A high time resolution technique like magnetoencephalography or electroencephalography is therefore required as well as new data processing algorithms for detecting different coherent brain areas cooperating for one cognitive task. Our experiments show that no algorithm for the inverse problem solution is immune from bias. We propose therefore, as a possible solution, our software LOCANTO (LOcalization and Coherence ANalysis TOol). This new package features a set of tools for the detection of coherent areas. For such a task, as a default, it employs the algorithm with best performances for the neural landscape to be detected. If the neural landscape under attention involves more than two interacting areas the SLoreta algorithm is used. Our study shows in fact that SLoreta performance is not biased when the correlation among multiple sources is high. On the other hand, the Beamforming algorithm is more precise than SLoreta at localizing single or double sources but it gets a relevant localization bias when the sources are more than three and are highly correlated.     

On the aggregation across subjects in analyzing individual choice behavior

Authors dealing with models of individual choice behavior often point out the pitfalls of using group data to test such models. However, it is possible to derive properties of the choice behavior of the population based on these models of individual choice behavior. The present paper develops a procedure for the aggregation of choice data which takes into account individual differences. The utility of using aggregated choice data and the properties of the present procedure in comparison to other methods which utilized group data in analyzing choice behavior are discussed.     

Teaching students to model neural circuits and neural networks using an electronic spreadsheet simulator

An electronic spreadsheet simulator can be used to enable students to conduct simulated microelectrode recording experiments. In addition, it can be used both to let students explore the operation of models of hypothetical neural networks and to let them design and develop their own neural models.     

(Physio)logical circuits: the intellectual origins of the McCulloch-Pitts neural networks

This article examines the intellectual and institutional factors that contributed to the collaboration of neuropsychiatrist Warren McCulloch and mathematician Walter Pitts on the logic of neural networks, which culminated in their 1943 publication, "A Logical Calculus of the Ideas Immanent in Nervous Activity." Historians and scientists alike often refer to the McCulloch-Pitts paper as a landmark event in the history of cybernetics, and fundamental to the development of cognitive science and artificial intelligence. This article seeks to bring some historical context to the McCulloch-Pitts collaboration itself, namely, their intellectual and scientific orientations and backgrounds, the key concepts that contributed to their paper, and the institutional context in which their collaboration was made. Although they were almost a generation apart and had dissimilar scientific backgrounds, McCulloch and Pitts had similar intellectual concerns, simultaneously motivated by issues in philosophy, neurology, and mathematics. This article demonstrates how these issues converged and found resonance in their model of neural networks. By examining the intellectual backgrounds of McCulloch and Pitts as individuals, it will be shown that besides being an important event in the history of cybernetics proper, the McCulloch-Pitts collaboration was an important result of early twentieth-century efforts to apply mathematics to neurological phenomena.     

Severity of anxiety moderates the association between neural circuits and maternal behaviors in the postpartum period

Neuroimaging research has suggested that activity in the amygdala, center of the socioemotional network, and functional connectivity between the amygdala and cortical regions are associated with caregiving behaviors in postpartum mothers. Anxiety is common in the early postpartum period, with severity ranging from healthy maternal preoccupation to clinical disorder. However, little is known about the influence of anxiety on the neural correlates of early caregiving. We examined these relationships in a community cohort of 75 postpartum women (ages 18–22; predominantly low-SES, minority race) who listened to infant cry sounds while undergoing an fMRI assessment. Maternal self-reported symptoms of anxiety were mostly within the subclinical range. Positive and negative caregiving behaviors during filmed face-to-face mother–infant interactions were coded by independent observers. The results from whole-brain analyses showed that anxiety severity moderated the brain–maternal behavior relationships. Specifically, our results showed that the higher a mother’s anxiety, the stronger the association between positive caregiving (i.e., maternal warmth and involvement) and amygdala–right posterior superior temporal sulcus (amygdala–RpSTS) functional connectivity. These results remained significant when we controlled for symptoms of depression and contextual variables. These findings suggest that functional connectivity between the amygdala and a social perception region (RpSTS) plays a particularly important role for anxious mothers in facilitating their positive parenting. These findings extend our understanding of the specific neural circuits that support positive maternal caregiving in the context of maternal anxiety, and they may help inform the future design of personalized and effective interventions.     

The teacher-pupil interaction scale: An empirical method for analyzing the interactive effects of teacher and pupil behavior

The Teacher-Pupil Interaction Scale was developed for use by educational consultants to provide teachers with systematic data for analyzing and improving the learning of pupils. The scoring protocol provides for the simultaneous rating of sequential teacher-pupil interactions; a single mark is used to designate both teacher and pupil behavior at each five-second interval. Observations of a target pupil are alternated with other pupils in the classroom to serve as a point of comparison with the target pupil and to provide a general assessment of the classroom climate. Using the sequential record of their verbal and nonverbal interactions with students, teachers can pinpoint conditions serving both as antecedents and consequents of current student behavior. Armed with this information, teachers can effect changes in their instructional program to increase the rate of desired behaviors and eliminate or reduce unwanted responses.     

Three-dimensional spatiotemporal imaging of movement patterns: Another step toward analyzing the continuity of behavior

Computer-aided spatiotemporal imaging techniques, like those that are proving to be important in many other scientific fields, are being used to represent and study movement patterns of animals exposed to basic reinforcement contingencies. Data from a video-tracking system that provides real-time tracking of the position of an experimental animal as it moves about in a three-dimensional space can be plotted in up to three dimensions. When the data are plotted in two spatial dimensions and the time dimension, behavior is captured as continuous patterns or structures in space-time. Spatiotemporal imaging of movement patterns permits regularities to be observed that are not seen as readily in other ways such as watching videotapes of the experimental sessions or simply examining rate of responding. By providing a concise spatiotemporal representation of the movement patterns that occurred in a given experimental preparation, the imaging techniques described here represent an advancement in the scientific study of continuously flowing behavior. Although we concentrate here on movement patterns produced by basic reinforcement contingencies, the spatiotemporal imaging technology is applicable to any research topic in which movement patterns are of interest, such as foraging, place learning, sign language, and limb movement.     

How the brain understands intention: different neural circuits identify the componential features of motor and prior intentions

In this paper we present theoretical and experimental evidence for a set of mechanisms by which intention is understood. We propose that three basic aspects are involved in the understanding of intention. The first aspect to consider is intention recognition, i.e., the process by which we recognize other people's intentions, distinguishing among different types. The second aspect concerns the attribution of intention to its author: the existence of shared neural representations provides a parsimonious explanation of how we recognize other people's intentions (what they are doing), but in and of itself, is not sufficient to determine who the agent is. Once the intention has been recognized and attributed to an agent, the reasons for, and the aim of, the intention are to be considered. Hence, the third aspect concerns the aim-intention motivating the execution of a certain action. We discuss the neural basis of these three theoretical aspects suggesting a conceptual synthesis.     

Separate neural sites for d-amphetamine suppression of mouse killing and feeding behavior in rats

The present study was conducted to investigate several possible neural sites for d-amphetamine's effect on mouse killing and feeding behaviors. d-Amphetamine (10, 20, and 30 μg) injected into each lateral ventricle, suppressed mouse kiling, food, and water intake in a dose-dependent manner. Bilateral adminstration of d-amphetamine (20 μg) into the central amygdaloid nucleus abolished mouse killing behavior but did not affect feeding and drinking. By contrast, bilateral amphetamine injections into the substantia nigra, or into the ventral region of the caudate nucleus, did not suppress mouse killing behavior, but significantly decreased food and water intake. The lateral hypothalamus was sensitive to d-amphetamine injections, which suppressed mouse killing and food intake as well as water intake. d-Amphetamine injections into the nucleus accumbens produced inconsistent effects on mouse killing and feeding. Our observations suggest a differentiation of the neural sites that mediate feeding from those underlying mouse killing behavior.     

From neural responses to population behavior: neural focus group predicts population-level media effects

Can neural responses of a small group of individuals predict the behavior of large-scale populations? In this investigation, brain activations were recorded while smokers viewed three different television campaigns promoting the National Cancer Institute's telephone hotline to help smokers quit (1-800-QUIT-NOW). The smokers also provided self-report predictions of the campaigns' relative effectiveness. Population measures of the success of each campaign were computed by comparing call volume to 1-800-QUIT-NOW in the month before and the month after the launch of each campaign. This approach allowed us to directly compare the predictive value of self-reports with neural predictors of message effectiveness. Neural activity in a medial prefrontal region of interest, previously associated with individual behavior change, predicted the population response, whereas self-report judgments did not. This finding suggests a novel way of connecting neural signals to population responses that has not been previously demonstrated and provides information that may be difficult to obtain otherwise.     

A basis for analyzing test-retest reliability

Three sources of variation in experimental results for a test are distinguished: trials, persons, and items. Unreliability is defined only in terms of variation over trials. This definition leads to a more complete analysis than does the conventional one; Spearman's contention is verified that the conventional approach—which was formulated by Yule—introduces unnecessary hypotheses. It is emphasized that at least two trials are necessary to estimate the reliability coefficient. This paper is devoted largely to developinglower bounds to the reliability coefficient that can be computed from but asingle trial; these avoid the experimental difficulties of making two independent trials. Six different lower bounds are established, appropriate for different situations. Some of the bounds are easier to compute than are conventional formulas, and all the bounds assume less than do conventional formulas. The terminology used is that of psychological and sociological testing, but the discussion actually provides a general analysis of the reliability of the sum ofn variables.The writer is indebted to the members of his statistical seminar, to Professor Mark Kac, and to Professor Samuel A. Stouffer and his staff in the Research Branch, Information and Education Division, War Department, for their helpful comments on this paper.     

On structural models for analyzing change

The recommendation to base the analysis of multi-wave data upon explicit models for change is advocated. Several univariate and multivariate models are described, which emerge from an interaction between the classical test theory and the structural equation modeling approach. The resulting structural models for analyzing change reflect in some of their parameters substantively interesting aspects of intra- and interindividual change in follow-up studies. The models are viewed as an alternative to an ANOVA-based analysis of longitudinal data, and are illustrated on data from a cognitive intervention study of old adults (Bakes et al , 1986). The approach presents a useful means of analyzing change over time, and is applicable for purposes of (latent) growth curve analysis when analysis of variance assumptions are violated (e.g., Schaie & Hertzog, 1982; Morrison, 1976).     

Methods for analyzing performance of musical rhythm

Performance of musical rhythm was analyzed to test the hypothesis that such performance is characterized by certain systematic variations as regards the durations of the sound events in relation to strict mechanical regularity. Monophonic performances of 28 melodies in different notations were registered, and the duration data were treated by a special computer program for systematic variations and by factor analysis. The methods are illustrated for one of the melodies demonstrating pronounced systematic variations and different types of performance as found by factor analysis.     

Mathematical concepts for analyzing ambiguous situations

Abstract

We examine some mathematical tools for dealing with ambiguous situations. The main tool is the use of non-standard logic with truth-values in what is called a locale. This approach is related to fuzzy set theory, which we briefly discuss. We also consider probabilistic concepts. We include specific examples and describe the way a researcher can set up a suitable locale to analyse a concrete situation.