Comparing intervention outcomes in smokers treated for single versus multiple behavioral risks.

This study examined treatment outcomes among smokers with single versus multiple behavioral risks. Data were drawn from smokers (N = 2,326) participating in 3 population-based, multibehavioral studies targeting cigarette smoking, high-fat diets, and high-risk sun exposure. Outcomes were compared for participants at risk for smoking only (13%), smoking plus 1 additional risk factor (37%), and smoking plus 2 additional risk factors (50%). The smoking only group tended to be female, older, more educated, lighter smokers, in the preparation stage of change for smoking, with more previous quit attempts and longer periods of abstinence. At 12 and 24 months follow-up, treatment of 1 or 2 coexisting risk factors did not decrease the effectiveness of smoking cessation treatment, and treatment for the coexisting factors was effective.     

Comparing single and serial homicide offenses

Serial homicide has attracted much attention, but little empirical scientific investigation. This exploratory study reports demographic information on a large sample of serial homicide offenders (157 offenders, 608 victims), and compares a subsample of serial homicide offenses with a control group of single homicide offenses. Results show that serial homicide offenders target more women than men, and kill more strangers than family or friends. Single homicide offenders kill men and women in equal frequency, but kill family and friends more often than strangers. Serial homicide offenders kill for apparent sexual motivation more often than for any other reason, while single homicide offenders kill most often out of anger.     

Role of individual neurons and neural networks in cognitive functioning of the brain: a new insight

The prevailing concept in modern neuroscience is that neuron networks play a dominant role in the functioning of the nervous system, whereas the role of individual neurons is rather insignificant. This concept suggests that "individuality" of single neurons is primarily determined by their place in a network rather than their intrinsic properties. Here I argue that individual neurons may play an important, if not decisive, role in performing cognitive functions of the brain. This tentative viewpoint is supported by experimental and clinical insights into disorders of cognitive functions and by genetic studies of cognitive abilities and disabilities. The results obtained in these studies indicate that many specific cognitive functions are carried out by groups of highly specialized neurons whose roles in performing these functions are genetically predetermined and their activity could not be substituted by the activity of other neurons. In this context, the main role of neural networks and intercellular interactions is to form dynamic ensembles of neurons involved in performing a given cognitive function.     

Signaling in large-scale neural networks

We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages of this metabolically costly organization are analyzed by comparing with synaptically less intense networks driven by the intrinsic response properties of the network neurons.     

Signaling in large-scale neural networks

We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages of this metabolically costly organization are analyzed by comparing with synaptically less intense networks driven by the intrinsic response properties of the network neurons.     

Artificial neural networks in Neurosciences

This article shows that artificial neural networks are used for confirming the relationships between physiological and cognitive changes. Specifically, we explore the influence of a decrease of neurotransmitters on the behaviour of old people in recognition tasks. This artificial neural network recognizes learned patterns. When we change the threshold of activation in some units, the artificial neural network simulates the experimental results of old people in recognition tasks. However, the main contributions of this paper are the design of an artificial neural network and its operation inspired by the nervous system and the way the inputs are coded and the process of orthogonalization of patterns.     

Multivariate pattern analysis and the search for neural representations

Synthese - Multivariate pattern analysis, or MVPA, has become one of the most popular analytic methods in cognitive neuroscience. Since its inception, MVPA has been heralded as offering much more...     

Pictorial versus structural representations of ingroup and outgroup faces

Mental representations of unfamiliar faces may rely, to a greater or lesser extent, on pictorial cues that are closely linked to the specific image studied, and structural cues that allow for recognition across various transformations. Here, we test participants’ recognition of unfamiliar ingroup and outgroup faces, manipulating image transformation (pose) and exposure duration. The results showed that changes in pose reduced accuracy for outgroup faces but not for ingroup faces. Overall, accuracy increased as exposure duration increased. Accurate responses were made more rapidly than inaccurate responses for both ingroup and outgroup faces, showing that response latency is a useful postdictor of accuracy even for cross-ethnic identifications. The results are discussed in the framework of Bruce and Young's (1986) model of face recognition.     

Minimal models and canonical neural computations: the distinctness of computational explanation in neuroscience

In a recent paper, Kaplan (Synthese 183:339–373, 2011) takes up the task of extending Craver’s (Explaining the brain, 2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper I discuss work in computational neuroscience that creates difficulties for the mechanist project. Carandini and Heeger (Nat Rev Neurosci 13:51–62, 2012) propose that many neural response properties can be understood in terms of canonical neural computations. These are “standard computational modules that apply the same fundamental operations in a variety of contexts.” Importantly, these computations can have numerous biophysical realisations, and so straightforward examination of the mechanisms underlying these computations carries little explanatory weight. Through a comparison between this modelling approach and minimal models in other branches of science, I argue that computational neuroscience frequently employs a distinct explanatory style, namely, efficient coding explanation. Such explanations cannot be assimilated into the mechanistic framework but do bear interesting similarities with evolutionary and optimality explanations elsewhere in biology.     

Abstract versus modality-specific memory representations in processing auditory and visual speech

Serial recall of lip-read, auditory, and audiovisual memory lists with and without a verbal suffix was examined. Recency effects were the same in the three presentation modalities. The disrupting effect of a suffix was largest when it was presented in the same modality as the list items. The results suggest that abstract linguistic as well as modality-specific codes play a role in memory for auditory and visual speech.     

Kohonen neural networks and language.

Kohonen neural networks are a type of self-organizing network that recognizes the statistical characteristics of input datasets. The application of this type of neural network to language theory is demonstrated in the present note by showing three brief applications: recognizing word borders, learning the limited phonemes of one's native tongue, and category-specific naming impairments.     

Working memory and number line representations in single-digit addition: Approximate versus exact,nonsymbolic versus symbolic

How do kindergarteners solve different single-digit addition problem formats? We administered problems that differed solely on the basis of two dimensions: response type (approximate or exact), and stimulus type (nonsymbolic, i.e., dots, or symbolic, i.e., Arabic numbers). We examined how performance differs across these dimensions, and which cognitive mechanism (mental model, transcoding, or phonological storage) underlies performance in each problem format with respect to working memory (WM) resources and mental number line representations. As expected, nonsymbolic problem formats were easier than symbolic ones. The visuospatial sketchpad was the primary predictor of nonsymbolic addition. Symbolic problem formats were harder because they either required the storage and manipulation of quantitative symbols phonologically or taxed more WM resources than their nonsymbolic counterparts. In symbolic addition, WM and mental number line results showed that when an approximate response was needed, children transcoded the information to the nonsymbolic code. When an exact response was needed, however, they phonologically stored numerical information in the symbolic code. Lastly, we found that more accurate symbolic mental number line representations were related to better performance in exact addition problem formats, not the approximate ones. This study extends our understanding of the cognitive processes underlying children's simple addition skills.     

Applications of neural networks in training science

Training science views itself as an integrated and applied science, developing practical measures founded on scientific method. Therefore, it demands consideration of a wide spectrum of approaches and methods. Especially in the field of competitive sports, research questions are usually located in complex environments, so that mainly field studies are drawn upon to obtain broad external validity. Here, the interrelations between different variables or variable sets are mostly of a nonlinear character. In these cases, methods like neural networks, e.g., the pattern recognizing methods of Self-Organizing Kohonen Feature Maps or similar instruments to identify interactions might be successfully applied to analyze data. Following on from a classification of data analysis methods in training-science research, the aim of the contribution is to give examples of varied sports in which network approaches can be effectually used in training science. First, two examples are given in which neural networks are employed for pattern recognition. While one investigation deals with the detection of sporting talent in swimming, the other is located in game sports research, identifying tactical patterns in team handball. The third and last example shows how an artificial neural network can be used to predict competitive performance in swimming.     

The neural fate of individual item representations in visual working memory

Previous research has demonstrated an interaction between eye gaze and selected facial emotional expressions, whereby the perception of anger and happiness is impaired when the eyes are horizontally averted within a face, but the perception of fear and sadness is enhanced under the same conditions. The current study reexamined these claims over six experiments. In the first three experiments, the categorization of happy and sad expressions (Experiments 1 and 2) and angry and fearful expressions (Experiment 3) was impaired when eye gaze was averted, in comparison to direct gaze conditions. Experiment 4 replicated these findings in a rating task, which combined all four expressions within the same design. Experiments 5 and 6 then showed that previous findings, that the perception of selected expressions is enhanced under averted gaze, are stimulus and task-bound. The results are discussed in relation to research on facial expression processing and visual attention.     

Learning and encoding higher order rules in neural networks

Some researchers state that whereas neural networks are fine for pattern recognition and categorization, complex rule formation requires a separate “symbolic” level. However, the human brain is a connectionist system and, however imperfectly, does complex reasoning and inference. Familiar modeling principles (e.g., Hebbian or associative learning, lateral inhibition, opponent processing, neuromodulation) could recur, in different combinations, in architectures that can learn diverse rules. These rules include, for example, “go to the most novel object,” “alternate between two given objects,” and “touch three given objects, without repeats, in any order.” Frontal lobe damage interferes with learning all three of those rules. Hence, network models of rule learning and encoding should include a module analogous to the prefrontal cortex. They should also include modules analogous to the hippocampus for episode setting and analogous to the amygdala for emotional evaluation.     

Comparing single and cumulative dosing procedures in human triazolam discriminators.

This study evaluated a cumulative dosing procedure for drug discrimination with human participants. Four participants learned to discriminate triazolam (0.35 mg/70 kg) from placebo. A crossover design was used to compare the results under a single dosing procedure with results obtained under a cumulative dosing procedure. Under the single dosing procedure, a dose of triazolam (0, 0.05, 0.15, or 0.35 mg/70 kg) or secobarbital (0, 25, 75, or 175 mg/70 kg) was administered 45 min before assessment. Determining each dose-effect curve thus required four sessions. Under the cumulative dosing procedure, four doses of triazolam (0, 0.05, 0.10, and 0.20 mg/70 kg) or secobarbital (0, 25, 50, and 100 mg/70 kg) were administered approximately 55 min apart, producing a complete dose-effect curve in one four-trial session. Regardless of procedure, triazolam and secobarbital produced discriminative stimulus and self-reported effects similar to previous single dosing studies in humans. Shifts to the right in cumulative dose-effect curves compared to single dose-effect curves occurred on several self-report measures. When qualitative stimulus functions rather than quantitative functions are of interest, application of cumulative dosing may increase efficiency in human drug discrimination.     

The effects of single and dual representations on children's gesture production

Investigations that focus on children's hand gestures often conclude that gesture production arises as a result of having multiple representations. To date, the predictive validity of this notion has not been tested. In this study, we compared the gestures of 82 five-year-old children holding either a single or a dual representation. The children retold a story narrated to them, with pictures, by the experimenter. In one condition the children heard a false belief story and hence, when retelling, held two beliefs—or representations—concurrently. In the other conditions, the children retold a version of the story without the false belief component and therefore held single representations. Children were four times more likely to gesture in the false belief condition than in two comparable true belief conditions, supporting the notion that gestures may function to externalise some of the child's cognitive process, particularly when they hold multiple representations.