Latent Growth Modeling for Logistic Response Functions

Throughout much of the social and behavioral sciences, latent growth modeling (latent curve analysis) has become an important tool for understanding individuals' longitudinal change. Although nonlinear variations of latent growth models appear in the methodological and applied literature, a notable exclusion is the treatment of growth following logistic (sigmoidal; S-shape) response functions. Such trajectories are assumed in a variety of psychological and educational settings where learning occurs over time, and yet applications using the logistic model in growth modeling methodology have been sparse. The logistic function, in particular, may not be utilized as often because software options remain limited. In this article we show how a specialized version of the logistic function can be modeled using conventional structural equation modeling software. The specialization is a reparameterization of the logistic function whose new parameters correspond to scientifically interesting characteristics of the growth process. In addition to providing an example using simulated data, we show how this nonlinear functional form can be fit using transformed subject-level data obtained through a learning task from an air traffic controller simulation experiment. LISREL syntax for the empirical example is provided.     

A role for Synapsin in associative learning: the Drosophila larva as a study case

Synapsins are evolutionarily conserved, highly abundant vesicular phosphoproteins in presynaptic terminals. They are thought to regulate the recruitment of synaptic vesicles from the reserve pool to the readily-releasable pool, in particular when vesicle release is to be maintained at high spiking rates. As regulation of transmitter release is a prerequisite for synaptic plasticity, we use the fruit fly Drosophila to ask whether Synapsin has a role in behavioral plasticity as well; in fruit flies, Synapsin is encoded by a single gene (syn). We tackled this question for associative olfactory learning in larval Drosophila by using the deletion mutant syn^97CS, which had been backcrossed to the Canton-S wild-type strain (CS) for 13 generations. We provide a molecular account of the genomic status of syn^97CS by PCR and show the absence of gene product on Western blots and nerve-muscle preparations. We found that olfactory associative learning in syn^97CS larvae is reduced to ~50% of wild-type CS levels; however, responsiveness to the to-be-associated stimuli and motor performance in untrained animals are normal. In addition, we introduce two novel behavioral control procedures to test stimulus responsiveness and motor performance after “sham training.” Wild-type CS and syn^97CS perform indistinguishably also in these tests. Thus, larval Drosophila can be used as a case study for a role of Synapsin in associative learning.     

The role of associative history in models of associative learning: A selective review and a hybrid model

Associative learning theories strive to capture the processes underlying and driving the change in strength of the associations between representations of stimuli that develop as a result of experience of the predictive relationships between those stimuli. Historically, formal models of associative learning have focused on two potential factors underlying associative change, namely processing of the conditioned stimulus (in terms of changes in associability) and processing of the unconditioned stimulus (in terms of changes in error). This review constitutes an analysis of the proper role of these two factors, specifically with regard to the way in which they are influenced by associative history (the prior training undergone by cues). A novel “hybrid” model of associative learning is proposed and is shown to provide a more satisfactory account of the effects of associative history on subsequent learning than any previous single-process theory.     

Judgements of a 2 × 2 contingency table: Sequential processing and the learning curve

Shanks (1985) has used a video game to investigate how subjects estimate the effect of their behaviour in a task defined by a 2×2 contingency table. The subjects were able to distinguish positive and negative contingencies from zero contingencies. In addition, they showed a learning curve and a bias to rate zero contingencies with a high outcome density higher than low-density zero contingencies. He interpreted these data as being consistent with associative models derived from animal learning. In Experiment 1 we replicated these results using a task and instructions similar to his. In a second experiment we showed that the subjects' tendency to overestimate high-density zero contingencies did not arise because the “game” was so difficult that it interfered with processing the events. In this experiment subjects were given tables of the outcome frequencies that had been determined by the earlier subjects. These subjects were, if anything, less accurate in rating the zero contingencies. We point out several logical problems with Shanks's initial task. The task did not represent a true 2×2 contingency, and aspects of it were physically impossible. In Experiment 3 we modified the task to represent a true 2×2 contingency. Using this task, we found a similar pattern of results, except that there was no evidence of the learning curve predicted by the associative models. We conclude that there is little in our data to rule out a “rule-based” analysis of contingency judgements.     

The smart gut: Tracking affective associative learning with measures of “liking”, facial electromyography, and preferential looking

We demonstrate here that initially neutral items can acquire specific value based on their associated outcomes, and that responses of physiological systems to such previously meaningless stimuli can rapidly reflect this associative history. Each participant participated in an associative learning task in which four neutral abstract pictures were each repeatedly paired with one of four foods that varied in valence and magnitude. Over the course of learning, participants’ “liking” ratings of and preferences for each picture came to reflect the value of the food with which it was paired. The abstract pictures also elicited physiological responses characteristic of the foods with which they were paired, including changes in facial electromyography (EMG) and preferential looking. A logistic modeling procedure showed that learning parameters, such as the rate at which participants learned the values associated with the pictures, were similar across food outcomes of different value.     

Retrospective revaluation as simple associative learning

Backward blocking, unovershadowing, and backward conditioned inhibition are examples of retrospective revaluation phenomena that have been suggested to involve more than simple associative learning. Models of these phenomena have thus used additional concepts, for example, appealing to attentional effects or more elaborate learning mechanisms. The author shows that a suitable representation of stimuli, paired with a careful analysis of the discriminations faced by animals, leads to an account of these and other phenomena in terms of a simple elemental model of associative learning, with essentially the same learning mechanism as the R. A. Rescorla and A. R. Wagner (1972) model. The author concludes with a discussion of some implications for theories of learning.     

Modeling cross-situational word-referent learning: prior questions

Both adults and young children possess powerful statistical computation capabilities--they can infer the referent of a word from highly ambiguous contexts involving many words and many referents by aggregating cross-situational statistical information across contexts. This ability has been explained by models of hypothesis testing and by models of associative learning. This article describes a series of simulation studies and analyses designed to understand the different learning mechanisms posited by the 2 classes of models and their relation to each other. Variants of a hypothesis-testing model and a simple or dumb associative mechanism were examined under different specifications of information selection, computation, and decision. Critically, these 3 components of the models interact in complex ways. The models illustrate a fundamental tradeoff between amount of data input and powerful computations: With the selection of more information, dumb associative models can mimic the powerful learning that is accomplished by hypothesis-testing models with fewer data. However, because of the interactions among the component parts of the models, the associative model can mimic various hypothesis-testing models, producing the same learning patterns but through different internal components. The simulations argue for the importance of a compositional approach to human statistical learning: the experimental decomposition of the processes that contribute to statistical learning in human learners and models with the internal components that can be evaluated independently and together.     

Associative learning predicts intelligence above and beyond working memory and processing speed

Recent evidence suggests the existence of multiple cognitive mechanisms that support the general cognitive ability factor (g). Working memory and processing speed are the two best established candidate mechanisms. Relatively little attention has been given to the possibility that associative learning is an additional mechanism contributing to g. The present study tested the hypothesis that associative learning ability, as assessed by psychometrically sound associative learning tasks, would predict variance in g above and beyond the variance predicted by working memory capacity and processing speed. This hypothesis was confirmed in a sample of 169 adolescents, using structural equation modeling. Associative learning, working memory, and processing speed all contributed significant unique variance to g, indicating not only that multiple elementary cognitive processes underlie intelligence, but also the novel finding that associative learning is one such process.     

Speaking of pain in Greek: Implications for the cognitive permeation of emotions

ABSTRACT

Although item-memory for emotional information is enhanced, memory for associations between items is often impaired for negative, emotionally arousing compared to neutral information. We tested two possible mechanisms underlying this impairment, using picture pairs: 1) higher confidence in one’s own ability to memorise negative information may cause participants to under-study negative pairs; 2) better interactive imagery for neutral pairs could facilitate associative memory for neutral pairs more than for negative pairs. Tested with associative recognition, we replicated the impairment of associative memory for negative pairs. We also replicated the result that confidence in future memory (judgments of learning) was higher for negative than neutral pairs. Inflated confidence could not explain the impairment of associative recognition memory: Judgements of learning were positively correlated with associative memory success for both negative and neutral pairs. However, neutral pairs were rated higher in their conduciveness to interactive imagery than negative pairs, and this difference in interactive imagery showed a robust relationship to the associative memory difference. Thus, associative memory reductions for negative information are not due to differences in encoding effort. Instead, interactive imagery may be less effective for encoding of negative than neutral pairs.     

Out of control: an associative account of congruency effects in sequence learning

The demonstration of a sequential congruency effect in sequence learning has been offered as evidence for control processes that act to inhibit automatic response tendencies (Jiménez, Lupiáñez, &; Vaquero, 2009) via unconscious conflict monitoring. Here we propose an alternative interpretation of this effect based on the associative learning of chains of sequenced contingencies. This account is supported by simulations with a Simple Recurrent Network, an associative (connectionist) model of sequence learning. We argue that the control- and associative-based accounts differ in their predictions concerning the magnitude of the sequential congruency effect across training. These predictions are tested by reanalysing data from a study by Shanks, Wilkinson, and Channon (2003). The results support the associative learning account which explains the sequential congruency effect without appealing to control processes (either conscious or unconscious).     

Differential‐associative processing: A new strategy for learning highly‐similar concepts

For many students concepts like fluid and crystallized intelligence are difficult to learn because they have highly‐similar definitions that are easy to confuse. The challenge of learning these highly‐similar, yet often confused concepts is further complicated by the fact that students are tested on exams about differences between the concepts. In this theoretically‐motivated research we test a new strategy for learning highly‐similar pairs of concepts, called differential‐associative processing. The results revealed that performance on multiple‐choice questions was higher when students learned highly‐similar concepts using differential‐associative processing rather than a strategy of their own choice, text‐based elaboration, or identifying similarities and differences. The results also revealed that students spontaneously transferred differential‐associative processing to a neutral control condition. Taken as a whole, the present study supports differential‐associative processing as a useful strategy for learning similar concepts. Copyright © 2009 John Wiley & Sons, Ltd.     

Theoretical accounts of spatial learning: A neurobiological view (commentary on Pearce, 2009)

Theories of learning have historically taken, as their starting point, the assumption that learning processes have universal applicability. This position has been argued on grounds of parsimony, but has received two significant challenges: first, from the observation that some kinds of learning, such as spatial learning, seem to obey different rules from others, and second, that some kinds of learning take place in processing modules that are separate from each other. These challenges arose in the behavioural literature but have since received considerable support from neurobiological studies, particularly single neuron studies of spatial learning, confirming that there are indeed separable (albeit highly intercommunicating) processing modules in the brain, which may not always interact (within or between themselves) according to classic associative principles. On the basis of these neurobiological data, reviewed here, it is argued that rather than assuming universality of associative rules, it is more parsimonious to assume sets of locally operating rules, each specialized for a particular domain. By this view, although almost all learning is associative in one way or another, the behavioural-level characterization of the rules governing learning may vary depending on which neural modules are involved in a given behaviour. Neurobiological studies, in tandem with behavioural studies, can help reveal the nature of these modules and the local rules by which they interact.     

Developmental differences in memory for cross‐modal associations

Associative learning is critical to normal cognitive development in children. However, young adults typically outperform children on paired‐associate tasks involving visual, verbal and spatial location stimuli. The present experiment investigated cross‐modal odour–place associative memory in children (7–10 years) and young adults (18–24 years). During the study phase, six odours were individually presented and paired with one of 12 spatial locations on a board. During the test phase, participants were presented with the six stimuli individually and were asked to place each stimulus on the correct spatial location. Children committed significantly more errors on the odour–place task than did young adults. However, item recognition memory for the odours or spatial locations involved in the odour–place associative memory task was similar between children and young adults. Therefore, poor odour–place associative memory in children did not result from impaired memory for the individual odours or spatial locations involved in the associations. The results suggest that cross‐modal associative memory is not fully developed in children.     

Individual differences in the acquisition of non‐linguistic audio‐visual associations in 5 year olds

Audio‐visual associative learning – at least when linguistic stimuli are employed – is known to rely on core linguistic skills such as phonological awareness. Here we ask whether this would also be the case in a task that does not manipulate linguistic information. Another question of interest is whether executive skills, often found to support learning, may play a larger role in a non‐linguistic audio‐visual associative task compared to a linguistic one. We present a new task that measures learning when having to associate non‐linguistic auditory signals with novel visual shapes. Importantly, our novel task shares with linguistic processes such as reading acquisition the need to associate sounds with arbitrary shapes. Yet, rather than phonemes or syllables, it uses novel environmental sounds – therefore limiting direct reliance on linguistic abilities. Five‐year‐old French‐speaking children (N = 76, 39 girls) were assessed individually in our novel audio‐visual associative task, as well as in a number of other cognitive tasks evaluating linguistic abilities and executive functions. We found phonological awareness and language comprehension to be related to scores in the audio‐visual associative task, while no correlation with executive functions was observed. These results underscore a key relation between foundational language competencies and audio‐visual associative learning, even in the absence of linguistic input in the associative task.     

Verbal knowledge, working memory, and processing speed as predictors of verbal learning in older adults

The present study aimed at modeling individual differences in a verbal learning task by means of a latent structured growth curve approach based on an exponential function that yielded 3 parameters: initial recall, learning rate, and asymptotic performance. Three cognitive variables-speed of information processing, verbal knowledge, working memory-and the participant's age were included in the model in order to explain individual differences in the learning parameters. The data come from the second wave of the Zurich Longitudinal Study on Cognitive Aging (D. Zimprich, Martin, et al., 2008) comprising 334 participants ranging in age from 66 to 81 years (M = 74.43, SD = 4.41). Among the logistic, the Gompertz, and the hyperbolic function, the exponential function described the data best. Reliable individual differences were found in all 3 learning parameters. The cognitive predictor variables affected the verbal learning parameters differentially: All 3 predictors affected positively initial recall, the asymptotic performance increased with better working memory and faster processing speed, and the learning rate was positively associated with verbal knowledge only. Age did not affect the learning parameters but correlated negatively with working memory and processing speed. The finding of large and reliable individual differences in learning is seen as evidence that the potential for positive change, or plasticity in adulthood is maintained and that it is worthwhile to enhance the determinants of learning or learning itself.     

Associative memory or algorithmic search: a comparative study on learning strategies of bats and shrews

Two common strategies for successful foraging are learning to associate specific sensory cues with patches of prey ("associative learning") and using set decision-making rules to systematically scan for prey ("algorithmic search"). We investigated whether an animal's life history affects which of these two foraging strategies it is likely to use. Natterer's bats (Myotis nattereri) have slow life-history traits and we predicted they would be more likely to use associative learning. Common shrews (Sorex araneus) have fast life-history traits and we predicted that they would rely more heavily on routine-based search. Apart from their marked differences in life-history traits, these two mammals are similar in body size, brain weight, habitat, and diet. We assessed foraging strategy, associative learning ability, and retention time with a four-arm maze; one arm contained a food reward and was marked with four sensory stimuli. Bats and shrews differed significantly in their foraging strategies. Most bats learned to associate the sensory stimuli with the reward and remembered this association over time. Most shrews searched the maze using consistent decision-making rules, but did not learn or remember the association. We discuss these results in terms of life-history traits and other key differences between these species. Our results suggest a link between an animal's life-history strategy and its use of associative learning.     

Transition and protective agency of early childhood learning behaviors as portents of later school attendance and adjustment

This article reports on the study of differential change trajectories for early childhood learning behaviors as they relate to future classroom adjustment and school attendance. A large sample (N = 2152) of Head Start children was followed through prekindergarten, kindergarten, and 1st grade. Classroom learning behaviors were assessed twice each year by teachers who observed gradual declines in Competence Motivation and Attentional Persistence as children transitioned through schooling. Cross-classified multilevel growth models revealed distinct transitional pathways for future adjustment versus maladjustment and sporadic versus chronic absenteeism. Generalized multilevel logistic modeling and receiver operating characteristic curve analyses showed that teachers' earliest assessments were substantially predictive of eventual good classroom adjustment and school attendance, with increasing accuracy for prediction of future sociobehavioral adjustment as time progressed.     

Olfactory learning in individually assayed Drosophila larvae

Insect and mammalian olfactory systems are strikingly similar. Therefore, Drosophila can be used as a simple model for olfaction and olfactory learning. The brain of adult Drosophila, however, is still complex. We therefore chose to work on the larva with its yet simpler but adult-like olfactory system and provide evidence for olfactory learning in individually assayed Drosophila larvae. We developed a differential conditioning paradigm in which odorants are paired with positive (“+” fructose) or negative (“-” quinine or sodium chloride) gustatory reinforcers. Test performance of individuals from two treatment conditions is compared—one received odorant A with the positive reinforcer and odorant B with a negative reinforcer (A+/B-); animals from the other treatment condition were trained reciprocally (A-/B+). During test, differences in choice between A and B of individuals having undergone either A+/B- or A-/B+ training therefore indicate associative learning. We provide such evidence for both combinations of reinforcers; this was replicable across repetitions, laboratories, and experimenters. We further show that breaks improve performance, in accord with basic principles of associative learning. The present individual assay will facilitate electrophysiological studies, which necessarily use individuals. As such approaches are established for the larval neuromuscular synapse, but not in adults, an individual larval learning paradigm will serve to link behavioral levels of analysis to synaptic physiology.     

Unblocking in Pavlovian fear conditioning

Six experiments used rats to study blocking and unblocking of fear learning. An excitatory stimulus (A) blocked fear learning to a neutral stimulus (B). Unblocking of B occurred if the AB compound signaled an increase in unconditioned stimulus (US) intensity or number. Assessments of associative change during blocking showed that more was learned about B than A. Such assessments during unblocking revealed that more was learned about B than A following an increase in US intensity but not US number. These US manipulations had no differential effects on single-cue learning. The results show that variations in US intensity or number produce unblocking of fear learning, but for each there is a different profile of associative change and a potentially different mechanism.