The tractable contribution of synapses and their component molecules to individual differences in learning

Measuring the replicability of our own research

In the study of transgenic mouse models of neurodevelopmental and neurodegenerative disorders, we use batteries of tests to measure deficits in behaviour and from the results of these tests, we make inferences about the mental states of the mice that we interpret as deficits in "learning", "memory", "anxiety", "depression", etc. This paper discusses the problems of determining whether a particular transgenic mouse is a valid mouse model of disease X, the problem of background strains, and the question of whether our behavioural tests are measuring what we say they are. The problem of the reliability of results is then discussed: are they replicable between labs and can we replicate our results in our own lab? This involves the study of intra- and inter- experimenter reliability. The variables that influence replicability and the importance of conducting a complete behavioural phenotype: sensory, motor, cognitive and social emotional behaviour are discussed. Then the thorny question of failure to replicate is examined: Is it a curse or a blessing? Finally, the role of failure in research and what it tells us about our research paradigms is examined.

Intrinsic plasticity and birdsong learning

Although information processing and storage in the brain is thought to be primarily orchestrated by synaptic plasticity, other neural mechanisms such as intrinsic plasticity are available. While a number of recent studies have described the plasticity of intrinsic excitability in several types of neurons, the significance of non-synaptic mechanisms in memory and learning remains elusive. After reviewing plasticity of intrinsic excitation in relation to learning and homeostatic mechanisms, we focus on the intrinsic properties of a class of basal-ganglia projecting song system neurons in zebra finch, how these related to each bird's unique learned song, how these properties change over development, and how they are maintained dynamically to rapidly change in response to auditory feedback perturbations. We place these results in the broader theme of learning and changes in intrinsic properties, emphasizing the computational implications of this form of plasticity, which are distinct from synaptic plasticity. The results suggest that exploring reciprocal interactions between intrinsic and network properties will be a fruitful avenue for understanding mechanisms of birdsong learning.

Déjà vu All Over Again: A Unitary Biological Mechanism for Intelligence Is (Probably) Untenable

Nearly a century ago, Spearman proposed that “specific factors can be regarded as the ‘nuts and bolts’ of cognitive performance…, while the general factor is the mental energy available to power the specific engines”. Geary (2018; 2019) takes Spearman’s analogy of “mental energy” quite literally and doubles-down on the notion by proposing that a unitary energy source, the mitochondria, explains variations in both cognitive function and health-related outcomes. This idea is reminiscent of many earlier attempts to describe a low-level biological determinant of general intelligence. While Geary does an admirable job developing an innovative theory with specific and testable predictions, this new theory suffers many of the shortcomings of previous attempts at similar goals. We argue that Geary’s theory is generally implausible, and does not map well onto known psychological and genetic properties of intelligence or its relationship to health and fitness. While Geary’s theory serves as an elegant model of “what could be”, it is less successful as a description of “what is”.

Individual differences: Case studies of rodent and primate intelligence

Early in the 20th century, individual differences were a central focus of psychologists. By the end of that century, studies of individual differences had become far less common, and attention to these differences played little role in the development of contemporary theory. To illustrate the important role of individual differences, here we consider variations in intelligence as a compelling example. General intelligence (g) has now been demonstrated in at least 2 distinct genera: primates (including humans, chimpanzees, bonobos, and tamarins) and rodents (mice and rats). The expression of general intelligence varies widely across individuals within a species; these variations have tremendous functional consequence, and are attributable to interactions of genes and environment. Here we provide evidence for these assertions, describe the processes that contribute to variations in general intelligence, as well as the methods that underlie the analysis of individual differences. We conclude by describing why consideration of individual differences is critical to our understanding of learning, cognition, and behavior, and illustrate how attention to individual differences can contribute to more effective administration of therapeutic strategies for psychological disorders. (PsycINFO Database Record

Young and older good learners have higher levels of brain nicotinic receptor binding

Neuronal alphabeta heteromeric and alpha7 homomeric nicotinic acetylcholine receptors (nAChRs) were compared in 4- and 27-month rabbits selected for learning proficiency. Sixty 4- and 60 27-month rabbits received the alpha7 nAChR agonist (MEM-3389), galantamine, or vehicle during training in trace eyeblink classical conditioning. Brain tissue from the best and worst young and older learners was analyzed with radioligand binding. Vehicle-treated 4- and 27-month good learners had higher alphabeta heteromeric nAChR binding in hippocampus and temporal-parietal cortex than poor learners, and this result was replicated in both age groups of rabbits treated with galantamine. Results indicate that anatomically more numerous nAChRs or functional activation of a greater number of nAChRs may characterize animals demonstrating optimal learning. During normal aging the expression of high-affinity binding sites declines. Age-related changes in the expression of hippocampal alphabeta heteromeric nAChRs may account for some of the documented age-related impairment in learning. However, individual differences in alphabeta heteromeric nAChRs also exist early in life, as better learning in 4-month rabbits was associated with significantly higher binding.

Individual differences in the expression of a "general" learning ability in mice

Human performance on diverse tests of intellect are impacted by a "general" regulatory factor that accounts for up to 50% of the variance between individuals on intelligence tests. Neurobiological determinants of general cognitive abilities are essentially unknown, owing in part to the paucity of animal research wherein neurobiological analyses are possible. We report a methodology with which we have assessed individual differences in the general learning abilities of laboratory mice. Abilities of mice on tests of associative fear conditioning, operant avoidance, path integration, discrimination, and spatial navigation were assessed. Tasks were designed so that each made unique sensory, motor, motivational, and information processing demands on the animals. A sample of 56 genetically diverse outbred mice (CD-1) was used to assess individuals' acquisition on each task. Indicative of a common source of variance, positive correlations were found between individuals' performance on all tasks. When tested on multiple test batteries, the overall performance ranks of individuals were found to be highly reliable and were "normally" distributed. Factor analysis of learning performance variables determined that a single factor accounted for 38% of the total variance across animals. Animals' levels of native activity and body weights accounted for little of the variability in learning, although animals' propensity for exploration loaded strongly (and was positively correlated) with learning abilities. These results indicate that diverse learning abilities of laboratory mice are influenced by a common source of variance and, moreover, that the general learning abilities of individual mice can be specified relative to a sample of peers.

The genetics of g in human and mouse

The g factor refers to the substantial overlap that exists between individual differences in diverse cognitive processes in humans. In this article, I argue that a mouse model of g could provide a powerful analytic tool for exploring cognitive processes that are linked functionally by genes.