The primate hippocampus: ontogeny, early insult and memory

Recent evidence suggests that in primates, as in rodents, the hippocampus shows a developmental continuum that affects memory abilities from infancy to adulthood. In primates relatively few hippocampal-dependent abilities (e.g. some aspects of recognition memory) are present in early infancy, whereas others (e.g. relational memory) begin to show adult-like characteristics around 2 years of age in monkeys and 5-7 years in humans. Profound and persistent memory loss resulting from insult to the hippocampus in infancy becomes evident in everyday behavior only later in childhood. This pattern of results suggests a maturational gradient within the medial temporal lobe memory system, with most abilities crucially dependent upon the hippocampus emerging in later stages of development, supporting a model of hierarchical organization of memory within the medial temporal lobe.     

Response by H. H. Pattee to Jon Umerez’s Paper: “Where Does Pattee’s “How Does a Molecule Become a Message?” Belong in the History of Biosemiotics?”

Umerez’s analysis made me aware of the fundamental differences in the culture of physics and molecular biology and the culture of semiotics from which the new field of biosemiotics arose. These cultures also view histories differently. Considering the evolutionary span and the many hierarchical levels of organization that their models must cover, models at different levels will require different observables and different meanings for common words, like symbol, interpretation, and language. These models as well as their histories should be viewed as complementary rather than competitive. The relation of genetic language and human language is the central issue. They are separated by 4 billion years and require entirely different models. Nevertheless, these languages have in common a unique unlimited expressive power that allows open-ended evolution and creative thought. Understanding the nature of this expressive power and how it arises remains a basic unsolved problem of biosemiotics.     

Memory,language, and ageing.

This overview provides both theoretical and empirical reasons for emphasizing practice and familiar skills as a practical strategy for enhancing cognitive functioning in old age. Our review of empirical research on age-related changes in memory and language reveals a consistent pattern of spared and impaired abilities in normal old age. Relatively preserved in old age is memory performance involving highly practised skills and familiar information, including factual, semantic and autobiographical information. Relatively impaired in old age is memory performance that requires the formation of new connections, for example, recall of recent autobiographical experiences, new facts or the source of newly acquired facts. This pattern of impaired new learning versus preserved old learning cuts across distinctions between semantic memory, episodic memory, explicit memory and perhaps also implicit memory. However, familiar verbal information is not completely preserved when accessed on the output side rather than the input side: aspects of language production, namely word finding and spelling, exhibit significant age-related declines. This emerging pattern of preserved and impaired abilities presents a fundamental challenge for theories of cognitive ageing, which must explain why some aspects of language and memory are more vulnerable to the effects of ageing than others. Information-universal theories, involving mechanisms such as general slowing that are independent of the type or structure of the information being processed, require additional mechanisms to account for this pattern of cognitive aging. Information-specific theories, where the type or structure of the postulated memory units can influence the effects of cognitive ageing, are able to account for this emerging pattern, but in some cases require further development to account for comprehensive cognitive changes such as general slowing.     

Structure Learning in a Sensorimotor Association Task

Learning is often understood as an organism''s gradual acquisition of the association between a given sensory stimulus and the correct motor response. Mathematically, this corresponds to regressing a mapping between the set of observations and the set of actions. Recently, however, it has been shown both in cognitive and motor neuroscience that humans are not only able to learn particular stimulus-response mappings, but are also able to extract abstract structural invariants that facilitate generalization to novel tasks. Here we show how such structure learning can enhance facilitation in a sensorimotor association task performed by human subjects. Using regression and reinforcement learning models we show that the observed facilitation cannot be explained by these basic models of learning stimulus-response associations. We show, however, that the observed data can be explained by a hierarchical Bayesian model that performs structure learning. In line with previous results from cognitive tasks, this suggests that hierarchical Bayesian inference might provide a common framework to explain both the learning of specific stimulus-response associations and the learning of abstract structures that are shared by different task environments.     

The predictive nature of individual differences in early associative learning and emerging social behavior

Across the first year of life, infants achieve remarkable success in their ability to interact in the social world. The hierarchical nature of circuit and skill development predicts that the emergence of social behaviors may depend upon an infant's early abilities to detect contingencies, particularly socially-relevant associations. Here, we examined whether individual differences in the rate of associative learning at one month of age is an enduring predictor of social, imitative, and discriminative behaviors measured across the human infant's first year. One-month learning rate was predictive of social behaviors at 5, 9, and 12 months of age as well as face-evoked discriminative neural activity at 9 months of age. Learning was not related to general cognitive abilities. These results underscore the importance of early contingency learning and suggest the presence of a basic mechanism underlying the ontogeny of social behaviors.     

Sex hormones influence human cognitive pattern

The major sex differences in cognitive skills are summarized, and the role of sex hormones in early organization and possible maintenance of these differences is discussed. Using animal models and human hormonal anomalies, a good case can be made that prenatal androgens strongly influence adult cognitive pattern, though the relation between baseline androgens and spatial ability, for example, need not be linear. Moreover, men and women remain sensitive to variation in hormonal state, as evidenced in the fluctuations in cognitive and motor performance across natural diurnal, menstrual and circannual rhythms. Evidence from administration of exogenous hormones in humans is more equivocal, though this field ultimately should yield useful information.     

Aversive phototaxic suppression: evaluation of a short‐term memory assay in Drosophila melanogaster

Drosophila melanogaster is increasingly being used to model human conditions that are associated with cognitive deficits including fragile‐X syndrome, Alzheimer’s disease, Parkinson’s disease, sleep loss, etc. With few exceptions, cognitive abilities that are known to be modified in these conditions in humans have not been evaluated in fly models. One reason is the absence of a simple, inexpensive and reliable behavioral assay that can be used by laboratories that are not expert in learning and memory. Aversive phototaxic suppression (APS) is a simple assay in which flies learn to avoid light that is paired with an aversive stimulus (quinine/humidity). However, questions remain about whether the change in the fly’s behavior reflects learning an association between light and quinine/humidity or whether the change in behavior is because of nonassociative effects of habituation and/or sensitization. We evaluated potential effects of sensitization and habituation on behavior in the T‐maze and conducted a series of yoked control experiments to further exclude nonassociative effects and determine whether this task evaluates operant learning. Together these experiments indicate that a fly must associate the light with quinine/humidity to successfully complete the task. Next, we show that five classic memory mutants are deficient in this assay. Finally, we evaluate performance in a fly model of neurodegenerative disorders associated with the accumulation of Tau. These data indicate that APS is a simple and effective assay that can be used to evaluate fly models of human conditions associated with cognitive deficits.     

Precise-Spike-Driven Synaptic Plasticity: Learning Hetero-Association of Spatiotemporal Spike Patterns

A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.     

The role of visual field position in pattern-discrimination learning.

Invariance of object recognition to translation in the visual field is a fundamental property of human pattern vision. In three experiments we investigated this capability by training subjects to distinguish between random checkerboard stimuli. We show that the improvement of discrimination performance does not transfer across the visual field if learning is restricted to a particular location in the retinal image. Accuracy after retinal translation shows no sign of decay over time and remains at the same level it had at the beginning of the training. It is suggested that in two-dimensional translation invariance-as in three-dimensional rotation invariance-the human visual system is relying on memory-intensive rather than computation-intensive processes. Multiple position- and stimulus-specific learning events may be required before recognition is independent of retinal location.     

Top-Down Feedback in an HMAX-Like Cortical Model of Object Perception Based on Hierarchical Bayesian Networks and Belief Propagation

Hierarchical generative models, such as Bayesian networks, and belief propagation have been shown to provide a theoretical framework that can account for perceptual processes, including feedforward recognition and feedback modulation. The framework explains both psychophysical and physiological experimental data and maps well onto the hierarchical distributed cortical anatomy. However, the complexity required to model cortical processes makes inference, even using approximate methods, very computationally expensive. Thus, existing object perception models based on this approach are typically limited to tree-structured networks with no loops, use small toy examples or fail to account for certain perceptual aspects such as invariance to transformations or feedback reconstruction. In this study we develop a Bayesian network with an architecture similar to that of HMAX, a biologically-inspired hierarchical model of object recognition, and use loopy belief propagation to approximate the model operations (selectivity and invariance). Crucially, the resulting Bayesian network extends the functionality of HMAX by including top-down recursive feedback. Thus, the proposed model not only achieves successful feedforward recognition invariant to noise, occlusions, and changes in position and size, but is also able to reproduce modulatory effects such as illusory contour completion and attention. Our novel and rigorous methodology covers key aspects such as learning using a layerwise greedy algorithm, combining feedback information from multiple parents and reducing the number of operations required. Overall, this work extends an established model of object recognition to include high-level feedback modulation, based on state-of-the-art probabilistic approaches. The methodology employed, consistent with evidence from the visual cortex, can be potentially generalized to build models of hierarchical perceptual organization that include top-down and bottom-up interactions, for example, in other sensory modalities.     

Guppies learn faster to discriminate between red and yellow than between two shapes

Animal species are expected to evolve specialised cognitive abilities to solve the tasks that are critical for their fitness. The literature contains several examples of specialised cognitive abilities, but few regard fish. The guppy, Poecilia reticulata, is a freshwater fish in which females choose their mates based on colouration, and orange‐coloured fruits are important diet enrichments for both sexes. For these reasons, we expect that this species has evolved enhanced learning abilities in colour discrimination compared to other types of discrimination. The comparison between studies in which guppies were tested for colour discrimination and studies in which guppies were tested for shape discrimination seems to support this hypothesis, but direct testing is still lacking. We experimentally compared the learning performance of guppies trained in a red–yellow colour discrimination learning task and that of guppies trained in a shape discrimination learning task using the same, automated conditioning procedure. Guppies trained in the colour discrimination showed greater learning performance, which provides support to the hypothesis that guppies possess enhanced colour discrimination abilities. Moreover, we found that male guppies performed better than females in both shape and colour discrimination learning.     

Age-Related Decline in Associative Learning in Healthy Chinese Adults

Paired associates learning (PAL) has been widely used in aging-related research, suggesting an age-related decline in associative learning. However, there are several cognitive processes (attention, spatial and recognition memory, strategy, and associative learning) involved in PAL. It is unclear which component contributes to the decline in PAL performance associated with age effects. The present study determines whether age effects on associative learning are independent of other cognitive processes involved in PAL. Using a validated computerized cognitive program (CANTAB), we examined cognitive performance of associative learning, spatial and recognition memory, attention and strategy use in 184 Singaporean Chinese adults aged from 21 to 80 years old. Linear regression revealed significant age-related decline in associative learning, spatial and recognition memory, and the level of strategy use. This age-related decline in associative learning remains even after adjusting for attention, spatial and recognition memory, and strategy use. These results show that age effects on associative learning are independent of other cognitive processes involved in PAL.     

Visual Recognition Software for Binary Classification and Its Application to Spruce Pollen Identification

Discriminating between black and white spruce (Picea mariana and Picea glauca) is a difficult palynological classification problem that, if solved, would provide valuable data for paleoclimate reconstructions. We developed an open-source visual recognition software (ARLO, Automated Recognition with Layered Optimization) capable of differentiating between these two species at an accuracy on par with human experts. The system applies pattern recognition and machine learning to the analysis of pollen images and discovers general-purpose image features, defined by simple features of lines and grids of pixels taken at different dimensions, size, spacing, and resolution. It adapts to a given problem by searching for the most effective combination of both feature representation and learning strategy. This results in a powerful and flexible framework for image classification. We worked with images acquired using an automated slide scanner. We first applied a hash-based “pollen spotting” model to segment pollen grains from the slide background. We next tested ARLO’s ability to reconstruct black to white spruce pollen ratios using artificially constructed slides of known ratios. We then developed a more scalable hash-based method of image analysis that was able to distinguish between the pollen of black and white spruce with an estimated accuracy of 83.61%, comparable to human expert performance. Our results demonstrate the capability of machine learning systems to automate challenging taxonomic classifications in pollen analysis, and our success with simple image representations suggests that our approach is generalizable to many other object recognition problems.     

Did tool-use evolve with enhanced physical cognitive abilities?

The use and manufacture of tools have been considered to be cognitively demanding and thus a possible driving factor in the evolution of intelligence. In this study, we tested the hypothesis that enhanced physical cognitive abilities evolved in conjunction with the use of tools, by comparing the performance of naturally tool-using and non-tool-using species in a suite of physical and general learning tasks. We predicted that the habitually tool-using species, New Caledonian crows and Galápagos woodpecker finches, should outperform their non-tool-using relatives, the small tree finches and the carrion crows in a physical problem but not in general learning tasks. We only found a divergence in the predicted direction for corvids. That only one of our comparisons supports the predictions under this hypothesis might be attributable to different complexities of tool-use in the two tool-using species. A critical evaluation is offered of the conceptual and methodological problems inherent in comparative studies on tool-related cognitive abilities.     

Sexual selection and the evolution of male and female cognition: A test using experimental evolution in seed beetles*

The mating system is thought to be important in shaping animal intelligence and sexual selection has been depicted as a driver of cognitive evolution, either directly by promoting superior cognitive ability during mate competition, or indirectly via genic capture of sexually selected traits. However, it remains unclear if intensified sexual selection leads to general improvements in cognitive abilities. Here, we evaluated this hypothesis by applying experimental evolution in seed beetles. Replicate lines, maintained for 35 generations of either enforced monogamy (eliminating sexual selection) or polygamy, were challenged to locate and discriminate among mates (male assays) or host seeds (female assays) in a spatial chemosensory learning task. All lines displayed learning between trials. Moreover, polygamous males outperformed monogamous males, providing evidence that sexual selection can lead to the evolution of improved male cognition. However, there were no differences between regimes in rates of male learning, and polygamous females showed no improvement in host search and even signs of reduced learning. Hence, sexual selection increased performance in cognitively demanding mate search, but it did not lead to general increases in cognitive abilities. We discuss the possibility that sexually antagonistic selection is an important factor maintaining abundant genetic variation in cognitive traits.     

Evolution of protolinguistic abilities as a by-product of learning to forage in structured environments

The skills required for the learning and use of language are the focus of extensive research, and their evolutionary origins are widely debated. Using agent-based simulations in a range of virtual environments, we demonstrate that challenges of foraging for food can select for cognitive mechanisms supporting complex, hierarchical, sequential learning, the need for which arises in language acquisition. Building on previous work, where we explored the conditions under which reinforcement learning is out-competed by seldom-reinforced continuous learning that constructs a network model of the environment, we now show that realistic features of the foraging environment can select for two critical advances: (i) chunking of meaningful sequences found in the data, leading to representations composed of units that better fit the prevalent statistical patterns in the environment; and (ii) generalization across units based on their contextual similarity. Importantly, these learning processes, which in our framework evolved for making better foraging decisions, had been earlier shown to reproduce a range of findings in language learning in humans. Thus, our results suggest a possible evolutionary trajectory that may have led from basic learning mechanisms to complex hierarchical sequential learning that can support advanced cognitive abilities of the kind needed for language acquisition.     

A Reciprocal Model of Face Recognition and Autistic Traits: Evidence from an Individual Differences Perspective

Although not a core symptom of the disorder, individuals with autism often exhibit selective impairments in their face processing abilities. Importantly, the reciprocal connection between autistic traits and face perception has rarely been examined within the typically developing population. In this study, university participants from the social sciences, physical sciences, and humanities completed a battery of measures that assessed face, object and emotion recognition abilities, general perceptual-cognitive style, and sub-clinical autistic traits (the Autism Quotient (AQ)). We employed separate hierarchical multiple regression analyses to evaluate which factors could predict face recognition scores and AQ scores. Gender, object recognition performance, and AQ scores predicted face recognition behaviour. Specifically, males, individuals with more autistic traits, and those with lower object recognition scores performed more poorly on the face recognition test. Conversely, university major, gender and face recognition performance reliably predicted AQ scores. Science majors, males, and individuals with poor face recognition skills showed more autistic-like traits. These results suggest that the broader autism phenotype is associated with lower face recognition abilities, even among typically developing individuals.     

Modeling neural mechanisms of vertebrate habituation: Locus specificity and pattern discrimination

A critical problem in neurobiology is to explain how the central nervous system coordinates pattern discrimination and locus specificity in learning. This problem is investigated in anuran amphibians who demonstrate both locus specificity and pattern discrimination in visual habituation. A neural mechanism is proposed whereby neural circuitry for pattern discrimination is shared by a spatial memory system. Such learning processes are argued to occur in the medial pallium (MP), the anuran's homolog of mammalian hippocampus. Necessary mapping from the shared network to spatial memory is set up by a mechanism that forms topographical connections, with desired orientation determined by activity gradient in presynaptic and postsynaptic layers. The model of MP is tested on both locus and stimulus specific habituation, which involve short-term as well as long-term synaptic plasticity. Successful modeling yields a set of predictions concerning MP organization and learning properties.     

Mapping human brain networks with cortico-cortical evoked potentials

The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex.