LIDA: A Working Model of Cognition

In this paper we present the LIDA architecture as a working model of cognition. We argue that such working models are broad in scope and address real world problems in comparison to experimentally based models which focus on specific pieces of cognition. While experimentally based models are useful, we need a working model of cognition that integrates what we know from neuroscience, cognitive science and AI. The LIDA architecture provides such a working model. A LIDA based cognitive robot or software agent will be capable of multiple learning mechanisms. With artificial feelings and emotions as primary motivators and learning facilitators, such systems will ‘live’ through a developmental period during which they will learn in multiple ways to act in an effective, human-like manner in complex, dynamic, and unpredictable environments. We discuss the integration of the learning mechanisms into the existing IDA architecture as a working model of cognition

Semantic similarity dissociates shortfrom long-term recency effects: testing a neurocomputational model of list memory

The finding that recency effects can occur not only in immediate free recall (i.e., short-term recency) but also in the continuous-distractor task (i.e., long-term recency) has led many theorists to reject the distinction between short- and long-term memory stores. Recently, we have argued that long-term recency effects do not undermine the concept of a short-term store, and we have presented a neurocomputational model that accounts for both short- and long-term recency and for a series of dissociations between these two effects. Here, we present a new dissociation between short- and long-term recency based on semantic similarity, which is predicted by our model. This dissociation is due to the mutual support between associated items in the short-term store, which takes place in immediate free recall and delayed free recall but not in continuous-distractor free recall

Self-organized learning in multi-layer networks

We present a framework for the self-organized formation of high level learning by a statistical preprocessing of features. The paper focuses first on the formation of the features in the context of layers of feature processing units as a kind of resource-restricted associative multiresolution learning We clame that such an architecture must reach maturity by basic statistical proportions, optimizing the information processing capabilities of each layer. The final symbolic output is learned by pure association of features of different levels and kind of sensorial input. Finally, we also show that common error-correction learning for motor skills can be accomplished also by non-specific associative learning. Keywords: feedforward network layers, maximal information gain, restricted Hebbian learning, cellular neural nets, evolutionary associative learnin

Evolution of associative learning in chemical networks

Organisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning – the ability to detect correlated features of the environment – has been studied extensively in nervous systems, where the underlying mechanisms are reasonably well understood, mechanisms within single cells that could allow associative learning have received little attention. Here, using in silico evolution of chemical networks, we show that there exists a diversity of remarkably simple and plausible chemical solutions to the associative learning problem, the simplest of which uses only one core chemical reaction. We then asked to what extent a linear combination of chemical concentrations in the network could approximate the ideal Bayesian posterior of an environment given the stimulus history so far? This Bayesian analysis revealed the ’memory traces’ of the chemical network. The implication of this paper is that there is little reason to believe that a lack of suitable phenotypic variation would prevent associative learning from evolving in cell signalling, metabolic, gene regulatory, or a mixture of these networks in cells

Incorporating characteristics of human creativity into an evolutionary art algorithm

A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically

Contributions of the ventromedial prefrontal cortex to goal-directed action selection

In this article, it will be argued that one of the key contributions of the ventromedial prefrontal cortex (vmPFC) to goal-directed action selection lies both in retrieving the value of goals that are the putative outcomes of the decision process and in establishing a relative preference ranking for these goals by taking into account the value of each of the different goals under consideration in a given decision-making scenario. These goal-value signals are then suggested to be used as an input into the on-line computation of action values mediated by brain regions outside of the vmPFC, such as parts of the parietal cortex, supplementary motor cortex, and dorsal striatum. Collectively, these areas can be considered to be constituent elements of a multistage decision process whereby the values of different goals must first be represented and ranked before the value of different courses of action available for the pursuit of those goals can be computed

EPS mid-career prize 2018: Inference within episodic memory reflects pattern completion

Recollection of episodic memories is a process of reconstruction where coherent events are inferred from subsets of remembered associations. Here, we investigated the formation of multielement events from sequential presentation of overlapping pairs of elements (people, places, and objects/animals), interleaved with pairs from other events. Retrievals of paired associations from a fully observed event (e.g., AB, BC, AC) were statistically dependent, indicating a process of pattern completion, but retrievals from a partially observed event (e.g., AB, BC, CD) were not. However, inference for unseen "indirect" associations (i.e., AC, BD or AD) from a partially observed event showed strong dependency with each other and with linking direct associations from that event. In addition, inference of indirect associations correlated with the product of performance on the linking direct associations across events (e.g., AC with ABxBC) but not on the non-linking association (e.g., AC with CD). These results were seen across three experiments, with greater differences in dependency between indirect and direct associations when they were separately tested, but similar results following single and repeated presentations of the direct associations. The results could be accounted for by a simple auto-associative network model of hippocampal memory function. Our findings suggest that pattern completion supports recollection of fully observed multielement events and the inference of indirect associations in partly observed multielement events, mediated via the directly observed linking associations (although the direct associations themselves were retrieved independently). Together with previous work, our results suggest that associative inference plays a key role in reconstructive episodic memory and does so through hippocampal pattern completion

The hippocampus and inferential reasoning: building memories to navigate future decisions

A critical aspect of inferential reasoning is the ability to form relationships between items or events that were not experienced together. This review considers different perspectives on the role of the hippocampus in successful inferential reasoning during both memory encoding and retrieval. Intuitively, inference can be thought of as a logical process by which elements of individual existing memories are retrieved and recombined to answer novel questions. Such flexible retrieval is sub-served by the hippocampus and is thought to require specialized hippocampal encoding mechanisms that discretely code events such that event elements are individually accessible from memory. In addition to retrieval-based inference, recent research has also focused on hippocampal processes that support the combination of information acquired across multiple experiences during encoding. This mechanism suggests that by recalling past events during new experiences, connections can be created between newly formed and existing memories. Such hippocampally mediated memory integration would thus underlie the formation of networks of related memories that extend beyond direct experience to anticipate future judgments about the relationships between items and events. We also discuss integrative encoding in the context of emerging evidence linking the hippocampus to the formation of schemas as well as prospective theories of hippocampal function that suggest memories are actively constructed to anticipate future decisions and actions

Context reinstatement in recognition: memory and beyond

Context effects in recognition tests are twofold. First, presenting familiar contexts at a test leads to an attribution of context familiarity to a recognition probe, which has been dubbed ‘context-dependent recognition’. Second, reinstating the exact study context for a particular target in a recognition test cues recollection of an item-context association, resulting in 'context-dependent discrimination'. Here we investigated how these two context effects are expressed in metacognitive monitoring (confidence judgments) and metacognitive control ('don’t know' responding) of retrieval. We used faces as studied items, landscape photographs as study and test contexts and both free- and forced-report 2AFC recognition tests. In terms of context-dependent recognition, the results document that presenting familiar contexts at test leads to higher confidence and lower rates of 'don’t know responses compared to novel contexts, while having no effect on forced-report recognition accuracy. In terms of context-dependent discrimination, the results show that reinstated contexts further boost confidence and reduce 'don’t know' responding compared to familiar contexts, while affecting forced-report recognition accuracy only when contribution of recollection to recognition performance is high. Together, our results demonstrate that metacognitive measures are sensitive to context effects, sometimes even more so than recognition measures