View: session overviewtalk overview

neuroscience

neuroscience

10:40 | A causal role for right frontopolar cortex in directed, but not random, exploration SPEAKER: unknown ABSTRACT. The explore-exploit dilemma occurs anytime we must choose between exploring unknown options for information and exploiting known resources for reward. Previous work suggests that people use two different strategies to solve the explore-exploit dilemma: directed exploration, driven by information seeking, and random exploration, driven by decision noise. Here, we show that these two strategies rely on different neural systems. Using transcranial magnetic stimulation to inhibit the right frontopolar cortex, we were able to selectively inhibit directed exploration while leaving random exploration intact. This suggests a causal role for right frontopolar cortex in directed, but not random, exploration and that directed and random exploration rely on (at least partially) dissociable neural systems. |

11:00 | A Neural Accumulator Model of Antisaccade Performance of Healthy Controls and Obsessive-Compulsive Disorder Patients SPEAKER: Vassilis Cutsuridis ABSTRACT. Antisaccade performance in obsessive-compulsive disorder (OCD) is related to a dysfunctional network of brain structures including the (pre)frontal and posterior parietal cortices, basal ganglia, and superior colliculus. Previously recorded antisaccade performance of healthy and OCD subjects is re-analyzed to show greater variability in mean latency and variance of corrected antisaccades as well as in shape of antisaccade and corrected antisaccade latency distributions and increased error rates of OCD patients relative to healthy participants. Then a well-established neural accumulator model of antisaccade performance is employed to uncover the mechanisms giving rise to these observed OCD deficits. The model shows: i) increased variability in latency distributions of OCD patients is due to a more noisy accumulation of information by both correct and erroneous decision signals; (ii) OCD patients are almost as confident about their decisions as healthy controls; ii) competition via local lateral inhibition between the correct and erroneous decision processes, and not a third top-down STOP signal of the erroneous response, accounts for both the antisaccade performance of healthy controls and OCD patients. |

11:20 | A Neurocomputational Model of Learning to Select Actions SPEAKER: unknown ABSTRACT. We present an extension of a schema-based architecture for action selection, where competition between schemas is resolved using a variation of a neuroanatomically detailed model of the basal ganglia. The extended model implements distinct learning mechanisms for cortical schemas and for units within the basal ganglia. We demonstrate the functionality of the proposed mechanisms by applying the model to two classic neuropsychological tasks, the Wisconsin Card Sorting Task (WCST) and the Probabilistic Reversal Learning Task (PRLT). We discuss how the model captures existing behavioural data in neurologically healthy subjects and PD patients and how to overcome its shortcomings. |

11:40 | Gaps Between Human and Artificial Mathematics SPEAKER: Aaron Sloman ABSTRACT. The Turing-inspired Meta-morphogenesis project begun in 2011 was partly motivated by deep gaps in our understanding of mathematical cognition and other aspects of human and non-human intelligence and our inability to model them. The project attempts to identify previously unnoticed evolutionary transitions in biological information processing related to gaps in our current understanding of cognition. Analysis of such transitions may also shed light on gaps in current AI. This is very different from attempts to study human mathematical cognition directly, e.g. via observation, experiment, neural imaging, etc. Fashionable ideas about ``embodied cognition'', ``enactivism'', and ``situated cognition'', focus on shallow products of evolution, ignoring pressures to evolve increasingly {\em dis}embodied forms of cognition to meet increasingly complex and varied challenges produced by articulated physical forms, multiple sensory capabilities, geographical and temporal spread of important information and other resources, and ``other-related meta-cognition'' concerning mental states, processes and capabilities of other individuals. Computers are normally thought of as good at mathematics: they perform logical, arithmetical and statistical calculations and manipulate formulas, at enormous speeds, but still lack abilities in humans and other animals to perceive and understand geometrical and topological possibilities and constraints that (a) are required for perception and use of affordances, and (b) play roles in mathematical, and proto-mathematical, discoveries made by ancient mathematicians, human toddlers and other intelligent animals. Neurally inspired, statistics-based (e.g.``deep learning'') models cannot explain recognition and understanding of mathematical {\em necessity} or {\em impossibility}. A partial (neo-Kantian) analysis of types of evolved biological information processing capability still missing from our models may inspire new kinds of research helping to fill the gaps. Had Turing lived long enough to develop his ideas on morphogenesis, he might have done this. |

reasoning

14:40 | Noisy Reasoning: a Model of Probability Estimation and Inferential Judgment SPEAKER: unknown ABSTRACT. We describe a computational model of two central aspects of people’s probabilistic reasoning: descriptive probability estimation and inferential probability judgment. This model assumes that people’s reasoning follows standard frequentist probability theory, but is subject to random noise. This random noise has a regressive effect in probability estimation, moving probability estimates away from normative probabilities and towards the center of the probability scale. This regressive effect explains various reliable and systematic biases seen in people’s probability estimation. This random noise has an anti-regressive effect in inferential judgment, however. This model predicts that these contrary effects will tend to cancel out in tasks that involve both descriptive probability estimation and inferential probability judgment, leading to unbiased responses in those tasks. We test this model by applying it to one such task, described by Gallistel et al. (2014). Participants’ median responses in this task were unbiased, agreeing with normative probability theory over the full range of responses. Our model captures the pattern of unbiased responses in this task, while simultaneously explaining systematic biases away from normatively correct probabilities seen in other tasks. |

15:00 | Cognitive Computational Models for Conditional Reasoning SPEAKER: unknown ABSTRACT. Premises in conditional reasoning consist of an ``if'' statement (e.g., ``if I can catch the bus, I won't be late'') and a fact (e.g., I can catch the bus). Such types of simple inference have been studied empirically and formally for about a century. In the past five decades, several cognitive theories have been proposed to explain why humans deviate from predictions of conditional logic. In this article, we (i) describe existing theories, (ii) develop multinomial processing tree (MPT) models for these theories and systematically extend the theories with guessing subtrees to test the predictive power of cognitive models. The models are evaluated with G^2, Akaike’s (AIC) and Bayesian Information Criteria (BIC), and Fisher’s Information Approximation (FIA). Mental model theory with directionality for indicative conditionals while the independence model for counterfactuals provide the best fits to data from psychological studies. |

15:20 | Beyond the Visual Impedance Effect SPEAKER: unknown ABSTRACT. Whether the mental representation of reasoning problems is spatial or visual (or mixed) in nature has been the subject of considerable debate for years. The visual impedance effect found in Knauff & Johnson-Laird (2002) has provided us with new insights into this question. The study found that the forming of excessive visual images induced by the premises can impede relational reasoning. This study aimed at investigating the factor of complexity on the visual impedance effect in two folds, namely number of term series (i.e., total number of premises plus the conclusion) and whether the entities in the premises are presented in a continuous manner (i.e., whether the subject of the argument is the same as the object of the previous argument). In line with previous studies, relational category, number of term series and successiveness were the main factors of the response time. Results of the parameter estimation by generalized estimating equation showed that visual relations, 5-term series and discontinuous problems were the only significant parameters. The results again suggested that irrelevant visual images can hinder reasoning processes, in addition to the complexity of the problem. We proposed a combined cognitive model of ACT-R and PRISM for the findings in this study. |

15:40 | Implementing Mental Model Updating in ACT-R SPEAKER: Sabine Prezenski ABSTRACT. This paper demonstrates how mental models and updates of mental models due to system changes can be modeled with the cognitive architecture ACT-R using explicit mechanisms. The mental model building and updating is modeled with a representation chunk and a control chunk. The representation chunk holds the strategy, the expected outcome and an evaluation mechanism of the strategy. The control chunk holds information over environmental conditions and the learning history. This modeling approach was developed and tested for smartphone application tasks and then implemented in a dynamic decision making task investigating strategy development with complex stimuli. The later task used different multi-feature auditory stimuli material. The modeling approach explained data of participants in the smartphone studies very well and met the trends found in the dynamic decision making task. |

decision making