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More than 50 years ago, Bongard introduced 100 visual concept learning problems as a challenge for artificial vision systems. These problems are now known as Bongard problems. Although they are well known in cognitive science and artificial intelligence, only very little progress has been made toward building systems that can solve a substantial subset of them. In the system presented here, visual features are extracted through image processing and then translated into a symbolic visual vocabulary. We introduce a formal language that allows representing compositional visual concepts based on this vocabulary. Using this language and Bayesian inference, concepts can be induced from the examples that are provided in each problem. We find a reasonable agreement between the concepts with high posterior probability and the solutions formulated by Bongard himself for a subset of 35 problems. While this approach is far from solving Bongard problems like humans, it does considerably better than previous approaches. We discuss the issues we encountered while developing this system and their continuing relevance for understanding visual cognition. For instance, contrary to other concept learning problems, the examples are not random in Bongard problems; instead they are carefully chosen to ensure that the concept can be induced, and we found it helpful to take the resulting pragmatic constraints into account. [ABSTRACT FROM AUTHOR]

Discrimination performance in perceptual choice tasks is known to reflect both sensory discriminability and nonsensory response bias. In the framework of signal detection theory, these aspects of discrimination performance are quantified through separate measures, sensitivity (d′) for sensory discriminability and decision criterion (c) for response bias. However, it is unknown how response bias (i.e., criterion) changes at the single‐trial level as a consequence of reinforcement history. We subjected rats to a two‐stimulus two‐response conditional discrimination task with auditory stimuli and induced response bias through unequal reinforcement probabilities for the two responses. We compared three signal‐detection‐theory‐based criterion learning models with respect to their ability to fit experimentally observed fluctuations of response bias on a trial‐by‐trial level. These models shift the criterion by a fixed step (1) after each reinforced response or (2) after each nonreinforced response or (3) after both. We find that all three models fail to capture essential aspects of the data. Prompted by the observation that steady‐state criterion values conformed well to a behavioral model of signal detection based on the generalized matching law, we constructed a trial‐based version of this model and find that it provides a superior account of response bias fluctuations under changing reinforcement contingencies. [ABSTRACT FROM AUTHOR]

Problem solving research has encountered an impasse. Since the seminal work of Newell und Simon (1972) researchers do not seem to have made much theoretical progress (Batchelder and Alexander, 2012; Ohlsson, 2012). In this paper we argue that one factor that is holding back the field is the widespread rejection of introspection among cognitive scientists. We review evidence that introspection improves problem solving performance, sometimes dramatically. Several studies suggest that self-observation, self-monitoring, and self-reflection play a key role in developing problem solving strategies. We argue that studying these introspective processes will require researchers to systematically ask subjects to introspect. However, we document that cognitive science textbooks dismiss introspection and as a consequence introspective methods are not used in problem solving research, even when it would be appropriate. We conclude that research on problem solving would benefit from embracing introspection rather than dismissing it.

When solving problems people flexibly apply different tactics. Here, we use Sudoku to study this flexibility. In a think-aloud study participants use at least two tactics, a cell-based and a number-based tactic, and have personal preferences for one or the other. Response times in two follow-up experiments indicate that participants can be biased towards either tactic by task instructions and task requirements. We argue that previous research often used biasing task designs and therefore underestimated participants' flexibility and overestimated the importance of a problem's complexity. Furthermore, our experiments demonstrate that if a tactic does not lead to a solution, participants are able to switch to the other. We model each tactic and we show that only by incorporating switching we can fit the data. [ABSTRACT FROM AUTHOR]

Combining experts' subjective probability estimates is a fundamental task with broad applicability in domains ranging from finance to public health. However, it is still an open question how to combine such estimates optimally. Since the beta distribution is a common choice for modeling uncertainty about probabilities, here we propose a family of normative Bayesian models for aggregating probability estimates based on beta distributions. We systematically derive and compare different variants, including hierarchical and non-hierarchical as well as asymmetric and symmetric beta fusion models. Using these models, we show how the beta calibration function naturally arises in this normative framework and how it is related to the widely used Linear-in-Log-Odds calibration function. For evaluation, we provide the new Knowledge Test Confidence data set consisting of subjective probability estimates of 85 forecasters on 180 queries. On this and another data set, we show that the hierarchical symmetric beta fusion model performs best of all beta fusion models and outperforms related Bayesian fusion models in terms of mean absolute error. [ABSTRACT FROM AUTHOR]

Mounting evidence suggests that the role of sensory cortices in perceptual decision making goes beyond the mere representation of the discriminative stimuli and additionally involves the representation of nonsensory variables such as reward expectation. However, the relevance of these representations for behavior is not clear. To address this issue, we trained rats to discriminate sounds in a single-interval forced-choice task and then confronted the animals with unsignaled blockwise changes of reward probabilities. We found that unequal reward probabilities for the two choice options led to substantial shifts in response bias without concomitant reduction in stimulus discrimination. Although decisional biases were on average less extreme than required to maximize overall reinforcement, a model-based analysis revealed that rats managed to harvest >97% of rewards. Neurons in auditory cortex recorded during task performance weakly differentiated the discriminative stimuli but more strongly the subsequent goal-directed movement. Although 10–20% of units exhibited significantly different firing rates between task epochs with different response biases, control experiments showed this to result from inflated false positive rates due to unspecific temporal correlations of spiking activity rather than changing reinforcement contingencies. Transient pharmacological inactivation of auditory cortex reduced sound discriminability without affecting other measures of performance, whereas inactivation of medial prefrontal cortex affected both discriminability and bias. Together, these results suggest that auditory cortex activity only weakly reflects decisional variables during flexible updating of stimulus-response-outcome contingencies and does not play a crucial role in sound-cued adaptive behavior, beyond the representation of the discriminative stimuli. NEW & NOTEWORTHY Recent evidence suggests that sensory cortex represents nonsensory variables such as reward expectation, but the relevance of these representations for behavior is not well understood. We show that rat auditory cortex (AC) is modulated during movement and reward anticipation in a sound-cued reward tracking task, whereas AC inactivation only impaired discrimination without affecting reward tracking, consistent with a predominantly sensory role of AC. [ABSTRACT FROM AUTHOR]

A model for hematopoiesis is presented that explicitly includes the erythrocyte, granulocyte, and thrombocyte lineages and their common precursors. A small number of stem cells proliferate and differentiate through different compartments to produce the vast number of blood cells needed every day. Growth factors regulate the proliferation of cells dependent on the current demand. We provide a steady state analysis of the model and rough parameter estimates. Furthermore, we extend the model to include mutations that alter the replicative capacity of cells and introduce differentiation blocks. With these mutations the model develops signs of acute myeloid leukemia. [ABSTRACT FROM AUTHOR]

Because the growth of internet of things (IOT) technology, stakeholders who come from different research areas (information modeling area, dynamic process modeling area, and so on) will very easily and simultaneously evaluate and control the manufacturing process from all over the world. Therefore, there is an urgent need for a comprehensive enterprise modeling methodology with the integration of information modeling and dynamic process modeling method. However, it is known that there is limited research on the realization of a modeling methodology that can simultaneously handle information modeling and dynamic process modeling. The method for object-oriented business process optimization (MO2GO) system performs modeling in terms of information and process. However, the process modeling part in this system is static. Meanwhile, the Petri net mathematical modeling language has strong dynamic simulation capability. Thus, our main contribution is to analyze the characteristics of Petri net, which would be helpful to the dynamic process modeling realization in the MO2GO system, and integrate Petri net engine into the MO2GO system to allow a static process model to become dynamic. In here, the system can not only display a simulated manufacturing process but also calculate actual information (time and cost) for a final manufactured product. Therefore, it is possible for the system to handle both information modeling and dynamic process modeling. Finally, the MO2GO system will be more competitive in the future industry. [ABSTRACT FROM AUTHOR]

In neuroscience, data are typically generated from neural network activity. The resulting time series represent measurements from spatially distributed subsystems with complex interactions, weakly coupled to a high-dimensional global system. We present a statistical framework to estimate the direction of information flow and its delay in measurements from systems of this type. Informed by differential topology, gaussian process regression is employed to reconstruct measurements of putative driving systems from measurements of the driven systems. These reconstructions serve to estimate the delay of the interaction by means of an analytical criterion developed for this purpose. The model accounts for a range of possible sources of uncertainty, including temporally evolving intrinsic noise, while assuming complex nonlinear dependencies. Furthermore, we show that if information flow is delayed, this approach also allows for inference in strong coupling scenarios of systems exhibiting synchronization phenomena. The validity of themethod is demonstrated with a variety of delay-coupled chaotic oscillators. In addition, we show that these results seamlessly transfer to local field potentials in cat visual cortex. [ABSTRACT FROM AUTHOR]

Exemplar theories of categorization depend on similarity for explaining subjects' ability to generalize to new stimuli. A major criticism of exemplar theories concerns their lack of abstraction mechanisms and thus, seemingly, of generalization ability. Here, we use insights from machine learning to demonstrate that exemplar models can actually generalize very well. Kernel methods in machine learning are akin to exemplar models and are very successful in real-world applications. Their generalization performance depends crucially on the chosen similarity measure. Although similarity plays an important role in describing generalization behavior, it is not the only factor that controls generalization performance. In machine learning, kernel methods are often combined with regularization techniques in order to ensure good generalization. These same techniques are easily incorporated in exemplar models. We show that the generalized context model (Nosofsky, 1986) and ALCOVE (Kruschke, 1992) are closely related to a statistical model called kernel logistic regression. We argue that generalization is central to the enterprise of understanding categorization behavior, and we suggest some ways in which insights from machine learning can offer guidance. [ABSTRACT FROM AUTHOR]

Abstract: Performance on psychophysical tasks is influenced by a variety of non-sensory factors, most notably the magnitude or probability of reinforcement following correct responses. When reinforcement probability is unequal for hits and correct rejections, signal detection theory specifies an optimal decision criterion which maximizes the number of reinforcers. We subjected pigeons to a task in which six different stimuli (shades of gray) had to be assigned to one of two categories. Animals were confronted with asymmetric reinforcement schedules in which correct responses to five of the stimuli were reinforced with a probability of 0.5, while correct responses to the remaining stimulus were extinguished. The subjects’ resultant choice probabilities clearly deviated from those predicted by a maximization account. More specifically, the magnitude of the choice bias increased with the distance of the to-be-extinguished stimulus to the category boundary, a pattern opposite to that posited by maximization. The present and a previous set of results in which animals performed optimally can be explained by a simple choice mechanism in which a variable decision criterion is constantly updated according to a leaky integration of incomes attained from both response options. [Copyright &y& Elsevier]