Active Sensing as Bayes-Optimal Sequential Decision Making

Sensory inference under conditions of uncertainty is a major problem in both machine learning and computational neuroscience. An important but poorly understood aspect of sensory processing is the role of active sensing. Here, we present a Bayes-optimal inference and control framework for active sensing, C-DAC (Context-Dependent Active Controller). Unlike previously proposed algorithms that optimize abstract statistical objectives such as information maximization (Infomax) [Butko & Movellan, 2010] or one-step look-ahead accuracy [Najemnik & Geisler, 2005], our active sensing model directly minimizes a combination of behavioral costs, such as temporal delay, response error, and effort. We simulate these algorithms on a simple visual search task to illustrate scenarios in which context-sensitivity is particularly beneficial and optimization with respect to generic statistical objectives particularly inadequate. Motivated by the geometric properties of the C-DAC policy, we present both parametric and non-parametric approximations, which retain context-sensitivity while significantly reducing computational complexity. These approximations enable us to investigate the more complex problem involving peripheral vision, and we notice that the difference between C-DAC and statistical policies becomes even more evident in this scenario.Comment: Scheduled to appear in UAI 201

Pesky Pests of the Great Lakes State: Is Public Participation Influenced by Geographic Differences?

In Michigan, environmental issues, such as invasive species, are not geographically constrained, affecting citizens throughout the state. Regulations and management plans organized by scientists and officials are intended to address issues statewide, but these policies may not adequately tackle the threat from invasive species as it impacts different parts of the state at different times. Participation and contributions from citizens can offer insight into the impacts and changes non-native species have on the local ecosystem. However, chances to participate and contribute may be influenced by geographic location in the state. To understand if this was the case, this research studied publicly available documents and completed participant observations and semistructured interviews with participants, leaders, and officials included in invasive species management. Between the two study locations, Metro Detroit and the Western Upper Peninsula of Michigan, locational differences had some impact on opportunities to contribute to invasive species management. Population and the differences in the type of advertising used to alert citizens about events influenced access to participation opportunities. This research also revealed that this public policy issue lacks public involvement and contributions. Between the two locations, more involvement opportunities and organizations were present in Metro Detroit. However, it was the organizations themselves and their limited political involvement, and not geographic location, which had a greater impact on citizens\u27 lack of participation in invasive species management

Biocompatible parylene neurocages developing a robust method for live neural network studies

We present a refined method and design for fabricating parylene neurocages for in vitro studies of live neural networks. Parylene neurocages are biocompatible and very robust, making them ideally suited for studying the synaptic connections netween individual neurons to gain insight into learning and memory. The neurocage fabrication process is significantly less complex than earlier versions. Previous neurocage designs achieved limited neuronal outgrowth; however, the long-term cell survival rate was 50%

Assessing the spatial and temporal variability of the Detroit River and harmful algal blooms in western Lake Erie

Despite efforts to reduce the occurrence of harmful algal blooms (HABs) in western Lake Erie, blooms recur annually due to agricultural runoff, storms with high winds and heavy rains, and weak lake circulation patterns. The influence from river inputs on the spatial and temporal characteristics of HABs remains relatively unknown. The Detroit River, which contributes about 80% of the basin\u27s total inflow can have a large influence on the spatial and temporal distribution of the bloom. To understand this, optically classified imagery, in situ water measurements, and meteorological and river discharge observations were compiled and synthesized to examine the spatiotemporal variability of the Detroit River, HABs, and their interaction. Results indicate the presence of a defined Detroit River plume, which varies in size depending on wind and water current conditions within the lake. While high discharge of the river has an impact on the entire basin, strong winds in the spring, fall, and during summer pushes the Detroit River further south into the basin. This increases the spatiotemporal interaction between the Detroit River and HAB by limiting northerly bloom extent and diluting bloom water conditions. These results reveal the importance of Detroit River impact on blooms. Here, I present a greater understanding of the Detroit River and its role in the lake aiding the ability to improve predictions of bloom spatial variability

Development of biocompatible parylene neurocages

We present a refined method and design for building parylene neurocages for in vitro studies of live neural networks. Parylene neurocages are biocompatible and very robust, making them ideally suited for studying the synaptic connections between individual neurons to gain insight into learning and memory. The neurocage fabrication process is significantly less complex than earlier versions. Previous neurocage designs achieved limited neuronal outgrowth; however, the long-term cell survival rate was 50%

Stop paying attention: the need for explicit stopping in inhibitory control

Inhibitory control, the ability to stop inappropriate actions, isan important cognitive function often investigated via the stop-signal task, in which an infrequent stop signal instructs the sub-ject to stop a default go response. Previously, we proposed arational decision-making model for stopping, suggesting theobserver makes a repeated Go versus Wait choice at each in-stant, so that a Stop response is realized by repeatedly choosingto Wait. We propose an alternative model here that incorpo-rates a third choice, Stop. Critically, unlike the Wait action,choosing the Stop action not only blocks a Go response at thecurrent moment but also for the remainder of the trial – thedisadvantage of losing this flexibility is balanced by the bene-fit of not having to pay attention anymore. We show that thisnew model both reproduces known behavioral effects and hasinternal dynamics resembling presumed Go neural activationsin the brain

A Conceptual Model for Enhancing Intra-Group Knowledge Sharing

Knowledge sharing is a social action involving the collective behavior of a group of people. However, prior research on knowledge predominately focused on individual behavior. Furthermore, previous studies did not capture the multiple facets of this group behavior. In this research, we propose and justify a framework for the explanation of knowledge sharing in organization context as a social action, integrating multiple theories, i.e., social capital theory, institutional theory, and adaptive technology structuration theory. A series of propositions are proposed and discussed

Rational Decision-Making in Inhibitory Control

An important aspect of cognitive flexibility is inhibitory control, the ability to dynamically modify or cancel planned actions in response to changes in the sensory environment or task demands. We formulate a probabilistic, rational decision-making framework for inhibitory control in the stop signal paradigm. Our model posits that subjects maintain a Bayes-optimal, continually updated representation of sensory inputs, and repeatedly assess the relative value of stopping and going on a fine temporal scale, in order to make an optimal decision on when and whether to go on each trial. We further posit that they implement this continual evaluation with respect to a global objective function capturing the various reward and penalties associated with different behavioral outcomes, such as speed and accuracy, or the relative costs of stop errors and go errors. We demonstrate that our rational decision-making model naturally gives rise to basic behavioral characteristics consistently observed for this paradigm, as well as more subtle effects due to contextual factors such as reward contingencies or motivational factors. Furthermore, we show that the classical race model can be seen as a computationally simpler, perhaps neurally plausible, approximation to optimal decision-making. This conceptual link allows us to predict how the parameters of the race model, such as the stopping latency, should change with task parameters and individual experiences/ability