Dynamics and robustness of familiarity memory

When presented with an item or a face, one might have a sense of recognition without the ability to recall when or where the stimulus has been encountered before. This sense of recognition is called familiarity memory. Following previous computational studies of familiarity memory, we investigate the dynamical properties of familiarity discrimination and contrast two different familiarity discriminators: one based on the energy of the neural network and the other based on the time derivative of the energy. We show how the familiarity signal decays rapidly after stimulus presentation. For both discriminators, we calculate the capacity using mean field analysis. Compared to recall capacity (the classical associative memory in Hopfield nets), both the energy and the slope discriminators have bigger capacity, yet the energy-based discriminator has a higher capacity than one based on its time derivative. Finally, both discriminators are found to have a different noise dependence

Roles of familiarity and novelty in visual preference judgments are segregated across object categories

Understanding preference decision making is a challenging problem because the underlying process is often implicit and dependent on context, including past experience. There is evidence for both familiarity and novelty as critical factors for preference in adults and infants. To resolve this puzzling contradiction, we examined the cumulative effects of visual exposure in different object categories, including faces, natural scenes, and geometric figures, in a two-alternative preference task. The results show a clear segregation of preference across object categories, with familiarity preference dominant in faces and novelty preference dominant in natural scenes. No strong bias was observed in geometric figures. The effects were replicated even when images were converted to line drawings, inverted, or presented only briefly, and also when spatial frequency and contour distribution were controlled. The effects of exposure were reset by a blank of 1 wk or 3 wk. Thus, the category-specific segregation of familiarity and novelty preferences is based on quick visual categorization and cannot be caused by the difference in low-level visual features between object categories. Instead, it could be due either to different biological significances/attractiveness criteria across these categories, or to some other factors, such as differences in within-category variance and adaptive tuning of the perceptual system

Fluid Tasks and Fluid Teams: The Impact of Diversity in Experience and Team Familiarity on Team Performance

In this paper, we consider how the structures of tasks and teams interact to affect team performance. We study the effects of diversity in experience on a team's ability to respond to task changes, by separately examining interpersonal team diversity (i.e., differences in experience across the entire team) and intrapersonal team diversity (i.e., whether individuals on the team are more or less specialized). We also examine whether team familiarity - team members' prior experience working with one another - helps teams to better manage challenges created by task changes and greater interpersonal team diversity. Using detailed project- and individual-level data from an Indian software services firm, we find that the interaction of task-change with intrapersonal diversity is related to improved project performance, while the interaction of task-change with interpersonal diversity is related to diminished performance. Additionally, the interaction of team familiarity with interpersonal diversity is related to improved project performance in some cases. Our results highlight a need for more nuanced approaches to leveraging experience in team management.Diversity, Knowledge Work, Project Flexibility, Task Change, Team Familiarity

Representation of Time-Varying Stimuli by a Network Exhibiting Oscillations on a Faster Time Scale

Sensory processing is associated with gamma frequency oscillations (30–80 Hz) in sensory cortices. This raises the question whether gamma oscillations can be directly involved in the representation of time-varying stimuli, including stimuli whose time scale is longer than a gamma cycle. We are interested in the ability of the system to reliably distinguish different stimuli while being robust to stimulus variations such as uniform time-warp. We address this issue with a dynamical model of spiking neurons and study the response to an asymmetric sawtooth input current over a range of shape parameters. These parameters describe how fast the input current rises and falls in time. Our network consists of inhibitory and excitatory populations that are sufficient for generating oscillations in the gamma range. The oscillations period is about one-third of the stimulus duration. Embedded in this network is a subpopulation of excitatory cells that respond to the sawtooth stimulus and a subpopulation of cells that respond to an onset cue. The intrinsic gamma oscillations generate a temporally sparse code for the external stimuli. In this code, an excitatory cell may fire a single spike during a gamma cycle, depending on its tuning properties and on the temporal structure of the specific input; the identity of the stimulus is coded by the list of excitatory cells that fire during each cycle. We quantify the properties of this representation in a series of simulations and show that the sparseness of the code makes it robust to uniform warping of the time scale. We find that resetting of the oscillation phase at stimulus onset is important for a reliable representation of the stimulus and that there is a tradeoff between the resolution of the neural representation of the stimulus and robustness to time-warp. Author Summary Sensory processing of time-varying stimuli, such as speech, is associated with high-frequency oscillatory cortical activity, the functional significance of which is still unknown. One possibility is that the oscillations are part of a stimulus-encoding mechanism. Here, we investigate a computational model of such a mechanism, a spiking neuronal network whose intrinsic oscillations interact with external input (waveforms simulating short speech segments in a single acoustic frequency band) to encode stimuli that extend over a time interval longer than the oscillation's period. The network implements a temporally sparse encoding, whose robustness to time warping and neuronal noise we quantify. To our knowledge, this study is the first to demonstrate that a biophysically plausible model of oscillations occurring in the processing of auditory input may generate a representation of signals that span multiple oscillation cycles.National Science Foundation (DMS-0211505); Burroughs Wellcome Fund; U.S. Air Force Office of Scientific Researc

Team Familiarity and Productivity in Cardiac Surgery Operations: The Effect of Dispersion, Bottlenecks and Task Complexity

Fluid teams are commonly used by a variety of organizations to perform similar and repetitive yet highly critical and knowledge-intensive tasks. Such teams operate for a limited time, after which they dissolve and some of their members may work together again as part of another team. Using a granular dataset of 6,206 cardiac surgeries from a private hospital in Europe over seven years, our study offers a new and detailed account of how team familiarity (i.e., shared work experience) influences team productivity. We highlight the role of nuanced team composition dynamics beyond average team familiarity. We observe that teams with high dispersion of pairwise familiarity exhibit lower team productivity, and the existence of a "bottleneckpair" may significantly hinder overall knowledge transfer capability, thus, productivity of fluid teams. In addition, we find that the higher the percentage of familiarity gained from complex tasks, the higher the productivity of the team. Finally, our results suggest that the positive effect of average team familiarity on productivity is enhanced when performing more complicated tasks. Our study provides new operational insights to improve productivity of fluid teams with better team composition strategies

Feature-Based Change Detection Reveals Inconsistent Individual Differences in Visual Working Memory Capacity

Visual working memory (VWM) is a key cognitive system that enables people to hold visual information in mind after a stimulus has been removed and compare past and present to detect changes that have occurred. VWM is severely capacity limited to around 3–4 items, although there are robust individual differences in this limit. Importantly, these individual differences are evident in neural measures of VWM capacity. Here, we capitalized on recent work showing that capacity is lower for more complex stimulus dimension. In particular, we asked whether individual differences in capacity remain consistent if capacity is shifted by a more demanding task, and, further, whether the correspondence between behavioral and neural measures holds across a shift in VWM capacity. Participants completed a change detection (CD) task with simple colors and complex shapes in an fMRI experiment. As expected, capacity was significantly lower for the shape dimension. Moreover, there were robust individual differences in behavioral estimates of VWM capacity across dimensions. Similarly, participants with a stronger BOLD response for color also showed a strong neural response for shape within the lateral occipital cortex, intraparietal sulcus (IPS), and superior IPS. Although there were robust individual differences in the behavioral and neural measures, we found little evidence of systematic brain-behavior correlations across feature dimensions. This suggests that behavioral and neural measures of capacity provide different views onto the processes that underlie VWM and CD. Recent theoretical approaches that attempt to bridge between behavioral and neural measures are well positioned to address these findings in future work

Shopping for new glasses: looking beyond jazz in the study of organization improvisation

This article calls for research on organizational improvisation to go beyond the currently dominant jazz metaphor in theory development. We recognize the important contribution that jazz improvisation has made and will no doubt continue to make in understanding the nature and complexity of organizational improvisation. This article therefore presents some key lessons from the jazz metaphor and then proceeds to identify the possible dangers of building scientific inquiry upon a single metaphor. We then present three alternative metaphors Indian music, therapy and role theory. We explore the nature of these metaphors and seek to identify ways in which they differ from the jazz metaphor. This analysis leads us to identify not merely how these alternative metaphors fill the gaps left by the jazz metaphor but also how they complement the contribution from the jazz metaphor thus further strengthening theory-building in this genre. Ultimately, our understanding of organizational improvisation will be sharpened by more incisive theoretical analysis and empirical research.