Power laws, memory capacity, and self-tuned critical branching in an LIF model with binary synapses

Both fluctuations and distributions of spontaneous neural spiking activity have been observed to closely follow a variety of power laws. Multiple explanations have been offered for each observation, but few lead to mechanisms that encompass their widespread occurrence. A canonical, leaky integrate-and-fire model is presented in which synapses are updated based on the timing of pre- and post-synaptic spikes in order to maintain a state of critical branching. Results showed that 1) the self-tuning algorithm maintained critical branching under a range of parameters; 2) power laws were obtained in spiking activity fluctuations (1/f scaling), size distributions of network bursts (neural avalanches), and temporal correlations in interspike intervals (Allan factor); 3) power laws disappeared once the self-tuning algorithm was disabled; and 4) critical branching was adaptive in that it maximized the network’s memory capacity when assessed as a liquid state machine

Walking across Wikipedia: a scale-free network model of semantic memory retrieval.

Semantic knowledge has been investigated using both online and offline methods. One common online method is category recall, in which members of a semantic category like "animals" are retrieved in a given period of time. The order, timing, and number of retrievals are used as assays of semantic memory processes. One common offline method is corpus analysis, in which the structure of semantic knowledge is extracted from texts using co-occurrence or encyclopedic methods. Online measures of semantic processing, as well as offline measures of semantic structure, have yielded data resembling inverse power law distributions. The aim of the present study is to investigate whether these patterns in data might be related. A semantic network model of animal knowledge is formulated on the basis of Wikipedia pages and their overlap in word probability distributions. The network is scale-free, in that node degree is related to node frequency as an inverse power law. A random walk over this network is shown to simulate a number of results from a category recall experiment, including power law-like distributions of inter-response intervals. Results are discussed in terms of theories of semantic structure and processing

The segment as the minimal planning unit in speech production and reading aloud: evidence and implications.

Speech production and reading aloud studies have much in common, especially the last stages involved in producing a response. We focus on the minimal planning unit (MPU) in articulation. Although most researchers now assume that the MPU is the syllable, we argue that it is at least as small as the segment based on negative response latencies (i.e., response initiation before presentation of the complete target) and longer initial segment durations in a reading aloud task where the initial segment is primed. We also discuss why such evidence was not found in earlier studies. Next, we rebut arguments that the segment cannot be the MPU by appealing to flexible planning scope whereby planning units of different sizes can be used due to individual differences, as well as stimulus and experimental design differences. We also discuss why negative response latencies do not arise in some situations and why anticipatory coarticulation does not preclude the segment MPU. Finally, we argue that the segment MPU is also important because it provides an alternative explanation of results implicated in the serial vs. parallel processing debate

Spectral convergence in tapping and physiological fluctuations: coupling and independence of 1/f noise in the central and autonomic nervous systems.

When humans perform a response task or timing task repeatedly, fluctuations in measures of timing from one action to the next exhibit long-range correlations known as 1/f noise. The origins of 1/f noise in timing have been debated for over 20 years, with one common explanation serving as a default: humans are composed of physiological processes throughout the brain and body that operate over a wide range of timescales, and these processes combine to be expressed as a general source of 1/f noise. To test this explanation, the present study investigated the coupling vs. independence of 1/f noise in timing deviations, key-press durations, pupil dilations, and heartbeat intervals while tapping to an audiovisual metronome. All four dependent measures exhibited clear 1/f noise, regardless of whether tapping was synchronized or syncopated. 1/f spectra for timing deviations were found to match those for key-press durations on an individual basis, and 1/f spectra for pupil dilations matched those in heartbeat intervals. Results indicate a complex, multiscale relationship among 1/f noises arising from common sources, such as those arising from timing functions vs. those arising from autonomic nervous system (ANS) functions. Results also provide further evidence against the default hypothesis that 1/f noise in human timing is just the additive combination of processes throughout the brain and body. Our findings are better accommodated by theories of complexity matching that begin to formalize multiscale coordination as a foundation of human behavior

Spatial Memory and Foraging: How Perfect Spatial Memory Improves Foraging Performance

Foraging is a search process common to all mobile organisms. Spatial memory can improve foraging efficiency and efficacy, and evidence indicates that many species—including humans—actively utilize spatial memory to aid in their foraging, yet most current models of foraging do not include spatial memory. In this study, a simple online foraging game was used to attempt to replicate and extend findings from a recent study (Kerster, Rhodes, & Kello, 2016) to further investigate the role of spatial memory in foraging. The game involved searching a simple 2d space by clicking the mouse to try and find as many resources as possible in 300 clicks. Spatial information was displayed that provided complete information about search history in order test how “perfect” spatial memory improves search performance. Over 1000 participants were recruited to participate in the task using Amazon’s Mechanical Turk, which allowed this test to be performed across a wide parameter space of different resource distributions. Results replicated many of the findings of earlier studies, and demonstrated that spatial memory can have a dramatic effect on search performance

Social Foraging in Groups of Search Agents with Human Intervention

Intelligent agents coordinate and cooperate flexibly when rules and dynamics of interaction can change over time and across different tasks and environmental conditions. Loose coupling emerges among agents when the rules of interaction are weak enough for agents to act independently or interdependently, and patterns of interaction vary as a function of conditions. Here, we examine collective foraging among simulated agents with and without human intervention. We find that loose coupling among search agents improved group foraging success, and that human players improved performance partly by subtle, indirect effects on group interactions. Analyses of movement patterns showed that loose coupling enabled collections of agents to self-organize and reorganize into a greater diversity of ad hoc groupings

Individual exploration and selective social learning: balancing exploration–exploitation trade-offs in collective foraging

Search requires balancing exploring for more options and exploiting the ones previously found. Individuals foraging in a group face another trade-off: whether to engage in social learning to exploit the solutions found by others or to solitarily search for unexplored solutions. Social learning can better exploit learned information and decrease the costs of finding new resources, but excessive social learning can lead to over-exploitation and too little exploration for new solutions. We study how these two trade-offs interact to influence search efficiency in a model of collective foraging under conditions of varying resource abundance, resource density and group size. We modelled individual search strategies as Lévy walks, where a power-law exponent (μ) controlled the trade-off between exploitative and explorative movements in individual search. We modulated the trade-off between individual search and social learning using a selectivity parameter that determined how agents responded to social cues in terms of distance and likely opportunity costs. Our results show that social learning is favoured in rich and clustered environments, but also that the benefits of exploiting social information are maximized by engaging in high levels of individual exploration. We show that selective use of social information can modulate the disadvantages of excessive social learning, especially in larger groups and when individual exploration is limited. Finally, we found that the optimal combination of individual exploration and social learning gave rise to trajectories with μ ≈ 2 and provide support for the general optimality of such patterns in search. Our work sheds light on the interplay between individual search and social learning, and has broader implications for collective search and problem-solving

Exploring the movement dynamics of deception

Both the science and the everyday practice of detecting a lie rest on the same assumption: hidden cognitive states that the liar would like to remain hidden nevertheless influence observable behavior. This assumption has good evidence. The insights of professional interrogators, anecdotal evidence, and body language textbooks have all built up a sizeable catalog of non-verbal cues that have been claimed to distinguish deceptive and truthful behavior. Typically, these cues are discrete, individual behaviors—a hand touching a mouth, the rise of a brow—that distinguish lies from truths solely in terms of their frequency or duration. Research to date has failed to establish any of these non-verbal cues as a reliable marker of deception. Here we argue that perhaps this is because simple tallies of behavior can miss out on the rich but subtle organization of behavior as it unfolds over time. Research in cognitive science from a dynamical systems perspective has shown that behavior is structured across multiple timescales, with more or less regularity and structure. Using tools that are sensitive to these dynamics, we analyzed body motion data from an experiment that put participants in a realistic situation of choosing, or not, to lie to an experimenter. Our analyses indicate that when being deceptive, continuous fluctuations of movement in the upper face, and somewhat in the arms, are characterized by dynamical properties of less stability, but greater complexity. For the upper face, these distinctions are present despite no apparent differences in the overall amount of movement between deception and truth. We suggest that these unique dynamical signatures of motion are indicative of both the cognitive demands inherent to deception and the need to respond adaptively in a social context