Toward a “Standard Model” of Early Language Learning

A standard model is a theoretical framework that synthesizes observables into a quantitative consensus. Have researchers made progress toward this kind of synthesis for children’s early language learning? Many computational models of early vocabulary learning assume that individual words are learned through an accumulation of environmental input. This assumption is also implicit in empirical work that emphasizes links between language input and learning outcomes. However, models have typically focused on average performance, whereas empirical work has focused on variability. To model individual variability, we relate the tradition of research on accumulator models to item response theory models from psychometrics. This formal connection reveals that currently available data sets do not allow researchers to test the resulting models fully, illustrating a critical need for theory to contribute to shaping new data collection and creating and testing an eventual standard model.

Toward a “Standard Model” of Early Language Learning

A "standard model" is a theoretical framework that synthesizes observables into a quantitative consensus. Have we made progress towards this kind of synthesis for children’s early language learning? Many computational models of early vocabulary learning assume that individual words are learned through an accumulation of environmental input. This assumption is also implicit in empirical work that emphasizes links between language input and learning outcomes. However, models have typically focused on average performance, while empirical work has focused on variability. To model individual variability, we relate the tradition of research on accumulator models to Item-Response Theory models from psychometrics. This formal connection reveals that currently available datasets cannot allow us to fully test these models, illustrating a critical need for theory in shaping new data collection and in creating and testing an eventual "standard model."

The Role of Situation Criticality in Affecting the Effect of Cognitive Load on Drivers’ Brake responses: A Driving Simulator Based Study

A dramatic increase in talking on the phone whilst driving has been seen over the past decades, which posed a significant safety threat on the whole society consequently. Studies on the topic regarding the effect of phone conversations on drivers’ driving performances have never come to a cease, especially on the studies of drivers’ brake response times. However, few studies focus on the relationship between situation criticality and the effect of cognitive load on drivers’ brake responses. To better understand it, a driving simulator experiment with two braking scenarios corresponding to two levels of situation criticality was conducted in this study. Participants were asked to follow a lead vehicle as they normally did and answer arithmetic problems (simple and complex) in three phone modes (baseline, hands-free, and handheld) in the meantime. Drivers’ brake response times to the lead vehicle under five conditions were collected and fitted in accumulator models, in which visual looming and brake lights onset were included as the sensory cues. Results demonstrated that the previously proposed mechanistically explicit simulation model was able to predict drivers’ brake response times on different levels of cognitive load and the increased effect of cognitive load on drivers’ brake response times in less critical situations was demonstrated in this paper as well.

Influence of Hydraulic Accumulator Performance on the Hydraulic Hybrid Powertrain

Owing to its high power density, hydraulic hybrid is considered as an effective approach to reducing the fuel consumption of heavy duty vehicles. A gas-charged hydraulic accumulator serves as the power buffer, storing and releasing hydraulic power through gas. An accurate hydraulic accumulator model is crucial to predict its actual performance. There are two widely used accumulator models: isothermal and adiabatic models. Neither of these models are practical to reflect its real performance in the hydraulic hybrid system. Therefore, the influence of an accumulator model considering thermal hysteresis on a hydraulic hybrid wheel loader has been studied in this paper. The difference of three accumulator models (isothermal, adiabatic and energy balance) has been identified. A dynamic simulation model of the hydraulic hybrid wheel loader has been developed. The fuel consumptions of the hydraulic hybrid wheel loader with three accumulator models has been compared. The influence of heat transfer coefficient of the accumulator housing has also been studied.

The Neurodynamic Decision Variable in Human Multi-alternative Perceptual Choice

The neural dynamics underpinning binary perceptual decisions and their transformation into actions are well studied, but real-world decisions typically offer more than two response alternatives. How does decision-related evidence accumulation dynamically influence multiple action representations in humans? The heightened conservatism required in multiple compared with binary choice scenarios suggests a mechanism that compensates for increased uncertainty when multiple choices are present by suppressing baseline activity. Here, we tracked action representations using corticospinal excitability during four- and two-choice perceptual decisions and modeled them using a sequential sampling framework. We found that the predictions made by leaky competing accumulator models to accommodate multiple choices (i.e., reduced baseline activity to compensate increased uncertainty) were borne out by dynamic changes in human action representations. This suggests a direct and continuous influence of interacting evidence accumulators, each favoring a different decision alternative, on downstream corticospinal excitability during complex choice.

Optimal evidence accumulation on social networks

A fundamental question in biology is how organisms integrate sensory and social evidence to make decisions. However, few models describe how both these streams of information can be combined to optimize choices. Here we develop a normative model for collective decision making in a network of agents performing a two-alternative forced choice task. We assume that rational (Bayesian) agents in this network make private measurements, and observe the decisions of their neighbors until they accumulate sufficient evidence to make an irreversible choice. As each agent communicates its decision to those observing it, the flow of social information is described by a directed graph. The decision-making process in this setting is intuitive, but can be complex. We describe when and how the absence of a decision of a neighboring agent communicates social information, and how an agent must marginalize over all unobserved decisions. We also show how decision thresholds and network connectivity affect group evidence accumulation, and describe the dynamics of decision making in social cliques. Our model provides a bridge between the abstractions used in the economics literature and the evidence accumulator models used widely in neuroscience and psychology.

FRONTAL EYE FIELD INACTIVATION DIMINISHES SUPERIOR COLLICULUS ACTIVITY, BUT DELAYED SACCADIC ACCUMULATION GOVERNS REACTION TIME INCREASES

Stochastic accumulator models provide a comprehensive framework for how neural activity could produce behavior. Neural activity within the frontal eye fields (FEF) and intermediate layers of the superior colliculus (iSC) support such models for saccade initiation, by relating variations in saccade reaction time (SRT) to variations in parameters such as baseline, rate of accumulation of activity, or threshold. Here, by recording iSC activity during reversible cryogenic inactivation of the FEF in non-human primates, we causally test which parameter(s) best explains concomitant increases in SRT. While FEF inactivation decreased all aspects of ipsilesional iSC activity, decreases in accumulation rate and threshold poorly predicted accompanying increases in SRT. Instead, SRT increases best correlated with delays in the onset of saccade-related accumulation. We conclude that FEF signals govern the onset of saccade-related accumulation within the iSC, and that the onset of accumulation is a relevant parameter for stochastic accumulation models of saccade initiation.Significance StatementThe superior colliculus (SC) and frontal eye fields (FEF) are two of the best-studied areas in the primate brain. Surprisingly, little is known about what happens in the SC when the FEF is temporarily inactivated. Here, we show that temporary FEF inactivation decreases all aspects of functionally-related activity in the SC. This combination of techniques also allowed us to relate changes in SC activity to concomitant increases in saccadic reaction time (SRT). Although stochastic accumulator models relate SRT increases to reduced rates of accumulation or increases in threshold, such changes were not observed in the SC. Instead, FEF inactivation delayed the onset of saccade-related accumulation, emphasizing the importance of this parameter in biologically-plausible models of saccade initiation.

How to discriminate conclusively among different models of decision making?

A popular and successful class of decision-making models (the “evidence accumulator” models) has been recently challenged by a new hypothesis called the urgency-gating model. Hawkins et al. ( J Neurophysiol 114: 40–47, 2015) used a sophisticated curve-fitting procedure to show that these models are discriminable and thus testable in constant evidence tasks. In this Neuro Forum article I raise possible limitations of such an approach, discuss some of its implications, and propose alternative solutions.