Using a Simple Neural Network to Delineate Some Principles of Distributed Economic Choice

Distinct neural activations correlate with maximization of reward magnitude versus frequency

Choice selection strategies and decision-making are typically investigated using multiple-choice gambling paradigms that require participants to maximize expected value of rewards. However, research shows that performance in such paradigms suffers from individual biases towards the frequency of gains such that users often choose smaller frequent gains over larger rarely occurring gains, also referred to as melioration. To understand the basis of this subjective tradeoff, we used a simple 2-choice reward task paradigm in 186 healthy human adult subjects sampled across the adult lifespan. Cortical source reconstruction of simultaneously recorded electroencephalography suggested distinct neural correlates for maximizing reward magnitude versus frequency. We found that activations in the parahippocampal and entorhinal areas, which are typically linked to memory function, specifically correlated with maximization of reward magnitude. In contrast, maximization of reward frequency was correlated with activations in the lateral orbitofrontal cortices and operculum, typical areas involved in reward processing. These findings reveal distinct neural processes serving reward frequency versus magnitude maximization that can have clinical translational utility to optimize decision-making.

Choice-relevant information transformation along a ventrodorsal axis in the medial prefrontal cortex

Choice-relevant brain regions in prefrontal cortex may progressively transform information about options into choices. Here, we examine responses of neurons in four regions of the medial prefrontal cortex as macaques performed two-option risky choices. All four regions encode economic variables in similar proportions and show similar putative signatures of key choice-related computations. We provide evidence to support a gradient of function that proceeds from areas 14 to 25 to 32 to 24. Specifically, we show that decodability of twelve distinct task variables increases along that path, consistent with the idea that regions that are higher in the anatomical hierarchy make choice-relevant variables more separable. We also show progressively longer intrinsic timescales in the same series. Together these results highlight the importance of the medial wall in choice, endorse a specific gradient-based organization, and argue against a modular functional neuroanatomy of choice.

Partial integration of the components of value in anterior cingulate cortex

Evaluation often involves integrating multiple determinants of value, such as the different possible outcomes in risky choice. A brain region can be placed either before or after a presumed evaluation stage by measuring how responses of its neurons depend on multiple determinants of value. A brain region could also, in principle, show partial integration, which would indicate that it occupies a middle position between (preevaluative) nonintegration and (postevaluative) full integration. Existing mathematical techniques cannot distinguish full from partial integration and therefore risk misidentifying regional function. Here we use a new Bayesian regression-based approach to analyze responses of neurons in dorsal anterior cingulate cortex (dACC) to risky offers. We find that dACC neurons only partially integrate across outcome dimensions, indicating that dACC cannot be assigned to either a pre- or postevaluative position. Neurons in dACC also show putative signatures of value comparison, thereby demonstrating that comparison does not require complete evaluation before proceeding. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Excitatory-inhibitory tone shapes decision strategies in a hierarchical neural network model of multi-attribute choice

We are constantly faced with decisions between alternatives defined by multiple attributes, necessitating an evaluation and integration of different information sources. Time-varying signals in multiple brain areas are implicated in decision-making; but we lack a rigorous biophysical description of how basic circuit properties, such as excitatory-inhibitory (E/I) tone and cascading nonlinearities, shape attribute processing and choice behavior. Furthermore, how such properties govern choice performance under varying levels of environmental uncertainty is unknown. We investigated two-attribute, two-alternative decision-making in a dynamical, cascading nonlinear neural network with three layers: an input layer encoding choice alternative attribute values; an intermediate layer of modules processing separate attributes; and a final layer producing the decision. Depending on intermediate layer E/I tone, the network displays distinct regimes characterized by linear (I), convex (II) or concave (III) choice indifference curves. In regimes I and II, each option's attribute information is additively integrated. In regime III, time-varying nonlinear operations amplify the separation between offer distributions by selectively attending to the attribute with the larger differences in input values. At low environmental uncertainty, a linear combination most consistently selects higher valued alternatives. However, at high environmental uncertainty, regime III is more likely than a linear operation to select alternatives with higher value. Furthermore, there are conditions where readout from the intermediate layer could be experimentally indistinguishable from the final layer. Finally, these principles are used to examine multi-attribute decisions in systems with reduced inhibitory tone, leading to predictions of different choice patterns and overall performance between those with restrictions on inhibitory tone and neurotypicals.

Oscillations as a window into neuronal mechanisms underlying dorsal anterior cingulate cortex function

The function of dorsal Anterior Cingulate Cortex (dACC) remains poorly understood. While many methods, spanning bottom-up and top-down approaches, have been deployed, the view they offer is often conflicting. Integrating bottom-up and top-down approaches requires an intermediary with sufficient explanatory power, theoretical development, and empirical support. Oscillations in the local field potential (LFP) provide such a link. LFP oscillations arise from empirically well-characterized neuronal circuit motifs. Synchronizing the firing of individual units has appealing properties to bind disparate brain regions and propagate information, including gating, routing, and coding. Moreover, the LFP, rather than single unit activity, more closely relates to macro-scale recordings, such as the electroencephalogram and functional magnetic resonance imaging. Thus, LFP oscillations are a critical link that allow for the inference of neuronal micro-circuitry underlying macroscopic brain recordings.

BP neural network trade volume prediction and enterprises HRM optimization model based on ES-LM training

In this paper, the author analyze the neural network trade volume prediction and enterprises HRM optimization model based on ES-LM training. In order to improve the quality of human resource performance management, international trade enterprises need to introduce the concept of fine management, so as to create a good working environment for employees and effectively improve the effect of performance management. Moreover, this study uses a city’s import and export trade quota forecast to verify the prediction accuracy of the improved algorithm network. In addition, this study decomposes data into training data and test data based on normalization and principal component extraction. Finally, this study conducted a comparative analysis of the algorithm performance analysis of this study. The research results show that the proposed algorithm has certain effects, and it meets the forecasting requirements in terms of convergence speed and prediction accuracy and can provide theoretical reference for subsequent related research.

Heuristics and optimal solutions to the breadth-depth dilemma

In multialternative risky choice, we are often faced with the opportunity to allocate our limited information-gathering capacity between several options before receiving feedback. In such cases, we face a natural trade-off between breadth-spreading our capacity across many options-and depth-gaining more information about a smaller number of options. Despite its broad relevance to daily life, including in many naturalistic foraging situations, the optimal strategy in the breadth-depth trade-off has not been delineated. Here, we formalize the breadth-depth dilemma through a finite-sample capacity model. We find that, if capacity is small (∼10 samples), it is optimal to draw one sample per alternative, favoring breadth. However, for larger capacities, a sharp transition is observed, and it becomes best to deeply sample a very small fraction of alternatives, which roughly decreases with the square root of capacity. Thus, ignoring most options, even when capacity is large enough to shallowly sample all of them, is a signature of optimal behavior. Our results also provide a rich casuistic for metareasoning in multialternative decisions with bounded capacity using close-to-optimal heuristics.

Economic Choice as an Untangling of Options into Actions

We propose that economic choice can be understood as a gradual transformation from a domain of options to one of the actions. We draw an analogy with the idea of untangling information in the form vision system and propose that form vision and economic choice may be two aspects of a larger process that sculpts actions based on sensory inputs. From this viewpoint, choice results from the accumulated effect of repetitions of simple computations. These may consist primarily of relative valuations (evaluations relative to the value of rejection, perhaps in a manner akin to divisive normalization) applied to individual offers. With regard to economic choice, cortical brain regions differ primarily in their position and in what information they prioritize, and do not-with a few exceptions-have categorically distinct roles. Each region's specific contribution is determined largely by its inputs; thus, understanding connectivity is crucial for understanding choice. This view suggests that there is no single site of choice, that there is no meaningful distinction between pre- and post-decisionality, and that there is no explicit representation of value in the brain.

Perceptual Dominance in Brief Presentations of Mixed Images: Human Perception vs. Deep Neural Networks

Visual perception involves continuously choosing the most prominent inputs while suppressing others. Neuroscientists induce visual competitions in various ways to study why and how the brain makes choices of what to perceive. Recently deep neural networks (DNNs) have been used as models of the ventral stream of the visual system, due to similarities in both accuracy and hierarchy of feature representation. In this study we created non-dynamic visual competitions for humans by briefly presenting mixtures of two images. We then tested feed-forward DNNs with similar mixtures and examined their behavior. We found that both humans and DNNs tend to perceive only one image when presented with a mixture of two. We revealed image parameters which predict this perceptual dominance and compared their predictability for the two visual systems. Our findings can be used to both improve DNNs as models, as well as potentially improve their performance by imitating biological behaviors.