Meta-Learned Models of Cognition.

Psychologists and neuroscientists extensively rely on computational models for studying and analyzing the human mind. Traditionally, such computational models have been hand-designed by expert researchers. Two prominent examples are cognitive architectures and Bayesian models of cognition. While the former requires the specification of a fixed set of computational structures and a definition of how these structures interact with each other, the latter necessitates the commitment to a particular prior and a likelihood function which - in combination with Bayes' rule - determine the model's behavior. In recent years, a new framework has established itself as a promising tool for building models of human cognition: the framework of meta-learning. In contrast to the previously mentioned model classes, meta-learned models acquire their inductive biases from experience, i.e., by repeatedly interacting with an environment. However, a coherent research program around meta-learned models of cognition is still missing to this day. The purpose of this article is to synthesize previous work in this field and establish such a research program. We accomplish this by pointing out that meta-learning can be used to construct Bayes-optimal learning algorithms, allowing us to draw strong connections to the rational analysis of cognition. We then discuss several advantages of the meta-learning framework over traditional methods and reexamine prior work in the context of these new insights.

Using cognitive psychology to understand GPT-3.

We study GPT-3, a recent large language model, using tools from cognitive psychology. More specifically, we assess GPT-3's decision-making, information search, deliberation, and causal reasoning abilities on a battery of canonical experiments from the literature. We find that much of GPT-3's behavior is impressive: It solves vignette-based tasks similarly or better than human subjects, is able to make decent decisions from descriptions, outperforms humans in a multiarmed bandit task, and shows signatures of model-based reinforcement learning. Yet, we also find that small perturbations to vignette-based tasks can lead GPT-3 vastly astray, that it shows no signatures of directed exploration, and that it fails miserably in a causal reasoning task. Taken together, these results enrich our understanding of current large language models and pave the way for future investigations using tools from cognitive psychology to study increasingly capable and opaque artificial agents.

Time-Resolved Ultrafast Interatomic Coulombic Decay in Superexcited Sodium-Doped Helium Nanodroplets.

The autoionization dynamics of superexcited superfluid He nanodroplets doped with Na atoms is studied by extreme-ultraviolet (XUV) time-resolved electron spectroscopy. Following excitation into the higher-lying droplet absorption band, the droplet relaxes into the lowest metastable atomic 1s2s 1,3S states from which interatomic Coulombic decay (ICD) takes place either between two excited He atoms or between an excited He atom and a Na atom attached to the droplet surface. Four main ICD channels are identified, and their decay times are determined by varying the delay between the XUV pulse and a UV pulse that ionizes the initial excited state and thereby quenches ICD. The decay times for the different channels all fall in the range of ∼1 ps, indicating that the ICD dynamics are mainly determined by the droplet environment. A periodic modulation of the transient ICD signals is tentatively attributed to the oscillation of the bubble forming around the localized He excitation.

Heuristics from bounded meta-learned inference.

Numerous researchers have put forward heuristics as models of human decision-making. However, where such heuristics come from is still a topic of ongoing debate. In this work, we propose a novel computational model that advances our understanding of heuristic decision-making by explaining how different heuristics are discovered and how they are selected. This model-called bounded meta-learned inference (BMI)-is based on the idea that people make environment-specific inferences about which strategies to use while being efficient in terms of how they use computational resources. We show that our approach discovers two previously suggested types of heuristics-one reason decision-making and equal weighting-in specific environments. Furthermore, the model provides clear and precise predictions about when each heuristic should be applied: Knowing the correct ranking of attributes leads to one reason decision-making, knowing the directions of the attributes leads to equal weighting, and not knowing about either leads to strategies that use weighted combinations of multiple attributes. In three empirical paired comparison studies with continuous features, we verify predictions of our theory and show that it captures several characteristics of human decision-making not explained by alternative theories. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Unravelling the full relaxation dynamics of superexcited helium nanodroplets.

The relaxation dynamics of superexcited superfluid He nanodroplets is thoroughly investigated by means of extreme-ultraviolet (XUV) femtosecond electron and ion spectroscopy complemented by time-dependent density functional theory (TDDFT). Three main paths leading to the emission of electrons and ions are identified: droplet autoionization, pump-probe photoionization, and autoionization induced by re-excitation of droplets relaxing into levels below the droplet ionization threshold. The most abundant product ions are He2+, generated by droplet autoionization and by photoionization of droplet-bound excited He atoms. He+ appear with some pump-probe delay as a result of the ejection He atoms in their lowest excited states from the droplets. The state-resolved time-dependent photoelectron spectra reveal that intermediate excited states of the droplets are populated in the course of the relaxation, terminating in the lowest-lying metastable singlet and triplet He atomic states. The slightly faster relaxation of the triplet state compared to the singlet state is in agreement with the simulation showing faster formation of a bubble around a He atom in the triplet state.

High-gain harmonic generation with temporally overlapping seed pulses and application to ultrafast spectroscopy.

Collinear double-pulse seeding of the High-Gain Harmonic Generation (HGHG) process in a free-electron laser (FEL) is a promising approach to facilitate various coherent nonlinear spectroscopy schemes in the extreme ultraviolet (XUV) spectral range. However, in collinear arrangements using a single nonlinear medium, temporally overlapping seed pulses may introduce nonlinear mixing signals that compromise the experiment at short time delays. Here, we investigate these effects in detail by extending the analysis described in a recent publication (Wituschek et al., Nat. Commun., 11, 883, 2020). High-order fringe-resolved autocorrelation and wave packet interferometry experiments at photon energies > 23 eV are performed, accompanied by numerical simulations. It turns out that both the autocorrelation and the wave-packet interferometry data are very sensitive to saturation effects and can thus be used to characterize saturation in the HGHG process. Our results further imply that time-resolved spectroscopy experiments are feasible even for time delays smaller than the seed pulse duration.

Effects of high pulse intensity and chirp in two-dimensional electronic spectroscopy of an atomic vapor.

The effects of high pulse intensity and chirp on two-dimensional electronic spectroscopy signals are experimentally investigated in the highly non-perturbative regime using atomic rubidium vapor as clean model system. Data analysis is performed based on higher-order Feynman diagrams and non-perturbative numerical simulations of the system response. It is shown that higher-order contributions may lead to a fundamental change of the static appearance and beating-maps of the 2D spectra and that chirped pulses enhance or suppress distinct higher-order pathways. We further give an estimate of the threshold intensity beyond which the high-intensity effects become visible for the system under consideration.

Tracking attosecond electronic coherences using phase-manipulated extreme ultraviolet pulses.

The recent development of ultrafast extreme ultraviolet (XUV) coherent light sources bears great potential for a better understanding of the structure and dynamics of matter. Promising routes are advanced coherent control and nonlinear spectroscopy schemes in the XUV energy range, yielding unprecedented spatial and temporal resolution. However, their implementation has been hampered by the experimental challenge of generating XUV pulse sequences with precisely controlled timing and phase properties. In particular, direct control and manipulation of the phase of individual pulses within an XUV pulse sequence opens exciting possibilities for coherent control and multidimensional spectroscopy, but has not been accomplished. Here, we overcome these constraints in a highly time-stabilized and phase-modulated XUV-pump, XUV-probe experiment, which directly probes the evolution and dephasing of an inner subshell electronic coherence. This approach, avoiding any XUV optics for direct pulse manipulation, opens up extensive applications of advanced nonlinear optics and spectroscopy at XUV wavelengths.

Delocalized excitons and interaction effects in extremely dilute thermal ensembles.

Long-range interparticle interactions are revealed in extremely dilute thermal atomic ensembles using highly sensitive nonlinear femtosecond spectroscopy. Delocalized excitons are detected in the atomic systems at particle densities where the mean interatomic distance (>10 µm) is much greater than the laser wavelength and multi-particle coherences should destructively interfere over the ensemble average. With a combined experimental and theoretical analysis, we identify an effective interaction mechanism, presumably of dipolar nature, as the origin of the excitonic signals. Our study implies that even in highly-dilute thermal atom ensembles, significant transition dipole-dipole interaction networks may form that require advanced modeling beyond the nearest neighbor approximation to quantitatively capture the details of their many-body properties.

Coherent multidimensional spectroscopy of dilute gas-phase nanosystems.

Two-dimensional electronic spectroscopy (2DES) is one of the most powerful spectroscopic techniques with unique sensitivity to couplings, coherence properties and real-time dynamics of a quantum system. While successfully applied to a variety of condensed phase samples, high precision experiments on isolated systems in the gas phase have been so far precluded by insufficient sensitivity. However, such experiments are essential for a precise understanding of fundamental mechanisms and to avoid misinterpretations. Here, we solve this issue by extending 2DES to isolated nanosystems in the gas phase prepared by helium nanodroplet isolation in a molecular beam-type experiment. This approach uniquely provides high flexibility in synthesizing tailored, quantum state-selected model systems of single and many-body character. In a model study of weakly-bound Rb2 and Rb3 molecules we demonstrate the method's unique capacity to elucidate interactions and dynamics in tailored quantum systems, thereby also bridging the gap to experiments in ultracold quantum science.

Phase-synchronous undersampling in nonlinear spectroscopy.

We introduce the concept of phase-synchronous undersampling in nonlinear spectroscopy. The respective theory is presented and validated experimentally in a phase-modulated quantum beat experiment by sampling high phase modulation frequencies with low laser repetition rates. The advantage of undersampling in terms of signal quality and reduced acquisition time is demonstrated, and breakdown conditions are identified. The presented method is particularly beneficial for experimental setups with limited signal/detection rates.