Identifying category representations for complex stimuli using discrete Markov chain Monte Carlo with people

Abstract

With the explosion of “big data,” digital repositories of texts and images are growing rapidly. These datasets present new opportunities for psychological research, but they require new methodologies before researchers can use these datasets to yield insights into human cognition. We present a new method that allows psychological researchers to take advantage of text and image databases: a procedure for measuring human categorical representations over large datasets of items, such as arbitrary words or pictures. We call this method discrete Markov chain Monte Carlo with people (d-MCMCP). We illustrate our method by evaluating the following categories over datasets: emotions as represented by facial images, moral concepts as represented by relevant words, and seasons as represented by images drawn from large online databases. Three experiments demonstrate that d-MCMCP is powerful and flexible enough to work with complex, naturalistic stimuli drawn from large online databases. Electronic supplementary material The online version of this article (10.3758/s13428-019-01201-9) contains supplementary material, which is available to authorized users.

Prior knowledge elicitation: The past, present, and future

“…A very promising perspective in psychology treats human judgements as a result of sampling from their subjective probabilities. This viewpoint has been successfully applied in the Markov chain Monte Carlo with people (MCMCP) approach (Sanborn and Griffiths, 2008;Sanborn et al, 2010) and its variants (Hsu et al, 2012;León-Villagrá et al, 2020;Harrison et al, 2020) to elicit beliefs about how stimuli from a multidimensional stimulus space (e.g., n-dimensional stick figures) maps onto a target category (e.g, 'shape of a cat'). In MCMCP participants take the place of an MCMC acceptance function, and repeatedly accept or reject proposals regarding the category membership of the sampled stimuli.…”

Representing absence of evidence: why algorithms and representations matter in models of language and cognition

“…For example, tools can allow for representations to be inferred (e.g. from behavioural data, or the performance of DNNs) which can subsequently be correlated against further test sets of brain and behavioural data (Battleday et al, 2019;Houlsby et al, 2013;Hsu et al, 2019;Ma & Peters, 2020;Sanders & Nosofsky, 2020;Schatz et al, 2019;Yamins et al, 2014;Zheng et al, 2019). From the perspective described here, this appears to be a promising direction of research, since it offers the possibility of ultimately empirically constraining the search space for representations, and might even lead to the development of tools for objectively testing some representational choices, in some domains at least.…”

Reinforcement Learning Rebirth, Techniques, Challenges, and Resolutions

“…The penalty area of QL is to absorb a policy, which expresses an agent pardons action to take under what surroundings that does not even necessitate a model of the environment and it can grip difficulties with stochastic transitions and plunders, deprived from necessitating adaptations [20], [120]. [23] IoT representation annotation [24] Data-driven management [25] Data and Feedback validation [26] Visualization and understanding [27] Learning environment detection [28] Fraud detection [29] Prediction of the performance [50] Classification of capability [51] Tolerance related acquisition [52] IoT crime forensics [53] Fraud detection in IoT application [54] IoT decision process and making [55] LA Intrusion prediction [30] IoT representation annotation [31] Data-driven management [32] Data and Feedback validation [33] Visualization and understanding [34] Learning environment detection [35] Fraud detection [36] Predicting Software Defects on IoTs [56] Prediction of behavioral changes [57] Signature verification [58] Analysis and decisions [59] Auto-selection of IoT task [60] Traffic incident detection [61] Telecommunication [62] Internet networks [63] MDP Intrusion prediction [37] IoT representation annotation [38] Data-driven management [39] Data and Feedback validation [40] Visualization and understanding [41] Learning environment detection [42] Fraud detection [43] Re...…”