Function estimation: Quantifying individual differences of hand-drawn functions.

Graphical perception is an important part of the scientific endeavour, and the interpretation of graphical information is increasingly important among educated consumers of popular media, who are often presented with graphs of data in support of different policy positions. However, graphs are multidimensional and data in graphs are comprised not only of overall global trends but also local perturbations. We presented a novel function estimation task in which scatterplots of noisy data that varied in the number of data points, the scale of the data, and the true generating function were shown to observers. 170 psychology undergraduates with mixed experience of mathematical functions were asked to draw the function that they believe generated the data. Our results indicated not only a general influence of various aspects of the presented graph (e.g., increasing the number of data points results in smoother generated functions) but also clear individual differences, with some observers tending to generate functions that track the local changes in the data and others following global trends in the data.

Against naïve induction from experimental data.

This commentary argues against the indictment of current experimental practices such as piecemeal testing, and the proposed integrated experiment design (IED) approach, which we see as yet another attempt at automating scientific thinking. We identify a number of undesirable features of IED that lead us to believe that its broad application will hinder scientific progress.

Recognition memory decisions made with short- and long-term retrieval.

In the present research, we produce a coherent account of the storage and retrieval processes in short- and long-term event memory, and long-term knowledge, that produce response accuracy and response time in a wide variety of conditions in our studies of recognition memory. Two to nine pictures are studied sequentially followed by a target or foil test picture in four conditions used in Nosofsky et al. Journal of Experimental Psychology: Learning, Memory, and Cognition, 47, 316-342, (2021) and in our new paradigm: VM: target and foil responses to a given stimulus change from trial to trial; CM: the responses do not change from trial to trial; AN: every trial uses new stimuli; MIXED: combinations of VM, CN, and AN occur on each trial. In the new paradigm a given picture is equally often tested as old or new, but only in CM is the response key the same and learnable. Our model has components that have appeared in a variety of prior accounts, including learning and familiarity, but are given support by our demonstration that accuracy and response time data from a large variety of conditions can be predicted by these processes acting together, with parameter values that largely are unchanged. A longer version of this article, containing information not found here due to space, is available online  https://doi.org/10.31234/osf.io/h8msp .The avalibility of the data (supplement materials), info and link is attached at the end section ( https://psyarxiv.com/h8msp .).

The distinction between long-term knowledge and short-term control processes is valid and useful.

The binary distinction De Neys questions has been put forward many times since the beginnings of psychology, in slightly different forms and under different names. It has proved enormously useful and has received detailed empirical support and careful modeling. At heart the distinction is that between knowledge in long-term memory and control processes in short-term memory.

Two case studies of very long-term retention.

Here, we present two case studies of extremely long-term retention. In the first, Richard C. Atkinson (RCA) had learned word sequences during experiments for his dissertation. Sixty-seven years later, RCA relearned the same words either in the original order or in a scrambled order. RCA reported no conscious awareness that the words were those used in the dissertation, but his relearning was considerably better for the words in the original order. In the second case study, Denis Cousineau had searched displays of objects for the presence of a target. The targets and foils had been novel at the beginning of training, and his search rate improved markedly over about 70 sessions. After 22 years, retraining showed retention of much of this gain in rate of search, and the rate was markedly faster than search for new objects with the same structure as the trained set. We consider interpretations of these case studies for our understanding of long-term retention.

Is it Reasonable to Study Decision-Making Quantitatively?

Scientists studying decision-making often provide a set of choices, each specified with values or distributions of values, and probabilities or distributions of probabilities. For example, "Would you prefer $100 with probability 1.0 or $1 with probability .9 and $1,000 with probability 0.1?" Other decision research examines choices made in the absence of most quantitative information; for example, "Would you prefer a Ford now or a Porsche a year from now?," "Which food would you prefer," but models the findings with precise quantitative assumptions. Yet other research does neither; for example, modeling verbally stated choices with verbally stated heuristics. This article asks about the relevance of the first two research approaches for much of the decision-making made in life. The use of quantitative research and modeling is unsurprising, given that this approach underlies most of science. In life, values and probabilities are almost always partly or wholly vague and qualitative rather than quantitative. For example, when deciding which house to buy, there are relevant features such as size, color, neighborhood schools, construction materials, attractiveness, and many more, but the decision-maker finds it difficult and of little use to assign these precise values or weights. Nonetheless, humans have evolved to make decisions in such vaguely specified settings. I provide an example showing how a very high degree of uncertainty can defeat the application of quantitative decision-making, but such a demonstration is not critical if quantitative research and modeling produce a good understanding of and a good approximation to decision-making in the natural environment. This perspective addresses these issues.

Extraordinary claims, extraordinary evidence? A discussion.

Roberts (2020, Learning & Behavior, 48[2], 191-192) discussed research claiming honeybees can do arithmetic. Some readers of this research might regard such claims as unlikely. The present authors used this example as a basis for a debate on the criterion that ought to be used for publication of results or conclusions that could be viewed as unlikely by a significant number of readers, editors, or reviewers.

Modeling short- and long-term memory contributions to recent event recognition.

Participants gave recognition judgments for short lists of pictures of everyday objects. Pictures in a given list were an equal mixture of three types that varied according to the way they were used as targets and foils earlier in the same session. Under consistent-mapping (CM), targets and foils never switch roles; under varied-mapping (VM), targets and foils switch roles randomly across trials; whereas all-new (AN) items are novel on each trial of the experiment. Past research has shown that markedly enhanced performance occurs in CM conditions, leading to conclusions that item-response learning takes place in CM, perhaps automatically. However, almost all past research has compared CM, VM, and AN performance in between-blocks designs in which participants may adopt different cognitive strategies and criterion settings across the conditions. The present mixed-list design holds constant the strategy and criterion settings that are used for CM, VM, and AN items, and produced patterns of performance dramatically different than those observed in pure-list control conditions. We develop an extended version of an exemplar-based random-walk model of probe recognition to account for the major qualitative effects in the data. The data and the modeling provide evidence for strong item-response learning for CM foils but weak item-response learning for CM targets. We consider possible explanations for these effects in our General Discussion. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

What are the appropriate axioms of rationality for reasoning under uncertainty with resource-constrained systems?

When constrained by limited resources, how do we choose axioms of rationality? The target article relies on Bayesian reasoning that encounter serious tractability problems. We propose another axiomatic foundation: quantum probability theory, which provides for less complex and more comprehensive descriptions. More generally, defining rationality in terms of axiomatic systems misses a key issue: rationality must be defined by humans facing vague information.

50 years of research sparked by Atkinson and Shiffrin (1968).

In this article we review the framework proposed in 1968 by Atkinson and Shiffrin. We discuss the prior context that led to its production, including the advent of cognitive and mathematical modeling, its principal concepts, the subsequent refinements and elaborations that followed, and the way that the framework influenced other researchers to test the ideas and, in some cases, propose alternatives. The article illustrates the large amount of research and the large number of memory models that were directly influenced by this chapter over the past 50 years.

Scientific progress despite irreproducibility: A seeming paradox.

It appears paradoxical that science is producing outstanding new results and theories at a rapid rate at the same time that researchers are identifying serious problems in the practice of science that cause many reports to be irreproducible and invalid. Certainly, the practice of science needs to be improved, and scientists are now pursuing this goal. However, in this perspective, we argue that this seeming paradox is not new, has always been part of the way science works, and likely will remain so. We first introduce the paradox. We then review a wide range of challenges that appear to make scientific success difficult. Next, we describe the factors that make science work-in the past, present, and presumably also in the future. We then suggest that remedies for the present practice of science need to be applied selectively so as not to slow progress and illustrate with a few examples. We conclude with arguments that communication of science needs to emphasize not just problems but the enormous successes and benefits that science has brought and is now bringing to all elements of modern society.

Item frequency in probe-recognition memory search: Converging evidence for a role of item-response learning.

In short-term probe-recognition tasks, observers make speeded old-new recognition judgments for items that are members of short lists. However, long-term memory (LTM) for items from previous lists influences current-list performance. The current experiment pursued the nature of these long-term influences-in particular, whether they emerged from item-familiarity or item-response-learning mechanisms. Subjects engaged in varied-mapping (VM) and consistent-mapping (CM) short-term probe-recognition tasks (e.g., Schneider & Shiffrin, Psychological Review, 84, 1-66, 1977). The key manipulation was to vary the frequency with which individual items were presented across trials. We observed a striking dissociation: Whereas increased presentation frequency led to benefits in performance for both old and new test probes in CM search, it resulted in interference effects for both old and new test probes in VM search. Formal modeling suggested that a form of item-response learning took place in both conditions: Each presentation of a test probe led to the storage of that test probe-along with its associated "old" or "new" response-as an exemplar in LTM. These item-response pairs were retrieved along with current-list items in driving observers' old-- recognition judgments. We conclude that item-response learning is a core component of the LTM mechanisms that influence CM and VM memory search.

A dynamic approach to recognition memory.

We present a dynamic model of memory that integrates the processes of perception, retrieval from knowledge, retrieval of events, and decision making as these evolve from 1 moment to the next. The core of the model is that recognition depends on tracking changes in familiarity over time from an initial baseline generally determined by context, with these changes depending on the availability of different kinds of information at different times. A mathematical implementation of this model leads to precise, accurate predictions of accuracy, response time, and speed-accuracy trade-off in episodic recognition at the levels of both groups and individuals across a variety of paradigms. Our approach leads to novel insights regarding word frequency, speeded responding, context reinstatement, short-term priming, similarity, source memory, and associative recognition, revealing how the same set of core dynamic principles can help unify otherwise disparate phenomena in the study of memory. (PsycINFO Database Record

The rational status of quantum cognition.

Classic probability theory (CPT) is generally considered the rational way to make inferences, but there have been some empirical findings showing a divergence between reasoning and the principles of classical probability theory (CPT), inviting the conclusion that humans are irrational. Perhaps the most famous of these findings is the conjunction fallacy (CF). Recently, the CF has been shown consistent with the principles of an alternative probabilistic framework, quantum probability theory (QPT). Does this imply that QPT is irrational or does QPT provide an alternative interpretation of rationality? Our presentation consists of 3 parts. First, we examine the putative rational status of QPT using the same argument as used to establish the rationality of CPT, the Dutch Book (DB) argument, according to which reasoners should not commit to bets guaranteeing a loss. We prove the rational status of QPT by formulating it as a particular case of an extended form of CPT, with separate probability spaces produced by changing context. Second, we empirically examine the key requirement for whether a CF can be rational or not; the results show that participants indeed behave rationally, at least relative to the representations they employ. Finally, we consider whether the conditions for the CF to be rational are applicable in the outside (nonmental) world. Our discussion provides a general and alternative perspective for rational probabilistic inference, based on the idea that contextuality requires either reasoning in separate CPT probability spaces or reasoning with QPT principles. (PsycINFO Database Record

A Bootstrapping Model of Frequency and Context Effects in Word Learning.

Prior research has shown that people can learn many nouns (i.e., word-object mappings) from a short series of ambiguous situations containing multiple words and objects. For successful cross-situational learning, people must approximately track which words and referents co-occur most frequently. This study investigates the effects of allowing some word-referent pairs to appear more frequently than others, as is true in real-world learning environments. Surprisingly, high-frequency pairs are not always learned better, but can also boost learning of other pairs. Using a recent associative model (Kachergis, Yu, & Shiffrin, 2012), we explain how mixing pairs of different frequencies can bootstrap late learning of the low-frequency pairs based on early learning of higher frequency pairs. We also manipulate contextual diversity, the number of pairs a given pair appears with across training, since it is naturalistically confounded with frequency. The associative model has competing familiarity and uncertainty biases, and their interaction is able to capture the individual and combined effects of frequency and contextual diversity on human learning. Two other recent word-learning models do not account for the behavioral findings.

The development of automaticity in short-term memory search: Item-response learning and category learning.

In short-term-memory (STM)-search tasks, observers judge whether a test probe was present in a short list of study items. Here we investigated the long-term learning mechanisms that lead to the highly efficient STM-search performance observed under conditions of consistent-mapping (CM) training, in which targets and foils never switch roles across trials. In item-response learning, subjects learn long-term mappings between individual items and target versus foil responses. In category learning, subjects learn high-level codes corresponding to separate sets of items and learn to attach old versus new responses to these category codes. To distinguish between these 2 forms of learning, we tested subjects in categorized varied mapping (CV) conditions: There were 2 distinct categories of items, but the assignment of categories to target versus foil responses varied across trials. In cases involving arbitrary categories, CV performance closely resembled standard varied-mapping performance without categories and departed dramatically from CM performance, supporting the item-response-learning hypothesis. In cases involving prelearned categories, CV performance resembled CM performance, as long as there was sufficient practice or steps taken to reduce trial-to-trial category-switching costs. This pattern of results supports the category-coding hypothesis for sufficiently well-learned categories. Thus, item-response learning occurs rapidly and is used early in CM training; category learning is much slower but is eventually adopted and is used to increase the efficiency of search beyond that available from item-response learning. (PsycINFO Database Record