How are Bayesian models really used? Reply to Frank (2013)

In response to the proposal that cognitive phenomena might be best understood in terms of cognitive theories (Endress, 2013), Frank (2013) outlined an important research program, suggesting that Bayesian models should be used as rigorous, mathematically attractive implementations of psychological theories. This research program is important and promising. However, I show that it is not followed in practice. I then turn to Frank's defense of the assumption that learners prefer more specific rules (the "size principle"), and show that the results allegedly supporting this assumption do not provide any support for it. Further, I demonstrate that, in contrast to Frank's criticisms, there is no circularity in an account of rule-learning based on "common-sense psychology", and that Frank's other criticisms of this account are unsupported. I conclude that the research program outlined by Frank is important and promising, but needs to be followed in practice. Be that as it might, the rule-learning experiments discussed by Frank are still better explained by simple psychological mechanisms

Linguistics, cognitive psychology, and the now-or-never bottleneck

Christiansen & Chater (CC)’s key premise is that “if linguistic information is not processed rapidly, that information is lost for good”. From this “Now-or-Never Bottleneck” (NNB), CC derive “wide-reaching and fundamental implications for language processing, acquisition and change as well as for the structure of language itself”. We question both the premise and the consequentiality of its purported implications

Bayesian learning and the psychology of rule induction

In recent years, Bayesian learning models have been applied to an increasing variety of domains. While such models have been criticized on theoretical grounds, the underlying assumptions and predictions are rarely made concrete and tested experimentally. Here, I use Frank and Tenenbaum’s (2011) Bayesian model of rule-learning as a case study to spell out the underlying assumptions, and to confront them with the empirical results Frank and Tenenbaum (2011) propose to simulate, as well as with novel experiments. While rule-learning is arguably well suited to rational Bayesian approaches, I show that their models are neither psychologically plausible nor ideal observer models. Further, I show that their central assumption is unfounded: humans do not always preferentially learn more specific rules, but, at least in some situations, those rules that happen to be more salient. Even when granting the unsupported assumptions, I show that all of the experiments modeled by Frank and Tenenbaum (2011) either contradict their models, or have a large number of more plausible interpretations. I provide an alternative account of the experimental data based on simple psychological mechanisms, and show that this account both describes the data better, and is easier to falsify. I conclude that, despite the recent surge in Bayesian models of cognitive phenomena, psychological phenomena are best understood by developing and testing psychological theories rather than models that can be fit to virtually any data

Something from (almost) nothing: Buildup of object memory from forgettable single fixations

We can recognize thousands of individual objects in scores of familiar settings, and yet we see most of them only through occasional glances that are quickly forgotten. How do we come to recognize any of these objects? Here, we show that when objects are presented intermittently for durations of single fixations, the originally fleeting memories become gradually stabilized, such that, after just eight separated fixations, recognition memory after half an hour is as good as during an immediate memory test. However, with still shorter presentation durations, memories take more exposures to stabilize. Our results thus suggest that repeated glances suffice to remember the objects of our environment

A simple, biologically plausible feature detector for language acquisition

Language has a complex grammatical system we still have to understand computationally and biologically (Hauser et al., 2002; Yang, 2013). However, some evolutionarily ancient mechanisms have been repurposed for grammar (Dehaene & Cohen, 2007; Endress, Cahill, et al., 2009; Endress, Nespor, et al., 2009; Fitch, 2017) so that we can use insight from other taxa into possible circuit level mechanisms of grammar. Drawing upon recent evidence for the importance of disinhibitory circuits across taxa and brain regions (Chevalier & Deniau, 1990; Letzkus et al., 2015; Hangya et al., 2014; Xu et al., 2013; Goddard et al., 2014; Mysore & Knudsen, 2012; Koyama et al., 2016; Koyama & Pujala, 2018), I suggest a simple circuit that explains the acquisition of core grammatical rules used in 85% of the world’s languages (Rubino, 2013): grammatical rules based on sameness/difference relations. This circuit acts as a sameness-detector. Different items are suppressed through inhibition, but presenting two identical items leads to inhibition of inhibition. The items are thus propagated for further processing. This sameness-detector thus acts as a feature detector for a grammatical rule. I suggest that having a set of feature detectors for elementary grammatical rules might make language acquisition feasible based on relatively simple computational mechanisms

The cost of proactive interference is constant across presentation conditions

Proactive interference (PI) severely constrains how many items people can remember. For example, Endress and Potter (2014a) presented participants with sequences of everyday objects at 250 ms/picture, followed by a yes/no recognition test. They manipulated PI by either using new images on every trial in the unique condition (thus minimizing PI among items), or by re-using images from a limited pool for all trials in the repeated condition (thus maximizing PI among items). In the low-PI unique condition, the probability of remembering an item was essentially independent of the number of memory items, showing no clear memory limitations; more traditional working memory-like memory limitations appeared only in the high-PI repeated condition. Here, we ask whether the effects of PI are modulated by the availability of long-term memory (LTM) and verbal resources. Participants viewed sequences of 21 images, followed by a yes/no recognition test. Items were presented either quickly (250 ms/image) or sufficiently slowly (1500 ms/image) to produce LTM representations, either with or without verbal suppression. Across conditions, participants performed better in the unique than in the repeated condition, and better for slow than for fast presentations. In contrast, verbal suppression impaired performance only with slow presentations. The relative cost of PI was remarkably constant across conditions: relative to the unique condition, performance in the repeated condition was about 15% lower in all conditions. The cost of PI thus seems to be a function of the relative strength or recency of target items and interfering items, but relatively insensitive to other experimental manipulations

Learning multiple rules simultaneously: affixes are more salient than reduplications

Language learners encounter numerous opportunities to learn regularities, but need to decide which of these regularities to learn, because some are not productive in their native language. Here, we present an account of rule learning based on perceptual and memory primitives (Endress, Dehaene-Lambertz, & Mehler, 2007; Endress, Nespor, & Mehler, 2009), suggesting that learners preferentially learn regularities that are more salient to them, and that the pattern of salience reflects the frequency of language features across languages. We contrast this view with previous artificial grammar learning research, which suggests that infants “choose” the regularities they learn based on rational, Bayesian criteria (Frank & Tenenbaum, 2011; Gerken, 2006, 2010). In our experiments, adult participants listened to syllable strings starting with a syllable reduplication and always ending with the same “a!x” syllable, or to syllable strings starting with this “a!x” syllable and ending with the “reduplication.” Both a!xation and reduplication are frequently used for morphological marking across languages. We find three crucial results. First, participants learned both regularities simultaneously. Second, a!xation regularities seemed easier to learn than reduplication regularities. Third, regularities in sequence o↵sets were easier to learn than regularities at sequence onsets. We show that these results are inconsistent with previous Bayesian rule learning models, but mesh well with the perceptual or memory primitives view. Further, we show that the pattern of salience revealed in our experiments reflects the distribution of regularities across languages. Ease of acquisition might thus be one determinant of the frequency of regularities across languages

Learning multiple rules simultaneously: affixes are more salient than reduplications

International audienc

Learning multiple rules simultaneously: affixes are more salient than reduplications

International audienc

Learning multiple rules simultaneously: affixes are more salient than reduplications

International audienc