Does the familiarity bias hypothesis explain why there is no masked priming for “NO” decisions?

Traditional "activation" views of masked priming explain the identity priming effect in terms of facilitation due to ‘pre-activation’ of stored representations. Norris and Kinoshita's (2008) Bayesian Reader theory of masked priming instead explains priming in terms of the evidence that the prime contributes towards the decision required to the target. In support of the Bayesian Reader account, Norris and Kinoshita showed that the absence of priming for nonwords in the lexical decision task and for targets requiring a Different decision in the same–different match task can be explained based on a single principle. Against this, Bowers (2010) argued that the absence of priming should be explained instead by a combination of sublexical priming and "familiarity bias". As evidence, Bowers cited Bodner and Masson’s (1997) finding that nonword priming did emerge with targets presented in visually unfamiliar cAsE-AlTeRnAtEd format. We present evidence that this finding was due to the use of an ambiguous letter in case-alternated format; when using unambiguous letters, we consistently failed to find priming of case-alternated nonwords. We suggest that the Bayesian Reader, rather than the familiarity bias hypothesis, explains the absence of priming.     

Shortlist B: a Bayesian model of continuous speech recognition

A Bayesian model of continuous speech recognition is presented. It is based on Shortlist (D. Norris, 1994; D. Norris, J. M. McQueen, A. Cutler, & S. Butterfield, 1997) and shares many of its key assumptions: parallel competitive evaluation of multiple lexical hypotheses, phonologically abstract prelexical and lexical representations, a feedforward architecture with no online feedback, and a lexical segmentation algorithm based on the viability of chunks of the input as possible words. Shortlist B is radically different from its predecessor in two respects. First, whereas Shortlist was a connectionist model based on interactive-activation principles, Shortlist B is based on Bayesian principles. Second, the input to Shortlist B is no longer a sequence of discrete phonemes; it is a sequence of multiple phoneme probabilities over 3 time slices per segment, derived from the performance of listeners in a large-scale gating study. Simulations are presented showing that the model can account for key findings: data on the segmentation of continuous speech, word frequency effects, the effects of mispronunciations on word recognition, and evidence on lexical involvement in phonemic decision making. The success of Shortlist B suggests that listeners make optimal Bayesian decisions during spoken-word recognition.     

Facilitation versus inhibition in the masked priming same-different matching task

In the past years, growing attention has been devoted to the masked priming same-different task introduced by Norris and Kinoshita ( 2008 , Journal of Experimental Psychology: General). However, a number of researchers have raised concerns on the nature of the cognitive processes underlying this task-in particular the suspicion that masked priming effects in this task are mostly inhibitory in nature and may be affected by probe-prime contingency. To examine the pattern of facilitative/inhibitory priming effects in this task, we conducted two experiments with an incremental priming paradigm using four stimulus-onset asynchronies (13, 27, 40, and 53?ms). Experiment 1 was conducted under a predictive-contingency scenario (probe-prime-target; i.e., "same" trials: HOUSE-house-HOUSE vs. house-water-HOUSE; "different" trials: field-house-HOUSE vs. field-water-HOUSE), while Experiment 2 employed a zero-contingency scenario (i.e., "same" trials: HOUSE-house-HOUSE vs. house-water-HOUSE; "different" trials: field-field-HOUSE vs. field-water-HOUSE). Results revealed that, for "same" responses, both facilitation and inhibition increased linearly with prime duration in the two scenarios, whereas the pattern of data varied for "different" responses, as predicted by the Bayesian Reader model.     

Perception as evidence accumulation and Bayesian inference: insights from masked priming

The authors argue that perception is Bayesian inference based on accumulation of noisy evidence and that, in masked priming, the perceptual system is tricked into treating the prime and the target as a single object. Of the 2 algorithms considered for formalizing how the evidence sampled from a prime and target is combined, only 1 was shown to be consistent with the existing data from the visual word recognition literature. This algorithm was incorporated into the Bayesian Reader model (D. Norris, 2006), and its predictions were confirmed in 3 experiments. The experiments showed that the pattern of masked priming is not a fixed function of the relations between the prime and the target but can be changed radically by changing the task from lexical decision to a same-different judgment. Implications of the Bayesian framework of masked priming for unconscious cognition and visual masking are discussed.     

Masked priming effect reflects evidence accumulated by the prime

In the same–different match task, masked priming is observed with the same responses but not different responses. Norris and Kinoshita's (2008) Bayesian reader account of masked priming explains this pattern based on the same principle as that explaining the absence of priming for nonwords in the lexical decision task. The pattern of priming follows from the way the model makes optimal decisions in the two tasks; priming does not depend on first activating the prime and then the target. An alternative explanation is in terms of a bias towards responding “same” that exactly counters the facilitatory effect of lexical access. The present study tested these two views by varying both the degree to which the prime predicts the response and the visibility of the prime. Unmasked primes produced effects expected from the view that priming is influenced by the degree to which the prime predicts the response. In contrast, with masked primes, the size of priming for the same response was completely unaffected by predictability. These results rule out response bias as an explanation of the absence of masked priming for different responses and, in turn, indicate that masked priming is not a consequence of automatic lexical access of the prime.     

Non‐Bayesian Noun Generalization in 3‐ to 5‐Year‐Old Children: Probing the Role of Prior Knowledge in the Suspicious Coincidence Effect

It is unclear how children learn labels for multiple overlapping categories such as “Labrador,” “dog,” and “animal.” Xu and Tenenbaum (2007a) suggested that learners infer correct meanings with the help of Bayesian inference. They instantiated these claims in a Bayesian model, which they tested with preschoolers and adults. Here, we report data testing a developmental prediction of the Bayesian model—that more knowledge should lead to narrower category inferences when presented with multiple subordinate exemplars. Two experiments did not support this prediction. Children with more category knowledge showed broader generalization when presented with multiple subordinate exemplars, compared to less knowledgeable children and adults. This implies a U‐shaped developmental trend. The Bayesian model was not able to account for these data, even with inputs that reflected the similarity judgments of children. We discuss implications for the Bayesian model, including a combined Bayesian/morphological knowledge account that could explain the demonstrated U‐shaped trend.     

Merging information in speech recognition: feedback is never necessary

Top-down feedback does not benefit speech recognition; on the contrary, it can hinder it. No experimental data imply that feedback loops are required for speech recognition. Feedback is accordingly unnecessary and spoken word recognition is modular. To defend this thesis, we analyse lexical involvement in phonemic decision making. TRACE (McClelland & Elman 1986), a model with feedback from the lexicon to prelexical processes, is unable to account for all the available data on phonemic decision making. The modular Race model (Cutler & Norris 1979) is likewise challenged by some recent results, however. We therefore present a new modular model of phonemic decision making, the Merge model. In Merge, information flows from prelexical processes to the lexicon without feedback. Because phonemic decisions are based on the merging of prelexical and lexical information, Merge correctly predicts lexical involvement in phonemic decisions in both words and nonwords. Computer simulations show how Merge is able to account for the data through a process of competition between lexical hypotheses. We discuss the issue of feedback in other areas of language processing and conclude that modular models are particularly well suited to the problems and constraints of speech recognition.     

A Bayesian hierarchical diffusion model decomposition of performance in Approach–Avoidance Tasks

Common methods for analysing response time (RT) tasks, frequently used across different disciplines of psychology, suffer from a number of limitations such as the failure to directly measure the underlying latent processes of interest and the inability to take into account the uncertainty associated with each individual's point estimate of performance. Here, we discuss a Bayesian hierarchical diffusion model and apply it to RT data. This model allows researchers to decompose performance into meaningful psychological processes and to account optimally for individual differences and commonalities, even with relatively sparse data. We highlight the advantages of the Bayesian hierarchical diffusion model decomposition by applying it to performance on Approach–Avoidance Tasks, widely used in the emotion and psychopathology literature. Model fits for two experimental data-sets demonstrate that the model performs well. The Bayesian hierarchical diffusion model overcomes important limitations of current analysis procedures and provides deeper insight in latent psychological processes of interest.     

Facilitation versus inhibition in the masked priming same–different matching task

In the past years, growing attention has been devoted to the masked priming same–different task introduced by Norris and Kinoshita (2008 Norris, D. and Kinoshita, S. 2008. Perception as evidence accumulation and Bayesian inference: Insights from masked priming. Journal of Experimental Psychology: General, 137: 433–455. [Crossref], [Web of Science ®] , [Google Scholar], Journal of Experimental Psychology: General). However, a number of researchers have raised concerns on the nature of the cognitive processes underlying this task—in particular the suspicion that masked priming effects in this task are mostly inhibitory in nature and may be affected by probe–prime contingency. To examine the pattern of facilitative/inhibitory priming effects in this task, we conducted two experiments with an incremental priming paradigm using four stimulus–onset asynchronies (13, 27, 40, and 53 ms). Experiment 1 was conducted under a predictive-contingency scenario (probe–prime–target; i.e., “same” trials: HOUSE–house–HOUSE vs. house–water–HOUSE; “different” trials: field–house–HOUSE vs. field–water–HOUSE), while Experiment 2 employed a zero-contingency scenario (i.e., “same” trials: HOUSE–house–HOUSE vs. house–water–HOUSE; “different” trials: field–field–HOUSE vs. field–water–HOUSE). Results revealed that, for “same” responses, both facilitation and inhibition increased linearly with prime duration in the two scenarios, whereas the pattern of data varied for “different” responses, as predicted by the Bayesian Reader model.     

How the conjunction fallacy is tied to probabilistic confirmation: Some remarks on Schupbach (2009)

Crupi et al. (Think Reason 14:182–199, 2008) have recently advocated and partially worked out an account of the conjunction fallacy phenomenon based on the Bayesian notion of confirmation. In response, Schupbach (2009) presented a critical discussion as following from some novel experimental results. After providing a brief restatement and clarification of the meaning and scope of our original proposal, we will outline Schupbach’s results and discuss his interpretation thereof arguing that they do not actually undermine our point of view if properly construed. Finally, we will foster such a claim by means of some novel data.     

Meaning is in the beholder’s eye: Morpho-semantic effects in masked priming

A substantial body of literature indicates that, at least at some level of processing, complex words are broken down into their morphemes solely on the basis of their orthographic form (e.g., Rastle, Davis, & New, Psychonomic Bulletin and Review 11:1090–1098, 2004). Recent evidence has shown that this process might not be obligatory, as indicated by the fact that morpho-orthographic effects were not found in a cross-case same–different task—that is, when lexical access was not necessarily required (Duñabeitia, Kinoshita, Carreiras, & Norris, Language and Cognitive Processes 26:509–529, 2011). In this study, we employed a task that required understanding a series of words and, thus, implied lexical access. Masked primes were shown very briefly right before the appearance of the target word; prime–target pairs entertained a morpho-semantic (dealer–DEAL), a morpho-orthographic (corner–CORN), or a purely orthographic (brothel–BROTH) relationship. Eye fixation times clearly indicated facilitation for transparent pairs, but not for opaque pairs (or for orthographic pairs, which were used as a baseline). Conversely, the usual morpho-orthographic pattern was found in a control experiment, employing a lexical decision task. These results indicate that the access to a morpho-orthographic level of representation is not always necessary for lexical identification, which challenges models of visual word identification that cannot account for task-induced effects.     

Observing evidence accumulation during multi-alternative decisions

Most decision-making research has focused on choices between two alternatives. For choices between many alternatives, the primary result is Hick’s Law—that mean response time increases logarithmically with the number of alternatives. Various models for this result exist within specific paradigms, and there are some more general theoretical results, but none of those have been tested stringently against data. We present an experimental paradigm that supports detailed examination of multi-choice data, and analyze predictions from a Bayesian ideal observer model for this paradigm. Data from the experiment deviate from the predictions of the Bayesian model in interesting ways. A simple heuristic model based on evidence accumulation provides a good account for the data, and has attractive properties as a limit case of the Bayesian model.     

Want to block earworms from conscious awareness?B(u)y gum!

In a recent article, Jamieson and Mewhort (2009) proposed a novel account of artificial grammar learning (AGL), which is based on a multitrace model of episodic memory, the Minerva 2 model. According to this account, test performance in AGL is based on an assessment of global similarity of the test strings to the memory traces of the training strings. In this article, simulation studies are presented, showing for three different AGL experiments that the predictions of the Minerva 2 model strikingly deviate from participants' performance. It is argued that participants' test performance is not generally based on general similarity.     

Updating a progic

This paper presents a progic, or probabilistic logic, in the sense of Haenni et al. [8]. The progic presented here is based on Bayesianism, as the progic discussed by Williamson [15]. However, the underlying generalised Bayesianism differs from the objective Bayesianism used by Williamson, in the calibration norm, and the liberalisation and interpretation of the reference probability in the norm of equivocation. As a consequence, the updating dynamics of both Bayesianisms differ essentially. Whereas objective Bayesianism is based on a probabilistic re-evaluation, orthodox Bayesianism is based on a probabilistic revision. I formulate a generalised and iterable orthodox Bayesian revision dynamics. This allows to define an updating procedure for the generalised Bayesian progic. The paper compares the generalised Bayesian progic and Williamson's objective Bayesian progic in strength, update dynamics and with respect to language (in)sensitivity.     

Avoiding the dangers of averaging across subjects when using multidimensional scaling

Ashby, Maddox and Lee (Psychological Science, 5 (3) 144) argue that it can be inappropriate to fit multidimensional scaling (MDS) models to similarity or dissimilarity data that have been averaged across subjects. They demonstrate that the averaging process tends to make dissimilarity data more amenable to metric representations, and conduct a simulation study showing that noisy data generated using one distance metric, when averaged, may be better fit using a different distance metric. This paper argues that a Bayesian measure of MDS models has the potential to address these difficulties, because it takes into account data-fit, the number of dimensions used by an MDS representation, and the precision of the data. A method of analysis based on the Bayesian measure is demonstrated through two simulation studies with accompanying theoretical analysis. In the first study, it is shown that the Bayesian analysis rejects those MDS models showing better fit to averaged data using the incorrect distance metric, while accepting those that use the correct metric. In the second study, different groups of simulated ‘subjects’ are assumed to use different underlying configurations. In this case, the Bayesian analysis rejects MDS representations where a significant proportion of subjects use different configurations, or when their dissimilarity judgments contain significant amounts of noise. It is concluded that the Bayesian analysis provides a simple and principled means for systematically accepting and rejecting MDS models derived from averaged data.     

Leaping to Conclusions: Why Premise Relevance Affects Argument Strength

Everyday reasoning requires more evidence than raw data alone can provide. We explore the idea that people can go beyond this data by reasoning about how the data was sampled. This idea is investigated through an examination of premise non‐monotonicity, in which adding premises to a category‐based argument weakens rather than strengthens it. Relevance theories explain this phenomenon in terms of people's sensitivity to the relationships among premise items. We show that a Bayesian model of category‐based induction taking premise sampling assumptions and category similarity into account complements such theories and yields two important predictions: First, that sensitivity to premise relationships can be violated by inducing a weak sampling assumption; and second, that premise monotonicity should be restored as a result. We test these predictions with an experiment that manipulates people's assumptions in this regard, showing that people draw qualitatively different conclusions in each case.     

Refining the two‐parameter testlet response model by introducing testlet discrimination parameters

For testlet response data, traditional item response theory (IRT) models are often not appropriate due to local dependence presented among items within a common testlet. Several testlet‐based IRT models have been developed to model examinees' responses. In this paper, a new two‐parameter normal ogive testlet response theory (2PNOTRT) model for dichotomous items is proposed by introducing testlet discrimination parameters. A Bayesian model parameter estimation approach via a data augmentation scheme is developed. Simulations are conducted to evaluate the performance of the proposed 2PNOTRT model. The results indicated that the estimation of item parameters is satisfactory overall from the viewpoint of convergence. Finally, the proposed 2PNOTRT model is applied to a set of real testlet data.     

Bayesian inference for the information gain model

One of the most popular paradigms to use for studying human reasoning involves the Wason card selection task. In this task, the participant is presented with four cards and a conditional rule (e.g., “If there is an A on one side of the card, there is always a 2 on the other side”). Participants are asked which cards should be turned to verify whether or not the rule holds. In this simple task, participants consistently provide answers that are incorrect according to formal logic. To account for these errors, several models have been proposed, one of the most prominent being the information gain model (Oaksford & Chater, Psychological Review, 101, 608–631, 1994). This model is based on the assumption that people independently select cards based on the expected information gain of turning a particular card. In this article, we present two estimation methods to fit the information gain model: a maximum likelihood procedure (programmed in R) and a Bayesian procedure (programmed in WinBUGS). We compare the two procedures and illustrate the flexibility of the Bayesian hierarchical procedure by applying it to data from a meta-analysis of the Wason task (Oaksford & Chater, Psychological Review, 101, 608–631, 1994). We also show that the goodness of fit of the information gain model can be assessed by inspecting the posterior predictives of the model. These Bayesian procedures make it easy to apply the information gain model to empirical data. Supplemental materials may be downloaded along with this article from .