If we are eventually to understand the capability of higher organisms for perceptual recognition, generalization, recall, and thinking, we must first have answers to three fundamental questions: 1. How is information about the physical world sensed, or detected, by the biological system? 2. In what form is information stored, or remembered? 3. How does information contained in storage, or in memory, influence recognition and behavior? The first of these questions is in the.

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. Key Words: computational modeling; feedback; lexical processing; modularity; phonemic decisions; reading; speech recognition; word recognition. (shrink)

Traditional explanations of multistable visual phenomena (e.g. ambiguous figures, perceptual rivalry) suggest that the basis for spontaneous reversals in perception lies in antagonistic connectivity within the visual system. In this review, we suggest an alternative, albeit speculative, explanation for visual multistability – that spontaneous alternations reflect responses to active, programmed events initiated by brain areas that integrate sensory and non-sensory information to coordinate a diversity of behaviors. Much evidence suggests that perceptual reversals are themselves more closely related to the expression (...) of a behavior than to passive sensory responses: (1) they are initiated spontaneously, often voluntarily, and are influenced by subjective variables such as attention and mood; (2) the alternation process is greatly facilitated with practice and compromised by lesions in non-visual cortical areas; (3) the alternation process has temporal dynamics similar to those of spontaneously initiated behaviors; (4) functional imaging reveals that brain areas associated with a variety of cognitive behaviors are specifically activated when vision becomes unstable. In this scheme, reorganizations of activity throughout the visual cortex, concurrent with perceptual reversals, are initiated by higher, largely non-sensory brain centers. Such direct intervention in the processing of the sensory input by brain structures associated with planning and motor programming might serve an important role in perceptual organization, particularly in aspects related to selective attention. (shrink)

Traditional explanations of multistable visual phenomena (e.g. ambiguous figures, perceptual rivalry) suggest that the basis for spontaneous reversals in perception lies in antagonistic connectivity within the visual system. In this review, we suggest an alternative, albeit speculative. explanation for visual multistability - that spontaneous alternations reflect responses to active, programmed events initiated by brain areas that integrate sensory and non-sensory information to coordinate a diversity of behaviors. Much evidence suggests that perceptual reversals are themselves more closely related to the expression (...) of a behavior than to passive sensory responses: (1) they are initiated spontaneously, often voluntarily, and are influenced by subjective variables such as attention and mood; (2) the alternation process is greatly facilitated with practice and compromised by lesions in non- visual cortical areas; (3) the alternation process has temporal dynamics similar to those of spontaneously initiated behaviors; (4) functional imaging reveals that brain areas associated with a variety of cognitive behaviors are specifically activated when vision becomes unstable. In this scheme, reorganizations of activity throughout the visual cortex, concurrent with perceptual reversals, are initiated by higher, largely non- sensory brain centers. Such direct intervention In the processing of the sensory input by brain structures associated with planning and motor programming might serve an important role in perceptual organization, particularly in aspects related to selective attention. (shrink)

The question of when and how bottom‐up input is integrated with top‐down knowledge has been debated extensively within cognition and perception, and particularly within language processing. A long running debate about the architecture of the spoken‐word recognition system has centered on the locus of lexical effects on phonemic processing: does lexical knowledge influence phoneme perception through feedback, or post‐perceptually in a purely feedforward system? Elman and McClelland (1988) reported that lexically restored ambiguous phonemes influenced the perception of the following phoneme, (...) supporting models with feedback from lexical to phonemic representations. Subsequently, several authors have argued that these results can be fully accounted for by diphone transitional probabilities in a feedforward system (Cairns et al., 1995; Pitt & McQueen, 1998). We report results strongly favoring the original lexical feedback explanation: lexical effects were present even when transitional probability biases were opposite to those of lexical biases. (shrink)

A fundamental claim associated with parallel distributed processing theories of cognition is that knowledge is coded in a distributed manner in mind and brain. This approach rejects the claim that knowledge is coded in a localist fashion, with words, objects, and simple concepts, that is, coded with their own dedicated representations. One of the putative advantages of this approach is that the theories are biologically plausible. Indeed, advocates of the PDP approach often highlight the close parallels between distributed representations learned (...) in connectionist models and neural coding in brain and often dismiss localist theories as biologically implausible. The author reviews a range a data that strongly challenge this claim and shows that localist models provide a better account of single-cell recording studies. The author also contrast local and alternative distributed coding schemes and argues that common rejection of grandmother cell theories in neuroscience is due to a misunderstanding about how localist models behave. The author concludes that the localist representations embedded in theories of perception and cognition are consistent with neuroscience; biology only calls into question the distributed representations often learned in PDP models. (shrink)

In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and standard assumptions about optimality: People often appear to make decisions based on just one or a few samples from the appropriate posterior probability distribution, rather than using the full distribution. Although sampling-based approximations are a common way to implement Bayesian (...) inference, the very limited numbers of samples often used by humans seem insufficient to approximate the required probability distributions very accurately. Here, we consider this discrepancy in the broader framework of statistical decision theory, and ask: If people are making decisions based on samples—but as samples are costly—how many samples should people use to optimize their total expected or worst-case reward over a large number of decisions? We find that under reasonable assumptions about the time costs of sampling, making many quick but locally suboptimal decisions based on very few samples may be the globally optimal strategy over long periods. These results help to reconcile a large body of work showing sampling-based or probability matching behavior with the hypothesis that human cognition can be understood in Bayesian terms, and they suggest promising future directions for studies of resource-constrained cognition. (shrink)

The effects of lexical context on phonological processing are pervasive and there have been indications that such effects may be modulated by attention. However, attentional modulation in speech processing is neither well documented nor well understood. Experiment 1 demonstrated attentional modulation of lexical facilitation of speech sound recognition when task and critical stimuli were identical across attention conditions. We propose modulation of lexical activation as a neurophysiologically plausible computational mechanism that can account for this type of modulation. Contrary to the (...) claims of critics, this mechanism can account for attentional modulation without violating the principle of interactive processing. Simulations of the interactive TRACE model extended to include two different ways of modulating lexical activation showed that each can account for attentional modulation of lexical feedback effects. Experiment 2 tested conflicting predictions from the two implementations and provided evidence that is consistent with bias input as the mechanism of attentional control of lexical activation. (shrink)

A perceptual learning experiment provides evidence that the mental lexicon cannot consist solely of detailed acoustic traces of recognition episodes. In a training lexical decision phase, listeners heard an ambiguous [f–s] fricative sound, replacing either [f] or [s] in words. In a test phase, listeners then made lexical decisions to visual targets following auditory primes. Critical materials were minimal pairs that could be a word with either [f] or [s] (cf. English knife–nice), none of which had been heard in training. (...) Listeners interpreted the minimal pair words differently in the second phase according to the training received in the first phase. Therefore, lexically mediated retuning of phoneme perception not only influences categorical decisions about fricatives (Norris, McQueen, & Cutler, 2003), but also benefits recognition of words outside the training set. The observed generalization across words suggests that this retuning occurs prelexically. Therefore, lexical processing involves sublexical phonological abstraction, not only accumulation of acoustic episodes. (shrink)

It is possible to learn multiple layers of non-linear features by backpropagating error derivatives through a feedforward neural network. This is a very effective learning procedure when there is a huge amount of labeled training data, but for many learning tasks very few labeled examples are available. In an effort to overcome the need for labeled data, several different generative models were developed that learned interesting features by modeling the higher order statistical structure of a set of input vectors. One (...) of these generative models, the restricted Boltzmann machine (RBM), has no connections between its hidden units and this makes perceptual inference and learning much simpler. More significantly, after a layer of hidden features has been learned, the activities of these features can be used as training data for another RBM. By applying this idea recursively, it is possible to learn a deep hierarchy of progressively more complicated features without requiring any labeled data. This deep hierarchy can then be treated as a feedforward neural network which can be discriminatively fine-tuned using backpropagation. Using a stack of RBMs to initialize the weights of a feedforward neural network allows backpropagation to work effectively in much deeper networks and it leads to much better generalization. A stack of RBMs can also be used to initialize a deep Boltzmann machine that has many hidden layers. Combining this initialization method with a new method for fine-tuning the weights finally leads to the first efficient way of training Boltzmann machines with many hidden layers and millions of weights. (shrink)