The indispensability of precollision kinematics in the visual perception of relative mass

The visible kinematic pattern that occurs when objects collide contains information about the relative mass of the objects. Recently, Gilden and Proffitt (1989) have claimed that perceivers are limited to the use of simultaneous elemental cues and that therefore mass discrimination judgments are independent of whether the precollision epoch is visible or not. The present paper argues that their experimental results are irrelevant because crucial parameters were held constant, and that false conclusions were drawn because their data were not treated for what they are: threshold measurements obtained with the method of constant stimuli. Three experiments are reported, showingthat a high level of performance is attainable only when the whole event is visible, and thus that the effective information is extended over time. In addition, modified PROBIT analysis was used to determine what information observers use when the precollision epoch is occluded. Analytically complex invariants, based on vector components in the abstract collision-axis system, proved to have the best fit to the data. Thus the elementaristic premises in Gilden and Proffitt’s cue-heuristic model for relative mass perception are defeated.     

Stimulus-dependent dopamine release in attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is related to an attenuated and dysfunctional dopamine system. Normally, a high extracellular dopamine level yields a tonic dopaminergic input that down-regulates stimuli-evoked phasic dopamine responses through autoreceptors. Abnormally low tonic extracellular dopamine in ADHD up-regulates the autoreceptors so that stimuli-evoked phasic dopamine is boosted. The authors propose that these boosted phasic responses yield hypersensitivity to environmental stimuli in ADHD. Stimuli evoking moderate brain arousal lead to well-functioning performance, whereas either too little or too much stimuli attenuate cognitive performance. Strong, salient stimuli may easily disrupt attention, whereas an environment with impoverished stimuli causes low arousal, which is typically compensated for by hyperactivity. Stochastic resonance is the phenomenon that makes a moderate noise facilitate stimulus discrimination and cognitive performance. Computational modeling shows that more noise is required for stochastic resonance to occur in dopamine-deprived neural systems in ADHD. This prediction is supported by empirical data.     

The variance theory of the mirror effect in recognition memory

The mirror effect refers to a rather general empirical finding showing that, for two classes of stimuli, the class with the higher hit rates also has a lower false alarm rate. In this article, a parsimonious theory is proposed to account for the mirror effect regarding, specifically, high- and low-frequency items and the associated receiver-operating curves. The theory is implemented in a recurrent network in which one layer represents items and the other represents contexts. It is shown that the frequency mirror effect is found in this simple network if the decision is based on counting the number of active nodes in such a way that performance is optimal or near optimal. The optimal performance requires that the number of active nodes is low, only nodes active in the encoded representation are counted, the activation threshold is set between the old and the new distributions, and normalization is based on the variance of the input. Owing to the interference caused by encoding the to-be-recognized item in several preexperimental contexts, the variance of the input to the context layer is greater for highthan for low-frequency items, which yields lower hit rates and higher false alarm rates for high- than for low-frequency items. Although initially the theory was proposed to account for the mirror effect with respect to word frequency, subsequent simulations have shown that the theory also accounts for strength-based mirror effects within a list and between lists. In this case, consistent with experimental data, the variance theory suggests that focusing attention to the more difficult class within a list affects the hit rate, but not the false alarm rate and not the standard deviations of the underlying density, leading to no mirror effect.     

Support for the cue-heuristic model is based on suboptimal observer performance: Response to Gilden and Proffitt (1994)

Gilden and Proffitt (1994) have derived support for their updated cue-heuristic model of mass discrimination in observed collisions from two experiments reported by Runeson and Vedeler (1993). However, these experiments are inadequate for theory testing because of confounding and lack of representative variation in parameters. Instead, extended analyses of the third of Runeson and Vedeler’s experiments are presented. It is shown that observers exclusively use collision-axis velocity components in conditions where the heuristic model asserts that only trajectory speeds are available. Moreover, the patterns of error predicted by the heuristic model do not occur. Overall, performance is closer to the ideal than to the predictions from any conceivable model based on elementaristic cues; hence an explanation in terms of advanced informative invariants is the more viable approach. The final discussion concerns the distortion of theory development that might be engendered by empirical data that represent suboptimal observer performance due to unfavorable testing conditions.     

The role of sparsely distributed representations in familiarity recognition of verbal and olfactory materials

We present the generalized signal detection theory (GSDT), where familiarity is described by a sparse binomial distribution of binary node activity rather than by normal distribution of familiarity. Items are presented in a distributed representation, where each node receives either noise only, or signal and noise. An old response (i.e., a “yes” response) is made if at least one node receives signal plus noise that is larger than the activation threshold, and item variability is determined by the distribution of activated nodes as the threshold is varied. A distinct representation leads to better performance and a lower ratio of new to old item variability, than a more distributed and less distinct representations. Here we apply the GSDT to empirical data on verbal and olfactory memory and suggest that verbal memory relies on a distinct neural item representation, whereas olfactory memory has a fuzzy neural representation leading to poorer memory and inducing a larger ratio of new to old item variability.     

The Isolation, Primacy, and Recency Effects Predicted by an Adaptive LTD/LTP Threshold in Postsynaptic Cells

An item that stands out (is isolated) from its context is better remembered than an item consistent with the context. This isolation effect cannot be accounted for by increased attention, because it occurs when the isolated item is presented as the first item, or by impoverished memory of nonisolated items, because the isolated item is better remembered than a control list consisting of equally different items. The isolation effect is seldom experimentally or theoretically related to the primacy or the recency effects—that is, the improved performance on the first few and last items, respectively, on the serial position curve. The primacy effect cannot easily be accounted for by rehearsal in short-term memory because it occurs when rehearsal is eliminated. This article suggests that the primacy, the recency, and the isolation effects can be accounted for by experience-dependent synaptic plasticity in neural cells. Neurological empirical data suggest that the threshold that determines whether cells will show long-term potentiation (LTP) or long-term depression (LTD) varies as a function of recent postsynaptic activity and that synaptic plasticity is bounded. By implementing an adaptive LTP-LTD threshold in an artificial neural network, the various aspects of the isolation, the primacy, and the recency effects are accounted for, whereas none of these phenomena are accounted for if the threshold is constant. This theory suggests a possible link between the cognitive and the neurological levels.     

Forgetting curves: implications for connectionist models

Forgetting in long-term memory, as measured in a recall or a recognition test, is faster for items encoded more recently than for items encoded earlier. Data on forgetting curves fit a power function well. In contrast, many connectionist models predict either exponential decay or completely flat forgetting curves. This paper suggests a connectionist model to account for power-function forgetting curves by using bounded weights and by generating the learning rates from a monotonically decreasing function. The bounded weights introduce exponential forgetting in each weight and a power-function forgetting results when weights with different learning rates are averaged. It is argued that these assumptions are biologically reasonable. Therefore power-function forgetting curves are a property that may be expected from biological networks. The model has an analytic solution, which is a good approximation of a power function displaced one lag in time. This function fits better than any of the 105 suggested two-parameter forgetting-curve functions when tested on the most precise recognition memory data set collected by. Unlike the power-function normally used, the suggested function is defined at lag zero. Several functions for generating learning rates with a finite integral yield power-function forgetting curves; however, the type of function influences the rate of forgetting. It is shown that power-function forgetting curves cannot be accounted for by variability in performance between subjects because it requires a distribution of performance that is not found in empirical data. An extension of the model accounts for intersecting forgetting curves found in massed and spaced repetitions. The model can also be extended to account for a faster forgetting rate in item recognition (IR) compared to associative recognition in short but not long retention intervals.     

The big two dictionaries: Capturing agency and communion in natural language

Four studies developed and validated two dictionaries to capture agentic and communal expressions in natural language. Their development followed the Linguistic Inquiry and Word Count (LIWC) approach (Study 1) and we tested their validity with frequency-based analyses and semantic similarity measures. The newly developed Agency and Communion dictionaries were aligned with LIWC categories related to agency and communion (Study 2), and corresponded with subjective ratings (Study 3), confirming their convergent validity. Very low or absent correspondence between proposed dictionaries and unrelated LIWC categories demonstrated their discriminant validity (Study 2). Finally, we applied both dictionaries to language used in advertisements. In correspondence to gender stereotypes, male-dominated jobs were advertised with more agentic than communal words, and female-dominated jobs with more communal than agentic words (Study 4). Both dictionaries represent reliable tools for quantifying agentic and communal content in natural language, and will improve and facilitate future research on agency and communion.     

Power Function Forgetting Curves as an Emergent Property of Biologically Plausible Neural Network Models

Empirical forgetting curve data have been shown to follow a power function. In contrast, many connectionist models predict either an exponential decay or flat forgetting curves. This paper simulates power function forgetting curves in a Hopfield network modified to incorporate the more biologically realistic assumptions of bounded weights and a distribution of learning rates. The modified model produces power function forgetting curves. The bounded weights introduce exponential decay for individual weights, and a power function forgetting curve when summing exponential decays with different learning rates. Because these assumptions are biologically reasonable, power function forgetting curves may be an emergent property of biological networks. The results fit empirical data and indicate that forgetting curves restrict possible implementation of models of memory.