Comment on Ryder's SINBAD Neurosemantics: Is Teleofunction Isomorphism the Way to Understand Representations?

The merit of the SINBAD model is to provide an explicit mechanism showing how the cortex may come to develop detectors responding to correlated properties and therefore corresponding to the sources of these correlations. Here I argue that, contrary to the article, SINBAD neurosemantics does not need to rely on teleofunctions to solve the problem of misrepresentation. A number of difficulties for the teleofunction theories of content are reviewed and an alternative theory based on categorization performance and statistical relations is argued to provide a better account and to come closer to the practice in neuroscience and to powerful intuitions on swampkinds and on broad/narrow content

The Effect of synchronized inputs at the single neuron level

It is commonly assumed that temporal synchronization of excitatory synaptic inputs onto a single neuron increases its firing rate. We investigate here the role of synaptic synchronization for the leaky integrate-and-fire neuron as well as for a biophysically and anatomically detailed compartmental model of a cortical pyramidal cell. We find that if the number of excitatory inputs, N, is on the same order as the number of fully synchronized inputs necessary to trigger a single action potential, N_t, synchronization always increases the firing rate (for both constant and Poisson-distributed input). However, for large values of N compared to N_t, ''overcrowding'' occurs and temporal synchronization is detrimental to firing frequency. This behavior is caused by the conflicting influence of the low-pass nature of the passive dendritic membrane on the one hand and the refractory period on the other. If both temporal synchronization as well as the fraction of synchronized inputs (Murthy and Fetz 1993) is varied, synchronization is only advantageous if either N or the average input frequency, ƒ(in), are small enough

The impact of the mode of thought in complex decisions: intuitive decisions are better

A number of recent studies have reported that decision quality is enhanced under conditions of inattention or distraction (unconscious thought; Dijksterhuis, 2004; Dijksterhuis and Nordgren, 2006; Dijksterhuis et al., 2006). These reports have generated considerable controversy, for both experimental (problems of replication) and theoretical reasons (interpretation). Here we report the results of four experiments. The first experiment replicates the unconscious thought effect, under conditions that validate and control the subjective criterion of decision quality. The second and third experiments examine the impact of a mode of thought manipulation (without distraction) on decision quality in immediate decisions. Here we find that intuitive or affective manipulations improve decision quality compared to analytic/deliberation manipulations. The fourth experiment combines the two methods (distraction and mode of thought manipulations) and demonstrates enhanced decision quality, in a situation that attempts to preserve ecological validity. The results are interpreted within a framework that is based on two interacting subsystems of decision-making: an affective/intuition based system and an analytic/deliberation system

Semantic similarity dissociates shortfrom long-term recency effects: testing a neurocomputational model of list memory

The finding that recency effects can occur not only in immediate free recall (i.e., short-term recency) but also in the continuous-distractor task (i.e., long-term recency) has led many theorists to reject the distinction between short- and long-term memory stores. Recently, we have argued that long-term recency effects do not undermine the concept of a short-term store, and we have presented a neurocomputational model that accounts for both short- and long-term recency and for a series of dissociations between these two effects. Here, we present a new dissociation between short- and long-term recency based on semantic similarity, which is predicted by our model. This dissociation is due to the mutual support between associated items in the short-term store, which takes place in immediate free recall and delayed free recall but not in continuous-distractor free recall

Evidence integration and decision confidence are modulated by stimulus consistency

Evidence integration is a normative algorithm for choosing between alternatives with noisy evidence, which has been successful in accounting for vast amounts of behavioural and neural data. However, this mechanism has been challenged by non-integration heuristics, and tracking decision boundaries has proven elusive. Here we first show that the decision boundaries can be extracted using a model-free behavioural method termed decision classification boundary, which optimizes choice classification based on the accumulated evidence. Using this method, we provide direct support for evidence integration over non-integration heuristics, show that the decision boundaries collapse across time and identify an integration bias whereby incoming evidence is modulated based on its consistency with preceding information. This consistency bias, which is a form of pre-decision confirmation bias, was supported in four cross-domain experiments, showing that choice accuracy and decision confidence are modulated by stimulus consistency. Strikingly, despite its seeming sub-optimality, the consistency bias fosters performance by enhancing robustness to integration noise

Extracting Summary Statistics of Rapid Numerical Sequences

We examine the ability of observers to extract summary statistics (such as the mean and the relative-variance) from rapid numerical sequences of two digit numbers presented at a rate of 4/s. In four experiments (total N = 100), we find that the participants show a remarkable ability to extract such summary statistics and that their precision in the estimation of the sequence-mean improves with the sequence-length (subject to individual differences). Using model selection for individual participants we find that, when only the sequence-average is estimated, most participants rely on a holistic process of frequency based estimation with a minority who rely on a (rule-based and capacity limited) mid-range strategy. When both the sequence-average and the relative variance are estimated, about half of the participants rely on these two strategies. Importantly, the holistic strategy appears more efficient in terms of its precision. We discuss implications for the domains of two pathways numerical processing and decision-making

Synchronization, oscillations, and 1/f noise in networks of spiking neurons

We investigate a model for neural activity that generates long range temporal correlations, 1/ f noise, and oscillations in global activity. The model consists of a two-dimensional sheet of leaky integrate-and- fire neurons with feedback connectivity consisting of local excitation and surround inhibition. Each neuron is independently driven by homogeneous external noise. Spontaneous symmetry breaking occurs, resulting in the formation of "hotspots" of activity in the network. These localized patterns of excitation appear as clusters that coalesce, disintegrate, or fluctuate in size while simultaneously moving in a random walk constrained by the interaction with other clusters. The emergent cross-correlation functions have a dual structure, with a sharp peak around zero on top of a much broader hill. The power spectrum associated with single units shows a 1/ f decay for small frequencies and is flat at higher frequencies, while the power spectrum of the spiking activity averaged over many cells-equivalent to the local field potential-shows no 1/ f decay but a prominent peak around 40 Hz

Using time-varying evidence to test models of decision dynamics: bounded diffusion vs. the leaky competing accumulator model

When people make decisions, do they give equal weight to evidence arriving at different times? A recent study (Kiani et al., 2008) using brief motion pulses (superimposed on a random moving dot display) reported a primacy effect: pulses presented early in a motion observation period had a stronger impact than pulses presented later. This observation was interpreted as supporting the bounded diffusion (BD) model and ruling out models in which evidence accumulation is subject to leakage or decay of early-arriving information. We use motion pulses and other manipulations of the timing of the perceptual evidence in new experiments and simulations that support the leaky competing accumulator (LCA) model as an alternative to the BD model. While the LCA does include leakage, we show that it can exhibit primacy as a result of competition between alternatives (implemented via mutual inhibition), when the inhibition is strong relative to the leak. Our experiments replicate the primacy effect when participants must be prepared to respond quickly at the end of a motion observation period. With less time pressure, however, the primacy effect is much weaker. For 2 (out of 10) participants, a primacy bias observed in trials where the motion observation period is short becomes weaker or reverses (becoming a recency effect) as the observation period lengthens. Our simulation studies show that primacy is equally consistent with the LCA or with BD. The transition from primacy-to-recency can also be captured by the LCA but not by BD. Individual differences and relations between the LCA and other models are discussed

Dynamics of decision-making: from evidence accumulation to preference and belief

Decision-making is a dynamic process that begins with the accumulation of evidence and ends with the adjustment of belief. Each step is itself subject to a number of dynamic processes, such as planning, information search and evaluation. Furthermore, choice behavior reveals a number of challenging patterns, such as order effects and contextual preference reversal. Research in this field has converged toward a standard computational framework for the process of evidence integration and belief updating, based on sequential sampling models, which under some conditions are equivalent to normative Bayesian theory (Gold and Shadlen, 2007). A variety of models have been developed within the sequential sampling framework that can account for accuracy, response-time distributional data, and the speed-accuracy trade-off (Busemeyer and Townsend, 1993; Usher and Mcclelland, 2001; Brown and Heathcote, 2008; Ratcliff and McKoon, 2008). Yet there are differences between these models with regard to the mechanism of decision-termination, the optimality of the decision and the temporal weighting of the evidence. There is also a need to extend this framework to preference type of decisions (where the criteria are up to the judge) and to enrich it so as to include control processes (such as exploration/exploitation), information search, and adaptation to the environment, thereby allowing it to capture richer decision problems; for example, when alternatives are not pre-defined, or when the decision-maker is not just accumulating evidence but also adapting beliefs about the data-generating process. This Research Topic presents new work that investigates the dynamical and mathematical properties of evidence integration and its neural mechanisms and extends this framework to more complex decisions, such as those that occur during risky choice, preference formation, and belief updating. We hope these articles will encourage researchers to explore the computational and normative aspects of the decision process and the observed deviations. We briefly review here the contributions in this collection, starting from simple perceptual decisions in which the information flow is externally controlled to more complex decisions, which allow the observer to control the information flow and other learning strategies, and following on with preference formation