The current failure to construct an artificial intelligence (AI) agent with the capacity for domain-general learning is a major stumbling block in the attempt to build conscious robots. Taking an evolutionary approach, we previously suggested that the emergence of consciousness was entailed by the evolution of an open-ended domain-general form of learning, which we call unlimited associative learning (UAL). Here, we outline the UAL theory and discuss the constraints and affordances that seem necessary for constructing an AI machine exhibiting UAL. We argue that a machine that is capable of domain-general learning requires the dynamics of a UAL architecture and that a UAL architecture requires, in turn, that the machine is highly sensitive to the environment and has an ultimate value (like self-persistence) that provides shared context to all its behaviors and learning outputs. The implementation of UAL in a machine may require that it is made of “soft” materials, which are sensitive to a large range of environmental conditions, and that it undergoes sequential morphological and behavioral co-development. We suggest that the implementation of these requirements in a human-made robot will lead to its ability to perform domain-general learning and will bring us closer to the construction of a sentient machine.

Many decisions result from the accumulation of decision-relevant information (evidence) over time. Even when maximizing decision accuracy requires weighting all the evidence equally, decision-makers often give stronger weight to evidence occurring early or late in the evidence stream. Here, we show changes in such temporal biases within participants as a function of intermittent judgments about parts of the evidence stream. Human participants performed a decision task that required a continuous estimation of the mean evidence at the end of the stream. The evidence was either perceptual (noisy random dot motion) or symbolic (variable sequences of numbers). Participants also reported a categorical judgment of the preceding evidence half-way through the stream in one condition or executed an evidence-independent motor response in another condition. The relative impact of early versus late evidence on the final estimation flipped between these two conditions. In particular, participants' sensitivity to late evidence after the intermittent judgment, but not the simple motor response, was decreased. Both the intermittent response as well as the final estimation reports were accompanied by nonluminance-mediated increases of pupil diameter. These pupil dilations were bigger during intermittent judgments than simple motor responses and bigger during estimation when the late evidence was consistent than inconsistent with the initial judgment. In sum, decisions activate pupil-linked arousal systems and alter the temporal weighting of decision evidence. Our results are consistent with the idea that categorical choices in the face of uncertainty induce a change in the state of the neural circuits underlying decision-making. NEW & NOTEWORTHY The psychology and neuroscience of decision-making have extensively studied the accumulation of decision-relevant information toward a categorical choice. Much fewer studies have assessed the impact of a choice on the processing of subsequent information. Here, we show that intermittent choices during a protracted stream of input reduce the sensitivity to subsequent decision information and transiently boost arousal. Choices might trigger a state change in the neural machinery for decision-making.

According to the thesis of transparency, subjects can attend only to the representational content of perceptual experience, never to the intrinsic properties of experience that carry this representational content, i.e., to “mental paint.” So far, arguments for and against transparency were conducted from the armchair, relying mainly on introspective observations. In this paper, we argue in favor of transparency, relying on the cognitive neuroscience of attention. We present a trilemma to those who hold that attention can be directed to mental paint. Such attention is either first-order sensory, higher-order cognitive, or higher-order sensory attention. We argue that the notion of first-order sensory attention to mental paint is incompatible with the neuroscience of sensory attention; that higher-order cognitive attention to mental paint is irrelevant to transparency; and that the notion of higher-order sensory attention to mental paint has an apparently incoherent prediction. Via elimination, these considerations support the thesis of transparency.

The question of whether conscious experience is restricted by cognitive access and exhausted by report, or whether it overflows it—comprising more information than can be reported—is hotly debated. Recently, we provided evidence in favor of Overflow, showing that observers discriminated the color‐diversity (CD) of letters in an array, while their working‐memory and attention were dedicated to encoding and reporting a set of cued letters. An alternative interpretation is that CD‐discriminations do not entail conscious experience of the underlying colors. Here we argue, based on conceptual considerations and consistency with neuroscience and phenomenology, in favor of the Overflow interpretation.

Humans possess a remarkable ability to rapidly form coarse estimations of numerical averages. This ability is important for making decisions that are based on streams of numerical or value-based information, as well as for preference formation. Nonetheless, the mechanism underlying rapid approximate numerical averaging remains unknown, and several competing mechanism may account for it. Here, we tested the hypothesis that approximate numerical averaging relies on perceptual-like processes, instantiated by population coding. Participants were presented with rapid sequences of numerical values (four items per second) and were asked to convey the sequence average. We manipulated the sequences' length, variance, and mean magnitude and found that similar to perceptual averaging, the precision of the estimations improves with the length and deteriorates with (higher) variance or (higher) magnitude. To account for the results, we developed a biologically plausible population-coding model and showed that it is mathematically equivalent to a population vector. Using both quantitative and qualitative model comparison methods, we compared the population-coding model to several competing models, such as a step-by-step running average (based on leaky integration) and a midrange model. We found that the data support the population-coding model. We conclude that humans' ability to rapidly form estimations of numerical averages has many properties of the perceptual (intuitive) system rather than the arithmetic, linguistic-based (analytic) system and that population coding is likely to be its underlying mechanism.

We contrast two theoretical positions on the relation between phenomenal and access consciousness. First, we discuss previous data supporting a mild Overflow position, according to which transient visual awareness can overflow report. These data are open to two interpretations: (i) observers transiently experience specific visual elements outside attentional focus without encoding them into working memory; (ii) no specific visual elements but only statistical summaries are experienced in such conditions. We present new data showing that under data-limited conditions observers cannot discriminate a simple relation (same versus different) without discriminating the elements themselves and, based on additional computational considerations, we argue that this supports the first interpretation: summary statistics (same/different) are grounded on the transient experience of elements. Second, we examine recent data from a variant of 'inattention blindness' and argue that contrary to widespread assumptions, it provides further support for Overflow by highlighting another factor, 'task relevance', which affects the ability to conceptualize and report (but not experience) visual elements.This article is part of the theme issue 'Perceptual consciousness and cognitive access'.

The quantity and nature of the processes underlying recognition memory remains an open question. A majority of behavioral, neuropsychological, and brain studies have suggested that recognition memory is supported by two dissociable processes: recollection and familiarity. It has been conversely argued, however, that recollection and familiarity map onto a single continuum of mnemonic strength and hence that recognition memory is mediated by a single process. Previous electrophysiological studies found marked dissociations between recollection and familiarity, which have been widely held as corroborating the dual-process account. However, it remains unknown whether a strength interpretation can likewise apply for these findings. Here we describe an ERP study, using a modified remember-know (RK) procedure, which allowed us to control for mnemonic strength. We find that ERPs of high and low mnemonic strength mimicked the electrophysiological distinction between R and K responses, in a lateral positive component (LPC), 500-1000 msec poststimulus onset. Critically, when contrasting strength with RK experience, by comparing weak R to strong K responses, the electrophysiological signal mapped onto strength, not onto subjective RK experience. Invoking the LPC as support for dual-process accounts may, therefore, be amiss.

BACKGROUND:Social anxiety disorder (SAD) is characterized by intense fear when facing a crowd. Processing biases of crowd-related information have been suggested as contributing to the etiology and maintenance of the disorder. Here we tested whether patients with SAD display aberrant patterns of extracting the mean emotional tone from sets of faces.METHODS:Twenty-one participants with SAD and 24 unanxious control participants had to determine the average emotion expression of sets of six different morphed faces ranging from happy to angry. In 20% of trials the six faces were randomly sampled from the entire happy-angry range. The remaining 80% of trials, considered the critical trials, had an emotional outlier: five faces were sampled from one-half of the emotional range, whereas the sixth face was sampled from the opposite emotional range.RESULTS:Participants with SAD were less accurate than controls in extracting the mean emotional tone from sets of faces. Unanxious participants underweighted negative outliers and overweighed positive outliers when extracting the mean, whereas participants with SAD exhibited no such biases.CONCLUSIONS:Results suggest a possible mechanism associated with the anxiety experienced by socially anxious individuals when facing a crowd.

The parietal cortex has been implicated in a variety of numerosity and numerical cognition tasks and was proposed to encompass dedicated neural populations that are tuned for analogue magnitudes as well as for symbolic numerals. Nonetheless, it remains unknown whether the parietal cortex plays a role in approximate numerical averaging (rapid, yet coarse computation of numbers' mean)—a process that is fundamental to preference formation and decision-making. To causally investigate the role of the parietal cortex in numerical averaging, we have conducted a transcranial direct current stimulation (tDCS) study, in which participants were presented with rapid sequences of numbers and asked to convey their intuitive estimation of each sequence's average. During the task, the participants underwent anodal (excitatory) tDCS (or sham), applied either on a parietal or a frontal region. We found that, although participants exhibit above-chance accuracy in estimating the average of numerical sequences, they did so with higher precision under parietal stimulation. In a second experiment, we have replicated this finding and confirmed that the effect is number-specific rather than domain-general or attentional. We present a neurocomputational model postulating population-coding underlying rapid numerical averaging to account for our findings. According to this model, stimulation of the parietal cortex elevates neural activity in number-tuned dedicated detectors, leading to increase in the system's signal-to-noise level and thus resulting in more precise estimations.

We suggest an approach to studying consciousness that focuses on its evolutionary origins. The proposed framework is inspired by the study of the transition from inanimate matter to life, which proved extremely useful for understanding what 'life' entails. We follow the theoretical and methodological scheme put forward by Tibor Ganti, who suggested a marker for the transition to life an evolved feature that is sufficient for ascribing dynamic persistence to a minimal living system and that can serve as a point of departure for reconstructing it. We apply this approach to consciousness and suggest an overt behavioural trait unlimited associative learning (UAL) - as a candidate transition marker. We show that the enabling system of UAL instantiates the set of properties that are considered jointly sufficient for minimal consciousness, and discuss the experimental predictions and the implications of our approach for the taxonomic distribution of consciousness in the animal world.

The minimal state of consciousness is sentience. This includes any phenomenal sensory experience - exteroceptive, such as vision and olfaction; interoceptive, such as pain and hunger; or proprioceptive, such as the sense of bodily position and movement. We propose unlimited associative learning (UAL) as the marker of the evolutionary transition to minimal consciousness (or sentience), its phylogenetically earliest sustainable manifestation and the driver of its evolution. We define and describe UAL at the behavioral and functional level and argue that the structural-anatomical implementations of this mode of learning in different taxa entail subjective feelings (sentience). We end with a discussion of the implications of our proposal for the distribution of consciousness in the animal kingdom, suggesting testable predictions, and revisiting the ongoing debate about the function of minimal consciousness in light of our approach.

Perceptual decisions are thought to be mediated by a mechanism of sequential sampling and integration of noisy evidence whose temporal weighting profile affects the decision quality. To examine temporal weighting, participants were presented with two brightness-fluctuating disks for 1, 2 or 3 seconds and were requested to choose the overall brighter disk at the end of each trial. By employing a signal-perturbation method, which deploys across trials a set of systematically controlled temporal dispersions of the same overall signal, we were able to quantify the participants’ temporal weighting profile. Results indicate that, for intervals of 1 or 2 sec, participants exhibit a primacy-bias. However, for longer stimuli (3-sec) the temporal weighting profile is non-monotonic, with concurrent primacy and recency, which is inconsistent with the predictions of previously suggested computational models of perceptual decision-making (drift-diffusion and Ornstein-Uhlenbeck processes). We propose a novel, dynamic variant of the leaky-competing accumulator model as a potential account for this finding, and we discuss potential neural mechanisms.

Behavioral epigenetics is part of the thriving field of epigenetics, which describes the study of developmental processes that lead to persistent changes in the states of organisms, their components, and their lineages. Such developmental, context-sensitive changes are mediated by epigenetic mechanisms that establish and maintain the changes in patterns of gene expression and cellular structures that occur during ontogeny in both nondividing cells, such as most mature neurons, and dividing cells such as stem cells. When information is vertically transmitted to cells during cell division, or horizontally between cells through migrating reproducing molecules (like small RNAs), and when variations in the transmitted information are not determined by variations in DNA sequence (i.e., the same DNA sequence has more than one cell-heritable epigenetic state), epigenetic inheritance is said to occur. Behavioral epigenetics investigates the role of behavior in the shaping of developmental epigenetic states and the reciprocal role of epigenetic factors and mechanisms in the shaping of the behavior of human and nonhuman animals, at the short-, middle-, and long-term (ontogenetic, ecological, and evolutionary) time scales. The focus is on the molecular-epigenetic study of the interactions between environmental factors, such as ecological factors and habitual activities such as lifestyles and learning, with genetic variation and the neurobiological and physiological mechanisms that mediate between the regulation of gene expression and behavior. This range of epigenetic processes therefore includes, but is not limited to, studies involving epigenetic inheritance and the direct and indirect evolutionary effects of epigenetic developmental mechanisms. The neural-behavioral aspects that occur during ontogeny through the mediation of epigenetic mechanisms are central to behavioral epigenetics and are the main focus of neural epigenetics.

Behavioural studies over half a century indicate that making categorical choices alters beliefs about the state of the world. People seem biased to confirm previous choices, and to suppress contradicting information. These choice-dependent biases imply a fundamental bound of human rationality. However, it remains unclear whether these effects extend to lower level decisions, and only little is known about the computational mechanisms underlying them. Building on the framework of sequential-sampling models of decision-making, we developed novel psychophysical protocols that enable us to dissect quantitatively how choices affect the way decision-makers accumulate additional noisy evidence. We find robust choice-induced biases in the accumulation of abstract numerical (experiment 1) and low-level perceptual (experiment 2) evidence. These biases deteriorate estimations of the mean value of the numerical sequence (experiment 1) and reduce the likelihood to revise decisions (experiment 2). Computational modelling reveals that choices trigger a reduction of sensitivity to subsequent evidence via multiplicative gain modulation, rather than shifting the decision variable towards the chosen alternative in an additive fashion. Our results thus show that categorical choices alter the evidence accumulation mechanism itself, rather than just its outcome, rendering the decision-maker less sensitive to new information.

We investigated the mechanism with which humans estimate numerical averages. Participants were presented with 4, 8 or 16 (two-digit) numbers, serially and rapidly (2 numerals/second) and were instructed to convey the sequence average. As predicted by a dual, but not a singlecomponent account, we found a non-monotonic influence of set-size on accuracy. Moreover, we observed a marked decrease in RT as set-size increases and RT-accuracy tradeoff in the 4-,but not in the 16-number condition. These results indicate that in accordance with the normative directive, participants spontaneously employ analytic/sequential thinking in the 4-number condition and intuitive/holistic thinking in the 16-number condition. When the presentation rate is extreme (10 items/sec) we find that, while performance still remains high, the estimations are now based on intuitive processing. The results are accounted for by a computational model postulating populationcoding underlying intuitive-averaging and working-memory-mediated symbolic procedures underlying analytical-averaging, with flexible allocation between the two.

A key characteristic of learning and neural plasticity is state-dependent acquisition dynamics reflected by the non-linear learning curve that links increase in learning with practice. Here we propose that the manner by which epigenetic states of individual cells change during learning contributes to the shape of the neural and behavioral learning curve. We base our suggestion on recent studies showing that epigenetic mechanisms such as DNA methylation, histone acetylation and RNA-mediated gene regulation are intimately involved in the establishment and maintenance of long-term neural plasticity, reflecting specific learning-histories and influencing future learning. Our model, which is the first to suggest a dynamic molecular account of the shape of the learning curve, leads to several testable predictions regarding the link between epigenetic dynamics at the promoter, gene-network and neural-network levels. This perspective opens up new avenues for therapeutic interventions in neurological pathologies.

The distinction between access consciousness and phenomenal consciousness is a subject of intensive debate. According to one view, visual experience overflows the capacity of the attentional and working memory system: We see more than we can report. According to the opposed view, this perceived richness is an illusion-we are aware only of information that we can subsequently report. This debate remains unresolved because of the inevitable reliance on report, which is limited in capacity. To bypass this limitation, this study utilized color diversity-a unique summary statistic-which is sensitive to detailed visual information. Participants were shown a Sperling-like array of colored letters, one row of which was precued. After reporting a letter from the cued row, participants estimated the color diversity of the noncued rows. Results showed that people could estimate the color diversity of the noncued array without a cost to letter report, which suggests that color diversity is registered automatically, outside focal attention, and without consuming additional working memory resources.