One of the overarching lessons of complexity theory is that complex behaviors often emerge out of less complex local rule structures. Evolutionary theory holds that complex phenotypes evolve out of less complex phenotypes. This work presents evidence for the evolution of cognition out of simple molecular structures initially operating in the control of spatial foraging behavior. The evidence is constructed from and helps to unify observations from behavioral ecology, mathematical biology, molecular genetics, neuroscience, attention studies, and research on human goaldirected pathologies. Similarities in foraging behavior across eumetazoans (i.e., vertebrates, insects, and mollusks) suggest the early evolution of a foraging behavior called area-restricted search. Area-restricted search is characterized by initially concentrated searching around local areas of highest historical payoff, followed by more global and less focused searching as payoffs become infrequent. Mathematical models of area-restricted search show that it is optimal when resources are clumped and when only temporal information is available about resource density. I present a genetic algorithm that supports the mathematical findings and describes minimal molecular structures necessary for the evolution of area-restricted search. I show how these structures are present in existing neural pathways controlling area-restricted search and are internalized in the control of goal-directed behaviors in more recent vertebrates. Human pathologies of goal-directed behavior (e.g., ADHD, obsessive compulsive disorder, schizophrenia, drug addiction, and Parkinson’s Disease) share molecular similarities with foraging behavior, involve both motor and cognitive dysfunctions, and also appear to organize themselves along the gradient of behavior described by area-restricted search, from perseverative to interrupted. Studies of priming, memory chunking, and the prefrontal cortex provide evidence for the existence of hierarchical cognitive neighborhoods. Taken together, this work suggests that cognitive neighborhoods are the evolutionary emergent world of the foraging mind.

Animal foraging and the evolution of goal-directed cognition 2
1.0 Introduction
Stanislaw Ulam, the famous Russian mathematician, once observed that the mind is like a pack of dogs (Ulam, 1991). When the mind seeks a solution, it lets loose the dogs, who sniff around in the multidimensional cognitive space of our cortex, searching for the answer. Until very recently, the metaphor of cognitive exploration as a kind of foraging behavior has existed strictly at the level of analogy.
The analogy is not new to psychology. William James used it in the following way:
"...we make search in our memory for a forgotten idea, just as we rummage our house for a lost object. In both cases we visit what seems to us the probable neighborhood of that which we miss." (p654).
Evidence gathered in a number of fields is beginning to suggest that this relationships is not just one of analogy, but of evolutionary homology. That is, molecular machinery that was initially used to control foraging behavior was co-opted over evolutionary time to navigate internal, cognitive maps and the control of more general goal-directed behaviors.
In an evolutionary context, homology refers to a shared evolutionary history, in the same way that bats, humans, and whales all have five fingered limbs. The evolutionary explanation is that the limbs shared the same function in a common ancestor. An alternative would be the function of eyes and teeth, which does not share a common functional ancestry. In the case of cognitive foraging and spatial foraging, the evidence suggests that these too shared the same function in a common ancestor and the one (cognition) evolved from the other (foraging). What was once foraging in physical space is now foraging in cognitive space.
It is the goal of this paper to outline this evidence from the perspective of various fields and also to integrate this evidence into a theory of goal-directed cognition. The fundamental goal is not so much to ‘get it right,’ as it is to present a framework for integrating evidence from various fields in order to begin to understand the evolution of cognition and its associated phylogenetic predispositions. Moreover, the field of cognitive science is in general interdisciplinary, and this may be enhanced by understanding how mathematical biology and behavioral ecology can further provide insight into human cognition. Foremost, however, is the goal of making headway into the self-organization of cognitive structure and function by focusing on the rules of evolutionary precursors, which provide us with a starting point for thinking about the ordered complexity of cognition.
2.0 The Foraging Behavior: Area-restricted Search
Area-restricted search is characterized by a concentration of searching effort around areas where resources have been found in the past. When resources are encountered less frequently, behavior changes such that the search becomes less sensitive but covers more space. In an ecological context, animals using area-restricted search will turn more frequently following an encounter with food, restricting their search around the area where food was last encountered. As the period of time since the last food

Animal foraging and the evolution of goal-directed cognition 3 encounter increases, animals turn less frequently and begin to move away, following a more linear path.
Area-restricted search is observed in a wide variety of animals including house-flies (White et al., 1984), leeches (O’Gara et al., 1991), moths (Vickers, 2000), ladybug beetles (Kareiva and Odell, 1987), rodents (Benedix, 1993), nematodes (Hills et al., 2004), students in classrooms (Hills and Stroup, 2004) and other animals (see
Bell, 1991). The observation that so many animals do this behavior leads to a natural question. Why?
One kind of answer is that area-restricted search is an optimal search strategy under a common set of environmental conditions. Mathematical models of arearestricted search suggest that this is true when resources are clumpy and when information is limited with regards to the direction of those resources (Kareiva and
Odell, 1987; Walsh, 1996; Grunbaum, 2000). Without any other kind of information, when resources are found, the best place to look for more is nearby. Biological environments are prone to clumpy resource distributions because living organisms grow, reproduce, and disperse in spatially auto-correlated ways. So one answer to the why question is that conditions that support life tend to generate clumpy resource distributions, which in turn select for appropriate foraging strategies, like arearestricted search.
To further address the evolutionary likelihood of this behavior, I developed a genetic algorithm (Hills, 2004), using NetLogo (Wilensky, 1999), that allows agents to search for food in a two-dimensional space. The goal of the genetic algorithm was to understand the conditions where area-restricted search is likely to evolve and also to understand the physiological parameters required for the behavior's evolution. In this simulation, the agents have three genes, which control turning angle per time step when they are on food, turning angle per time step when they are off food, and a memory depth which describes the number of time steps it takes for the animal to progress from the on food to the off food turning angle once they’ve left food (the inverse of memory depth is the slope of change in turning angle per time step). The initial population is generated with a random 24 bit genome per individual (8 bits per gene). The genes assign the foraging rules each generation. After an appropriate lifespan, individuals mate and recombine--disproportionately according to their fitness-and a new generation is created, subject to a small mutation rate.
When resources are spatially correlated, the evolution of area-restricted search is an inevitable consequence (Figure 1a). Agents evolve towards high on-food turning angles and low-off food turning angles. Memory depth appears to be more sensitive to the random initial distribution of resource clumps, as clumps may be more or less close to one another, and the rate at which an agent ‘gives up’ is likely influence the rate at which it encounters nearby clumps. Nonetheless, area-restricted search is a robust outcome in a wide variety of resource distributions, and is not dependent on specific turning distributions or slope-formulations for memory-depth
(data not shown, but the algorithm is freely available, see Hills, 2004).

Animal foraging and the evolution of goal-directed cognition 4
Figure 1: Typical results from the foraging genetic algorithm. The top image shows the path of the agent with the most successful parent in the previous generation. The environment is on the surface of a torus (i.e., agents pass from one side of the screen to the other). Food is represented by diamonds, of which the agent “eats” individual pixels. The path is shown for the one hundredth generation. Cumulative data is shown below. “Off Food Turniness” represents the average turning angle when the animal is off food and has no memory of food. “On Food Turniness” is the average turning angle when the animal is on food. The turning angle adjust from the “On
Food” to the “Off Food” value linearly per pixel step with a slope equal to the inverse of the “Memory Depth.” For example, it would take 40 pixel steps to move from the average “On Food Turniness” to the average “Off Food Turniness” if the “Memory
Depth” were 40.
The only exception is when resources are spatially uncorrelated. In uncorrelated resource environments, the behavior fails to converge. This may be

Animal foraging and the evolution of goal-directed cognition 5 because search behaviors are highly sensitive to the initial random distribution of resources. Recent work on random searches suggests that in an uncorrelated twodimensional environment, optimal search paths should follow an inverse power law distribution of run lengths (Vishwanathan et al., 1999). This is impossible to evolve under the conditions of the genetic algorithm outlined above.
The genetic algorithm acts as an independent control on the mathematical theory. It provides us with evidence that the behavior will readily evolve under common resource distributions and that roughly three parameters are sufficient for its production. These three parameters correspond to specific physiological prerequisites required for the development of area-restricted search. If the resource being searched for turns out not to have disappeared, organisms need a way to determine when to start looking elsewhere (i.e., a clock that keeps track of time elapsed since they started searching). And when they start looking elsewhere, they need to modulate their search behavior appropriately such that more area is covered in less time. The particular molecular mechanisms associated with these behaviors are the subject of the next section.
The answer to the evolutionary question appears to be that the behavior is readily evolved when resources are spatially auto-correlated and that the behavior itself is not complicated to produce (more or less, depending on how one thinks about pre-existing conditions). Based on this evidence alone, the evolutionary argument is still open. If the behavior is so easily generated, then we may expect it to have evolved independently along multiple lineages. However, if a molecular predisposition for modulating behavior with respect to external stimuli arose early in the evolution of eumetazoans, its function might have been conserved and carried over in the function of related behaviors in more recent species. If this is true, we should find evidence of molecular and functional similarities in existing species. This is the topic of the next section.
3.0 The Evolution of Goal-directed Behavior
If foraging behavior in physical space is an evolutionary precursor of goaldirected behaviors in general, then we should find evidence of strong molecular similarities between the molecular control of area-restricted search and that of cognition. As stated in the last section, area-restricted search requires a clock connected to a behavioral modulator. The clock needs to start when the organism leaves food and the cumulative time then needs to be transformed into a downstream turning behavior. I have described molecular clocks associated with ecological behaviors elsewhere (Hills, 2003). In the present case, we are interested in molecular clocks specifically associated with the temporal modulation of turning in response to external stimuli.
The most primitive example of the temporal modulation of turning behavior is found in coliform bacteria like Escherichia coli and Salmonella typhimurium .
These bacteria use the direction of flagellar motors to control “run and tumble” movement. Runs provide forward motion, while tumbles create random turns.
Receptor proteins in the membrane bind to external stimuli and then signal, using a phosphorylation cascade, to proteins in the flagellar motor, which in turn modulates between run and tumble behavior (Stock & Surette, 1996). In effect, binding changes

Animal foraging and the evolution of goal-directed cognition 6 the shape of the proteins and influences their ability to interact with other proteins, which at the far end of the chain of reactions leads to changes in the motor’s direction.
This allows bacteria to move up chemical gradients using runs and to avoid moving towards repellents or low resource environments by using tumbles. The phosphorylation chain consists of several steps, which are points of further modulation by other factors that the bacterium may need to integrate into its behavior. This is necessary when a bacterium needs to move away from one nutrient source and towards another.
Bacteria do use temporal information to detect gradients (Macnab &
Koshland, 1972). In part, this comes as a consequence of dephosphorylation rates, which allow proteins to move to an inactive state after a short period of activation.
Behaviorally, this is evidenced by the fact that when gradients are rapidly shifted, E. coli will continue their run for a few seconds before they turn. This first turn after removal from food is at least the beginning of an area-restricted search. However, recent work by Korobkova et al. (2004) suggests that E. coli probably don’t perform an area-restricted search over longer time intervals, but instead search with the optimal random search described above (Vishwanathan, et al. 1999).
The most basal eumetazoan for which we have molecular details about arearestricted search is the nematode Caenorhabditis elegans (Figure 2). C. elegans performs an area-restricted search upon removal from food, showing initially a high frequency of turns which, over a period of 30 minutes, is modulated to a lower frequency. Recent work has exposed some of the molecular and neural mechanisms underlying this behavior (Hills et al., 2004; Sawin et al., 2000). The neural circuit described consists of 8 sensory neurons presynaptic to 8 interneurons. The interneurons are known to coordinate forward and backward movement (Chalfie et al.,
1985; Zheng et al., 1999). Similar to E. coli , C. elegans changes directions most dramatically by brief intervals of backwards movement (i.e., reversals), which are followed by pirouettes. The sensory neurons use the neurotransmitter dopamine to modulate glutamate receptor function in the contol interneurons, which leads to a change in the frequency of reversals. For example, exogenous applications of dopamine increase turning, whereas applications of dopamine antagonists reduce turning and eliminate area-restricted search. It is suggested that at some time immediately before or after animals are removed from food, they release dopamine, which leads to increased switching behavior in interneurons, and more turns. When off food, dopaminergic activity is reduced, and the interneurons reduce their switching frequency, leading to fewer turns.
The co-occurance of dopamine and glutamate in modulatory relationships is a common feature among many eumetazoan clades (Fienberg et al., 1998; Acerbo et al., 2002; Hills et al., 2004; Cleland and Selverston, 1997). Other evidence suggests that many of the general relationships described for C. elegans are conserved across eumetazoans, in particular, the use of dopamine to modulate locomotory and feeding behaviors. In mollusks and crustaceans, dopamine modulates the control of pattern generating interneurons (Barria et al., 1997; Harris-Warrick et al., 1995). Adult onset loss of dopaminergic neurons in Drosophila melanogaster leads to locomotor dysfunction (Feany and Bender, 2000) In Aplysia , dopamine modulates feeding response (Kabotyanski et al., 2000).
When we look in vertebrates, the evidence shifts from 'simple' circuit

Animal foraging and the evolution of goal-directed cognition 7 behaviors to complex behaviors associated with goal-directed behaviors in general.
For example, studies involving the genetic knock-outs of a gene responsible for the production of dopamine (tyrosine hydroylase) lead to hypophagic behavior, in which mice fail to eat or seek food normally (Szczypka et al., 1999). However, when these animals are given exogenous dopamine, they are temporarily able to attend to foraging related behaviors (Szczypka et al., 1999). Knocking out genes responsible for removing dopamine from the synapse lead to repetitive motor patterns, or perseveration (Ralph et al., 2001). In pigeons, dopamine acts with glutamate to produce pecking behavior (Acerbo et al., 2002). Amphetamines, which cause the release of dopamine, increase food seeking behavior in non-human primates (Fultin,
2001). As well, animals in all major clades of eumetazoans (i.e., insects, mollusks, and vertebrates) are used as models of drug addiction, specifically because they show characteristic dopaminergic responses to drugs seen in humans (reviewed in Wolf and
Heberlein, 2003; Betz et al., 2000).
H. sapiens
Drosophila
Mus
Aplysia
Daphnia
C. elegans
Figure 2. A phylogenetic tree of eumetazoans. The tree is reconstructed from Raible and Arendt, 2004 and Blair et al., 2002.
In vertebrates, more is known about the specific neural and molecular relationships between dopamine and glutamate. The most striking observation is that the relationship is not as linear as that suggested by the limited work that has been done in C. elegans . For example, the areas of the brain involved in modulating goaldirected behavior are numerous and feedback on one another in a complex ways involving dopamine, glutamate, and a host of other neurotransmitters (Greif et al.,
1995; Scott et al., 2002; Mansvelder and McGehee, 2000; Kretschmer, 1999). This is

Animal foraging and the evolution of goal-directed cognition 8 consistent with the internalization of previously external sensory circuits, which must now provide modulated feedback, mediating multiple response scenarious, in an internal space. Nonetheless, dopamine and glutamate are consistent components of the control of goal-directed behavior (Jackson and Moghaddam, 2001; reviewed in
Fuster, 2001 and Miller and Cohen, 2001; Kiyatckin, 2002). Because the dopaminergic neurons are internalized in these systems, it follows naturally that they would be downstream of their own modulatory outputs, that is, of organizational centers that control attention and subsequent behavioral and cognitive outcomes. I describe this in further detail in section 5.0.
Overall, the evidence strongly supports the evolution of dopaminergic like substances in early eumetazoans acting specifically in the modulation of foraging or goal-directed behaviors. Initially, dopaminergic mechanisms were used to control foraging in physical space, much like E. coli use membrane bound receptors to modulate tumbling behavior. Over time, these mechanisms were co-opted and internalized in the vertebrate nervous system to operate in the control of attention to specific goals.
This theory makes a number of predictions. Foremost, we should find evidence of a wide variety of goal-directed or attentional disorders that are dopaminergic in origin and can be treated with dopamine or its antagonists. Given the dynamic modulatory function of dopamine in the control of goal-directed behavior, it also expected that treatments involving dopamine might 'cover up' for biases towards perseverative or sporadic behavior created by other sources of dysfunction. Secondly, in order for area-restricted search like behavior to have meaning in a cognitive space, there must be some way to assess cognitive proximity or a neighborhood of ideas around any given idea. In the following sections, I address the evidence for goaldirected pathologies of dopaminergic origin and the existence of cognitive neighborhoods.
4.0 The Evidence from Pathologies of Goal-directed Behavior
Pathologies of area-restricted search should fall into two broad categories: those involving perseverative or stereotypic behaviors that endure past what might be considered their normal duration, and those behaviors that fail to persist for the necessary duration, or are otherwise distractible. These fall at the opposite ends of area-restricted search behavior and are consistent with high and low levels of dopamine, respectively, in, for example, C. elegans and the mouse (Hills et al., 2004;
Szczypka et al., 1999; Ralph et al., 2001). Based on the evolutionary argument, we should find pathologies of these two kinds at different levels of behavioral control, ranging from discrete motor events, to ritualized behavior, through persistence of ideas.
There are a number of goal-directed pathologies that fall into these categories. We can break these into the two characteristic classes, from stereotypic
(including schizophrenia, some types of autism, drug addiction and obsessive

Animal foraging and the evolution of goal-directed cognition 9 compulsive disorder) through failure to persist (including attention deficit hyperactivity and Parkinsons' disease). The claim is not that these behavior are controlled solely by dopamine, but that the perseverative aspects can either be attributed to dopaminergic defects or treated with dopaminergic drugs. The following disorders of attention were chosen because they are highly represented in the literature on goal-directed pathologies and because the information regarding their symptoms and treatment is reasonably well developed, though far from being completely understood.
4.1 Schizophrenia
Schizophrenia is characterized by stereotypies at multiple behavioral levels of control. These range from tics or motor spasms to highly ritualized behavior and idea persistence. The persistence of ideas in schizophrenia has been called a 'poverty of ideas' because the repetition of language patterns, from individual words to phrases, is confined to a specific content (reviewed in Ridley, 1994).
Failure to perform smooth visual pursuit and disinhibition of saccadic eye movements are also characteristic of schizophrenics (Hong et al., 2003). These characteristics of visual attention in schizophrenics are both early warning signs of schizophrenia as well as hereditary markers, as similar visual abnormalities are observed in first degree relatives (Hong et al., 2003). This evidence is also consistent with observations of hypofrontality (frontal metabolic and blood flow deficiencies) in schizophrenics (Davidson and Heinrichs, 2003), as eye movement is in part controlled by frontal lobe centers that maintain attention to specific objects
(LaBerge, 1998).
The most prevalent hypothesis for the biological basis of schizophrenia is called the 'dopamine hypothesis' and was originally based on the efficacy of antidopaminergic drugs used for its treatment, as well as the effects of high levels of dopaminergic drugs (Snyder, 1974). More recent evidence from a variety of sources suggest that deficits are associated with both dopaminergic and glutamatergic disfunction (reviewed in Laruelle et al., 2003).
4.2 Autism
One of the behavioral symptoms commonly associated with certain subtypes of autism is perseverative or stereotypic behavior, consisting of repeated motor patterns, often involving objects (Kaplan et al., 1994). Frith (1989) cites evidence that this stereotypic behavior is pervasive to the point of operating prior to the level of integrating perceptual features, what she called "weak central coherence." That is, autistics fail to solve the 'binding problem,' and instead appear to show abnormally focused attention on specific object features (Happe, 1999).
Whether or not autism is a disorder of dopaminergic function remains a subject of debate, but at least in practice, antidopaminergic treatments (e.g., haloperidol) help some autistic subjects to overcome stereotypic behavior and to integrate perceptual features in ways that allow for specific kinds of learning
(reviewed in Posey and McDougle, 2000).

Animal foraging and the evolution of goal-directed cognition 10
4.3 Drug Addiction
Drug addiction is characterized by apparently involuntary search for and consumption of drugs. Of principle interest with regards to the theory of cognitive foraging is its distinctly cognitive features, in which, individuals perseverate on the idea of the drug. Many accounts of this are available in the popular literature
(Burroughs, 1977; Davis and Troupe, 1990). The prevailing evidence regarding drug addiction suggests that drugs are addictive because they circumnavigate natural reward mechanisms and eventually modify dopaminergic pathways in the brain (Ritz et al., 1987,Volkow et al., 2004; Berke & Hyman, 2000). This may happen as a long term developmental result of manipulating dopamine concentrations at the synapse
(Berke & Hyman, 2000). Imaging studies further suggest that this acts to reduce frontal lobe response to non-drug related stimuli, while enhancing it (and reducing disinhibition) to drug related stimuli (Volkow et al., 2004). Again, other mechanisms have been implicated (Rocha et al., 1998), but the overall phenomenology is consistent with a dopamine induced persistence of ideas.
4.3 Attention Deficit Hyperactivity Disorder (ADHD)
ADHD shares symptoms that are largely the inverse of obsessive compulsive and addictive behaviors, although like many syndromes it has multiple subtypes.
Behavior associated with ADHD is correlated with specific polymorphisms in dopaminergic proteins (DRD4, DRD5, see Swanson et al., 2000; Lowe et al., 2004, respectively). As well, individuals diagnosed with ADHD also show elevated levels of the dopamine transporter, responsible for moving dopamine out of the synaptic cleft (Klaus-Herring et al., 2003).
For over sixty years the treatment of ADHD has involved sympathomimetics, which stimulate the release of dopamine (Kaplan et al., 1994; Markowitz et al., 2003).
One of the hypotheses associated with the efficacy of dopaminergic drugs in the treatment of ADHD is that dopamine increases the executive control of cognitive and behavioral inhibition (Kaplan et al., 1994). That is, dopamine improves attentional focus by inhibiting other possible sources of internal or external distraction.
Consistent with this hypothesis, recent work by Potter and Newhouse (2004) shows that the high prevalence of smoking among individuals with ADHD may be a result of increased positive cognitive and behavioral inhibition following treatment with nicotine, which increases the release of dopamine (Mansvelder et al, 2002)
4.4 Other Disorders
Obsessive compulsive disorder (OCD) involves components of both ideational perseveration and compulsive behaviors, which are usually performed to alleviate emotional factors associated with the obsessive idea (Kaplan et al, 1994;
Rapaport, 1990). Motor tics are often associated with OCD and again reveal a correlation between perseverative behavior and perseverative thoughts (Ridley, 1994).
Nearly half of OCD patients treated with serotonin uptake inhibitors fail to show

Animal foraging and the evolution of goal-directed cognition 11 positive clincal outcomes, but in cases involving tics the addition of dopamine antagonists shows significant improvement (McDougle et al., 1994).
The etiology of Parkinson's Disease is known to involve the degeneration of dopaminergic neurons in substantia nigra pars compacta (Parent and Cossette, 2001).
The disease can be characterized as the failure to voluntarily initiate action and is typically treated effectively, during the early stages of the disease, with L-dopa, which increases the supply of dopamine to the brain (Rascol et al., 2001). A possible explanation for the failure to initiate action is that PD so reduces the activation of specific behavior or ideational centers that voluntary behaviors fail to initiate.
Tourrette's Syndrome (TS) involves motor perseveration, such as sniffing or blinking, and the inability to inhibit specific behaviors, ranging from jumping to obscene language or mannerisms, sometimes called vocal tics (Graybiel and Canales,
2001). Pimozide, an antidopaminergic drug, is often successful in treatment (Sallee et al., 1997).
4.5 Conclusions from Pathologies of Goal-directed Behavior
All of the pathologies listed above appear to fall, at least from a clinical perspective, along an axis from perseverative to sporadic. Along this gradient fall the various disorders of attention listed above, falling to one side of the middle or the other, and their treatments entail prescribing the dopaminergic agonist (to increase persistance) or antagonist (to reduce persistance). Similarly, we see the same treatments, when applied to C. elegans or the mouse, move foraging behavior in similar directions (Hills et al., 2004; Szczypka et al., 2002; Wolf and Heberlein, 2003;
Betz et al., 2000). There is far more to all of these behaviors than dopamine--the treatment of OCD and schizophrenia being particularly obvious examples. Yet, taken as a whole, these disorders suggest that dopamine is a modulator of attentional persistance, both at the motor level, as in foraging behavior, and at the level of ideas.
5.0 The Evidence for the Existence of Cognitive Neighborhoods
We are concerned here with understanding exactly what the cognitive neighborhood looks like and what, in that space, an area-restricted search would look like. Cognitive neighborhood is used here to refer to the idea of nearness in a multidimensional cognitive space. Taking goal-directed pathologies as an example, we find that area-restricted search looks like perseverative motor activities in one context and the perseveration of ideas in another, operating under partially conserved molecular and neural control mechanisms. But, in the cognitive space, we have a limited conception of nearness, such that, searching the space does not contain the same meaning as searching a physical space. Moreover, this is not an argument about the anatomical proximity of brain activation, but rather one about the relationships between objects that the brain is used to represent.
I will provide evidence for cognitive nearness from a number of contexts. mostly from the literature of psychophysics and neurobiology. In this section we will find ourselves primarily interested in the prefrontal cortex (PFC) as it comes into play

Animal foraging and the evolution of goal-directed cognition 12 with other brain regions, as this is the area most clearly associated with executive voluntary action. The PFC is associated with goal-directed behavior in general. It focuses the attention and is most closely associated with working memory.
Some of the clearest evidence for cognitive nearness comes from studies of associative priming. In these studies, a subject is exposed to a stimulus that influences the meaning or reaction time associated with the identification of a future stimulus.
For example, patients with amnesia are unable to consciously remember seeing particular faces in the recent past, however, when asked to choose determine if two faces are the same or different, they respond more quickly when they have had recent experience with the face (Paller et al., 1992).
The priming stimulus does not, however, need to be identical with the recognized target. When individuals are asked to identify specific Arabic numerals, they do so more quickly when they have had recent experience with names associated with that number (e.g, 747 and Boeing, see Alameda et al., 2003). Identifying colors of objects is faster when the colors are typical of the object (Naor-Raz et al., 2003).
Visual priming occurs when pre-attentive visual images enhance or inhibit future object detection (Treisman, 1998). In still another context, evidence suggests that lexical priming can have various durations based on whether or not an item is primed by a single word or a sentence (Faust, 1998). Even music has been shown to prime for word recognition when the musical excerpts are related in 'subjective' meaning to the target word (e.g., "staircase" and ascending pitch steps, see Koelsch et al., 2004).
One interpretation of the evidence from priming is that conceptual objects
(e.g., banana) have associated neighborhoods (e.g., yellow, monkey, banana tree, slippery, etc). Attention to one is, at least, proximal attention to another. There is a sense in which these contexts are hierarchical or nested. For example, when asked to tell what you study, there are a number of ways to respond, ranging from a description a description of precise details (e.g., the phosphorylation rate of a specific protein), to a very general description of the field (e.g., natural sciences). Shiffrin (1970) referred to these associated contexts as "search sets" and used mathematical arguments based on random sampling from sets to explain diverse empirical findings of memory recall.
Evidence for cognitive neighborhoods also comes from the literature on memory chunking, in which categorization of objects according to specific familiar or
'natural' sets substantially aids recall. Evidence for this exists in pigeons, rats, and humans, suggesting that object chunking is a general feature of vertebrate nervous systems (Shettleworth, 1998; Ericcson et al., 1980). Skilled memory theory suggests that these chunks can be learned and that professionals are more capable of remembering items that are configured according to the rules of their domain (e.g., chess piece placement or figure skating choreography, see Chase and Simon, 1973 and Deakin and Allard, 1991, respectively), than they are to remember random configurations (Ericsson and Kintsch, 1995). Chunking and other kinds of memory skills associated with mnemonics appear to utilize features of existing cognitive neighborhoods to organize long-term memory in ways that can be readily accessed by working memory structures (Ericsson and Kintsch, 1995).
In many of the cases listed above, evidence suggests working memory operations in the prefrontal cortex, both in the mediation of voluntary and involuntary priming (LaBerge, 1998; Gabrieli et al., 1998), the organization of information into chunks (Bor et al., 2003), and the voluntary movement of animals in space (Seamans

Animal foraging and the evolution of goal-directed cognition 13 et al., 1998). The prefrontal cortex is also strongly implicated in general features of working memory, such as remembering or encoding information about place or strategy (reviewed in Eichenbaum and Cohen, 2001; Fuster, 2001). The mechanism of operation appears to be that cells in the PFC show sustained activation in brief memory tasks (Goldman-Rakic, 1995; Orlov et al., 1988; Cohen et al., 1997;
Courtney et al., 1997), which is believed to maintain specific associations during the interval of activation. Dopamine appears to be the major factor involved in the sustained activation (Seamans et al., 1998; Wang et al, 2004; Watanabe et al., 1997) and appears to operate by a variety of downstream mechanisms including glutamate
(Seamans et al., 2001; also see Durstewitz et al., 1999).
The prevailing evidence suggests that the PFC is multifunctional, highly interconnected with other areas of the brain, and involved in the production of behaviors associated with both concrete objects in the environment and abstract rules
(Cools et al., 2004; Fuster, 2001; Eichenbaum and Cohen, 2001). This has led to a number of models of the PFC, in which dopamine is used to modulate the access of its afferent connections (Miller and Cohen, 2001; Durstewitz et al., 1999). Presumably, this holds objects in attention, such that appropriate behaviors can be activated, and successful cognitive associations can be reinforced (Braver and Cohen, 2000).
Consistent with the claim of cognitive neighborhoods is evidence that existing solutions mediated by the PFC are more likely to be used when problemsolving than novel solutions (see Dehaene and Changeux, 1992). In this way, well worn cognitive paths play themselves out in our day to day behavior, ranging in complexity from the simple motor manipulations to verbal rants consisting of long strings apparently connected ideas. The PFC role appears to be in an executive mediation of these possible behavioral sequences (see Miller and Cohen, 2001).
When the PFC is damaged, mediation fails, and the consequences are consistently either perseveration or the failure of ideas to persist (see Eichenbaum and Cohen,
2001; Miller and Cohen, 2001).
Taken together, the evidence strongly supports the existence of both cognitive neighborhoods and a mechanism of navigating those neighborhoods, which uses dopaminergic pathways to modulate attention to specific goals. Figure 3 is a possible representation of the cognitive foraging space one might use if, upon arriving home after a conference, one realized one’s keys were missing. The first question might be, “Where did I see them last?” If it was the Hotel Room, one would initially perform the cognitive search around areas highly associated with the Hotel.
Following this, the cognitive search moves to the Airport. Notice, the search moves between neighborhoods, not necessarily through the temporal sequence (as this may not be remembered). The density of the arrows represents the most strongly associated nodes. Attentional mechanisms operate by spending time attending to nodes (and details downstream) in proportion to the expectation (or associated weights) of the node. New nodes are chosen because they are the next highest expectation. The evidence suggests that dopamine is used to attend to nodes and also to maintain, at the same time, the original goal: finding one’s keys. This further suggests a parallel cognitive structure which holds in attention both the goal and neighborhoods were a solution to that goal may be found.

Animal foraging and the evolution of goal-directed cognition 14
Figure 3: Representation of a cognitive area-restricted search, which begins when one realizes they lost track of their keys at some point during a recent conference.
Holding the goal of “keys” in one’s attention, one also then moves through a cognitive representation of the past visit to the place where the keys were seen last: “the Hotel
Room.” From here, details are searched at each node and the progression is always from highest expectation (largest arrows) to lowest expectation (narrowest arrows).
The path also moves from hubs, or local neighborhoods, represented by the “Hotel,”
“Airport,” and “Home.”
It is difficult to imagine how such neighborhoods could come into being if not for the means to navigate between them. Yet, once the navigational machinery is present, the evolution and access of new brain structures can be mediated by the this machinery.
Several predictions follow from theory of cognitive foraging. First, we should expect natural selection to act on attentional mechanisms along the gradient of perseveration, such that organisms with an evolutionary history of heterogenous and novel environments would show more global search strategies associated with finding

Animal foraging and the evolution of goal-directed cognition 15 new solutions to old problems. One source of evidence for such selection is found in humans, where positive selection has been identified in a dopamine receptor protein
(DRD4) associated with ADHD (Ding et al., 2002). The positive selection appears to correlate strongly with the survivors of early human migrations, or those most capable of dealing with environmental novelty (Harpending and Cochran, 2002). According to the theory of cognitive foraging, these individuals are least likely to perseverate on old solutions when a search for new solutions is clearly in order.
Another prediction is that when searching for answers to problems of freeassociative recall, external stimuli that shift the focus of attention to new cognitive neighborhoods should result in a new set of answers, that are recalled with the same functional shape as the previous set, initially high then low as the neighborhood is depleted. Recent studies of human creativity have observed exactly this, finding that when the search space is empty in a creative-recall task, introducing random associations is sufficient to reactivate initial recall rates (Howard-Jones and Murray,
2003).
6.0 Conclusions and Caveats
Much of the theory in cognitive science has been a search for the appropriate metaphor of mind. The theory of mind I have introduced here tries to go further than this, to understand the biological origins of cognition. To this end, the major focus of this work has been to lay out a detailed argument for the evolutionary homology of cognition and animal foraging behavior. The evidence came in five forms: 1) the similarities in foraging behavior across eumetazoans, suggesting the early evolution of area-restricted search. 2) the similarities among molecular mechanisms controlling area-restricted search-like behaviors and goal-directed behavior, along with the internalization of these mechanisms in vertebrates, suggesting the conservation of mechanisms in the control of goal directed behavior 3) pathologies of goal-directed behavior, which both share molecular mechanisms with foraging behavior and also appear to organize themselves along the gradient of behavior described by arearestricted search, 4) evidence for cognitive neighborhoods, from studies of priming and memory chunking, suggesting that neural structures have modeled themselves after external spatial structures, presumably in connection with the internalization of dynamic attention structures capable of navigating this space, and 5) evidence from molecular mechanisms of the prefrontal cortex which suggest that goals are maintained via dopaminergic activity, much like turning behavior in C. elegans , which operates to create the persistence of ideas.
There are a number of possible forms of negative evidence for the argument I have outlined here. One of these would come in the form of an alternate (nonforaging) functionality for dopaminergic modulatory mechanisms that had a strong presence in eumetazoans and looked more like the internalized cognitive structures we find in more recent vertebrates. As an example, it is possible that the dopaminergic modulatory mechanisms we see in extant species shared a common ancestor who used them to modulate gut retention. As well, there may be no such thing as standing associative neighborhoods in the nervous system, but that such neighborhoods, when

Animal foraging and the evolution of goal-directed cognition 16 we see them, are actually constructed on the fly out of unadulturated sensory impressions, in the same way that one may rewind a movie to discover who won the horse race. Which is to say, maybe there is no conceptual horse, there are only 'real' horses, and they are not close to one another in our minds unless they were standing that way when we saw them.
The implications of the evolution of cognition out of foraging behavior are multifold, but pivot around the idea that studies of cognition and foraging can inform one another. The relationship inspires a host of questions. What does it mean to forget something? Perhaps that you can't get there from here. Are there islands in the mind that cannot be approached? Can one get lost in one's own mind? Can one construct waypoints in the space of ideas, as some kind of plan or argument? If such waypoints were used frequently, creating a well worn path, how does this play itself out in our attention? Could such well worn paths eventually become the stories we tell ourselves about the world we live in? Where does one 'place' start and another end? What does it mean to have competing ideas? Do these ideas lead away from one another, posing different trajectories from the same starting point? Are there emotional consequences associated with neural paths that lead away from one another? Can we learn optimal thinking strategies? Are the stories we construct in our minds, for example when we are dreaming or try to make sense of the behavior of others, composed of meandering paths through near neighborhoods, guided by a specific logic of emotion or reason? Do different drugs lead to different kinds of paths? Is it possible to see the same idea from different sides? Does arriving at such a place after leaving competing ideas account for the feeling of insight? Certainly the mind has internalized spatial reference in a number of places, and perhaps the space of ideas is similarly constructed. This kind of thinking is not new, but here I have tried to spell out the biological foundations that support it, which perhaps not only make the argument sound, but possible to have in the first place.
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